A Mountain Of Data

So I heard this interesting perspective on information and data analytics...

Basically, it comes down to this: 

"Most organizations are data rich, but information/insight poor."

Or put another way:

"Data is collected, but not used."

Hence we don't know what we don't know and we end up making bad decisions based on poor information. 

Just imagine if we could actually make sense of all the data points, connect them, visualize them, and get good information from them.

How much better than a pile of rocks is that? 

(Source Photo: Andy Blumenthal)

A Mountain Of Data

Data Architecture Done Right

Data architecture done right provides for the discovery and exchange of data assets between producers and consumers of data.

Data discovery is enabled by data that is understandable, trusted, and visible.

Data exchange is facilitated by data that is accessible and interoperable.

Together, data discovery and exchange are the necessary ingredients for information sharing.

Why is it so hard?

Primarily it’s a coordination issue. We need to coordinate not only internally in our own organization (often already large and complex), but also externally, between organizations — horizontally and vertically. It’s quite a challenge to get everyone describing data (metadata) and cataloging data in the same way. Each of us, each office, each division, and so forth has its own standards and way of communicating. What is the saying, “you say poTAYtos, and I say poTAHtos”.

Can we ever get everyone talking the same language? And even if we could, do we really want to limit the diversity and creativity by which we express ourselves? One way to state a social security number is helpful for interoperability, but is there really only one "right" way to say it? How do we create data interoperability without creating only one right way and many wrong ways to express ourselves?

Perhaps, the future will bring artificial intelligence closer to being able to interpret many different ways of communicating and making them interoperable. Sort of like the universal translator on Star Trek.

Data Architecture Done Right

Why Isn’t There a Chief Data Architect in the Federal Government?

In the federal government, there is a Chief Enterprise Architect in the Office of Management and Budget (OMB) — this is a good thing.

But the question that I have is why there isn’t a Chief Data Architect as well?

We all know that one of the essentials to good architecture is having strong data architecture that provides for data descriptions (or metadata) to uniformly describe data, data context (or taxonomies) for discovery, and that supports data sharing (or exchange).

In the Federal Enterprise Architecture (FEA), there is a Data Reference Model (DRM). Moreover, in the FEA, data is the crucial touch point between on one hand, the business functions toward achieving desired performance outcomes, and on other hand, the services and technologies that serve up the data in order to perform the functions and activities of the enterprise.

Furthermore, in developing technology solutions of the enterprise, one very important question for the business is what their information and data requirements are. The answer to this helps drive the technology solution.

For the federal government, the benefits of maturing its data architecture could be significant, especially in being able to share vital information, and thereby fill gaps and reduce redundancy across the federal enterprise. Given the size and important scope of the federal government missions, the imperative is great!

The Chief Data Architect would focus on data issues and drive such things as data standardization, common lexicon, metadata development, exchange standards and directories, service oriented architecture, and overall information sharing.

What do you think--would a Federal Chief Data Architect be a good idea to help progress this?

Why Isn’t There a Chief Data Architect in the Federal Government?