How Your Analytics Measures UpHow Your Analytics Measures Up

Do you feel like your analytics is core to how your organization does business, with a dedicated staff that's constantly modeling, mining, and scraping to help predict and gain insight? If your answer is "no" you can rest assured that you are not alone.

A recent UBM Tech (the parent company of All Analytics) survey of 200 data management or analytics professionals conducted in April 2017 found that only 11% of them believed their organizations fit that "leading" definition when it comes to analytics. All Analytics invited analytics experts, consultants, and bloggers Pierre DeBois and Meta Brown to talk to All Analytics editors Jim Connolly and me during a session at Interop ITX in Las Vegas on May 18 about the survey results and how they align with these consultants' experience in the field.

Brown is president of A4A Brown, a consultancy that helps technical people communicate with executives and clients. She is a statistician and an engineer and also the author of Data Mining for Dummies.

[Learn more about AI and Machine Learning. Sign up now for the next edition of the A2 Academy.]

DeBois is founder of Zimana, a small business analytics consultancy that reviews data from Web analytics and social media dashboard solutions, and then provides recommendations and Web development action that improves marketing strategy and business profitability. He is an engineer by training and previously has worked in the auto industry.

Brown and DeBois provided their perspective during the All Analytics session titled What Analytics Means to Your Company in 2017 and Beyond.

Leading or Guiding

When it comes to organizational approach to data analysis, while few companies said they fit the "leading" profile, 41% characterized their organization as "guiding," indicating that analytics is core to almost every part of the organization, touching sales, customer service, and operations, but saying they are not quite there yet with predictive use.

A full 36% of survey respondents said that their experience was "limited," indicating that some groups dig into non-financial sources, but cross department analysis is limited to financial data.

Our panelists Brown and DeBois said that analytics implementation tends to be all over the board, from very integrated and sophisticated to more basic and limited for organizations that are starting out or that have placed less of an emphasis on the practice.

Brown said that she considers terms such as predictive and prescriptive to be marketing terms, and that organizations shouldn't get hung up on those labels.

The survey also asked who drove most of the new ideas for data analysis, and business executives came in at the top at 31%, with IT staff and management making up the second largest chunk at 27%. Brown said she believed that the marketing department should be the one driving the biggest share of new ideas for data analysis.

Those surveyed also expressed concern about data security. Half of all companies said they were either extremely concerned or moderately concerned about security as it relates to big data, and another 29% said they were moderately concerned.

DeBois said that this concern may be coming from all the attention to data privacy right now. For instance, privacy practices at retailer Target drew plenty of attention a few years ago after the New York Times published an account of how one teenaged girl's father found out she was pregnant because Target started sending her marketing mail for pregnant women. It turned out that Target's algorithms recognized the girl's behavior as matching that of pregnant customers.

Organizations were also asked about their barriers to success with their data analytics initiatives and 44% pointed to lack of staff expertise, 41% to data security concerns, and 34% to ensuring data quality. Brown, who presented a session earlier in the day that touched on the topic of data analytics failures, said that projects fail due to lack of focus. Organizations need to identify their revenue-generating or cost-savings goals before they go into a data analytics project, something that many organizations don't do.