Data Management Competency for Analytics at the Speed of Business

Welcome back to our series of Analytics Chalk Talk videos. I hope you enjoyed our last video:How to Manage the Complexity of Cloud Analytics”. Data can be the difference between winning and losing. In this weeks Chalk Talk, I’ll talk about how to deliver data management competency for analytics to meet the needs of the business.

Please watch the video or read the transcript. You might be able to tell I’m pretty passionate about this particular area, so share your views in the comments below or at Twitter–@leanlyle.

 

 

If data is the difference between winners and losers, how do we ensure that our organization makes a success of it?

I talk to dozens of companies every quarter and it seems for them the problem is still that complexity has built up over time. This hairball is preventing IT from moving at the speed of, and therefore supporting, the business.

The key is integration—specifically, agreement about the data. There’s no point joining sets together if the underlying data doesn’t match.

This is a competency that we need to be continually improving. When we do big IT projects that involve data we’ll often assign people to do the development or the integration aspect of the problem. But as soon as the project is over they disperse, and we lose all that institutional learning. Then next time around we use a completely different set of tools and solutions.

 

 

This means we’re not building our integration competency. We need to develop an integration center of excellence that acts as a service for the rest of the business. Gradually it should build toward an information competency where we’re delivering data at the speed the business requires. Ultimately that will lead to a business transformation competency that works at the speed the execs require.

This all comes from a foundation of integration competency and metadata management. So, how do you assess where you are and begin to develop these competencies?

It’s hard to align the business and IT in such a way that they’re able to make these improvements. With that in mind, a number of us pooled our experience and put together a framework for self-assessment based on the differing maturity levels between the business and IT.

 

Data Management Competency for Analytics

 

When there’s a difference in maturity level between the business and IT you naturally have different starting points. This is important because it determines the roadmap to your future state. It’s easy to draw future-state architecture; it’s incredibly difficult to figure out the steps that take you there.

 

Data Management Competency for Analytics

 

By looking at business-IT alignment along these five axes, we’re able to illustrate the different steps.

I haven’t mentioned technology yet for one simple reason—technology is the easy part. It’s people and process that are hard. We see this time and again.

Two years ago we bought a company that had reference models, a full meta-model and methodology for different vertical industries. That allowed us to map the business to IT and business strategy, all the way down to through the functions and into the systems and tech. We could see—as we can in metadata manager—how to change a report in a BI tool that affects the ETL, database, and so on. You can see the different parts of the organization, people, and processes that are going to be affected by your changes.

 

Data Management Competency for Analytics

 

This is so helpful for successful architecture-led planning because it allows you to take account of every aspect of the enterprise that’s going to be affected.

And ultimately, this is all about us being able to deliver data to the business at the speed they require.

Other videos in the series:

Part I: Analytics Chalk Talks: Data Warehouses—Past, Present, and Future

Part 2: Analytics Chalk Talks: What’s Next for Big Data Analytics?

Part 3: How to Manage the Complexity of Cloud Analytics

Part 5: Driving Analytics Data Management Competence with Metadata