If you’re just joining, welcome. Before launching in at the middle, it might be useful to go back and read Part 1, Part 2, and Part 3. I’ll wait.
Recapping very briefly: It seems that most everybody has become comfortable with the notion that the overwhelming majority of data warehouse, artificial intelligence, and machine learning projects will fail or fail to meet expectations. This is despite an abundance of available resources. The problem isn’t that we don’t know what to do or how to do it, but rather a deficiency in something foundational. Specifically, a lack of data understanding. Collecting the key details about the data … its definition and expected content … can be tedious, and the natural motivations for development teams and their business partners disincentivize their participation in the process. Furthermore, executive management is often reluctant to mandate information management activities or supports them only insofar as they don’t disrupt or delay any existing deliverables. If we want to use our data effectively, deliver reliably successful and sustainable analytics projects, and generate business value and competitive differentiation, then the underlying data must be understood. Since it’s not happening today, somebody has to do something different. Nobody else seems to be stepping up to it, so that somebody has to be us. Start profiling some data. Any data. Publish the results. Don’t worry about tools or technology. Run queries and attach a spreadsheet to the project update. Start now.
There. All caught up.
I guess I should also say before continuing that the conundrum presented in this series might not necessarily describe your situation. Many companies already have robust information management programs and understand their data very well. These are also typically the companies that deftly adjust to changing market conditions, quickly release new business capabilities, and effectively monetize their data both internally and externally. Your company may be one of those that has already crossed the Data Chasm. If so, then great! You’re welcome to continue with us, and I’d be interested in hearing about your experience getting to that point.
Moving on.
Many of our teams have already come to the realization that we must proceed on our own. We have already started profiling our data or documenting our business terms or organizing our data models. Then what. We continue to try to involve the development teams. We continue to share the results of our data content analyses. We continue to create beautiful data model cubicle posters. And we continue to get the same lack of engagement. And it gets frustrating. It seems we’re right back to where we started 3200 words ago.
So, we start performing for ourselves.
We form closed groups, usually consisting of data architects, data modelers, DBAs, and data analysts. Sometimes we call them Competency Centers or Capability Centers or Centers of Excellence (CoE). We profile data or document business terms or organize data models. We do a ton of really good work. I think that most data modelers can recall a time when they worked on a model, extracted the entities from the business requirements, debated and decided on the primary keys, associated the attributes, and tied up all the foreign keys so that everything looked and felt just right. It was a work of art. The model was then presented at the next Information Management CoE meeting. They clapped when you finished.
Maybe the model was posted on an internal website. Maybe it was printed on E1 plotter paper and hung on the wall. Maybe it was occasionally referenced, but most likely it languished on a digital bookshelf with a million other dusty, unused files. Everyone outside the room could not have cared less.
Information management professionals have been ignored for so long that we seek validation in each other.
But as long as we are focused inwardly within our own echo chamber, we will remain stuck in a cycle of quality deliverables and corporate irrelevance.
We must turn our efforts outward.
But didn’t you just say that the development teams and their business partners won’t engage? That’s true. The team might not engage, but you can find individuals that recognize or can be convinced of the benefits of data understanding to their own careers. Sometimes it just takes one.
Back around 1999, a Marketing executive recognized the benefits of having data at his fingertips when he was in senior leadership meetings. He worked with his analytics team to generate reports and extracts that he viewed on his Palm Pilot. That’s right, Palm Pilot. Such limited mobile data availability seems primitive today, but at a time when decisions were made mostly on instinct and experience it was a revolutionary concept. In those meetings he was able to show data that supported his conclusions, ideas, and plans, and more often than not he won the support of his boss if not also his peers. Other executives saw the benefits of supporting their conclusions, ideas, and plans with data and so they worked with their analytics teams to generate reports and extracts for them to view on their Palm Pilots. There was no corporate Palm Pilot mandate. In fact, very few employees outside of the C-suite had them. It happened organically when one person saw an opportunity that benefitted them, and others followed suit for the same reason.
Transform your Information Management Center of Excellence to an Information Management Center of Evangelism, focusing your efforts on finding and cultivating allies in the development and business areas as well as in management.
You probably know some of these allies already, and you can find more. Be creative in your outreach. One idea is to hold an informational webinar about some hot data topic. Talk about the application of prescriptive analytics or deep learning or large language models in your industry. You know what will resonate most strongly with the people in your company. But be sure to link success to data understanding. Then see who seems interested, who stays behind to ask questions, or who wants to dive into the subject more deeply.
Those who recognize the benefits will already be inclined to support you. You need to give them reasons to become more active in their support. To carve off some portion of their time to work with you. Time that they will be pressured not to divert from their current activities. You know what will resonate most strongly with the people in your company. Crystalize the benefits and help articulate them to their management.
Nurture these new relationships. Support your new recruits.
Direct your efforts toward their data domains and applications. Those who work regularly with volunteers will tell you that the worst thing that you can do is to not engage someone who shows interest.
Next time we’ll conclude this series with two of the best things that we can do to build upon these small victories and put ourselves on a path toward sustainable information management, finally building that bridge across the Data Chasm.
This is the fourth in a series of articles that explores the question of why we continue to see overwhelming numbers of analytics, artificial intelligence, machine learning, information management, and data warehouse project failures despite the equally overwhelming availability of resources, references, processes, SMEs, and tools…and what can be done about it.
