How the Most Successful Companies Effectively Leverage Data and Analytics for Problem Solving

by   |   February 10, 2017 5:30 am   |   0 Comments

Tom Pohlmann, Head of Values & Strategy, Mu Sigma

Tom Pohlmann, Head of Values and Strategy, Mu Sigma

Over the past few years, terms like analytics and data scientist have been on their way to becoming a household name within the world of business. To us data junkies, using analytics to generate actionable business insights is a no-brainer need, as it can significantly help companies improve all facets of the business. This is why we compiled a report on “The State of Analytics and Decision Science” and surveyed executives who lead or heavily influence data and analytics investment decisions at large U.S. enterprises in a variety of industries.

The report identifies gaps and shortcomings in traditional approaches to analytics and problem solving. It shows that many businesses are still misguidedly prioritizing data and technology needs over the need for better decision making. Changes in customer behaviors are leading to a scramble for new capabilities and offerings – which in turn fuels the need for analytics and insights. But because businesses aren’t paying enough attention to creative problem solving, they are falling short in analytics. We came up with some interesting results, some of which I will touch on in this post.

The New Art of Problem Solving

Organizations don’t approach analytics with the same rigor that they do other, more mature disciplines. But businesses face complex problems every day, and they are forced to solve them quickly and efficiently. That’s where decision science comes in. This market needs proven methodologies and frameworks to follow in order to materially affect business outcomes.

Of the companies surveyed, 39 percent don’t follow a consistent methodology for problem solving. However, we anticipate this number will drop significantly over the next decade as companies are beginning to realize the importance of solving organizational and structural deficiencies. Our report discovered:

41 percent of companies think their ability to drive actionable insights out of their analytics work could really improve.

23 percent would make developing a clear roadmap of analytics business problems to address in the coming year a top priority.

23 percent would prioritize identifying where analytics work is both sufficient and deficient in supporting business needs.

Once companies realize that they can take a more creative yet consistent approach to problem solving, decision makers in each organization will see the need for a serious discussion about leveraging the data for their benefit.

Top Challenges in Data and Analytics Have Shifted

As companies lean more on analytics to inform decision-making, data challenges persist, particularly issues with quality, consistency and usability. In fact, one-third (34 percent) of the companies surveyed noted that these data concerns are the most important issues plaguing their analytics initiatives.

Issues related to talent – shortages or lack of training – in data and analytics were the second-highest challenge (30 percent) the companies surveyed needed to overcome. In the past, lack of domain acumen in mathematics or statistics was always a top concern, but now, business acumen and communication skills have become noteworthy challenges as well.

Data Ownership and Governance Models Continue to Change

When it comes to responsibility for data and analytics, it’s the wild west. Of the companies surveyed, 23 percent noted that CIOs are in charge of data analytics, while 17 percent said it’s the CFO and 13 percent said it’s a relatively new position of Chief Analytics Officer (CAO).

Generally speaking, who is considered to have overall responsibility for analytics at your company?

Generally speaking, who is considered to have overall responsibility for analytics at your company?

Governance models also depend on the person responsible for data and analytics, however most organizations (44 percent) tend to have their sights on a centralized model, where a central group provides the analytics service to the entire company. The rest are broken down as follows: 22 percent using a decentralized model, 16 percent using a federated model and 15 using a mixed (i.e., lack of) model.

Although big data has been hyped for a few years now, we’re still in an early stage with respect to analytics and decision science as a discipline in the enterprise. There seems to be a concerted effort to corral analytics into centralized body, despite the reputation of many shared services functions like IT as not being fast or agile enough for the lines of business they serve. About 45 percent of companies intend to become more centralized if looking to change the governance and organization of analytics.

Analytics and decision science continue on their way to changing how businesses are run. Our goal with this survey was to truly understand how far we’ve come – and how far we have left to go – when it comes to the implementation of data analytics in the workplace. For now, it looks like we’re going in the right direction, but need to embrace analytics as a discipline that deserves rigor, structure, and consistency; and shift our focus past just the data to matters of problem solving. To view the full survey results, click here.

 

Tom Pohlmann is the head of values and strategy at Mu Sigma, a leading global provider of decision science and big data analytics solutions. In this role, he leads the company’s global brand and communications strategy, manages a portfolio of new client accounts, and oversees the development of programs that continuously align the company’s work with its values and vision. Recognized as an innovator and creative thought leader in the industry, Tom has more than 25 years of experience in strategy and product development, messaging and branding, profits and loss accountability, mergers and acquisition, customer insights and analytics, publishing, and public speaking.

 

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