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How sports analytics tactics can help businesses win big

Examine 10 ways winning teams use sports analytics to improve on their play, and put your company on the fast track to success.

It began as long ago as the 1940s, when Brooklyn Dodgers General Manager Branch Rickey hired statistician Allan Roth to evaluate player performance. Since then, baseball and other sports have increasingly relied on various forms of data analytics to build successful teams.

Sports analytics received a major boost in 2003 when the book "Moneyball," by Michael Lewis, described how Oakland Athletics General Manager Billy Beane used the technique to optimize the frequently imperfect science of baseball player evaluation. Today, virtually all major sports teams have analytics departments that crunch numbers to create actionable insights. General managers and scouts then use the information to determine which players they think will fit their club best and to maximize overall team performance.

Businesses can get more value out of their data analytics operations by following many of the techniques sports teams use to maximize their on-field success. Here are 10 steps to help you get started.

1. Aspire to be a top performer

Like any sports team, every business aims for success. To be one of the best in its particular "league," a business needs to use analytics across the organization, making decisions based on all available data, rather than relying on gut instincts. The NBA's Orlando Magic, for example, doesn't use analytics to just study player performance. According to analytics software provider SAS, the organization also studies the resale ticket market to better price tickets, predict season ticket holders at risk of defection (and bring them back), and analyze concession and product merchandise sales to make sure the organization has what the fans want every time they enter the arena. 

Your first step toward becoming a top performer should be to identify projects that are both promising and practical. For a hockey team, it might be locating prospects that combine power, speed, and precision using data that's already on hand. For your company, it might be finding store sites in locations with the most desirable spending patterns.

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2. Develop a goals-driven analytics team

Finding and hiring the right talent is the essential first step in creating a skilled analytics staff. Major sports teams hire data analysts from MIT, Caltech, and other top-tier schools. Your business doesn't have to aim quite that high. What is important is that the people you hire should understand your business and possess a deep understanding of analytical tools, formulas, and models that comes from hands-on experience.

Yet finding analytics-skilled individuals isn't always easy. A KPMG CIO Survey found that data analytics is the most in-demand technology skill, and nearly 40 percent of IT leaders say they suffer from a shortage of people possessing skills in this critical area.

3. Draw data from multiple sources

Data is the fuel that drives analytics. Sports teams acquire data from multiple sources, including published game statistics, scouting reports, schools, equipment sensors, companies that specialize in collecting sports statistics, and even other teams. Businesses, on the other hand, can inhale data from internal and commercial databases, mobile devices, remote sensors, point-of-sale systems, and myriad other sources.

While access to abundant amounts of big data is generally a good thing, it's also important not to become buried under mounds of essentially useless data. In the NBA, for instance, multiple video cameras can now track the movements of every player on the court, as well as the basketball itself, at speeds of up to 25 times per second. The downside is that the sheer volume of the data generated overwhelms most analytical tools and no one has yet been able to prove that tracking player movements can help a team win more games.

4. Focus on the team, not just the players

Sports analytics is primarily focused on players—who to draft, who to promote, who to trade, and who to play. The ultimate goal, however, is not simply to obtain the best players, but to create a team that wins games.

Businesses should take a similar outlook. Analytics can be used to do all sorts of useful things, including identifying unproductive business relationships, pinpointing underperforming employees, and uncovering supply chain kinks. Analytics can even be used to find the answer to a complex business problem, such as: How does a group or unit perform when a particular manager is overseeing it, and how does performance change when that manager leaves? Still, the eventual goal should always be to use analytics in ways that ultimately benefit the entire enterprise.

5. Spread analytics across your entire team<

One of the biggest analytics challenges enterprises face is trying to glean insights from data and analytical functions trapped inside silos scattered across multiple business units and organizational functions. Silos complicate and restrict data access and analysis, making it difficult to find valuable opportunities that spark enterprise growth. Data analytics should be the pulse of the organization, incorporated into all key decisions across sales, marketing, supply chain, customer experience, and other core functions

As Amiel Sawdaye, assistant GM of the Arizona Diamondbacks, told the Arizona Republic, "[Analytics] should impact every department, from amateur to international scouting to player development and medical. This takes years, not months, but our vision is to have this department provide key information for all the decision-makers in those respective departments.”

6. Use the best tools

Pro athletes are equipped with the highest quality gear: bats, gloves, helmets, and so on. Most sports teams also work hard to give their analytics staff access to the best and latest data sources and analytical tools. NBA teams, for instance, tap into qualitative, actionable data generated by cutting-edge eye-in-the-sky cameras and wearable technology to  decide which players to add or drop from a roster and which ones need to be rested.

Selecting analytics tools that provide the best fit for your company's activities and goals is critical to the success of all its future analytics projects. The selection process should include gathering and prioritizing requirements, as well as determining use cases and tool categories. In general, your organization should select analytics tools that provide the best fit for anticipated use cases and can be implemented with currently available or projected resources.

7. Build and maintain open channels

In sports, analytics experts must communicate in plain and straightforward language with people who tend to know more about the technique of hitting a low-hanging fastball over the outfield fence than algorithms and models.  “I think there currently exists a little bit of a disconnect between analytics departments of other organizations and the messaging or implementation of the data with players," former Minnesota Twins pitcher Craig Breslow told the Minnesota Post earlier this year. One way the Twins' analytics staff connects with players is by sending each pitcher a sheet with multiple charts, asking what information they needed or wanted to know more about.

The same approach can be used in business, where end users often have little or no grasp of analytics techniques and processes. Most sports analysts are dedicated sports fans as well as top-drawer data experts. Your analytics team must know enough about the company's business, including its key plans and challenges, to effectively interpret and explain key analytics-generated insights to managers and other company decision-makers. 

8. Work with your IT partners

Given player salaries and other financial encumbrances, many sports teams, particularly those based in smaller markets, can't afford the luxury of operating giant IT or analytics departments. Such organizations make every dollar count by reaching beyond their internal resources. Even well-heeled teams, like the Orlando Magic, see an advantage in building partnerships with data suppliers, analytics software providers, and hardware vendors to bolster their in-house number-crunching capabilities.

Your business can also benefit from strategic partnerships with external resources. It costs little or nothing to investigate the possibilities.  Important caveat: Be certain that your partners really understand your company's needs and goals.

9. Seek external support

Sports enthusiasts, such as in baseball and Roland Beech in basketball, helped spur sports teams into embracing analytics. Today, data-obsessed fans continue to take sports analytics to new levels of refinement and power. The Society for American Baseball Research (SABR), for example, is a collection of baseball statistics enthusiasts who crunch numbers for the sheer joy of uncovering interesting facts and correlations. The organization holds an annual SABR Analytics Conference where it brings the top minds of the baseball analytics community together under one roof to discuss, debate, and share insightful ways to analyze and examine baseball data. Another major get-together of teams and analytics experts is the annual MIT Sloan Sports Analytics Conference.

Your enterprise may not have a fan base it can draw on to provide analytics support, but it does likely have business partners and customers with their own analytics departments. They might be willing to put their teams to work to help solve tough problems that ultimately benefit both parties.

10. Stress the critical importance of analytics

New analytics initiatives often encounter strong resistance from managers who remain entrenched in traditional methods. Such individuals are convinced that old-school approaches to solving business problems remain superior to analytics, which many skeptics mistakenly view as a passing fad.

Actually, getting backing for data analytics projects may be more difficult than you think. A KPMG report, for instance, found that only 51 percent of C-suite executives fully support their organization’s data and analytics strategy. Gartner, meanwhile, estimates data and analytics projects fail 60 percent of the time, typically due to the fact that organizational structure and talent and are not properly aligned with the business strategy. Gartner notes that analytics chiefs must work with business leaders to identify problems that have to be addressed. All must jointly review the outcomes that drive the business and identify the decisions that could provide the biggest impact or, in many cases, the quickest payback.

As Michael Jordan once wisely observed, "Obstacles don’t have to stop you. If you run into a wall, don’t turn around and give up. Figure out how to climb it, go through it, or work around it."

This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.