By Dennis D. McDonald

The following Health Analytics survey findings will come as no surprise:

According to a new poll from ISACA, a global non-profit offering IT governance leadership and resources, only 53 percent of surveyed IT professionals across multiple industries believe their board rooms have a firm grasp on big data analytics and data-driven business intelligence principles.

Dig into the report and you'll see that "data literacy" here means way more than the ability to crunch numbers. It also includes a basic understanding of what it takes to gather, organize, manage, and analyze data.

For executive management today that has to include a basic understanding of the costs involved in data related business processes as well as what new and emerging technologies might mean for organizational security, effectiveness, and competitiveness. If management doesn't understand the basics of traditional database management or e-commerce operations, how can you expect them to understand the basics of blockchain, migration to the cloud, and internet of things?

It would be reasonable to ask, "What else is new? Hasn't management ALWAYS been behind the curve in accepting new technologies? Isn't that why middle management and younger employees have often led the charge for innovation?"

It is true that in my own research into what it takes to manage "big data" projects effectively, I did find that a lot of what is needed is good old fashioned meat-and-potatoes project planning and project management. Adopting new data related technologies will require change. Change in the right direction does not happen by itself.

If what you are really doing is making existing processes bigger, better, and faster, needed change and associated progress metrics can be tradition-based -- and understandable to management.

On the other hand, when we start thinking more strategically about the potential value of better data and improved data analytics to the organization, we do have to start thinking in strategic, technological, and process-oriented terms. When we do we may find that treating data like a valuable resource may require us to think differently about how the organization is structured and what the nature of its business is.

Some in management will embrace this perspective. Others may find it threatening. Categorizing management pushback as being due to "data illiteracy" may simply be too shortsighted and counterproductive. We have to explain what needs to be done in a language that management understands.

This starts with being clear and specific about what we are trying to accomplish with better data governance and data analytics. I addressed this in "A Framework for Defining the Scope of Data Governance Strategy Projects," Part 1 and Part 2 where I recommended a very targeted approach to developing a "data governance strategy":

1. Decide specifically what problem (“application area”) you want to solve.
2. Identify and understand the data, systems, and processes that need to work together to provide the needed data analytics to solve this problem.
3. Create and implement a project plan based on a rational consideration of possible alternative approaches to solving the problem.
4. Use what is learned from (3) to adapt and expand the strategy to other problem areas.

In other words, overcoming "data illiteracy" benefits by showing management a successful application, one where they are involved collaboratively from the start in defining the problem to be addressed and in overseeing the steps taken to solve the problem. Rocket science? No, basic project planning and management.

Copyright (c) 2017 by Dennis D. McDonald