Organisations are increasingly turning to data-driven decision making, but there is still a gap in their ability to do so, according to industry specialists.

“This makes it difficult for them to get value out of their data,” according to the author’s of the paper.

The report is based on a series of interviews and roundtable discussions throughout the year with leading data science practitioners and examines the changing place of data analytics and the challenges they are facing.

One of the top impediments to becoming a data-driven organisation is accessing the data, particularly in more developed organisations, which often employ a mix of old and new systems. This makes data extraction both challenging and frustrating, according to the whitepaper.

According to Polynomial director Anna Russell, “Very large corporates have generally found a way around this through data transformation projects, and SMEs are usually too small and lean to have legacy systems. But there’s a big tranche of organisations in the middle who struggle to draw value from many siloed or inaccessible pools of data.”

Data wrangling is not especially difficult, but it is time-consuming and often a tough sell to the board.

As a result, many interesting analytical opportunities risk getting put into the too-hard basket because there is so much work in pulling together the base data.

Executives often worry that, after all that work, they still won’t find anything useful.

Sandra Hogan, Director, former Business Analytics Advisory, SAS Australia and New Zealand and now Group Head, Customer Analytics, Origin Energy, identified challenges that get in the way, and also proposed some solutions.

Problems:

  • Too much time spent on getting data ingested and structured;
  • Insufficient consideration given to how the data will be used and resources prioritised;
  • Too much focus on tools and technology to solve business problems;
  • Systems/applications not integrated to support data-driven decisions to the people who need to execute/action.

Solutions:

  • More focus on use cases and measure of success — make people more accountable for the outcome they are driving towards;
  • More focus on people’s ability to know how to make data-based decisions;
  • Focus on processes to integrate systems/applications to enable insights to be closer to where business decisions are made.

The problems surrounding a shift to data-driven decision making stem from a lack of understanding of data and how to leverage it. Capturing data is not enough. This point is evidenced by the growing premium on those who can – analytics professionals.

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