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Slam Dunk Your Data Strategy: What College Basketball Can Teach You About Leading Data Initiatives

As this year’s NCAA basketball tournament draws to an end, I am reminded of parallels between building a successful sports team and organizing an efficient business team to run functions like data initiatives.

Upon receiving the 2008 NCAA Coach of the Year Award, The University of Kansas’ men’s basketball coach, Bill Self, remarked: “These things are nice, but I’m never hung up on them. They can’t happen unless you have good players. I care a lot more about winning a championship as a group than any individual award.” Melding 12 teammates into a cohesive championship team is akin to turning individual employees into a successful company that is greater than the sum of its parts. The same parallel applies to deriving value from data. In the era of the digital enterprise, where big data rules, establishing a holistic data practice is paramount to achieving championship-like success. So, what can companies learn from team sports strategies that can be applied to making sense of, and eliciting value from, data?

A robust, holistic data practice establishes an intelligent management strategy for data initiatives, puts the most suitable people in place to lead those initiatives and implements tools built to derive the most value from data.

See the Entire Court

Simply fielding five good basketball players doesn’t automatically mean the team will end the year with hardware. A coach must step back, analyze his players’ strengths and weaknesses and put those players in the best positions to succeed.

Before embarking on data initiatives, executives should take a similar step back. Simply ingesting and storing data isn’t enough to turn an organization into a data-savvy company. Having data doesn’t give it value. Rather, the ability to take data, analyze it and act quickly on the patterns it shows, is the hallmark of a truly data-driven organization. Data-focused executives must first understand how their organizations currently use data, and whether or not they make the most of that data. Once an executive understands his organization’s data processes, he will be able to more accurately address the potential problems plaguing the organization’s value-deriving mechanisms.

A Successful Season Happens One Game at a Time

Perhaps the most difficult obstacle in determining data value is the sheer volume of information organizations now have. Those in charge of determining data value are often overwhelmed with the mountain of data because they don’t know where to start. Data decision makers would do well to follow basketball’s lead, where teams practice one play at a time, then work toward their goals one game at a time, building up confidence. Rather than attempting to conquer every use case and effectively trying to play for the national championship on their first day of practice, organizations should pick a couple of high-value use cases out of the gate and narrowly focus on making them successful. This approach will help generate immediate value and experience from the process. Organizations can then they can move on from those first steps more confidently.

Let’s look at how to approach this: A retailer, for instance, collects customer data from its point-of-sale (POS) systems but might not use that data to power marketing campaigns. Rather than focus on every single piece of data it owns, the retailer can look at sales information that might be valuable from a marketing perspective. Organizations can leverage existing infrastructure components to see where they might be able to layer additional functionality and derive more value.

This layering approach helps solve two challenges. First, it attacks a problem without necessitating an entire rebuild. Organizations can focus on one area where they know they already have useful data and build from that pillar of infrastructure. This eliminates the tendency to believe that the entire mountain must be conquered at once — and early successes make it easier for management to secure buy-in to expand to more projects . Second, a layering approach helps knock down silo walls. Siloing is helpful and even necessary in some instances. But for the majority of data-driven decisions removing silos helps glean more value from data and quantify that value. Using data from different silos for a common purpose, or using data from one silo for a new purpose, can reveal hidden and underappreciated data value.

Your Team Needs the Right People in the Right Positions

The first step toward deriving value from data is to make your data actionable, as the retailer did with its POS data. Now that you’ve determined a starting point you need the appropriate personnel to manage and model that data. Some organizations make the mistake of placing this responsibility on the shoulders of developers they already employ. This move would be tantamount to asking your seven-foot-tall center to play point guard (a position usually reserved for shorter, quicker players) and lead the offense down the court, or hiring a water polo coach as an assistant basketball coach. Instead, the task of exploring the data to derive value should fall to a data (modeling) specialist.

Why? Organizations must understand that deriving value from data isn’t just about data access. It’s about the ability to look at the data and use those observations to take informed actions in as-close-to-real-time as possible. Consider the example of Wall Street. Financial heavyweights hire a large majority of economics and math majors because those students know how to model large data sets and derive value from incremental data. Similarly, organizations that have only worked with traditional, relational database management systems (RDBMSs) may not have the mindset or philosophy required to model unstructured data in a world NoSQL is better suited for. Once organizations identify where to derive additional value, they need to hire people with proven and appropriate skillsets to model and understand the data.

A Data-Driven Organization Comes From People, Processes and Tools

Establishing an effective data practice includes embracing a mindset that views data as actionable information. This mindset should permeate every aspect of the data strategy. When Self stood in front of his 2008 championship team — the winningest team in University of Kansas history — he told the players they had nothing to lose because no one could take that fact away from them. He also told the team that they had so much to gain. He developed a process that led the team through the season to that point, and he didn’t deviate from that process for the team’s biggest game.

Digital business leaders must instill a similar ideal: their organizations have so much to gain from their data. Instead of letting data sit, they can acquire and develop the appropriate tools and people to capitalize on its value to change and drive their businesses.

Organizations must choose the right tools. Using a relational database for big data initiatives, for example, would be like running basketball players through drills designed for football players. On the other hand, NoSQL databases offer flexibility and scalability and are a better fit for the dynamic, unstructured data filtering in through the myriad data sources organizations employ. Establishing a process, selecting the right people for the job and using the appropriate tools are the keys to winning a championship, er, realizing your data’s full potential.

Adam_WrayContributed by: Adam Wray, CEO, Basho Technologies. Adam is a cloud enterprise technology entrepreneur and executive with more than 20 years of experience. Most recently, he served as CEO and President of Tier 3 where he led the company through nearly $20 million in venture capital funding, growing it from a startup with a small client portfolio and revenue stream to an eight figure annual run-rate ($10M+). Prior to Tier 3, Wray held operational leadership roles at Amazon, Akamai Technologies and Limelight Networks. He currently sits on the Board of Directors for Basho Technologies and is a non-executive director of Cloudsoft Corporation, 6fusion USA and Observable Networks.

 

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