Member Article

Enterprises are failing to use analytics effectively - study

• CFO responsible for analytics in nearly a fifth of organisations

• ‘Failing fast and failing cheaply’ key to finding competitive edge

• Higher performing companies 4x more likely to follow a consistent approach

A new report by Mu Sigma, a leading global provider of decision science and big data analytics solutions, has revealed a diverse landscape when it comes to how multinational enterprises tackle problem solving and analytics - with the right approach proving an essential part to driving results and meeting stakeholders’ expectations.

The inaugural ‘State of Analytics and Decision Sciences’ report shows that while the majority of senior decision makers (65%) recognise the positive impact analytics can have on business growth, many are failing to manage and harness it effectively.

Part of that polarity is down to ownership. When asked who has overall responsibility for analytics within their organisation, 23 percent said it is their Chief Information Officer (CIO), while nearly a fifth said responsibility lay with the head of finance, the CFO (17%). The number of enterprises where ‘specialists’ are in charge of the function appears to still be in the minority, with only four percent saying they have a Chief Data Scientist looking after it, nine percent a Chief Data Officer and 13 percent a Chief Analytics Officer.

Perhaps unsurprisingly given this multitudinal approach to ownership, the report also highlights a diverse range of governance models among the 150 organisations that took part. Most use a centralised model (44%), where a central group provides analytics services to the rest of the company, while 22 percent said they use a decentralised model, where individual business units are responsible, and 16 percent adopt a federated one, a well-coordinated blend of the two.

When it comes to how companies approach analytical problem solving, surprisingly few companies said they begin with a business outcome in mind (26%); most (74%) said “we start with the data we know we have access to and go from there.” On average, the bulk (39%) of analytics work still centres on the descriptive – reporting on what’s happening in the business ‘here and now’ – as opposed to predictive analytics at 21%.

“The report shows that many businesses are misguidedly prioritising data and technology over better decisions,” said Tom Pohlmann, Head of Values and Strategy at Mu Sigma. “Many are forced to spend the bulk of their effort organising and reporting on what is happening in their business, and not enough time looking at the why and what’s next part of their story, which will better prepare them for the opportunities and challenges on the road ahead.”

A key ingredient to problem solving and analytics success lies in taking a more creative and experimental approach. Among the most successful companies, 60% practice a ‘fail fast and fail cheaply’ mentality to help them identify the right mix to their analytics to achieve a competitive edge. And, nearly 67% of companies that exceeded investor expectations said they look outside their industry for learnings and practices to make improvements to their business.

“Changes in customer behaviours are leading to a scramble for new capabilities and offerings – which in turn fuels the need for analytics and insights,” added Pohlmann. “While many enterprises are taking the right approach to meeting those challenges, many are still not paying enough attention to creative problem solving and consequently falling short in analytics.

“Organisations needs to understand the importance of decisions in order to gain truly valuable insights from their data – and sometimes you need to ‘think outside the box’ to get there.”

Looking forward, the overwhelming majority of participants (70%) acknowledged that, to varying degrees, they plan to make improvements to their approach and have a clearer roadmap of analytical business problems they want to address in the coming year.

Those who are planning to do so can take heart from statistics in the ‘State of Analytics and Decision Sciences’ report which show a connection between business performance and analytical rigor. Those firms who have met or exceeded stakeholders’ expectations are nearly four times (3.9) more likely to use a consistent methodology for analytical problem.

To download the full report, visit http://info.mu-sigma.com/the-2016-state-of-analytics-and-decision-sciences-report.

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Additional findings:

• When asked which challenge is most important for them to address in the next 12 months – 34% said data challenges (e.g. quality, consistency, availability); 30% skill set challenges (talent shortages, lack of training etc.); 20% organizational challenges (e.g. how to organize for and govern analytics), and 16% software challenges (analytics tools, data infrastructure etc.)

• When asked which skills set they are most looking to improve, 44% said business acumen (e.g, industry and/or functional); 36% said communications skills

• One-third (34 percent) of companies surveyed noted data quality, consistency and availability remain the most important issues plaguing their analytics initiatives

• Underperforming companies are twice as likely to identify skill set deficiencies as their most pressing challenge in analytics

• 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

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

• 24 percent of everyone that took part said they would make developing a clear roadmap of analytical business problems to address in the coming year a top priority

• Companies exceeding their investor’s expectations are 4x more likely than underperforming organizations to have a roadmap for solving their analytical business problems in the coming year.

Survey Methodology:

The survey was conducted online with 150 respondents during calendar Q1 of 2016. All respondents represent large multinational 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.

This was posted in Bdaily's Members' News section by Mu Sigma .