A Common Explaining Language Leads to Understanding

“I can explain it to you, but I can’t understand it for you.”

A good friend of mine recently alienated the person that pays his salary with the above statement. While you might question his wisdom – I know I did (after a quiet chuckle) – I’m reminded of how often the same thought has gone unsaid when business and IT “collaborate” on traditional data warehouse projects. The business is baffled by the technical jargon that IT uses, and the IT team wishes the business could articulate their requirements in a form from which they can build the solution. The lack of a common language to explain what each side means hinders this understanding. Teaching

We all know it’s important when building a data warehouse for providers (IT) and consumers (business sponsor) to collaborate, but it’s all too seldom done effectively. To do it properly you need the right ingredients – the right people, a collaborative environment and the right tools to bring them together. In the world of data warehousing, this is even more important because the data in the warehouse ultimately drives many other critical business decisions. Because of the data warehouse’s strategic nature, it’s immensely helpful, and important, to collaborate on requirements over a medium that can be understood by both parties. At Kalido we use the Business Information Modeler to support this conversation using a common language (don’t be shy, click the link, you can download it for free!) with the added benefit of automatically generating the physical warehouse environment.

Since data warehouses are all about the data, the solution does not begin to add value until you load real data, and reports are generated and presented to the users. Many data warehouses take months or even years to build the first report. In today’s world where the unit of measure for businesses to sense and respond is seconds, that’s simply not good enough. The solution needs to be ready to integrate data immediately and expose it to BI and discovery tools and other systems. We can’t continue to rely on teams of people investing months to understand the requirements and build the system before delivering results.

Data warehouse development needs to deliver a more rapid build-to-first-deployment. But then what happens? When you show the first report to the user the same day or week you modeled it, they point out the inevitable misinterpretations, enhancements and changes. Now what? In addition to the first build, you must be able to rapidly change it on the fly – without unloading and reloading the data. This is not the case in most existing traditionally-built warehouses we see.

The capability that overcomes these limitations as now being referred to as Data Warehouse Automation. Today, thousands of organizations are leveraging Data Warehouse Automation technology and approaches to achieve competitive advantages by removing the repetitive, time-consuming and resource-consuming (and boring) work from building and operating data warehouses. I believe that as the Big Data hype begins to settle down to a simmer and organizations focus their investments on projects with near-term ROI – we’ll see Data Warehouse Automation becoming mainstream.

If you want to “understand it” then please take a look at our videos. If you’d like a live demonstration or more details, we, unlike my now-unemployed friend, will be more than happy to explain it to you until you understand it.

0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply