In my experience there are a few consistent themes that emerge in data management and data governance work. Despite diversity of industry, culture and size, our clients face four common challenges in efforts to establish effective data management.
To paraphrase the DAMA Guide to the Data Management Body of Knowledge (DMBOK), data management means understanding enterprise data needs; collecting, storing, and protecting data, continually improving data quality, maintaining data security, and maximizing effective use and value of data assets.
Challenge #1: Get started
Even when business areas recognize and work around data quality problems, it is sometimes difficult to get improvement underway. Getting over the initial hump is hardest when the IT department drives the project. Most often, business teams rather than IT drive successful data management efforts that pursue hard dollar returns and significant business process improvements. Get started by finding and selling the business benefits.
Challenge #2: Identify tangible benefits
In a recent article I posed an example to illustrate the business value of data quality: what if a restaurateur found that 10% of customer checks didn't match kitchen orders? If you asked that chef to prove that data quality affected business results, the answer would be "Prove that it doesn't!" In most organizations a close review of areas affected by data quality problems reveals hidden ongoing costs in error correction or missed profit opportunities. Teams often find hard dollar ROI like reduced data correction, cleanup, and rework, and business process improvements enabled by improved data or resources freed from correction duties.
Challenge #3: Cross the silos
TDWI's Business Intelligence Maturity Model calls the transition from departmental to enterprise data management a "chasm". After crossing the chasm, organizations "achieve a consistent view of shared business information" that delivers "a more consistent set of corporate information and reports across all facets of the business". They call it a chasm because delivering that kind of integration typically means overcoming daunting political and technical challenges, and winning the battle for survival of the fittest priority project against other worthy efforts.
We recommend a business-focused approach emphasizing high-level support, risk/decision analysis, and a consensus business case. The business sponsor should have either authority or strong influence over the disparate areas to be integrated. Since enterprise data management requires enterprise buy-in, when it gets underway the project should incorporate Organizational Change Management (OCM) techniques to help break down barriers to success and make sure the organization is ready, willing, and able to use the new system when the time comes.
Challenge #4: Promote good practices
Finally, the fourth challenge is that proper data management means more than getting a new system approved and deploying it. An enterprise data warehouse and reporting system is a facility for integrating, cleansing, and making accessible business-critical data. Often, teams must learn new skills and priorities for maintaining data so that data from different sources remains consistent, accessible, and useful as the team adds new data sources and additional users come online. For example, consistent use of metadata dictionaries promote consistent data interpretation, analysis, and testing, and model standards for consistent addition of new data sources and maintenance of data integrity as the system changes over time. OCM approaches come in handy here again in educating the business on how they can use the data once effectively acquired and governed to ensure that the organization gets full value from the improvements.
In recent years the general sense seems to be that data quality problems are intractable. I don't agree: an organization can improve data management and meet key challenges by focusing on business results and effectively managing change.