How Data Governance Challenges Slow Us Down

The Struggle to Maximize Data Value Is Real

It’s become widely recognized that a company’s data is one of its most valuable assets. High-ROI ideas and new growth opportunities can be uncovered when companies make sense of the data in front of them. It can reveal the right customers to target for the highest return, new market segments to test, and risky partnerships to avoid.

To organize and use data for the purposes of extracting these valuable insights, most companies have turned to master data management software in recent years. Companies both large and small have eagerly sought a return from data this way. But often they end up struggling to make it work.

The inability to quantify data value creates a paradox: Companies acknowledge data governance as a dependency to obtain scale and value from their enterprise data, but because they don't measure the value of their data, they often struggle to justify a material or prolonged investment in governing it.

The Zombie Platform

When companies do attempt to make an investment in some sort of data governance program, they tend to start at the software level. This results in the dreaded “zombie platform” that will continue to run aimlessly as long as the hardware runs, but often with no direction or defined objective output. This usually occurs for two reasons:

• Companies approach the implementation of software as a one-time event or project, where the funding for the implementation is tied to the purchase of the software and the costs to get it up and running.
• Companies then fail to ensure that the budget for people to run and maintain these systems is accounted for in subsequent years or budget cycles — especially since the cost for people hits the bottom line immediately.

If you don’t define what a customer is, or what constitutes a hierarchy, or how your data is structured, then implementing software to automate customer data or hierarchy data management will fail. All software requires rules, and a governance program defines the “rules” for your enterprise data. Without these rules, the greatest software platform in the world won’t solve your problems.

Without people in place to run a data governance program, companies will eventually be left with another zombie software platform that runs and runs without generating tangible business value. People and software go hand in hand. In an efficient data ecosystem, they share a symbiotic relationship.

What’s Stopping Companies from Investing in the People to Manage Data?

In a nutshell, this “chicken or egg” paradox boils down to one question: How do you measure the value of data when you don’t invest in that same data's supervision, description, and storage?

This lack of clarity in ROI — causing a slowdown of further investment and thus, progress, in data — is caused by a common sequence of events:

1. People need policies and procedures in place to confidently use data to make important business decisions.
2. For that to happen, there needs to be a team monitoring it.
3. For there to be a team, there needs to be internal buy-in that this investment will be worthwhile for the company’s bottom line.

Say all the data in your CRM system disappeared tomorrow. What would the financial impact be on your business?

Most companies don’t know. They can’t answer how much an accurate account is worth, or if they put a new account into their CRM today, how much incremental revenue they’d generate from cross-sells or upsells.

That means, if you can’t put a value on your enterprise data, then it’s hard to justify prolonged investments in governing that data.

How Can Companies Break Through the Paradox and Prove ROI Opportunity?

In other words, what’s the cost of not managing the data? Recent studies have estimated that poor data quality may be costing an average organization as much as $14.2 million per year, and that the annual economic cost of bad data in the U.S alone may exceed $3 trillion. And time and time again, public examples related to data losses or data privacy breaches occur, with a focus on data security mistakenly being treated as an afterthought by some companies.

Essentially, the governance paradox makes companies feel stuck. They’re often on their second, third, or even fourth iteration of a project where they are trying gain control of their data and determine an ROI by implementing a software solution, but results fail to materialize. In addition, they’re still unable to accurately generate a single view of their customer.

To get any value out of enterprise data, over time, companies recognize that applying a governance program with people, policies, and procedures is the missing piece. An overarching program becomes the glue that brings the data and people together in a company — building a culture of trust and confidence as their business data becomes more accurate, timely, complete — and ultimately, usable.

How to Get Started: Small, Practical Steps to Build a Data Governance Program

The biggest mistake companies make in governing data is trying to do everything at once. The best way to improve the likelihood of both governance and MDM success is to start small. Dun & Bradstreet suggests tackling one initial category — let’s say, customer data — and establishing data quality standards.

Three of most common questions companies ask when beginning a data governance endeavor are:

1. What are the key components of an enterprise-wide data governance program?
2. What should my top priorities be in an early-stage governance program?
3. Why is data governance important to the overall success of a master data initiative?

Our data experts have put together a detailed guide, “The 5 Hurdles of Master Data Management,” to help you overcome obstacles on the path to extracting maximum value from data. Download the eBook and be sure to visit our Master Data Blog page for more blogs and podcasts on master data and data governance topics.