There is a wave of outrage over the internet about revelation that iPhones has a file with tracking information recording all the places it has been. How dare Apple track users of their products! I am afraid that this is a extremely naive attitude. The fact is that everybody is tracking you on iPhone and not only on a iPhone but on all smartphones and on many less than smart phones as well. Let me count the ways, starting off with the benign and moving to the egregious.
Firstly the carriers and handset makers collect data from phone to help improve their service. Last week we has a joint meeting of the SDForum Business Intelligence and Mobile SIGs on "Mobile Analytics". At that meeting Andrew Coward of CarrierIQ described how they embed their software in phones, usually at the carriers direction, to collect information that can be used to improve service. For example, he told us for example that it is quite normal for them to report to a carrier that their dropped call rate is 6% whereas the carrier's own engineers are telling management that their dropped call rate is 1%. They collect data on location so that the carrier knows where their users are using their phones from so that they can improve their service to that area.
In Europe, CDR laws require phone carriers to retain their Call Data Record (CDR) for all calls for a period of 1 or 2 years. The police can and do request information on all the calls made to or from a number to help with their enquiries into crime. While a CDR record does not usually contain specific location information, it can identify the cell tower and thus the approximate location of the caller. Police have successfully used location based CDR data to help with their investigations for well over a decade.
With the users permission, Google collects information from Android phones about their location. Google is the ultimate data collection company and I am always amazed at the creative ways they find for using that data. One Google service is the Traffic overlay on their Maps. This is derived from observing the change in location of Android phones. However, while Google says that they do not collect personally identifying information, they do need to distinguish between phones to make this application work, so they are tracking the movements of individuals, if only to provide the rest of us generic information on traffic flows. Google has plenty of other uses for this data. For example, they keep a database that locates every Wi-Fi hotspot is so that they can identify your location based on the Wi-Fi hotspot you using. Google can use data from Android phones to validate and update that database.
Mobile analytics and Apps is where the use of location based information starts to get interesting. Last year Flurry presented to the Business Intelligence SIG and we heard about their run in with Steve Jobs. You can read their press release to get the full story of what they did. In short Flurry has a free toolkit that developers install into their mobile Apps that collects information and sends the data back to Flurry. The developer can then access analytics reports about their app at the Flurry web site. However, Flurry retains the data that has been collected from the App, including location based data.
In January 2010, a couple of days before the iPad was announced, Flurry issued a press release saying that they saw a new Apple device that was was only being used in the Apple headquarters in Cupertino and gave some statistics on the number of different Apps that were being tested on this device. At this Steve Jobs blew his top and tried to get Flurry completely banned from iPhone Apps. Eventually Flurry and Apple settled their differences. The conclusion was that in the words of the iPhone developer agreement "The use of third party software in Your Application to collect and send Device Data to a third party for processing or analysis is expressly prohibited."
So lets parse this. Flurry is a company that has no direct relationship with the carriers, handset makers or the users of Apps, yet is is collecting data from all the Apps that it is included in. The data is available for use by the App developer and by Flurry. At the time of the iPad release they could identify that the device was different from all other devices and identify its location to within one set of buildings. Now, I am not trying to pick on Flurry specifically, there are several companies in this area. At the Business Intelligence SIG last week we heard from Apsalar, a recent start up in the same space, however, Flurry is the largest company that provides mobile analytics. Flurry estimates that they are included in up to 1 in 5 mobile Apps for the iPhone and Android. Because they are in so many Apps, they can provide aggregate data on all App usage.
The point of this is that we want location aware Apps, however we also want to preserve our privacy. As Apps are, these two goals are incompatible. To be location aware, the App has to know your location, and if the App knows your location, it can transmit that information back to the App developer or aggregator of analytics for the App developer. Thus they know where you are whether you want to or not. Android, has a profile that determines which information an App can access that is set when the App is installed. If it is allowed to access location information on installation, it can continue to do so until it is uninstalled.
Compared to what Apps know about what you are doing while you use the App, the location database that the iPhone is collecting seems to be a small matter. In fact it seems to be a good reason to limit the number of Apps that you can be running at any one time. At least if only one App is running then only one App knows where you are at any particular time.
Showing posts with label Business Intelligence. Show all posts
Showing posts with label Business Intelligence. Show all posts
Sunday, April 24, 2011
Saturday, February 19, 2011
Agile BI at Pentaho
Ian Fyfe, Chief Technology Evangelist at Pentaho showed us what the Pentaho Open Source Business Intelligence Suite is and where they are going with it when he spoke to the February meeting of the SDForum Business Intelligence SIG on "Agile BI". Here are my notes on Pentaho from the meeting.
Ian started off with positioning. Pentaho is an open source Business Intelligence Suite with a full set of data integration, reporting, analysis and data mining tools. Their perspective is that 80% of the work in a BI project is acquiring the data and getting it into a suitable form and then other 20% is reporting and analysis of the data. Thus the centerpiece of their suite is the Kettle data integration tool. They have a strong Mondrian OLAP analysis tool and Weka Data Mining tools. Their reporting tool is perhaps not quite as strong as other Open Source BI suites that started from a reporting tool. All the code is written in Java. It is fully embeddable in other applications and can be branded for that application.
Ian showed us a simple example of loading data from a spreadsheet, building a data model from the data and then generating reports from the data. All of these things could be done from within the data integration tool, although they can also be done with stand alone tools. Pentaho is working in the direction of a fully integrated set of tools with common metadata between them all. Currently some of the tools are thick clients and some web based clients. They are moving to have all their client tools be web based.
We had come to hear a presentation on agile BI and Ian gave us the Pentaho view. In an enterprise, the task of generating useful business intelligence is usually done by the IT department in consultation with the end users who want the product. The IT people are involved because they supposedly know the data sources and they own the expensive BI tools. Also, the tools are complicated and using them is usually too difficult for the end user. However, IT works to their own schedule, through their own processes and take their time to produce the product. Often, by the time IT has produced a report, the need for it has moved on.
Pentaho provides a tightly integrated set of tools with a common metadata layer so there is no need to export the metadata from one tool and import it into the next one. The idea is that that the end to end task of generating business intelligence from source data can be done within a single tool or with a tightly integrated suite of tools. This simplifies and speeds up the process of building BI products to the point that it can be delivered while it is still useful. In some cases, the task is simplified to such an extent that it may be done by a power user rather than being thrown over the wall to IT.
The audience was somewhat sceptical of the idea that a sprinkling of common metadata can make for agile BI. All the current BI suites, commercial and open source, have been pulled together from a set of disparate products and they all have rough edges in the way the components work together. I can see that deep and seamless integration between the tools in a suite will make the work of producing Business Intelligence faster and easier. Whether it will be fast enough to call agile we will have to learn from experience.
Ian started off with positioning. Pentaho is an open source Business Intelligence Suite with a full set of data integration, reporting, analysis and data mining tools. Their perspective is that 80% of the work in a BI project is acquiring the data and getting it into a suitable form and then other 20% is reporting and analysis of the data. Thus the centerpiece of their suite is the Kettle data integration tool. They have a strong Mondrian OLAP analysis tool and Weka Data Mining tools. Their reporting tool is perhaps not quite as strong as other Open Source BI suites that started from a reporting tool. All the code is written in Java. It is fully embeddable in other applications and can be branded for that application.
Ian showed us a simple example of loading data from a spreadsheet, building a data model from the data and then generating reports from the data. All of these things could be done from within the data integration tool, although they can also be done with stand alone tools. Pentaho is working in the direction of a fully integrated set of tools with common metadata between them all. Currently some of the tools are thick clients and some web based clients. They are moving to have all their client tools be web based.
We had come to hear a presentation on agile BI and Ian gave us the Pentaho view. In an enterprise, the task of generating useful business intelligence is usually done by the IT department in consultation with the end users who want the product. The IT people are involved because they supposedly know the data sources and they own the expensive BI tools. Also, the tools are complicated and using them is usually too difficult for the end user. However, IT works to their own schedule, through their own processes and take their time to produce the product. Often, by the time IT has produced a report, the need for it has moved on.
Pentaho provides a tightly integrated set of tools with a common metadata layer so there is no need to export the metadata from one tool and import it into the next one. The idea is that that the end to end task of generating business intelligence from source data can be done within a single tool or with a tightly integrated suite of tools. This simplifies and speeds up the process of building BI products to the point that it can be delivered while it is still useful. In some cases, the task is simplified to such an extent that it may be done by a power user rather than being thrown over the wall to IT.
The audience was somewhat sceptical of the idea that a sprinkling of common metadata can make for agile BI. All the current BI suites, commercial and open source, have been pulled together from a set of disparate products and they all have rough edges in the way the components work together. I can see that deep and seamless integration between the tools in a suite will make the work of producing Business Intelligence faster and easier. Whether it will be fast enough to call agile we will have to learn from experience.
Saturday, December 11, 2010
Now You See It: the Book
If you are of a data analytics bent or know someone who is and are looking for a book to put on the Christmas list, consider Now You See It: Simple Visualization Techniques for Quantitative Analysis
by Stephen Few. This is a beautiful book that would not look out of place on a coffee table, yet at the same time, is full of practical information about how to do analytics with charts, graphs and other visual tools.The book is divided into three sections. The first section covers visual perception and general visualization techniques for looking at data. Then the second section goes into more detail with chapters on specific techniques for different types of analysis including time-series analysis, ranking analysis, deviation analysis and multivariate analysis amongst others. Each chapter in this section ends with a summary of the techniques and best practices for that type of analysis. Finally the book ends with a shorter section that looks at promising new trends in visualization.
There are copious examples of graphs and charts drawn by different software tools. While some of these graphs come from high end tools like Tableau and Spotfire, others are drawn by Microsoft Excel. In fact there are several specific procedures for using features of Excel to do sophisticated analytics. That is not to say that the book suggests that you can do everything with a spreadsheet. The first part shows you what to look for in visual analytics software and it essential reading before going out and choosing which tool to use.
So, if you are looking for a quality and practical gift for an analytician, choose "Now You See It".
Sunday, October 31, 2010
The New OLAP
Just as there are new approaches to database management with the NoSQL movement, so is there a move to a new OLAP, although this movement is just emerging and has not taken a name yet. This month at the SDForum Business Intelligence SIG meeting, Flurry talked about how they put their data on mobile app usage in a giant data-cube. More recently, Chris Riccomini of LinkedIn spoke to the SDForum SAM SIG about the scalable data cubing system that they have developed. Here is what I learned about Avatara, the LinkedIn OLAP server. DJ Cline has also written a report of the event.
If you do not know what OLAP is, I had hoped to just point to an online explanation, but could not find any that made sense. The Wikipedia entries are pretty deplorable, so here is a short description. Conceptually, OLAP stores data in a multi-dimensional data cube, and this allows users to look at the data from different perspectives in real time. For example, take a simple cube of sales data has three dimensions, a date dimension, a sales person dimension, and a product dimension. In reality, OLAP cubes have more than dimensions than this. Each dimension contains a hierarchy, so the sales person dimension groups sales person by state then sales region, then country. At the base level the cube contains a data point called a measure for each sale of each product made by each sales person and the date when the sales was made. OLAP allows the user to look at the data in aggregate, and then drill down on the dimensions. In the example cube, a user could start by looking at the sales of all products grouped by quarter. Then they could drill down to look at the sales in the most recent quarter divided by sales region. Next they could drill down again to look at sales in the most recent quarter by sales person, comparing say the the Northern region to the Western region, and so on.
The new OLAP is enabled by the same forces that are changing databases with NoSQL. Firstly, the rise of commodity hardware that runs Linux, the commodity operating system, allows the creation of cheap server farms that encourages parallel distributed processing. Secondly, the inevitable march of Moore's law is increasing the size of main memory so that now you can spec a commodity server with more main memory that a commodity server had in disk space 10 years ago. An OLAP data cube can be partitioned along one or more of its dimensions to be distributed over a server farm, although at this stage partitioning is more of a research topic than standard practice. Huge main memory allows large cubes to reside in main memory, giving near instantaneous response to queries. For another perspective on in memory OLAP, read the free commentary by Nigel Pendse at the BI-Verdict (it used to be called the OLAP Report) on "What in-memory BI ‘revolution’?"
LinkedIn is a fast growing business oriented social networking site. They have developed Avatara to support their business needs and currently run several cubes on it. The plan is to to open source the code later this year. Avatara is an in memory OLAP server that uses partitioning to provide scalability beyond the capabilities of a single server.
The presentation was fast paced and it has taken me some time to appreciate the full implications of what was said. Here are some thoughts. Avatara offers an API that is reminiscent of Jolap rather than the MDX language that is the standard way of programming OLAP, probably because an API is easier to implement than a programming language. Avatara does not support hierarchies in its dimensions, but the number of dimension in a typical cube seems to be higher than usual. It may be that they use more dimensions rather than hierarchies within a dimension to represent the same information. This is a trade off of roll-up within the cube for slicing of dimensions. Slicing is probably more efficient and easier to implement while a hierarchy is easier for the user to understand as it allows for drill up and down.
Chris mentioned that most dimensions are small and that can be true, however the real problems with OLAP implementations start when you have more than one large dimension and you have to deal with the issue of sparsity in the data cube. Chris spent some time on the problem of a dimension with more than 4 billion elements and this seems to be a real requirement at LinkedIn. Current OLAP servers seem to be limited to 2 billion elements in a dimension, so they are going to be even more constraining than Avatara.
If you do not know what OLAP is, I had hoped to just point to an online explanation, but could not find any that made sense. The Wikipedia entries are pretty deplorable, so here is a short description. Conceptually, OLAP stores data in a multi-dimensional data cube, and this allows users to look at the data from different perspectives in real time. For example, take a simple cube of sales data has three dimensions, a date dimension, a sales person dimension, and a product dimension. In reality, OLAP cubes have more than dimensions than this. Each dimension contains a hierarchy, so the sales person dimension groups sales person by state then sales region, then country. At the base level the cube contains a data point called a measure for each sale of each product made by each sales person and the date when the sales was made. OLAP allows the user to look at the data in aggregate, and then drill down on the dimensions. In the example cube, a user could start by looking at the sales of all products grouped by quarter. Then they could drill down to look at the sales in the most recent quarter divided by sales region. Next they could drill down again to look at sales in the most recent quarter by sales person, comparing say the the Northern region to the Western region, and so on.
The new OLAP is enabled by the same forces that are changing databases with NoSQL. Firstly, the rise of commodity hardware that runs Linux, the commodity operating system, allows the creation of cheap server farms that encourages parallel distributed processing. Secondly, the inevitable march of Moore's law is increasing the size of main memory so that now you can spec a commodity server with more main memory that a commodity server had in disk space 10 years ago. An OLAP data cube can be partitioned along one or more of its dimensions to be distributed over a server farm, although at this stage partitioning is more of a research topic than standard practice. Huge main memory allows large cubes to reside in main memory, giving near instantaneous response to queries. For another perspective on in memory OLAP, read the free commentary by Nigel Pendse at the BI-Verdict (it used to be called the OLAP Report) on "What in-memory BI ‘revolution’?"
LinkedIn is a fast growing business oriented social networking site. They have developed Avatara to support their business needs and currently run several cubes on it. The plan is to to open source the code later this year. Avatara is an in memory OLAP server that uses partitioning to provide scalability beyond the capabilities of a single server.
The presentation was fast paced and it has taken me some time to appreciate the full implications of what was said. Here are some thoughts. Avatara offers an API that is reminiscent of Jolap rather than the MDX language that is the standard way of programming OLAP, probably because an API is easier to implement than a programming language. Avatara does not support hierarchies in its dimensions, but the number of dimension in a typical cube seems to be higher than usual. It may be that they use more dimensions rather than hierarchies within a dimension to represent the same information. This is a trade off of roll-up within the cube for slicing of dimensions. Slicing is probably more efficient and easier to implement while a hierarchy is easier for the user to understand as it allows for drill up and down.
Chris mentioned that most dimensions are small and that can be true, however the real problems with OLAP implementations start when you have more than one large dimension and you have to deal with the issue of sparsity in the data cube. Chris spent some time on the problem of a dimension with more than 4 billion elements and this seems to be a real requirement at LinkedIn. Current OLAP servers seem to be limited to 2 billion elements in a dimension, so they are going to be even more constraining than Avatara.
Sunday, October 24, 2010
Accidental Data Empires
In the new world of big data and analytics a winning business model is to find a novel way to collect interesting big data. Once you have the data, the ways to exploit it are endless. It is a phenomenon that I have seen several times, the latest example is Flurry, a company that collects and aggregates data from mobile applications. Peter Farago, VP Marketing, and Sean Byrnes, CTO abd Co-founder of Flurry spoke to the October meeting of the SDForum Business Intelligence SIG on "Your Company’s Mobile App Blind Spot".
The Flurry proposition is simple, they offer a toolkit that an app developer combines with their mobile app. The app developer goes to the Flurry website, creates a free account and downloads the toolkit. Whenever an instance of the app with the Flurry code is activated or used, it collects information about the usage that is sent back to the Flurry. The amount of information is small, usually about 1.2 kB compressed, so the burden of collection is small. At Flurry, the data is collected, cleansed and put in a gigantic data cube. At any time, an app developer can log into the Flurry website and get reports on how their application is being used. You can get a feel for their service by taking the short Analytics developer tour. Flurry have committed that their Analytics service will always be free.
While there are some issues with data collection that Flurry deals with, the quality of the data is great. Every mobile phone has a unique identifier so there is no problem with identifying individual usage patterns. As the service is free, there is very little friction to its use. Flurry estimates that they are in one in five mobile apps that are out there. In fact, for an app developer, the only reason for not using Flurry is that they have chosen to use a rival data collection service.
In the end however, the big winner is Flurry, who collect huge amounts of information about mobile app and phone usage. In the meeting Peter Farago gave us many different analyses of where the mobile smartphone market is and where it is going, including adoption rates for iPhones versus Android based phones and how the follow on market for apps on each platform is developing. You can get a mouthwatering feel for the information they presented by looking at their blog in which they publish a series of analyses from their data. As I write their latest post shows a graph on the "Revenue Shift from Advertising to Virtual Goods Sales" which shows that apps are growing their revenue from sales of virtual goods, while advertising revenue seems to be stagnant.
With data aggregators, there is always something creepy when you discover just how much data they have on you. Earlier this year there was an incident where a Flurry blog post described some details of the iPad a few days before it was announced that they had gleaned from apps running on these new devices in the Apple offices. Steve Jobs was so provoked by this that he called out Flurry by name and changed the iPhone app developer terms of service to prevent apps from collecting certain sorts of data. You can read more about this incident in the blog report on the meeting by my colleague Paul O'Rorke.
The title of this piece is a reference to the entertaining and still readable book Accidental Empires
by Robert X. Cringely about the birth of the personal computer industry and the rivalry between Steve Jobs and Bill Gates.
The Flurry proposition is simple, they offer a toolkit that an app developer combines with their mobile app. The app developer goes to the Flurry website, creates a free account and downloads the toolkit. Whenever an instance of the app with the Flurry code is activated or used, it collects information about the usage that is sent back to the Flurry. The amount of information is small, usually about 1.2 kB compressed, so the burden of collection is small. At Flurry, the data is collected, cleansed and put in a gigantic data cube. At any time, an app developer can log into the Flurry website and get reports on how their application is being used. You can get a feel for their service by taking the short Analytics developer tour. Flurry have committed that their Analytics service will always be free.
While there are some issues with data collection that Flurry deals with, the quality of the data is great. Every mobile phone has a unique identifier so there is no problem with identifying individual usage patterns. As the service is free, there is very little friction to its use. Flurry estimates that they are in one in five mobile apps that are out there. In fact, for an app developer, the only reason for not using Flurry is that they have chosen to use a rival data collection service.
In the end however, the big winner is Flurry, who collect huge amounts of information about mobile app and phone usage. In the meeting Peter Farago gave us many different analyses of where the mobile smartphone market is and where it is going, including adoption rates for iPhones versus Android based phones and how the follow on market for apps on each platform is developing. You can get a mouthwatering feel for the information they presented by looking at their blog in which they publish a series of analyses from their data. As I write their latest post shows a graph on the "Revenue Shift from Advertising to Virtual Goods Sales" which shows that apps are growing their revenue from sales of virtual goods, while advertising revenue seems to be stagnant.
With data aggregators, there is always something creepy when you discover just how much data they have on you. Earlier this year there was an incident where a Flurry blog post described some details of the iPad a few days before it was announced that they had gleaned from apps running on these new devices in the Apple offices. Steve Jobs was so provoked by this that he called out Flurry by name and changed the iPhone app developer terms of service to prevent apps from collecting certain sorts of data. You can read more about this incident in the blog report on the meeting by my colleague Paul O'Rorke.
The title of this piece is a reference to the entertaining and still readable book Accidental Empires
by Robert X. Cringely about the birth of the personal computer industry and the rivalry between Steve Jobs and Bill Gates.
Wednesday, October 13, 2010
A Critique of SQL
SQL is not a perfect solution as I told the audience at the SDForum Business Intelligence SIG September meeting, where I spoke about "Analytics: SQL or NoSQL". The presentation discusses the difference between SQL and structured data on the one hand versus the NoSQL movement and semi-structured data on the other hand. There is more to the presentation than I can fit in one blog post, so here is what I had to say about the SQL language itself. I will write more about the presentation at another time. You can download the presentation from the BI SIG web site.
Firstly the good. SQL has given us a model of a query language that seems so useful as to be essential. Every system that provides persistence has developed a query language. Here are a smattering of examples. The Hibernate object persistence system has Hibernate Query Language (HQL) which has been developed into the Java Persistence Query language (JPQL). Other Java based object oriented persistence systems either use JPQL or their own variant. Hive is a query interface built on top of the Hadoop Map-Reduce engine. Hive was initially developed by Facebook as a simplified way of accessing their Map-Reduce infrastructure when they discovered that many of the people who need to write queries did not have the programming skills to handle a raw Map-Reduce environment. XQuery is a language for querying a set of XML documents. It has been adopted into the SQL language and is also used with stand alone XML systems. If data is important enough to persist, there is almost always a requirement to provide a simple and easy to use reporting system on that data. A query language handles the simple reporting requirements easily.
On the other hand, SQL has many problems. Here is my thoughts on the most important ones. The first problem is that SQL is not a programming language, it is a data access language. SQL is not designed for writing complete programs, it is intended to fetch data from the database and then anything more than a simply formatted report is done in another programming language. This concept of a data access language for accessing a database goes back to the original concept of a database as promulgated by the CODASYL committee in the late 1960's.
While most implementations of SQL add extra features to make it a complete programming language, they do not solve the problem because SQL is a language unlike any of the other other programming language we have. Firstly, SQL is a relational language. Every statement in SQL starts with a table and results in a table. (Table means a table like in a document, a fixed number of columns and as many rows as are required to express the data.) This is a larger chunk of data than programmers are used to handling. The procedural languages that interface to SQL expects to deal with data at most a row at a time. Also, the rigid table of SQL does not fit well into the more flexible data structures of procedural languages.
Moreover SQL is a declarative language where you specify the desired results and the database system works out the best way to produce them. Our other programming languages are procedural where you describe to the system how it should produce the desired result. Programming SQL requires a different mindset from programming in procedural languages. Many programmers, most of whom just dabble in SQL as a sideline, have difficulty making the leap and are frustrated by SQL because it is just not like the programming languages that they are used to. The combination of a relational language and a declarative language creates a costly mismatch between SQL and our other programming systems.
Finally, SQL becomes excessively wordy, repetitive and opaque as the queries becomes more complicated. Sub-Queries start to abound and the need for correlated sub-queries, outer joins and pivoting data for presentation cause queries to explode in length and complexity. Analytics is the province of complicated queries so this is a particular problem for data analysts. In the past I have suggested that persistence is a ripe area for a new programming language, however although there are many new languages being proposed none of them are concerned with persistence or analytics. The nearest thing to an analytics programming language is R which is powerful but neither new nor easy to use.
Firstly the good. SQL has given us a model of a query language that seems so useful as to be essential. Every system that provides persistence has developed a query language. Here are a smattering of examples. The Hibernate object persistence system has Hibernate Query Language (HQL) which has been developed into the Java Persistence Query language (JPQL). Other Java based object oriented persistence systems either use JPQL or their own variant. Hive is a query interface built on top of the Hadoop Map-Reduce engine. Hive was initially developed by Facebook as a simplified way of accessing their Map-Reduce infrastructure when they discovered that many of the people who need to write queries did not have the programming skills to handle a raw Map-Reduce environment. XQuery is a language for querying a set of XML documents. It has been adopted into the SQL language and is also used with stand alone XML systems. If data is important enough to persist, there is almost always a requirement to provide a simple and easy to use reporting system on that data. A query language handles the simple reporting requirements easily.
On the other hand, SQL has many problems. Here is my thoughts on the most important ones. The first problem is that SQL is not a programming language, it is a data access language. SQL is not designed for writing complete programs, it is intended to fetch data from the database and then anything more than a simply formatted report is done in another programming language. This concept of a data access language for accessing a database goes back to the original concept of a database as promulgated by the CODASYL committee in the late 1960's.
While most implementations of SQL add extra features to make it a complete programming language, they do not solve the problem because SQL is a language unlike any of the other other programming language we have. Firstly, SQL is a relational language. Every statement in SQL starts with a table and results in a table. (Table means a table like in a document, a fixed number of columns and as many rows as are required to express the data.) This is a larger chunk of data than programmers are used to handling. The procedural languages that interface to SQL expects to deal with data at most a row at a time. Also, the rigid table of SQL does not fit well into the more flexible data structures of procedural languages.
Moreover SQL is a declarative language where you specify the desired results and the database system works out the best way to produce them. Our other programming languages are procedural where you describe to the system how it should produce the desired result. Programming SQL requires a different mindset from programming in procedural languages. Many programmers, most of whom just dabble in SQL as a sideline, have difficulty making the leap and are frustrated by SQL because it is just not like the programming languages that they are used to. The combination of a relational language and a declarative language creates a costly mismatch between SQL and our other programming systems.
Finally, SQL becomes excessively wordy, repetitive and opaque as the queries becomes more complicated. Sub-Queries start to abound and the need for correlated sub-queries, outer joins and pivoting data for presentation cause queries to explode in length and complexity. Analytics is the province of complicated queries so this is a particular problem for data analysts. In the past I have suggested that persistence is a ripe area for a new programming language, however although there are many new languages being proposed none of them are concerned with persistence or analytics. The nearest thing to an analytics programming language is R which is powerful but neither new nor easy to use.
Saturday, August 28, 2010
Mad Skills for Big Data
Big Data is a big deal these days, so it was with great interest that we welcomed Brian Dolan to the SDForum Business Intelligence SIG August meeting to speak on "MAD Skills: New Analysis Practices for Big Data". MAD is an acronym for Magnetic Agile Deep, and as Brian explained, these skills are all important in handling big data. Brian is a mathematician who came to Fox Interactive Media as Lead Analyst. There he had to help the marketing group with deciding how to price and serve advertisements to users. As they had tens of millions of users that they often knew quite a lot about, and served billions of advertisements per day, this was a big data problem. They used a 40 node Greenplum parallel database system and also had access to a 105 node map reduce cluster.
The presentation started with the three skills. Magnetic, means drawing the analyst in by giving them a free reign over their data and access to use their own methods. At Fox, Brian grappled with a button down DBA to establish his own his own private sandbox where he could access and manipulate his own data. There he could bring in his own data sets, both internal and external. Over time the analysts group established a set of mathematical operations that could be run in parallel over the data in the database system speeding up their analyses by orders of magnitude.
Agile means analytics that adjust react and learn from your business. Brian talked about the virtuous cycle of analytics, where the analyst first acquires new data to be analyzed, then runs analytics to improve performance and finally the analytics causes business practices to suit. He talked through the issues at each step in the cycle and led us through a case study of audience forecasting at Fox which illustrated problems with sampling and scaling results.
Deep analytics is about producing more than reports. In fact Brian pointed out that even data mining can concentrate on finding a single answer to a single problem where big analytics has the need to solve millions of problems at the same time. For example, he suggested that statistical density methods may be better at dealing with big analytics than other more focused techniques. Another problem with deep analysis of big data is that, given the volume of data, it is possible to find data that supports almost any conclusion. Brian used the parable of the Zen Tea Cup to illustrate the issue. The analyst needs to be to approach their analysis without preconceived notions or they will just find exactly what they are looking for.
Of all the topics that came up during the presentation, the one the caused most frissons with the audience was dirty data. Brian's experience has been that cleaning data can lose valuable information and that a good analyst can easily handle dirty data as a part of their analysis. When pressed by an audience member he said "well 'clean' only means that it fits your expectation". As an analyst is looking for the nuggets that do not meet obvious expectations, sanitizing data can lose those very nuggets. The recent trend to load data and then do the cleaning transformations in the database means that the original data is in the database as well as the cleaned data. If that original data is saved, the analyst can do their analysis with either data as they please.
Mad Skills also refers to the ability to do amazing and unexpected things, especially in motocross motor bike riding. Brian's personal sensibilities were more forged in punk rock, so you could say that he showed us the "kick out the jams" approach to analytics. You can get the presentation from the BI SIG web site. The original MAD Skills paper was presented at the 2009 VLDB conference and a version of it is available online.
The presentation started with the three skills. Magnetic, means drawing the analyst in by giving them a free reign over their data and access to use their own methods. At Fox, Brian grappled with a button down DBA to establish his own his own private sandbox where he could access and manipulate his own data. There he could bring in his own data sets, both internal and external. Over time the analysts group established a set of mathematical operations that could be run in parallel over the data in the database system speeding up their analyses by orders of magnitude.
Agile means analytics that adjust react and learn from your business. Brian talked about the virtuous cycle of analytics, where the analyst first acquires new data to be analyzed, then runs analytics to improve performance and finally the analytics causes business practices to suit. He talked through the issues at each step in the cycle and led us through a case study of audience forecasting at Fox which illustrated problems with sampling and scaling results.
Deep analytics is about producing more than reports. In fact Brian pointed out that even data mining can concentrate on finding a single answer to a single problem where big analytics has the need to solve millions of problems at the same time. For example, he suggested that statistical density methods may be better at dealing with big analytics than other more focused techniques. Another problem with deep analysis of big data is that, given the volume of data, it is possible to find data that supports almost any conclusion. Brian used the parable of the Zen Tea Cup to illustrate the issue. The analyst needs to be to approach their analysis without preconceived notions or they will just find exactly what they are looking for.
Of all the topics that came up during the presentation, the one the caused most frissons with the audience was dirty data. Brian's experience has been that cleaning data can lose valuable information and that a good analyst can easily handle dirty data as a part of their analysis. When pressed by an audience member he said "well 'clean' only means that it fits your expectation". As an analyst is looking for the nuggets that do not meet obvious expectations, sanitizing data can lose those very nuggets. The recent trend to load data and then do the cleaning transformations in the database means that the original data is in the database as well as the cleaned data. If that original data is saved, the analyst can do their analysis with either data as they please.
Mad Skills also refers to the ability to do amazing and unexpected things, especially in motocross motor bike riding. Brian's personal sensibilities were more forged in punk rock, so you could say that he showed us the "kick out the jams" approach to analytics. You can get the presentation from the BI SIG web site. The original MAD Skills paper was presented at the 2009 VLDB conference and a version of it is available online.
Saturday, August 14, 2010
Analytics at Work
Analytics has become a major driving force for competitive advantage in business. The new book "Analytics at Work: Smarter Decisions, Better Results
" by Thomas H. Davenport, Jeanne G. Harris and Robert Morison discusses what analytics can do for a business, how to manage analytics and how to make a business more analytical.
Analytics at Work has a useful introductory chapter and then divides into two parts. The first part discusses five major aspects of analytics in a business environment. The second part looks at the lifecycle of managing analytics in a business. The organization is good and there is no overlap between the topics in each part, however the order in which the information is presented seems designed to put the reader off.
The first part starts with a plodding chapter on what needs to be done to get the data organized and related topics, followed by a diffuse chapter called Enterprise. The interesting chapters in this part are the last two chapters. The Targets chapter discusses the important topic of picking targets for analytics. The Analysts chapter discusses how to effectively employ and organize analysts in a large enterprise. Similarly the second part of the book starts with a plodding chapter on how to Embed Analytics in Business Processes, followed by much more inspiring chapters on building an analytical culture, and the need to continually review a business comprehensively as part of an analytics push. If you find yourself stuck reading the book, try skipping to one of the interesting chapters that I have indicated.
Scattered throughout the book are many useful tools. In the introductory chapter there are the six key questions that an analyst asks. We come back to these questions from several places in the book. Running throughout the book is a five step capability maturity model for judging how analytical an organizations is and showing the path to making the organization more analytical. Each chapter in the first part ends with a discussion on how to take that aspect of the organization through the five steps.
It is important to understand the target audience. The book is aimed at senior management and executives, particularly in large enterprises. While the book contains many brief case studies as inspiration and it touches on all the important management issues that need to be considered, it does not go into great depth about what analytics is or the specific analytical techniques and how they can be used. This is not a book for analysts, unless they have ambitions to grow their career beyond analytics. I recommend this book to anyone in the target audience who wants to grow their organizations analytics capabilities.
" by Thomas H. Davenport, Jeanne G. Harris and Robert Morison discusses what analytics can do for a business, how to manage analytics and how to make a business more analytical.Analytics at Work has a useful introductory chapter and then divides into two parts. The first part discusses five major aspects of analytics in a business environment. The second part looks at the lifecycle of managing analytics in a business. The organization is good and there is no overlap between the topics in each part, however the order in which the information is presented seems designed to put the reader off.
The first part starts with a plodding chapter on what needs to be done to get the data organized and related topics, followed by a diffuse chapter called Enterprise. The interesting chapters in this part are the last two chapters. The Targets chapter discusses the important topic of picking targets for analytics. The Analysts chapter discusses how to effectively employ and organize analysts in a large enterprise. Similarly the second part of the book starts with a plodding chapter on how to Embed Analytics in Business Processes, followed by much more inspiring chapters on building an analytical culture, and the need to continually review a business comprehensively as part of an analytics push. If you find yourself stuck reading the book, try skipping to one of the interesting chapters that I have indicated.
Scattered throughout the book are many useful tools. In the introductory chapter there are the six key questions that an analyst asks. We come back to these questions from several places in the book. Running throughout the book is a five step capability maturity model for judging how analytical an organizations is and showing the path to making the organization more analytical. Each chapter in the first part ends with a discussion on how to take that aspect of the organization through the five steps.
It is important to understand the target audience. The book is aimed at senior management and executives, particularly in large enterprises. While the book contains many brief case studies as inspiration and it touches on all the important management issues that need to be considered, it does not go into great depth about what analytics is or the specific analytical techniques and how they can be used. This is not a book for analysts, unless they have ambitions to grow their career beyond analytics. I recommend this book to anyone in the target audience who wants to grow their organizations analytics capabilities.
Saturday, July 24, 2010
Data Management in the Cloud
Over the last couple of years, I have seen several presentations on the computing Cloud and how it is the next big thing. I realized that I still have a lot to learn from Daniel Graham's presentation "Data Management in the Cloud" at the July meeting of the Business Intelligence SIG. Dan leads Active Data Warehouse marketing programs for Teradata. If you have been living under a rock and do not know what cloud computing is, Wikipedia has a reasonable explanation. Dan distinguished between the public cloud as a rentable computing resource like Amazon's Elastic Computing Service and a private cloud which is your businesses computing resources in a datacenter behind the company firewall which uses virtualization software like VMWare to allow many applications to share hardware.
The big picture that Dan painted is that cloud computing is coming and that you need to get ready for it. By 2015, 20% of computing resources worldwide will be in the cloud. Start now by getting experience with the cloud to find out what works, what needs to be changed to make it work and what does not work. Teradata has been experimenting with cloud computing and is working with hardware and software vendors like VMWare and Amazon to ensure that Teradata database systems work well in the cloud. Informatica is another example of a software vendor that is working to ensure that their data integration software works well in the cloud and between clouds. NetFlix is an example of a company that has adopted cloud computing and recently announced that they were moving all their movie hosting into the Amazon computing cloud. The US Government is the leading user of cloud services having moved much of their computing needs into the cloud.
Cloud computing uses commodity hardware, which combined with the overhead of virtual machine software will not give you the best performance, however it is "good enough" for most applications. Dan took the well known quote from the movie Forrest Gump and bent it to his needs. “Clouds are like a box of chocolates. You never know what you're gonna get.” There is some high end software that is not suitable for cloud computing, the main problem coming from high IO requirements. The size and capabilities of a cloud computing host is often optimized to run a single instance Oracle database doing OLTP. In practice most applications are less demanding than this.
There were many other interesting tidbits in the presentation. Here are some examples. It is more expensive to get data out of a cloud than to bring it in. Why is unknown, but something to take into consideration when using a cloud. An interesting application for cloud computing is what Dan called "Workload Isolation". The idea is that when you have partners or consultants who need access to your data it is often preferable to put the data they need in the cloud rather than let them inside your firewall. In all the examples that Dan showed of Business Intelligence applications in the cloud, he talked about a Data Mart with the implication that a full Enterprise Data Warehouse was too large and demanding an application for the cloud for now.
The slides from the presentation are available at the SDForum Business Intelligence SIG web site.
The big picture that Dan painted is that cloud computing is coming and that you need to get ready for it. By 2015, 20% of computing resources worldwide will be in the cloud. Start now by getting experience with the cloud to find out what works, what needs to be changed to make it work and what does not work. Teradata has been experimenting with cloud computing and is working with hardware and software vendors like VMWare and Amazon to ensure that Teradata database systems work well in the cloud. Informatica is another example of a software vendor that is working to ensure that their data integration software works well in the cloud and between clouds. NetFlix is an example of a company that has adopted cloud computing and recently announced that they were moving all their movie hosting into the Amazon computing cloud. The US Government is the leading user of cloud services having moved much of their computing needs into the cloud.
Cloud computing uses commodity hardware, which combined with the overhead of virtual machine software will not give you the best performance, however it is "good enough" for most applications. Dan took the well known quote from the movie Forrest Gump and bent it to his needs. “Clouds are like a box of chocolates. You never know what you're gonna get.” There is some high end software that is not suitable for cloud computing, the main problem coming from high IO requirements. The size and capabilities of a cloud computing host is often optimized to run a single instance Oracle database doing OLTP. In practice most applications are less demanding than this.
There were many other interesting tidbits in the presentation. Here are some examples. It is more expensive to get data out of a cloud than to bring it in. Why is unknown, but something to take into consideration when using a cloud. An interesting application for cloud computing is what Dan called "Workload Isolation". The idea is that when you have partners or consultants who need access to your data it is often preferable to put the data they need in the cloud rather than let them inside your firewall. In all the examples that Dan showed of Business Intelligence applications in the cloud, he talked about a Data Mart with the implication that a full Enterprise Data Warehouse was too large and demanding an application for the cloud for now.
The slides from the presentation are available at the SDForum Business Intelligence SIG web site.
Saturday, June 26, 2010
Winning With Big Data
Michael Driscoll gave us Secrets of a Successful Data Scientist at the June meeting of the SDForum Business Intelligence SIG in his talk "Winning With Big Data". Michael is founder of a data consultancy Dataspora, where he has done work on projects ranging to analyzing baseball pitchers through helping cell phone companies understand their customer churn. You can see slides for the talk here, and follow Micheal's thoughts in his excellent blog on the Dataspora site.
After Michael revved up the crowd by giving the Hal Varian quote that "... the sexy job in the next ten years will be statisticians", he went through 9 ways to win as a Data Scientist. His first suggestion is to use the right tools. Michael uses a variety of tools including database systems, Hadoop and the R language. Large data takes a long time to process and often we can gain insights by just working with a sample of the data, however you have to be careful when taking a sample to ensure that it makes sense and that the results will scale. Which leads us to the another way to win, which is to know, understand and use statistics.
Statistics is a field of mathematics that is still developing and it is not easy, however statistics is a core competence of a Data Scientist. It is not enough to do the analysis, the Data Scientist has to be able to present the results and turn them into a compelling story. Both analysis and presentation requires good visualization tools and the knowledge of how to use them.
To illustrate his ways to win, Michael led us through a specific example of a successful data analysis that he had done. He had been asked by a cell phone company to investigate customer churn. Although he looked at the data in several different ways, his successful analysis went as follows. The starting point was Call Data Record (CDR) which records each call that a customer makes. Cell phone traffic generates billions of CDRs, so Michael first cut the data set down to a more manageable size by just looking at the CDRs for a single city. He then created social graphs between customers that call one another frequently, and was able to show that if one customer dropped service it was a predictor that other customers in that social graph would also leave the service. The study ended with a clever visualization of connected customers leaving the cell phone provider.
After Michael revved up the crowd by giving the Hal Varian quote that "... the sexy job in the next ten years will be statisticians", he went through 9 ways to win as a Data Scientist. His first suggestion is to use the right tools. Michael uses a variety of tools including database systems, Hadoop and the R language. Large data takes a long time to process and often we can gain insights by just working with a sample of the data, however you have to be careful when taking a sample to ensure that it makes sense and that the results will scale. Which leads us to the another way to win, which is to know, understand and use statistics.
Statistics is a field of mathematics that is still developing and it is not easy, however statistics is a core competence of a Data Scientist. It is not enough to do the analysis, the Data Scientist has to be able to present the results and turn them into a compelling story. Both analysis and presentation requires good visualization tools and the knowledge of how to use them.
To illustrate his ways to win, Michael led us through a specific example of a successful data analysis that he had done. He had been asked by a cell phone company to investigate customer churn. Although he looked at the data in several different ways, his successful analysis went as follows. The starting point was Call Data Record (CDR) which records each call that a customer makes. Cell phone traffic generates billions of CDRs, so Michael first cut the data set down to a more manageable size by just looking at the CDRs for a single city. He then created social graphs between customers that call one another frequently, and was able to show that if one customer dropped service it was a predictor that other customers in that social graph would also leave the service. The study ended with a clever visualization of connected customers leaving the cell phone provider.
Sunday, June 13, 2010
Reporting from the Production Database
Salesforce.com does their analytics directly out of their production database. For me, this was the interesting story that emerged from the talk on "Real Time Analytics at Salesforce.com" at the May meeting of the SDForum Business Intelligence SIG. Note that this post is not a report on the meeting, rather it is a reflection on a topic that came up during the meeting. Both my co-chair Paul O'Rorke and SIG member James Downey have written great summaries of the meeting.
Directly reporting from a production database is an issue that comes up from time to time. Deciding on whether to do it is a two step process. The first question is to ask whether it is possible. A database can be oriented to report the current state of affairs or alternatively to contain a record of how we got to the current state of affairs. In practice we need both views, and it is common to have a production database that is oriented to the maintaining the current status and a data warehouse that maintains the historical record. Typically an enterprise has several databases with production information and the historical record is combined in a single reporting data warehouse.
The tension between the requirements for production and reporting databases shows up in a number of ways. Production needs a fast transaction execution. One way to achieve this is to make the database small, cutting out anything that is not really needed. On the other hand, we want to keep as much information as possible for reporting, so that we can compare this time period with a year ago or maybe even two years ago. Reporting wants a simple database structure like a star schema that makes it straightforward to write ad-hoc queries that that generate good answers. Production databases tend to have more interlinked structures.
Salesforce.com is in the business of Customer Relationship Management (CRM), where it is useful to keep the historical record of interactions with each customer. As Salesforce.com has the historical record in their production database, reporting from that database makes perfect sense. In fact much of the impetus for real time data warehousing has come from CRM like applications. One common example is where a business wants to drive call center applications from data in their data warehouse.
The next question is whether it is a good idea to combine reporting and production queries in the same database. Production queries are short, usually reading a few records and then updating and inserting a few records. Reporting queries are read only, but they are longer running and may touch many records to produce aggregate results. A potential issue is that a longer running reporting query may interfere with production queries and prevent them from doing their job. This is the other major reason for doing reporting from a separate database than the production database.
The Oracle database used by Salesforce.com has optimistic read locking so that read only queries do not lock out queries that update the database. Also, as came out in the presentation, Salesforce.com has a multi-tenant database where each customer customizes their use of data fields in a different ways. Because of this, they sometimes copy the data out of the big table into a smaller temporary table to transform the data into the form that the customers query expects. Making a copy of the relevant data for further massaging is a common tactic in data reporting tools so this is not unusual. It also gets the reporting data out of the way of production data so they two do not interfere with one another.
Finally, Salesforce.com is large enough that they can afford a luxury of having a performance team whose sole purpose is to look at queries that take the longest to run or use up the most resources. Any database application requires some performance tuning, however it is especially important when doing reporting from a production database.
Directly reporting from a production database is an issue that comes up from time to time. Deciding on whether to do it is a two step process. The first question is to ask whether it is possible. A database can be oriented to report the current state of affairs or alternatively to contain a record of how we got to the current state of affairs. In practice we need both views, and it is common to have a production database that is oriented to the maintaining the current status and a data warehouse that maintains the historical record. Typically an enterprise has several databases with production information and the historical record is combined in a single reporting data warehouse.
The tension between the requirements for production and reporting databases shows up in a number of ways. Production needs a fast transaction execution. One way to achieve this is to make the database small, cutting out anything that is not really needed. On the other hand, we want to keep as much information as possible for reporting, so that we can compare this time period with a year ago or maybe even two years ago. Reporting wants a simple database structure like a star schema that makes it straightforward to write ad-hoc queries that that generate good answers. Production databases tend to have more interlinked structures.
Salesforce.com is in the business of Customer Relationship Management (CRM), where it is useful to keep the historical record of interactions with each customer. As Salesforce.com has the historical record in their production database, reporting from that database makes perfect sense. In fact much of the impetus for real time data warehousing has come from CRM like applications. One common example is where a business wants to drive call center applications from data in their data warehouse.
The next question is whether it is a good idea to combine reporting and production queries in the same database. Production queries are short, usually reading a few records and then updating and inserting a few records. Reporting queries are read only, but they are longer running and may touch many records to produce aggregate results. A potential issue is that a longer running reporting query may interfere with production queries and prevent them from doing their job. This is the other major reason for doing reporting from a separate database than the production database.
The Oracle database used by Salesforce.com has optimistic read locking so that read only queries do not lock out queries that update the database. Also, as came out in the presentation, Salesforce.com has a multi-tenant database where each customer customizes their use of data fields in a different ways. Because of this, they sometimes copy the data out of the big table into a smaller temporary table to transform the data into the form that the customers query expects. Making a copy of the relevant data for further massaging is a common tactic in data reporting tools so this is not unusual. It also gets the reporting data out of the way of production data so they two do not interfere with one another.
Finally, Salesforce.com is large enough that they can afford a luxury of having a performance team whose sole purpose is to look at queries that take the longest to run or use up the most resources. Any database application requires some performance tuning, however it is especially important when doing reporting from a production database.
Monday, April 26, 2010
Business Rules OK!
Performance Management Systems collect the data to make decisions but they do not make decisions, they do not ensure that decisions get made or even track the results of the decision so made. James Taylor (no relation) called this the "over-instrumented" enterprise when he spoke to the the April meeting of the SDForum Business Intelligence SIG on "Performance Management and Agility". James is CEO of Decision Management Solutions where he consults on using technology to better effect decision making.
James divides the decisions that an organization makes into three levels: strategic, tactical and operational. He is interested in the operation decisions, the little decisions that are taken all the time. An example of an operational decision is what offer to make to a customer that has called a call center. Every enterprise has their own set of operational decisions, however they have the characteristic that is a large number of them that in aggregate they represent a lot of value, so they are well worth managing.
Many operational decisions are or should be automated, and there are a set of principles that need to be recognized when decision making is automated. The first principle is that no decision is going to be forever, so the logic for making the decision should not be locked up into something inflexible such as program code. Much better to use a rules based decision engine which allows everybody to see the rules in a language that they can understand. Another principle is that making a decision is a business process and as such should be managed. A good business rules engine allows rules to be tested, measured and perhaps even simulated in action to understand what they are doing and how they can be optimized.
According to James, the purpose of the information gathered for a Performance Management Systems is to make decisions, so it should be used to make decisions. Too many enterprises are over-instrumented. They have spent all their effort to get and present the data, however they have no measurable ability to turn that data into actions. You can read more about these ideas in the book Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions
by James Taylor and Neil Raden. You can also read my co-chair Paul O'Rorke's take on the meeting in his blog.
James divides the decisions that an organization makes into three levels: strategic, tactical and operational. He is interested in the operation decisions, the little decisions that are taken all the time. An example of an operational decision is what offer to make to a customer that has called a call center. Every enterprise has their own set of operational decisions, however they have the characteristic that is a large number of them that in aggregate they represent a lot of value, so they are well worth managing.
Many operational decisions are or should be automated, and there are a set of principles that need to be recognized when decision making is automated. The first principle is that no decision is going to be forever, so the logic for making the decision should not be locked up into something inflexible such as program code. Much better to use a rules based decision engine which allows everybody to see the rules in a language that they can understand. Another principle is that making a decision is a business process and as such should be managed. A good business rules engine allows rules to be tested, measured and perhaps even simulated in action to understand what they are doing and how they can be optimized.
According to James, the purpose of the information gathered for a Performance Management Systems is to make decisions, so it should be used to make decisions. Too many enterprises are over-instrumented. They have spent all their effort to get and present the data, however they have no measurable ability to turn that data into actions. You can read more about these ideas in the book Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions
by James Taylor and Neil Raden. You can also read my co-chair Paul O'Rorke's take on the meeting in his blog.
Sunday, April 04, 2010
Web Analytics 2.0
Web analytics is changing fast as we discovered at the March meeting of the SDForum Business Intelligence SIG. Avinash Kaushik, Analytics Evangelist for Google, spoke on 'Web Analytics 2.0: Rethinking Decision Making in a "2.0" World'. Avinash started off by telling us how he became well known as a web analytics guru. A few years ago he started writing his blog "Occam's Razor". It soon gathered a large readership and a publisher approached him to write a book. His first book "Web Analytics: An Hour a Day
" was a distillation of his blog posts. The book is a best seller even although much of its contents is available for free on the web. His second book "Web Analytics 2.0
" came out recently.
Avinash is an excellent communicator with a strong personal style. One aspect of that style, quite obvious from his blog posts, is the urge to create lists of ideas. For his presentation, Avinash offered us a list of simple ideas on web sites metrics and analytics. Here are some of the ideas that he presented.
The first idea is simple and direct - Don't Suck. The suckage of a web page can be measured by a metric called Bounce. This is a relatively new metric that we had not previously heard discussed at the Business Intelligence SIG. Bounce measures the users whose experience of the web page and site is, as Avinash put it "I came. I puked. I left." and he showed us some pretty pukey pages that might back up this behavior. A typical analysis is to look at the pages with the highest bounce rate, determine why they cause that behavior and what can be done about it.
His next idea is Segment or Die. Analytics is about aggregating data to make sense of large datasets, however over-aggregation results in a single number and nothing to compare it with. Segmenting the data gives us a number of data items that we can compare. Avinash showed us a simple example where he took a hospital web site and classified the content into 8 segments and then compared the amount of content against the number of page views in each segment. It was immediately apparent where the effort should go into adding and improving content.
Analyzing your web logs only tells a part of the story, you also have to worry about what the analytics cannot tell you. Ex Defense Secretary Donald Rumsfeld is infamous for having talked about "the known knowns, the known unknowns, and the unknown unknowns". The unknown unknowns are the things that you don't even know that you don't know and therefore the thing you should be most worried about. You can start to get a handle on what you do not know by looking at your performance relative to your competitors. This is known as Benchmarking, or in the case of a deep study as Competitive Intelligence. For an example of what can be done, a recent post on the Occam's Razor blog discusses 8 sources for Competitive Intelligence data.
Most web site analytics only looks for the one big conversion from a web site, however there are many other small conversions that are tracked and worth evaluating because there may be hidden value lurking in the long tail. For example, recently Avinash wanted to know what his blog was worth so that he could defend taking time away from the family to write it. After determining a value for each reader, he started adding up all the other micro-conversion like people who subscribe to the RSS feed and advertisements for his books and the non-profit organizations that he supports. Overall he came up with a figure of about $26000 per month. Now Avinash does not make a penny from his blog, so this is notional money that adds to his personal brand value, but that value seems to make the effort of writing the blog well worth the time spent.
The next idea is Fail Faster. By this Avinash means do lots of different experiment, many of which will fail, to find out what works. He led us through an example from the Obama Presidential campaign. President Obama raised huge amounts of money from many small donations on his web site. The initial web page worked well. The experiments were to try some variations on the theme. Pages with video, and stirring video at that, did very badly. A simple picture of Obama with his family did a little better than the initial picture, so that one was chosen.
Avinash showed us this example to make a number of points. The Obama analytics team was tiny. Often the best work is done by a small agile team that has the freedom to experiment. The team used free tools. Avinash believes that that good people are much more important than good tools. His suggestion for dividing up the analytics budget is to spend 90% on people and 10% on tools. Sometimes, a web site design feature starts from a HiPPO (Highest Paid Persons Opinion), which can be destructively bad, and difficult to get around because in all organizations the highest paid persons opinion is taken very seriously. The best way to counter a HiPPO is to show that other ideas work better through the results of experiments that produce hard evidence.
While some may think that web analytics is a mostly solved problem, Avinash believes we are just starting to figure out what can be done, and that there is plenty of room for more innovation. I will continue to read Occam's Razor to find out where he takes us next.
" was a distillation of his blog posts. The book is a best seller even although much of its contents is available for free on the web. His second book "Web Analytics 2.0" came out recently.Avinash is an excellent communicator with a strong personal style. One aspect of that style, quite obvious from his blog posts, is the urge to create lists of ideas. For his presentation, Avinash offered us a list of simple ideas on web sites metrics and analytics. Here are some of the ideas that he presented.
The first idea is simple and direct - Don't Suck. The suckage of a web page can be measured by a metric called Bounce. This is a relatively new metric that we had not previously heard discussed at the Business Intelligence SIG. Bounce measures the users whose experience of the web page and site is, as Avinash put it "I came. I puked. I left." and he showed us some pretty pukey pages that might back up this behavior. A typical analysis is to look at the pages with the highest bounce rate, determine why they cause that behavior and what can be done about it.
His next idea is Segment or Die. Analytics is about aggregating data to make sense of large datasets, however over-aggregation results in a single number and nothing to compare it with. Segmenting the data gives us a number of data items that we can compare. Avinash showed us a simple example where he took a hospital web site and classified the content into 8 segments and then compared the amount of content against the number of page views in each segment. It was immediately apparent where the effort should go into adding and improving content.
Analyzing your web logs only tells a part of the story, you also have to worry about what the analytics cannot tell you. Ex Defense Secretary Donald Rumsfeld is infamous for having talked about "the known knowns, the known unknowns, and the unknown unknowns". The unknown unknowns are the things that you don't even know that you don't know and therefore the thing you should be most worried about. You can start to get a handle on what you do not know by looking at your performance relative to your competitors. This is known as Benchmarking, or in the case of a deep study as Competitive Intelligence. For an example of what can be done, a recent post on the Occam's Razor blog discusses 8 sources for Competitive Intelligence data.
Most web site analytics only looks for the one big conversion from a web site, however there are many other small conversions that are tracked and worth evaluating because there may be hidden value lurking in the long tail. For example, recently Avinash wanted to know what his blog was worth so that he could defend taking time away from the family to write it. After determining a value for each reader, he started adding up all the other micro-conversion like people who subscribe to the RSS feed and advertisements for his books and the non-profit organizations that he supports. Overall he came up with a figure of about $26000 per month. Now Avinash does not make a penny from his blog, so this is notional money that adds to his personal brand value, but that value seems to make the effort of writing the blog well worth the time spent.
The next idea is Fail Faster. By this Avinash means do lots of different experiment, many of which will fail, to find out what works. He led us through an example from the Obama Presidential campaign. President Obama raised huge amounts of money from many small donations on his web site. The initial web page worked well. The experiments were to try some variations on the theme. Pages with video, and stirring video at that, did very badly. A simple picture of Obama with his family did a little better than the initial picture, so that one was chosen.
Avinash showed us this example to make a number of points. The Obama analytics team was tiny. Often the best work is done by a small agile team that has the freedom to experiment. The team used free tools. Avinash believes that that good people are much more important than good tools. His suggestion for dividing up the analytics budget is to spend 90% on people and 10% on tools. Sometimes, a web site design feature starts from a HiPPO (Highest Paid Persons Opinion), which can be destructively bad, and difficult to get around because in all organizations the highest paid persons opinion is taken very seriously. The best way to counter a HiPPO is to show that other ideas work better through the results of experiments that produce hard evidence.
While some may think that web analytics is a mostly solved problem, Avinash believes we are just starting to figure out what can be done, and that there is plenty of room for more innovation. I will continue to read Occam's Razor to find out where he takes us next.
Sunday, December 20, 2009
BI Megatrends: Directions for Business Intelligence in 2010
Every year David Stodder, Research Fellow with Ventana Research and editor-at-large with Intelligent Enterprise writes a column on Business Intelligence Megatrends for the next year. This column looks back at what has happened in the last year and what he expects to happen in the next year. This year David also presented his thoughts to the December meeting of the SDForum Business Intelligence SIG. David talked about many topics, here I will just cover what he said about the big players.
Two years ago there was a huge wave of consolidation in Business Intelligence when the major independent BI vendors were bought up by IBM, SAP and Oracle, who along with Microsoft are the major enterprise software vendors. In the last year SAP has integrated Business Objects with SAP software to the point that SAP is now ready to threaten Oracle.
Consolidation has not finished. In 2009, two important mergers were announced. Firstly IBM bought SPSS to round out its analytics capabilities. This move threatens SAS which is in the same market, however SAS is a larger and more successful company that SPSS, also SAS is a private company which means that it does not necessarily need to respond to the pressures to consolidate.
The other merger is Oracle's offer to buy Sun and the effect that has on Oracle's relationship with HP. HP and Sun are bitter rivals for enterprise hardware, and HP was the launch partner for Oracle Exadata, the high end Oracle database. Now Oracle is pushing Sun hardware with Exadata, leaving HP in the lurch. David pointed out that there are plenty of up and coming companies with scalable database systems for HP to buy up. That list includes Aster Data Systems, GreenPlum, Infobright, ParAccell and Vertica. Expect to see something happen in this area in 2010.
Of the three major database vendors, Microsoft has the weakest offering, despite SQL Server 2008. However Microsoft does have the advantage of the Excel spreadsheet which remains the most used BI reporting tool. A new version of Excel is due in 2010. Also Microsoft is making a determined push in the direction of collaboration tools with SharePoint. As we heard at the BI SIG November meeting, collaboration is an important new direction for enterprise software capabilities.
Two years ago there was a huge wave of consolidation in Business Intelligence when the major independent BI vendors were bought up by IBM, SAP and Oracle, who along with Microsoft are the major enterprise software vendors. In the last year SAP has integrated Business Objects with SAP software to the point that SAP is now ready to threaten Oracle.
Consolidation has not finished. In 2009, two important mergers were announced. Firstly IBM bought SPSS to round out its analytics capabilities. This move threatens SAS which is in the same market, however SAS is a larger and more successful company that SPSS, also SAS is a private company which means that it does not necessarily need to respond to the pressures to consolidate.
The other merger is Oracle's offer to buy Sun and the effect that has on Oracle's relationship with HP. HP and Sun are bitter rivals for enterprise hardware, and HP was the launch partner for Oracle Exadata, the high end Oracle database. Now Oracle is pushing Sun hardware with Exadata, leaving HP in the lurch. David pointed out that there are plenty of up and coming companies with scalable database systems for HP to buy up. That list includes Aster Data Systems, GreenPlum, Infobright, ParAccell and Vertica. Expect to see something happen in this area in 2010.
Of the three major database vendors, Microsoft has the weakest offering, despite SQL Server 2008. However Microsoft does have the advantage of the Excel spreadsheet which remains the most used BI reporting tool. A new version of Excel is due in 2010. Also Microsoft is making a determined push in the direction of collaboration tools with SharePoint. As we heard at the BI SIG November meeting, collaboration is an important new direction for enterprise software capabilities.
Monday, November 30, 2009
Consumerization of IT
A new generation is entering the workforce and they are just not going to take it any more. Brian Gentile, CEO of Jaspersoft, did not say these exact words, but it conveys the intent of the introduction to his talk on "Consumerization of IT" at the November meeting of the SDForum Business Intelligence SIG.
Brian was talking about Generation Y, the first generation to have grown up with computers and instant communication to the extent that they take them for granted. More that that they have expectations about these tools and what they can do with them. Unfortunately, enterprise software has often created systems that are slow, ugly and so difficult to use that it can requires weeks of training. While previous generations have put up with difficult software because they know no better, Gen Y does know that it can be better and is not going to put up with software that does not match up.
Brian identified 4 characteristics that Business Intelligence, or any enterprise software must provide to meet the next generations expectations. They are:
While I have heard people argue that current Business Intelligence software does not provide a good user experience, Brian put a positive light on this trend, as if the change is for the good and the right thing to do. He is certainly positioning JasperSoft to provide these features and meet the requirements of the next generation.
Brian ended with another optimistic note. The cost of Information Technology is coming down with cheaper hardware and Open Source software. CIO's can direct the money they save to new innovative projects. A good example of this movement is Ingres talking about "The New Economics of IT" as they have been doing for some time.
Brian was talking about Generation Y, the first generation to have grown up with computers and instant communication to the extent that they take them for granted. More that that they have expectations about these tools and what they can do with them. Unfortunately, enterprise software has often created systems that are slow, ugly and so difficult to use that it can requires weeks of training. While previous generations have put up with difficult software because they know no better, Gen Y does know that it can be better and is not going to put up with software that does not match up.
Brian identified 4 characteristics that Business Intelligence, or any enterprise software must provide to meet the next generations expectations. They are:
- Elegant presentation.
- Easy access to data.
- Extensive Customization.
- Built In Collaboration.
While I have heard people argue that current Business Intelligence software does not provide a good user experience, Brian put a positive light on this trend, as if the change is for the good and the right thing to do. He is certainly positioning JasperSoft to provide these features and meet the requirements of the next generation.
Brian ended with another optimistic note. The cost of Information Technology is coming down with cheaper hardware and Open Source software. CIO's can direct the money they save to new innovative projects. A good example of this movement is Ingres talking about "The New Economics of IT" as they have been doing for some time.
Friday, October 23, 2009
Database Systems for Analytics
The question "what are the attributes of a database system for analytics?" came up during Omer Trajman's talk to the October meeting of the SDForum Business Intelligence SIG. The talk was titled "The Evolution of BI from Back Office to Business Critical Analytics". In the talk Omer gave several examples of applications that use real time analytics and explained the special attributes of each application. As he runs field engineering for Vertica, a Database Systems vendor, I am sure that these examples were based on his experience with Vertica deployments, however Omer was careful to keep his talk vendor neutral.
So what are the the attributes of a database system for analytics? Omer discussed three attributes. Firstly, an analytics database system cannot use the row level locking that is found in a traditional transaction processing database. The database system needs to provide snapshot isolation that gives a query a consistent view of the data while not preventing other operations like data loads. Having helped implement a system like this in the past, I am in total agreement with Omer.
The second attribute is the need to allow concurrency between loading and querying data. While this is related to the first attribute, it also comes with its own issues. Bulk loads are more efficient (particularly for a columnar database like Vertica), however, if you want access to the most up to minute data you need to do loads in small increments so that the data is available for query as soon as it is loaded. Managing this balance is difficult and as yet it has not been completely solved. Again, I have worked on this issue in several different systems.
The final attribute was scaleout, that is the ability to add more processing systems to handle more data and larger queries. We are building systems out of hundreds and thousands of computer systems. Scaleout is vital to effectively use these systems.
So what are the the attributes of a database system for analytics? Omer discussed three attributes. Firstly, an analytics database system cannot use the row level locking that is found in a traditional transaction processing database. The database system needs to provide snapshot isolation that gives a query a consistent view of the data while not preventing other operations like data loads. Having helped implement a system like this in the past, I am in total agreement with Omer.
The second attribute is the need to allow concurrency between loading and querying data. While this is related to the first attribute, it also comes with its own issues. Bulk loads are more efficient (particularly for a columnar database like Vertica), however, if you want access to the most up to minute data you need to do loads in small increments so that the data is available for query as soon as it is loaded. Managing this balance is difficult and as yet it has not been completely solved. Again, I have worked on this issue in several different systems.
The final attribute was scaleout, that is the ability to add more processing systems to handle more data and larger queries. We are building systems out of hundreds and thousands of computer systems. Scaleout is vital to effectively use these systems.
Sunday, July 26, 2009
LinkedIn's Data
LinkedIn has an extraordinary data resource. They have more than 40 million members and a complete job history of each member, in some cases going back 30 or 40 years. DJ Patil, Chief Scientist and Senior Director of Product Analytics at LinkedIn showed us some examples of their data when he spoke to a packed meeting of the SDForum Business Intelligence SIG on "The Analytics Behind LinkedIn" last week. Paul O'Rorke has written an excellent account of the meeting and here I am just adding my impression to that record.
DJ believes in the growing importance of the "data analyst" as a profession. He backed up that belief with some hard data when he shows us the growing importance of the job title over the last 35 years. Up to the mid 90's the appearance of that job title as a percentage of all job titles was flat, but since then it has been growing at a steady pace. As an aside, DJ told us that they use the Amazon Mechanical Turk service to do data cleansing of things like job titles. This is the first time I have heard of the service being used for this purpose.
We were shown other interesting examples of LinkedIn analytics including the change in the top five job titles over the Dot Com bust and an excellent display of the volume of cross country links between LinkedIn members. The big problem with this data is that we cannot have access to it because it is private to LinkedIn and they will keep it private to protect the privacy of their members.
DJ believes in the growing importance of the "data analyst" as a profession. He backed up that belief with some hard data when he shows us the growing importance of the job title over the last 35 years. Up to the mid 90's the appearance of that job title as a percentage of all job titles was flat, but since then it has been growing at a steady pace. As an aside, DJ told us that they use the Amazon Mechanical Turk service to do data cleansing of things like job titles. This is the first time I have heard of the service being used for this purpose.
We were shown other interesting examples of LinkedIn analytics including the change in the top five job titles over the Dot Com bust and an excellent display of the volume of cross country links between LinkedIn members. The big problem with this data is that we cannot have access to it because it is private to LinkedIn and they will keep it private to protect the privacy of their members.
Saturday, July 11, 2009
Graphs That Suck
Many years ago in the early days of the web, I learned about web site design by reading "Web Pages That Suck: Learn Good Design by Looking at Bad Design". It is a delightfully easy beginner level crawl through web site design, filled with examples ranging from excellent to awful with a capital 'A'. I would recommend the book
today except that the examples that make up the bulk of the book are way out of date.
For Business Intelligence the equivalent would be a book called something like "Graphs that Suck", and Stephen Few's Perceptual Edge blog is a good place to find examples of this genre. Recently they posted a spectacularly bad example, a pie chart put out by Business Objects to promote a user conference. I will not repeat the critique, however I will say that if this is an example of what Business Objects thinks their software should be used for, I would be leery of using it!
today except that the examples that make up the bulk of the book are way out of date.For Business Intelligence the equivalent would be a book called something like "Graphs that Suck", and Stephen Few's Perceptual Edge blog is a good place to find examples of this genre. Recently they posted a spectacularly bad example, a pie chart put out by Business Objects to promote a user conference. I will not repeat the critique, however I will say that if this is an example of what Business Objects thinks their software should be used for, I would be leery of using it!
Monday, June 22, 2009
Now You See It
While visualization can be an effective tool to understand data, too many software vendors seem to view visualization as an opportunity to "bling your graph" according to Stephen Few author, teacher and consultant. Few has written a new book just published called "Now You See It: Simple Visualization Techniques for Quantitative Analysis". He spoke to the SDForum Business Intelligence SIG June meeting.
Few took us on a quick tour of visualization. We saw a short Onion News Network video that satirized graphics displays in news broadcasts, followed by examples of blinged graphs and dashboards that were both badly designed and misleading in their information display. Not all visualizations are bad. An example of good visualization is the work of Hans Rosling who is a regular speaker at the TED conference (his presentations are well worth watching, and then you can go to Gapminder.org and play with the data just as he does). Another example of visualization used effectively to tell a story is in the Al Gore documentary "An Inconvenient Truth".
Next came a discussion of visual perception, leading up to the idea that we can only keep a few items in our short term memory at one time, however these items can be complex pieces of visual information. Given that data analysis is about comparing data, visual encoding allow us to see and compare more complex patterns than, for example, tabular data.
Any data display can only show us a small part of the picture. An analyst builds understanding of their data set by building up complex visualizations of the data, piece at a time. We saw some examples of these visualizations. Software should support the data analyst as they build up their visualizations without getting in the way. Few told us that the best software is rooted in academic research. He recommend several packages including Tableau and Spotfire, both of whom have presented to the Business Intelligence SIG in the past.
". He spoke to the SDForum Business Intelligence SIG June meeting.Few took us on a quick tour of visualization. We saw a short Onion News Network video that satirized graphics displays in news broadcasts, followed by examples of blinged graphs and dashboards that were both badly designed and misleading in their information display. Not all visualizations are bad. An example of good visualization is the work of Hans Rosling who is a regular speaker at the TED conference (his presentations are well worth watching, and then you can go to Gapminder.org and play with the data just as he does). Another example of visualization used effectively to tell a story is in the Al Gore documentary "An Inconvenient Truth".
Next came a discussion of visual perception, leading up to the idea that we can only keep a few items in our short term memory at one time, however these items can be complex pieces of visual information. Given that data analysis is about comparing data, visual encoding allow us to see and compare more complex patterns than, for example, tabular data.
Any data display can only show us a small part of the picture. An analyst builds understanding of their data set by building up complex visualizations of the data, piece at a time. We saw some examples of these visualizations. Software should support the data analyst as they build up their visualizations without getting in the way. Few told us that the best software is rooted in academic research. He recommend several packages including Tableau and Spotfire, both of whom have presented to the Business Intelligence SIG in the past.
Friday, May 29, 2009
Using BI to Manage Your Startup
We heard several different perspectives on how Start Ups use Business Intelligence at the May meeting of the SDForum Business Intelligence SIG. The meeting was a panel, moderated by Dan Scholnick of Trinity Ventures. Dan opened the meeting by introducing himself and then asking the panelists to introduce themselves.
The first panelist was Naghi Prasad, VP, Engineering & Operations at Offerpal Media, a start up that allows developers to monetize social applications and online games. Offerpal Media is a marketing company that does real time advertisement targeting and uses a variety of analytics techniques such as AB testing. Naghi told us that Business Intelligence is essential to the companies business and baked into their framework.
Next up was Lenin Gali, Director of Business Intelligence at ShareThis, a start up that allows people to share content with friends, family and their network via Email, SMS and social networking sites such as FaceBook, Twitter, MySpace and LinkedIn. ShareThis also uses AB testing, and as a content network has to deal with large amounts of data.
Third was Bill Lapcevic, VP of Business Development at New Relic, which provides Software as a Service (SaaS) performance management for the Ruby on Rails web development platform. New Relic has acquired 1700 customers over its first year as a start up with a single sales person. Their customers are technical and they use their platform to track the addiction or pain of each customer, and to estimate their potential budget.
The final panelist was Bill Grosso, CTO & VP of Engineering at Twofish, a start up that offers SaaS based virtual economies for Virtual Worlds and Massive Multiplayer Online Games (MMOG). For the operator, a virtual economy is Sam Walton's analytics dream, as you see into every players wallet and capture their every purchase and exchange. TwoFish uses their experience with running multiple virtual economies to tell their customers what they are doing right and wrong in developing a virtual economy.
Dan's first question was "What are some of the pitfalls of Business intelligence?" Bill Lapcevic told us that they have a real time reporting system that can track can track revenue by the minute. The problem is that you can become addicted to data and spend too much time with it. Sometime you need to get away from your screen and talk to the customer. Lenin agreed with this and added that they have problems with data quality. Naghi told us that while a benefit is the surprises that they find from the data, a problem is that they are never finished with their analytics. Bill Grosso was concerned with premature generalization. You need to wait until you have enough data to support conclusions and revisit the conclusions as more data arrives.
There was a wide variety of answers to the question of which tools each panel member used. According to Naghi Prasad, "MySQL is a killer app, it will kill your app!" Offerpal Media uses Oracle for their their database. While they like some of the features of Microsoft SQL Server, they are constrained to have only one Database Administrator (DBA) and DBAs are best when they specialize in one database system. They use open source Kettle for ETL and Microsoft Excel for data presentation. Naghi extolled the virtues of giving users data in a spreadsheet they were comfortable with and Excel pivot tables allows the user to manipulate their data at will. After surveying what was available, they implemented their own AB testing package.
ShareThis is on the leading edge of technology use. Lenin told us that they are 100% in the cloud, using the LAMP stack with MySQL and PHP. They have a 10 Terabyte in an Aster Data Systems database, and use both Microstrategy and Hadoop with Cascading for data analysis and reporting. Running this system takes about 1.5 system admins.
As might be expected, the New Relic system is built on Ruby on Rails and uses sharded MySQL to achieve the required database performance. In their experience it is sometimes worth paying a little more for hardware than optimizing the last ounce of performance from a system. They have developed many of their own analytics tools that they expect to sell as product to their customers.
As TwoFish does accounting for virtual worlds, their servers are not in the cloud, rather they are locked in their cage in a secure data center. While Bill Grosso lusts after some features in Microsoft SQL Server, they use MySQL with Kettle for ETL. They have developed their own visualization code that sits in front of the Mondrian OLAP engine. They expect to do more with the R language for statistical analysis and data mining.
Dan asked the panel how they get the organization to use their Business Intelligence product Bill Grosso lead by saying that adoption has to come from the top. If the CEO is not interested in Business intelligence, then nobody else will be either. He also called for simple metrics that make a point. Bill Lapcevic agreed that leadership should come from the top. The idea is to make the data addictive to users and to avoid to many metrics. Sharing data widely can help everyone understand how they can contribute to improving the numbers. Lenin thought that it was important to make decisions and avoid analysis paralysis. Naghi offered that Business Intelligence can scare non Business Intelligence users. You have to provide simple stuff, and make sure that you score some sure hits early on to encourage people. Finally remember that different people need different reports so make sure each report is specialized to the requirements of the person receiving it.
There were more questions asked, too many to describe in detail here. All in all, we had an informative discussion throughout the evening with a lot of good information shared.
The first panelist was Naghi Prasad, VP, Engineering & Operations at Offerpal Media, a start up that allows developers to monetize social applications and online games. Offerpal Media is a marketing company that does real time advertisement targeting and uses a variety of analytics techniques such as AB testing. Naghi told us that Business Intelligence is essential to the companies business and baked into their framework.
Next up was Lenin Gali, Director of Business Intelligence at ShareThis, a start up that allows people to share content with friends, family and their network via Email, SMS and social networking sites such as FaceBook, Twitter, MySpace and LinkedIn. ShareThis also uses AB testing, and as a content network has to deal with large amounts of data.
Third was Bill Lapcevic, VP of Business Development at New Relic, which provides Software as a Service (SaaS) performance management for the Ruby on Rails web development platform. New Relic has acquired 1700 customers over its first year as a start up with a single sales person. Their customers are technical and they use their platform to track the addiction or pain of each customer, and to estimate their potential budget.
The final panelist was Bill Grosso, CTO & VP of Engineering at Twofish, a start up that offers SaaS based virtual economies for Virtual Worlds and Massive Multiplayer Online Games (MMOG). For the operator, a virtual economy is Sam Walton's analytics dream, as you see into every players wallet and capture their every purchase and exchange. TwoFish uses their experience with running multiple virtual economies to tell their customers what they are doing right and wrong in developing a virtual economy.
Dan's first question was "What are some of the pitfalls of Business intelligence?" Bill Lapcevic told us that they have a real time reporting system that can track can track revenue by the minute. The problem is that you can become addicted to data and spend too much time with it. Sometime you need to get away from your screen and talk to the customer. Lenin agreed with this and added that they have problems with data quality. Naghi told us that while a benefit is the surprises that they find from the data, a problem is that they are never finished with their analytics. Bill Grosso was concerned with premature generalization. You need to wait until you have enough data to support conclusions and revisit the conclusions as more data arrives.
There was a wide variety of answers to the question of which tools each panel member used. According to Naghi Prasad, "MySQL is a killer app, it will kill your app!" Offerpal Media uses Oracle for their their database. While they like some of the features of Microsoft SQL Server, they are constrained to have only one Database Administrator (DBA) and DBAs are best when they specialize in one database system. They use open source Kettle for ETL and Microsoft Excel for data presentation. Naghi extolled the virtues of giving users data in a spreadsheet they were comfortable with and Excel pivot tables allows the user to manipulate their data at will. After surveying what was available, they implemented their own AB testing package.
ShareThis is on the leading edge of technology use. Lenin told us that they are 100% in the cloud, using the LAMP stack with MySQL and PHP. They have a 10 Terabyte in an Aster Data Systems database, and use both Microstrategy and Hadoop with Cascading for data analysis and reporting. Running this system takes about 1.5 system admins.
As might be expected, the New Relic system is built on Ruby on Rails and uses sharded MySQL to achieve the required database performance. In their experience it is sometimes worth paying a little more for hardware than optimizing the last ounce of performance from a system. They have developed many of their own analytics tools that they expect to sell as product to their customers.
As TwoFish does accounting for virtual worlds, their servers are not in the cloud, rather they are locked in their cage in a secure data center. While Bill Grosso lusts after some features in Microsoft SQL Server, they use MySQL with Kettle for ETL. They have developed their own visualization code that sits in front of the Mondrian OLAP engine. They expect to do more with the R language for statistical analysis and data mining.
Dan asked the panel how they get the organization to use their Business Intelligence product Bill Grosso lead by saying that adoption has to come from the top. If the CEO is not interested in Business intelligence, then nobody else will be either. He also called for simple metrics that make a point. Bill Lapcevic agreed that leadership should come from the top. The idea is to make the data addictive to users and to avoid to many metrics. Sharing data widely can help everyone understand how they can contribute to improving the numbers. Lenin thought that it was important to make decisions and avoid analysis paralysis. Naghi offered that Business Intelligence can scare non Business Intelligence users. You have to provide simple stuff, and make sure that you score some sure hits early on to encourage people. Finally remember that different people need different reports so make sure each report is specialized to the requirements of the person receiving it.
There were more questions asked, too many to describe in detail here. All in all, we had an informative discussion throughout the evening with a lot of good information shared.
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