Written by xScion
on July 10, 2018


We are in a very interesting time right now as it pertains to data and the promise that data holds for all organizations. We’re being inundated by buzz words like Machine Learning, Artificial Intelligence, Predictive Analytics and Data Science.

Many of us may not fully understand what these things really mean or how they should be applied to our organizations, but we’re drawn to them and feel an increasing pressure to harness their potential power. The mere sounds of these phrases and many others like them, elicits excitement and intrigue. Does this sound familiar?

Unfortunately, one of the most critical aspects of data maturity - one that should be the absolute foundation of any successful data modernization effort - is also one that doesn’t necessarily elicit excitement or intrigue. In fact, it sometimes has quite the opposite effect and often creates confusion, consternation and even disinterest among stakeholders. That term is Data Governance. Ugh!

With this being the case, it’s not surprising that most data modernization efforts put the cart before the horse when it comes to Data Governance. I can’t tell you how many conversations I’ve had with clients over the years where hundreds of thousands of dollars and even millions of dollars have been spent on new data warehouses, new software and tools, and even consulting; on just about everything data-related except for Data Governance. Without a plan to additionally address Data Governance (or to address Data Governance well), these expensive and time-consume efforts often end up failing to produce the expected ROI that was originally intended. Why does this happen and how you can you avoid this costly pitfall?


1. There is a human side of data maturity.  No matter how many data warehouses you build, or how many Machine Learning or Predictive Analytics tools you buy, at the end of the day human beings will be involved. If you don’t create a unique set of rules and methodologies for how data is categorized and handled at your organization, and you fail to put a group of cross-functional stakeholders in charge of those rules and methodologies, despite what tools and architecture you may have in place, you’re going to continue to have challenges with data quality and therefore, with the intended output of your data.

2. Governance drives decisions.  Without any governance in place, there is no foundation with which to make informed decisions about where to spend money on additional modernization efforts. Do you really need a data warehouse? If so, what data should go in there? How should that data be categorized? Who should have access to it? Who should manage it? What are the primary business drivers behind the need for data modernization at your organization? Who will lead this effort? Will the primary stakeholders be on the IT or business side? What data do you need to collect and why? I could go on and on, but you get the point.

3. Consistency is critical.  One of the most common complaints I hear at companies with little to no Data Governance is that despite all of the new and fancy tools and architecture that had been acquired over the last few years, they still had several different teams and departments handling and managing data in their own unique way. This makes it impossible to trust the data or to know with any degree of certainty that the data is producing and output that is true and accurate.

4. Buy-in at all levels is a requirement.  In most organizations, you typically have leadership or the Board asking for reports on certain metrics or KPIs that are critical to informed decision making, while Directors and Managers are the people who are actually handling this data. There can quite often be a huge chasm between the reality of the people tasked with collecting and preparing data, and those who are expected to use and consume that data. That being the case, you need to get buy-in at all levels within an organization in order to give true data maturity a realistic chance. This is from the CEO down to the individual analysts. Data Governance helps to bring together these different levels of the organization and create that shared buy-in that’s so critical.


To avoid these pitfalls, you simply have to put a plan in place to address data governance, which does not have to be some scary or overwhelming initiative. There are things that most organizations can start doing today, with minimal effort and for free, that will lay the foundation for effective Data Governance. Beyond that, a simple current state assessment and gap analysis can provide the feedback an organization needs to direct resources and decision making around Data Governance in the most cost-effective and efficient way possible. The bottom line is, Data Governance is a requisite for successful data maturity and waiting on addressing Data Governance will only create a bigger and more costly challenge down the road. You can find more insight and advice on implementing successful data maturity here in our recent white paper – How to Transform into a Data-Driven Organization Using The Agile Data Maturity Model.

Let me leave you with this. I can’t remember who said it, but it stuck with me. I was at a conference a few years ago listening to someone speak about data modernization and this person said the following at the end of his presentation, “Today, your data set is the smallest it will ever be in the history of your organization.” It sounds obvious, but the reality of that simple statement is substantial and is one that should really be driving all Data Governance conversations, no matter how badly the phrase itself makes you cringe!