When data and analytics come up in conversation it’s often in relation to topics such as artificial intelligence or machine learning; after all, these subjects are exciting, but are we trying to run before we can walk?

According to a recent study by Gartner, almost 90% or organisations have low business intelligence (BI) and analytics maturity. For organisations wanting to optimise the value of their data or exploit emerging analytics technologies such as AI or machine learning, this is a rather big obstacle to overcome.

“Low BI maturity severely constrains analytics leaders who are attempting to modernise BI,” said Melody Chien, senior director analyst at Gartner. “It also negatively affects every part of the analytics workflow. As a result, analytics leaders can struggle to accelerate and expand the use of modern BI capabilities and new technologies.”

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The survey based on 813 responses found that organisations with low maturity fall in “basic” or “opportunistic” levels on Gartner’s IT Score for Data Analytics.

Organisations deemed “basic” have BI capabilities that are predominantly spreadsheet-based analyses and personal data extracts. Those at the opportunistic level find that individual business units pursue their own data and analytics initiatives as stand-alone projects, lacking leadership and central guidance.

“Low maturity organisations can learn from the success of more mature organisations,” said Ms Chien. “Without reinventing the wheel and making the same mistakes, analytics leaders in low BI maturity organisations can make the most of their current resources to speed up modern BI deployment and start the journey toward higher maturity.”

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Gartner sets out four steps that data and analytics leaders can follow in the areas of strategy, people, governance and technology, to evolve their organisations’ capabilities for greater business impact.

1. Develop holistic data and analytics strategies with a clear vision
2. Create a flexible organisational structure, exploit analytics resources and implement ongoing analytics training
3. Implement a data governance programme
4. Create integrated analytics platforms that can support a broad range of uses