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Data cubes got it started

MM: Again, we were in the middle of reprising the development of business intelligence. You’d talked about the early days of data warehouses and then how ERP started to move through a lot of corporations, normalizing a lot of that data, giving rise to the need for a master data management as a way of harmonizing data among systems.

Then I think you were about to launch into the emergence of business intelligence tools or technologies such as Business Objects or Cognos or Microstrategy or things like that.

MB: These tools, and the companies around these tools, emerged over time. There was a big flurry of tools companies that came into existence around this idea called OLAP or On-Line Analytical Processing. Its central idea was something called “Data Cubes” which allow you to analyze and manipulate data. They give you many different ways of looking at data and organizing it along different dimensions that you need to look at it. You could look at items by vendor, by price or by profitability or also by geographic region, organizational roles or hierarchy, etc. The “cube” notion comes by analogy to being able to turn a cube around in your hands to look at it from different perspectives.

These tools have been implemented in a variety of ways. In the early days, people had to summarize the data to a considerable degree in order to get these tools to perform very well. As computing power and storage has become less expensive, people have discovered that you really no longer need to summarize the data. In fact these tools become a lot more useful if you can actually drill all the way down to the lowest level of detail.

You can drill down all the way to the details, and observe issues associated with the data at finer granularity.  Then you are using the tool to figure out what’s causing the problem and how to solve it. This results in a much more flexible, robust, and efficient solution with much faster response times.

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Breaking points

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MM: Could you give us a little bit of the history of business intelligence?

MB: Oco was formed to address the problems of existing BI tools, which were too difficult to develop and use. I can give you the historical perspective on that.

Back in the early 1990s, people started building data warehouses, because they didn’t have access to corporate information for the purposes of reporting and data analysis. They had lots of different operational systems, but they didn’t have systems that had data from all over the place gathered together.

These projects were originally pushing the relational database technology to the breaking point. Very large data warehouses were created, and every one of the vendors struggled to make these very large databases work.

But the software has matured now, allowing companies to put together quite, quite large data warehouses. There’s now an array of companies that offer BI tools. There’s also been some consolidation in the industry lately with SAP acquiring Business Objects and IBM acquiring Cognos and so forth.

Now there’s robust relational database software out there, and there are tools for accessing the information, but it has still been much too difficult. A recent report from Gartner estimates that still over 50% of these data warehousing or business intelligence projects fail.

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Professional background

MM: Let’s start with an introduction.

MB: My name is Mike Beckerle. Let me explain a little bit about my background. I joined in January of 2008. As the CTO, I’m responsible for product development of new products and technologies and the strategic direction for existing products.

I joined Oco from IBM, where I was involved in large-scale computing. The core of my experience comes from spending much of my career in developing large-scale parallel processing for commercial data processing workloads. The result of this work is now the core part of the IBM information server product. I was responsible for much of the scalability of that product.

I joined Oco because it was a very good fit with my background, and my experience is very relevant to Oco’s strategic direction. For example, during the dot-com era, I was involved in “SaaS,” at a startup called Fact City, although it wasn’t called that at the time. We did something that was fundamentally SaaS and quite similar to Oco’s solution in taking data in disparate forms and using it to construct a high-volume Web-accessed data service.

I decided that putting scalable commercial data processing and SaaS together would provide a great value proposition for the marketplace. I was thinking about launching this type of solution, and discovered that there was already a company doing it — Oco.