Super Bowl Tactics for a Winning Data Governance Strategy

January 29, 2019

4 minute read

The Super Bowl is here again—after the grind of a long regular season, and the nail-biting intensity of a successful play-off run—the championship is within reach for the two best teams in NFL. The teams that have made it this far were assembled by their GMs and coaches specifically to take the biggest prize in the sport. The game plans they developed and implemented throughout the season and play-offs have already made them champions in their respective conferences—giving them the chance to hoist the Super Bowl. In the same way, companies seeking to build a successful data governance strategy must assemble the right data, build a great game plan and execute in the face of changing conditions.

Read on to see how tactics from the best teams on the gridiron shed light on how executives can build a winning data governance strategy.

Pre-season efforts predict post-season success

The Super Bowl can be won on a spectacular one-handed grab made by a streaking wide receiver, but building that winning team starts in the preseason long before a single ball is snapped. In football, valuable off-season work involves drafting, trading and assembling the right squad. It also includes developing a vision for the way the team will play, depending on its strengths and weaknesses. Some teams have personnel that lend themselves to a pass-first game, while others consistently run the ball up the gut or rely on a strong defense to get the job done.

Like pre-season prep in football, developing a successful data governance strategy begins before any data analytics projects are ever initiated. A winning strategy starts with first understanding what data you have and where it is located. For most companies, this task is bigger than anticipated because more than 80 percent of their data is hiding in unstructured formats such as emails, MS Office applications, images, CAD format and within various file shares. This results in massive volumes of inaccessible data, too much to manually search. Instead, the data management process has to start with an automated crawl of all data repositories, file shares and ECM systems to identify all the data and remove ROT (redundant, obsolete and trivial) data.

Just like a football team may be looking at hundreds of players before training camp starts, and then winnows that group down to the core squad, enterprises must identify their redundant, obsolete and trivial (ROT) data, remove it to get to the essential data required. All the remaining unstructured data must be converted into a clean, structured format that can be used by the company’s data analytics engines. In the case of most businesses, this exploration and culling process needs to be carried out on a regular basis to offset the continuing ingestion and duplication of data.

Executing the game plan

Football teams don’t just let players run around the field doing whatever they want—they design and run plays that take advantage of the strengths of the team. In organizations, this means working on data to identify what has value and grouping it together prior to conducting any form of data analytics. The metadata should be cleaned and used to classify the content so that it’s easy to understand what it is and how it relates to other data—the equivalent of getting the right players on the field, in the right positions for a given play.

Avoid costly fumbles

Needless penalties and costly turnovers can cost teams games—if they make enough of them, the season can be ruined. In the same way, companies can fumble away their data governance strategy if they don’t adequately address the risk inherent in their content. Mishandling high-risk, unstructured data can run companies afoul of regulators (the industry referees) and legislation, like GDPR and the upcoming California Consumer Protection Act. Avoiding penalties requires that companies identify sensitive data (like PII) and apply appropriate security measures or redact confidential information.

Piling up the wins

Winning the Super Bowl requires calling the right plays and making real-time adjustments to changing conditions. It’s no different for winning organizations—unstructured data needs to be converted to structured data, insights need to be extracted and real-time refinements need to be made. Only then can the information be used as fuel for the powerful data analytics engines that will generate insights that lead to touchdowns, victories and competitive success.

LyondellBasell, one of the largest plastics, chemicals and refining companies, recently needed to create a solution for converting AutoCAD and MicroStation files into readable documents accessible to their global team. Implementing a new document conversion system for PDF rendering of their engineering drawings resulted in touchdown after touchdown. Converted PDF documents became accessible enterprise-wide on any device, scanned documents and PDFs became fully searchable based on content, and real-time monitoring capabilities of the document queue now allow administrators to prevent backlogs.

In the case of McKesson Corporation, a pharmaceutical distributor, they faced a discontinuation of the software application they had been using to convert and upload data into SharePoint, and were looking for a solution that offered long-term file accessibility. When they implemented a new document conversion solution, it set them up for a repeatable winning streak. A highly automated process for managing and converting large volumes of documents to PDF/A, guaranteed file longevity on critical documents, plus PDF/A conversion and validation capabilities enabled long-term archiving. Their form backlog was cleared and 11 months was saved in the conversion process.

Wrap up

No football team sits still in the off season, and the best teams, those that evolve into dynasties—like the Steelers, Cowboys and Patriots—are the ones that continually push for continuous improvement.

Companies that are able to use their data to generate business insights and gain advantage over their competition are those that define a winning data governance strategy, understand what unstructured data they have and automatically convert it to structured data, which they can leverage for data analytics engines in order to extract actionable business intelligence. But they recognize that, to stay on top in their market, they need to continually improve. They review their strategy and processes—and always seek to make the enhancements that will lead to better results.

Adlib’s Data Discovery Assessment will kick-start a winning data governance strategy. By conceptualizing the unstructured data within your ecosystem, we’ll shine a light on dark data and consolidate it into a dynamic view that can be instantly evaluated for data analytics that delivers valuable content clarity. Contact our team to get started.

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