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Data Management: Finding Common Ground between Business and IT

Businesses are discovering that their success is increasingly tied to the quality of their information. Organizations rely on data to make significant decisions that can affect customer retention, supply chain efficiency and regulatory compliance. As companies collect more and more information about their customers, products, suppliers, inventory and finances, it becomes more difficult to accurately maintain that information in a usable, logical framework.

The data management challenges facing today’s businesses stem from the way that IT systems have evolved. Enterprise data is frequently held in disparate applications across multiple departments and geographies. The confusion caused by this disjointed network of applications leads to poor customer service, redundant marketing campaigns, inaccurate product shipments and, ultimately, a higher cost of doing business. Add to that a sense of unrest and poor communication between departments regarding data quality and you’re at a standstill. Hear Tony Fisher discuss IT’s role in compliance.

How does one reconcile dealing with data that affects, to varying levels, multiple departments and facets of a corporation? More importantly, who will be responsible for turning a fragmented, disorganized data-driven chaos into a clean, manageable boon for efficiency? Will it be the infrastructural/technology management wizards in IT? Or perhaps the business/finance managers or analysts who use customer and product data in every aspect of their jobs and therefore must ensure that it’s of a certain quality? Well, unfortunately, recent trends show that there is a lot of uncertainty about who is, in fact, ultimately responsible.

The debate – is data quality an IT or a business problem? – has raged on for years. Proponents of IT data management argue that only technology-savvy overseers are capable of handling customer, product and master data; only they have the knowledge and expertise to govern data round-the-clock. Some IT executives themselves, however, will contend that they a) don’t have time to manage an entire company’s data, and b) shouldn’t have to clean up the business side’s messes.

Business executives are often eager to take control because data is essential to their day-to-day operations (and not necessarily so important for IT). These staff members can be frustrated that another department is pulling the strings on how quickly data can be integrated, cleaned and amalgamated to provide reporting and analysis. Nonetheless, the business side has a problem, too. They can’t single-handedly manage enterprise-wide data, which can lead to a fragmented, confused view of the organization.

Regardless, now that we’re on the brink of a recession – and wasting money on customer outreach and product management is the last thing executives want to do – it’s time we put an end to this confusion. The answer is simple: both IT and business sides are responsible for ensuring data quality. How does that work? Well, if IT and business haven’t been working together in the past, the best option is to bring in a referee. A referee, in this case, is known as a “data steward.”

Data stewards can help a company make significant gains in managing the data. Simply put, data stewards are people trained to exclusively handle the middle ground between IT and business – they are technically savvy individuals who understand the corporate goals for the department, division or enterprise. The role of a data steward is to govern customer, product, supplier and metadata across various silos, back-end and front-end systems and departments. They’re a solution to the IT vs. business debate because they’re neutral and can see both sides of the equations.

Conflicts in communication between IT and business departments will only be resolved if a data steward understands what both sides are aiming for and makes a constituted effort to appeal to them on an equal level. Here are some factors to consider when hiring a data steward to keep your information high quality and fit for purpose:

• Data stewards must not only possess strong technical acumen, but they must have a great understanding of how data affects an organization and the people skills to get the necessary results. A good data steward has often held positions – or at least “dotted line” responsibilities – with both groups.

• A key element to any data stewardship program is creating a culture where making and maintaining high quality data is a core discipline of the organization. People naturally support a program because they see a benefit, rather than supporting a program out of a sense of duty. Data stewards must accurately convey the real benefits of improved data quality – and help business and IT employees understand the impact of bad data.

• The best data stewards typically begin a new project by holding a meeting with key stakeholders and departments to discuss objectives and business drivers. The drivers could include the need to better cross-sell to its customer base or to have greater visibility into the organization’s product portfolio. This dialogue must be ongoing – data stewards frequently relay successes or failures, always linking these back to the effect on the business.

• Data stewards also make sure key executives are aligned with project ideals. People are more likely to commit to a project when they realize that the boss is on board. Having executive support instantly lends credibility to the data steward’s mission.

If your company’s data steward is competent in all of the above areas and yet still cannot get IT and finance to work together and communicate effectively when it comes to data responsibility, more measures must be taken. Typically, top management must outline the accountability of both departments in the ultimate success or failure of data management.

One of the best examples of this comes from a large high-tech manufacturing company that embarked on an immense internal data quality project. Rather than task its data stewards to create arbitrary standards for data quality, the company created service-level agreements (SLAs) between the IT and business groups. These agreements held both parties responsible. Business units created rules for monitoring the quality of corporate information; IT committed to a specific level of quality.

The SLAs included information about what metrics the data stewards would track and what thresholds were acceptable for different data points. The interplay between business and IT utilized the strength of each group. The business users were responsible for creating a blueprint of good data. The IT group, along with the data stewards, then enacted the data quality and data integration rules to make that a reality.

After an initial clean-up phase, the data stewards encapsulated the rules provided by the business users to create monitoring rules. These rules would scan incoming data in real time and fire system notifications or emails when data did not meet expectations. With this method, data quality problems were found farther upstream, before bad data could impact operations.

Savvy data stewards know that even the best technology in the world, although obviously helpful, isn’t enough to ensure smooth data management. In the end, the integrity of a company’s data hinges on the integrity of its employees’ maintenance practices. Enabling an environment conducive to respect and cooperation can go a long way in effecting quality data that drives quality business decisions.

About Tony Fisher: Tony Fisher joined DataFlux as president and general manager in 2000. In his years at DataFlux, he has guided the company through tremendous growth as DataFlux became a market-leading provider of data quality and data integration solutions. Prior to DataFlux, he was the director of data warehouse technology at SAS. Tony was a key technology leader at SAS, providing the engineering research and development direction that helped make SAS the market leader in business intelligence software. Tony is a native of North Carolina and earned a degree in computer science and mathematics from Duke University.

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