Why Do Some Companies Achieve More Predictive Analytics Success?

BrandPostBy Lisa Wiltshire, Principal, Insights & Foresights at GfK
Jun 29, 2022
Analytics Business Intelligence

Predictive analytics systems are designed to turn masses of data into actionable strategic insights. While many businesses face significant deployment challenges, other organisations are acting on several fundamental focus points to forge ahead with powerful predictions.

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Credit: GfK

There is growing belief that businesses are set to spend huge amounts of money on predictive analytics. While in 2021, the global market for corporate predictive analytics was worth $10 billion, it is forecast to balloon to $28 billion by 2026.

At this relatively early stage of adoption, many businesses are suffering from tough problems. Research shows nine in 10 businesses are not fully confident in their ability to make future-ready decisions around what they sell, with particular worries around understanding customer behaviour trends. Many lack the necessary quality of data, or the financial resources and internal talent to speedily turn that data into reliable, relevant insights. We frequently hear how they struggle with heavy manual efforts in writing and updating algorithms.

One thing is certain: the adoption of predictive analytics will continue. This is without a doubt, given executives’ insatiable appetite for systems that allow them to identify future risks and opportunities, and to determine actions that push their businesses ahead of competitors.

So, which attributes separate the businesses that are successfully running powerful predictive analytics, from those that are stumbling? Here is what we are seeing, in our extensive interactions with businesses worldwide:

  • The winners lay the right foundations: Successful adopters of predictive analytics know that deriving value from the software first requires an outstanding data and tech foundation. They acquire all the necessary information and unify it in one central warehouse. They move from manual to automated data wrangling, to deliver results in an easy-to-view format, achieve consistency and limit errors. They ensure superior quality of information, and they determinedly put in place the right tech stack. To augment how data drives decision making, these businesses ensure all information is safe and secure, with strong usage policies and controls. In maintaining this vision, governance, and change momentum, they ensure they overcome financial and timing obstacles
  • They develop the right culture: The most effective predictive analytics projects are those led by execs who recognise the need to start with cultural revolution within their organisations. To effect that cultural change, they can start small – building a team environment that embraces and fosters curiosity around data driven intelligence. They demonstrate the success that can be achieved by equipping each team member with direct access to a shared source of intelligence. This unlocks the ability to apply knowledge consistently across all teams, to take better decisions and accurately measure outcomes. This culture transformation can never be forced: these leaders achieve data democratisation by appreciating cultural sensitivities. They continually invest in the right skillsets, and tackle the shortage of available data science capabilities with a multi-pronged approach of new hires combined with re-skilling and upskilling existing teams.
  • They are sure to engender algo credibility: Even when the right tech, data, and people converge, there is another hurdle to face. Successful predictive analytics leaders must also overcome the natural psychological barriers that exist among individuals, teams and clients. These are particularly seen in people’s reaction to fully-automated solutions that require no (apparent) human intervention. Research shows that many individuals are instinctively averse to algorithms, even when shown proof that a particular code more accurately predicts future outcomes than humans can. In this setting, leaders must ensure the tools and insights they put into place have clear credibility and support throughout an organization. They must actively engender trust in the value these tools deliver in directly supporting – but not replacing – human decision making. The key is to balance the use of algorithms with human expertise, to engender the confidence in the technology that then drives increased adoption

As the impact of excellent predictive analytics on business success becomes ever clearer, project leaders of the future will focus closely on setting the right foundations, building excellent data cultures, and promoting true credibility in the algorithms they deploy.

To see more, watch our video, AI Adoption Barriers Across Organizations:
How to Solve Them & Implement a Data-Driven Strategy