Tablet Aspect Ratios

One important issue with tablet computers that is getting little attention is the screen aspect ratio. Some time ago I wrote about "aspect ratio hell" while trying to decide how to crop holiday photographs. The answer seems to be that you have to crop each photograph independently for each way the photograph is going to be output or displayed. For photographs, the variety of different aspect ratios is a perplexing problem that has no good answer.

Tablet computers have the same problem except that the responsibility lies with app developers who need to make their app work well with the aspect ratios of their target platforms. Aspect ratios for a tablet needs to take into consideration that it will be used in both portrait and landscape mode. The iPad has an aspect ratio of 4:3 (AR 1.33...), which is the same as the iPod Classic while the iPhone and iPod touch have an aspect ratio of 3:2 (AR 1.5). Anyone trying to develop apps for Apple products needs to take this difference into account. On the other hand, both Blackberry and Samsung has announced their Android based tablets with a 7 inch screen which has an aspect ratio of 128:75 (AR 1.706...), which is close to 16:9 (AR 1.77...).

When we look to media, television uses 16:9 and most cinema has a higher ratio like 2.40:1 except for iMax (AR 1.44) which is much squarer. Books and newspaper use a 3:2 ratio (AR 1.5) while magazines tend to be broader with a lower aspect ratio. Frankly, anything with an aspect ratio of more than 3:2 tends to look unnaturally skinny when viewed in portrait mode. A cell phone can get away with a higher aspect ratio because it has to be pocketable, but larger devices meant for viewing both media in both landscape and portrait mode needs to keep its aspect ratio to 3:2 or less. For example, the Kindle, which is mostly used in portrait mode has an aspect ratio of 4:3 (AR 1.33...). From this point of view, the Samsung and Blackberry tablets seem to be designed to be used in landscape mode and not in portrait mode. I hope that other tablet makers do not make the same mistake.

Understanding the iPad

Some people still struggle to understand the iPad. When it was first announced, there were shrieks of outrage from techies, complaining that it was not a free and open computer system and so nobody should buy one. Then it came out and was adopted by the millions. Steve Ballmer, CEO of Microsoft, expressed dismay that the iPad is easily outselling any tablet computer that Microsoft and ever had a hand in. More recently an executive from LG told the Wall Street Journal that they would bring out a Tablet that would be better than the iPad because it would be oriented towards content creation rather than content consumption.

Then there are many people who get it. For example, Jerry Kaplan, founder of Go Computing, an early slate computer in an interview with Chris O'Brian of the San Jose Mercury News understood that the iPad is oriented for media consumption as opposed to the more general purpose Go slate computer. My belief is that the iPad is a new category of device that addresses a new market.

Last year I wrote about Media Convergence, the idea that in the past, each type of media was different. Books were bound paper sold by booksellers, video was delivered as movies in movie theaters and broadcast as television, records were vinyl goods sold in record stores and heard over the radio, magazines were sold by booksellers or delivered by mail, newspapers had their own content delivery network to ensure that everybody got the previous days news by the following morning. With the digital revolution, all these different types of media are now the same. They are all just buckets of digital bits that are delivered through the Internet. Given this, the next thing we need are devices for consuming all this media. Audio just needs a device the size of your thumb and headphones, whereas video, books, magazines etc. need a screen that is big enough to see, and that is what the iPad is for.

When thinking about these things, I find it useful to draw up some requirements and use cases and then see how the offered devices match those requirements. Here is what I want from my Personal Information Appliance (PIA - remember that acronym).

1. Light enough that I can lie in bed and read or view media with it.
2. Instant on, long battery life, able to handle all media types.
3. Get media without having to plug it into anything else.
4. A screen large enough to read or view and small enough to make the device portable.

So how does the iPad match these requirements? At 1.5 pounds it is a little heavier than most "light" reading, but there are plenty of hardback books that weigh more. For the second requirement, Adobe Flash is the major missing media type, however there is probably an app to do that. As for screen size, we are going to have to resign ourselves to having multiple devices with different screen sizes until they work out the technology to project images directly onto the retina.

The funny thing is that even although the iPad is speced as a device for consuming media it turns out to be capable of much more. Computer games are the newest type of media and the iPad is a great games platform with a lot of future as Steve Jobs boasted in the recent iPod announcement event. There are many instances in the business world where it will be useful, for example in sales and marketing for giving a presentation or demonstration to an individual. The other day I was astonished to find my boss using his iPad for email while waiting for his laptop to be repaired.

Software Update Business Models

These days software updates are a fact of life. If we do not keep our software up to date we risk all sorts of horrendous infections and debilitating attacks. Unfortunately, the providers of our software know this and are starting to use software update to make money or at least remind us that they exist. I have done several software updates recently and noticed this in action.

Adobe just wants to remind me of their presence, so they insist on putting a shortcut to the Adobe Reader on my desktop every time they update. This is relatively benign as it is a matter of a few seconds at most to confirm that it is a shortcut and delete it. Apple is more pushy. I expect to get a new version of iTunes any day now, and I will need to carefully uncheck boxes to ensure that I do not get several applications more than I want. Most insidious is Java, now owned by Oracle. On one system they offered me the Yahoo tool bar, on another system which already had the Yahoo tool bar, they offered me some other software, so they obviously look to see what is installed to guide the offer. Judging by the fact that these offers were for third party software, I am sure that they get some sort of compensation for it.

Soon we will see advertisements and offers in the installer, and new ways to confuse us. The tactic that always gets me is to require some input that I forget to fill in, then when I go back to fill in this information, all the boxes I so carefully unchecked have been mysteriously filled in again. In a hurry, I just click "Install" not noticing that I am now getting all the extras that I had carefully tried to avoid. It is coming to a computer near you soon.

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.

End of Moore's Law

The recent announcement that Intel is buying McAfee, the security software company, has the analysts and pundits talking. The ostensible reason for the deal is that Intel wants the security company to help them add security to their chips. Now, while security is important, I do not believe that is the reason Intel bought McAfee. In my opinion, this purchase signals that Intel sees the coming end of Moore's Law.

In 2005, the Computer History Museum celebrated 40 years of Moore's Law, the technology trend that every 2 years, the number of transistors on a silicon chip, and thus its capabilities doubles. On the stage Gordon Moore told us that throughout the 40 years, "they have always been able to see out about 3 generations of manufacturing technology", where each generation is about 2 years. So Intel can see its technology path for about the next 6 years. At that time Moore told us that they could still see how they were going to carry on Moore's Law for the next three generations.

Now what would happen if Intel looked 6 years into the future and saw that it was no longer there. That they could see the end of Moore's law and that meant that they would no longer have the ability to create new and more powerful chips to keep their revenue growing. I believe that they would start looking to buy up other profitable companies in related lines of business to diversify their revenue.

McAfee is a large security software company, its main business is selling security solutions to large enterprises. If Intel had wanted to buy security technology they could have gone out and bought a security start-up with better technology than McAfee for a few hundred million dollars. Instead they are spending an expensive 8 billion dollars on an enterprise security software company. This deal does not make sense for the reasons given, however it does make sense if Intel wants to start buying its way into other lines of business.

Now there are many reasons that Intel wants diversify their business. Perhaps they see the profitable sales of processor chips disappearing as chips gain so many transistors that they do not know what to do with them. However the most likely reason is that they can see the end of Moore's Law and that it is now time to move on and add some other lines of business.

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.

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.

Another Angle on the iPhone Woes

In all the discussion over the antenna problems with the new Apple iPhone 4, there is one thing that I have not heard, and that is how few product lines that Apple has. The iPhone 4 is a prime example. It comes in two memory sizes and we are promised a second color real soon now. On the Apple site you can still buy the previous generation iPhone 3, but that still makes it 3 models available with another one or two to come. Compare this with BlackBerry which has six ranges and thirteen models. Blackberry is restrained in the number of models it offers compared to the likes of Motorola, Samsung or Nokia.

The same its true in other areas. With the Mac, Apple has three ranges of laptops, three ranges of desktops and one rackable server. Compared to HP, Dell or Acer this is a ridiculously small number of product lines. Again with the iPod there are 4 product ranges each with a couple of different memory size and and more variation on color.

There are a number of advantages in having a small number of product lines. Economy of scale will make the product cheaper to manufacture, however by the time you get to millions of devices, the additional advantage is not that great. More important are a brutally strong brand image and a lack of consumer confusion. There is no question about which version of the iPhone to get, the only question is whether you are willing to pay more for the extra memory.

However, there is one big disadvantage to having a single product line like the iPhone, and that is that you have all your eggs in one basket. If the product should prove to have a flaw, there is no other product line to take up the slack. If a consumer want to buy an iPhone now, they either have to go ahead and take the risk that it might be a lemon or wait until the problem is fixed. They cannot go out and buy the other Apple phone because it does not exist. For a big consumer goods company, Apple has had remarkably few dud products, but their life depends on getting each one right.

The HP Tablet and the Elephant

Recently HP bought Palm and in the acquisition press release announced that they are developing "... webOS based hardware products, from a robust smartphone roadmap to future slate PCs and netbooks". In all the discussion of this event, nobody seems to be discussing the elephant in the room, or more correctly, the elephant who is no longer in the room.

Ten years ago, HP would not have dared announce that it was going produce its own operating system (OS) in competition with the dominant Microsoft Windows OS. Then, most hardware developers had been cowed by Microsoft's aggressive and successful response to any attempt to develop a rival operating system. To give a couple of examples, in the early 90's the Go Corporation had developed its Penpoint OS for handheld computing. Then in 1992, Microsoft announced its own Windows for Pen Computing. Go Corporation faltered, was taken over by AT&T and then the project was shuttered. Another example is the fate of Be Inc. who had developed BeOS, initially to power their own hardware. In 2002, Be Inc. sued Microsoft claiming that Hitachi had been dissuaded from selling PCs loaded with BeOS, and that Compaq had been pressured to not market an Internet appliance in partnership with Be. The case was eventually settled out of court with no admission of liability on Microsoft's part. However by this time Be Inc had admitted defeat and sold its intellectual property to Palm Inc.

In the late 90's Microsoft was so dominant that no Silicon valley Venture Capital firm would fund a start up that would have the remotest chance of challenging Microsoft in any way. Since then Microsoft seems to have been transformed from a lithe competitor into a stumbling giant. The Vista version of the Windows OS is widely regarded as a failure, and was quickly replaced by Windows 7. While the Windows Mobile OS for smartphones has been around for a long time and gone through several versions, it has been losing market share for some time. Recently Microsoft introduced a new smartphone, the Kin with much ballyhoo, only to give up on it six week later. There are plenty other examples of Microsoft's left hand not knowing what the right hand was doing, like the PlayForSure debacle.

We have come to the point where Microsoft is so crippled by its own self inflicted wounds that one of its most important OEM customers is going to use its own operating system on future slate PCs and netbooks. The elephant is no longer in the room.

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.