Mu Sigma Unveils the “State of Analytics and Decision Science” Report, Uncovering Gaps and Trends in Data and Analytics

Mu Sigma, a leading global provider of decision science and big data analytics solutions, announced the results of its inaugural “State of Analytics and Decision Science” report, which identifies gaps and shortcomings in traditional approaches to analytics and problem solving. With the goal of understanding senior decision makers’ priorities and challenges in regards to analytics, Mu Sigma surveyed executives who lead or heavily influence data and analytics investment decisions at large U.S. enterprises across a variety of industries.

The report highlights that a majority of senior leadership believes analytics is affecting company strategy and results in positive ways. Companies that indicated they are underperforming against investors’ expectations tend to be more skeptical about the impact and benefit of data and analytics. However, the findings show that many companies are going about analytics the wrong way.

This report shows that many businesses are still misguidedly prioritizing data and technology needs over the need for better decision making,” said Tom Pohlmann, head of values and strategy, Mu Sigma. “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.”

Key findings from the State of Analytics and Decision Science report include:

Top Challenges in Data and Analytics are Changing

As companies become more reliant on analytics to inform their decision-making, data challenges persist, particularly issues with quality, consistency and usability. In addition to challenges with the data, many companies point to talent shortages and lack of training as a top obstacle. However, most skill set challenges faced by these companies are derived from a lack of soft skills – business acumen and communications skills – rather than a deficit in mathematics or stats knowledge.

• One-third (34 percent) of companies surveyed noted data quality, consistency and availability are the most important issues plaguing their analytics initiatives.
• Issues related to a dearth in available skills, whether due to talent shortages or lack of training, were the second-highest challenge (30 percent) faced by companies.
• Underperforming companies are twice as likely to identify skill set deficiencies as their most pressing challenge in analytics.
• Business acumen and communication skills are two of the top three skill set domains where businesses see the need for improvement.

Data Ownership and Governance Models are Changing

When it comes to who “owns” analytics, there is no clear answer. Responsibility over analytics is spread among the C-suite – Chief Information Officers (CIO), Chief Financial Officers (CFO), Chief Analytics Officers (CAO), Chief Marketing Officers (CMO), Chief Data Officers (CDO) and more.

Governance models also depend on the person responsible for the data and analytics. Most organizations have their sights set on a centralized model, where a central group provides analytics services to the rest of the company. Fewer use a federated model, combining the centralized and decentralized approaches with proper governance.

Although big data has been hyped for a few years now, we’re still in a period of nascency with respect to analytics and decision science as a discipline in the enterprise,” added Pohlmann. “That’s why there seems to be a concerted effort to corral analytics into centralized body, despite the reputation of many shared services functions as not being fast or agile enough for the lines of business they serve.”

• 23 percent noted that CIOs are in charge of data and analytics, while 17 percent said it’s the CFO and 13 percent said it’s the relatively new position of CAO.
• 44 percent use a centralized model, followed by 22 percent using a decentralized model, 16 percent using a federated model and 15 using a mixed (or lack of any) model.
• CIOs are more likely to lead centralized models, following a construct they’re comfortable with in traditional IT environments.
• The majority (45 percent) of companies will go more centralized when looking to change the governance and organization of analytics.

Leading with Data vs. Leading with Outcomes

Organizations have traditionally prioritized data over decisions, and the survey shows this mindset still prevails. An overwhelming majority of respondents indicate they lead with and sometimes limit themselves to the data they have available to them, rather than planning around the business outcomes they have in mind, when it comes to problem-solving.

• 74 percent of respondents indicate they lead with data when it comes to problem-solving, while 26 percent lead with their desired outcomes.
• CDOs (56 percent) are more likely to have this view than CAOs (41 percent).

It’s Time for a New Art of Problem Solving

Organizations don’t approach analytics with the same rigor that they do other, more mature disciplines. Many do not follow a consistent methodology for problem-solving, focusing instead on plugging holes in their data and workforce. But many are starting to recognize the importance of organizational and structural deficiencies when it comes to analytics. As organizations seek a more creative approach to problem solving, decision makers recognize the need for more collaboration in the analytics domain.

• 39 percent of respondents don’t follow a consistent methodology for problem solving.
• 41 percent think that their ability to drive actionable insights out of their analytics work could really improve.
• 24 percent would make developing a clear roadmap of analytical 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.

Survey Methodology:

The survey was conducted online with 150 respondents during calendar Q1 of 2016. All respondents represent U.S. firms with at least $500 million in annual revenue. Respondents are director level and above, 50 percent from shared services (Chief Data Officers, Chief Analytics Officers or Chief Information Officers) and 50 percent from lines of business (marketing, finance and supply chain leaders), and screened for heavy influence over data and analytics investment decisions.

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