How to Actually Put Your Data Analysis to Good Use

Executive Summary

A third of data analytics professionals say their company never, or only sometimes, puts their analyses to use. This calls into question the practicality of funnelling analyses through centralized teams focused on big-picture challenges. In our experience, most companies don’t need a small army of data scientists or bleeding-edge analytical techniques. What they do need are analyses that solve key commercial and operational problems. The good news is that the tools to do so are readily available — and relatively inexpensive. The same is true for processing power. Meanwhile, the vast majority of companies already store (but don’t analyze) vast amounts of commercially-relevant data and are collecting it at a faster rate than ever before. What’s missing, more often than not, is a clear strategy and operational model for using these capabilities in ways that are specific to the company’s business requirements. Any such effort depends on three basic components: People who can combine their commercial expertise with advanced analytics methods;  an evidence-based approach that translates analytical know-how and an understanding of the business problem into actionable insights; and a small team of analytics professionals (not necessarily data scientists) to develop appropriate analytical tools and techniques.

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Data and analytics professionals seem to be at the center of the next big race for talent. In 2015, there was a surplus of people with data science skills. Now there’s a significant shortage. By 2020, IBM expects broader demand for data and analytics talent to reach 2.7 million positions in the U.S. alone.

The competition for talent will be especially intense for companies for whom advanced analytics forms a core part of their proposition — think e-commerce giants, hedge funds and complex system engineers. For them, a dedicated, in-house team of data specialists can be a necessity.

But the rest of us? Not so much. Consider the findings of a Rexer Analytics survey in which more than a third of data analytics professionals say their company never, or only sometimes, puts their analyses to use. This calls into question the practicality of funnelling analyses through centralized teams focused on big-picture challenges.

Complete Integration with the Business

In our experience, most companies don’t need a small army of data scientists or bleeding-edge analytical techniques. What they do need are analyses that solve key commercial and operational problems. The good news is that the tools to do so are readily available — and relatively inexpensive. The same is true for processing power. Meanwhile, the vast majority of companies already store (but don’t analyze) vast amounts of commercially-relevant data and are collecting it at a faster rate than ever before.

What’s missing, more often than not, is a clear strategy and operational model for using these capabilities in ways that are specific to the company’s business requirements. Any such effort depends on three basic components:

  • People who can combine their commercial expertise with advanced analytics methods and applications in an effective way
  • An evidence-based approach that translates analytical know-how and an understanding of the business problem into actionable insights
  • A small team of analytics professionals (not necessarily data scientists) to develop appropriate analytical tools and techniques and enable the organization to deploy them through internal training and advice

Together, these form a solid foundation for closing the gap between technical skills and commercial thinking so that businesses can extract value from analytics. 

Building Internal Capabilities

So how to get started? We suggest taking your cue from companies that have been down this road before.

Start small. Aim for a single, key problem or an important (but limited) area of the business where predictive analytics can have a valuable and immediate impact. Then use the results to build credibility, excitement and momentum.

Brewing and beverage company SABMiller (now a division of Anheuser-Busch) took this track when they decided to make data and analysis available to their business units. To manage risk, they kept their initial investments modest. That way, they could readily abandon the failures while expanding the rollout of the tools and approaches that worked.

Keep it commercial. Tie analytics to the commercial and operational heart of your organization. (If it doesn’t address a core business need, analytics can be a hindrance more than a help.) While you’re at it, place the analytical capability as close as possible to those doing the commercial thinking.

Insight Center

Berlin-based Zalando, for instance, empowered its business units with a self-service analytics infrastructure paired with embedded analysts focused on product development. The result? Teams ranging from Fashion Store to Logistics now can extract the data-informed insights they need to make smart decisions in line with the firm’s top business priorities.

Identify your analytical capabilities — both existing and potential. Many companies already have functions dedicated to strategy, financial planning or business insights, as well as individuals who use analysis to solve business problems. Build on these assets by empowering them with new capabilities. Special qualifications aren’t required — just the ability (and desire) to become data literate and proficient in the tools you select.

When Jaguar Land Rover discovered pockets of self-service analytics activity across its departments, the company began offering in-house analytics training. The first 60-seat course offering filled to capacity. By last year, an estimated 1,800 and 2,500 employees had become “citizen analysts” — business users who created their own analytics as part of their day-to-day work.

Build a toolkit. Encourage business users to tap into analytics with self-service tools that preclude the need to learn how to code. The most effective of these have built-in algorithms to navigate company data, create charts and dashboards, and deliver insights to different audiences.

Nike put together a tailored set of software tools for business intelligence, analysis and visualization—all with an aim to reduce technical barriers and bring insights to users in the business.  The toolkit had to cover the full range of user requirements from those just wanting key dashboards to those looking for as much data as possible to perform their own ‘exploratory’ analysis.  All users benefit from a central team that provides the governance necessary to ensure a stable, secure and up-to-date environment.

Create evangelists. Don’t leave business users to figure out the commercial value of analytics on their own. Show it to them instead, via a network of advocates across the organization. Through these advocates, companies can proactively introduce the capabilities available to the business and provide expert support for those finding their way.

That’s what UK supermarket Sainsbury’s did when they created a new 60 strong internal team called the Humanalysts. The group’s mission? To identify data-driven opportunities for improvement—such as predicting shopper responses to new pricing strategies — and, along the way, make believers out of often-skeptical business users.

Making Use of What You Have

Wherever you start your journey, keep this in mind: Democratizing analytics is an unavoidably iterative process. Every step requires a look back to ensure the appropriate controls, training and delivery mechanisms are in place and working the way they need to be. As Voltaire famously observed, with great power comes great responsibility.

It’s also a good idea to borrow liberally from those in similar situations to your own. Make generous use of relevant case studies. Prioritize the insights that generate commercial benefits. When reporting back within your organisation, focus on output and impact rather than the underlying models. Inspire, and be inspired.

Finally, don’t give in to pressures to build great teams of scarce, highly-paid specialists without working out what makes sense for your business. There’s nothing so special about analytics that they must be kept from those most intimately familiar with the problems you need to solve, and who work for you already. Analytics should inherently empower anyone with the means to comprehend it. Put another way: Data analytics is for everyone — not just the few.