Poor data quality is undermining chance of gaining insights

Customers have been told that they need to use their data to generate business insights, but a significant number lack the quality of information needed to do that.

Research from data analytics specialist insightsoftware has shone a light on the challenge that customers are dealing with and the gap that many still have to bridge if they are to reach “data fluency”.

Once they get to that stage, customers would be able to understand the information they had and garner insights from it that would help in the decision-making process.

Problems that were holding users back included a lack of a complete picture of their data, inconsistent records and issues around accuracy, the research revealed.

Many also felt that their existing analytical tools were too complex and a lack of training undermined them further. The research also made it clear that users were looking for tools to be easier, with many saying they were looking for more intuitive offerings that would help with “enhancing custom reports”.

Some of the major analyst firms have highlighted data analytics as an area where customers will be investing next year. It is an area where recent changes to the working landscape have also spurred interest in getting on top of the data quality problem.

“As workforces shift to hybrid environments, businesses will continue to adopt cloud technology and as-a-service solutions,” said Monica Boydston, vice-president, product management at insightsoftware. “When these new solutions operate in tandem with legacy systems, the result is a large pool of data from multiple sources – ‘polluted’ data that requires cleaning.”

She added that there were tools the channel could employ that would help users improve their position and get into a situation where they could unlock more insights from their data.

“The growing recognition of the value of data fluency within organisations is positive, but also puts pressure and creates expectations across all departments,” said Boydston. “Employees need to be properly trained, be able to piece different data concepts together, and possess soft skills to communicate that widely across departments. Quality of data, however, remains the most critical concern.”