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Predictive analytics: how to achieve success for companies 

by Lisa Wiltshire , 24.01.2023

Predictive analytics systems are designed to turn masses of data into optimized, actionable insights - and do it fast. Many businesses struggle with the significant challenges of setting up such systems - so here are the core focus points to follow, if you want to forge ahead with powerful predictions  

There is a growing belief that businesses are set to spend huge amounts of money on predictive analytics. The global market for corporate predictive analytics is forecast to balloon to $28 billion by 2026 - up from $10 billion in 2021. 

Problems faced by companies setting up predictive analytics to support business decision making

However, many businesses are struggling to set up the systems that support data-based decision making. Research shows nine in 10 businesses are not fully confident in their ability to make future-ready decisions about what to sell - with particular worries about fully understanding customer behavior trends.

Some lack the necessary quality of data. Others lack the financial resources or internal talent to speedily turn that data into reliable, relevant, and actionable insights. We frequently hear how organisations are overwhelmed by the heavy manual efforts required in writing and updating data-analysis algorithms. Without these algorithms in place, companies aren’t able to generate reliably powerful predictions to improve their business.  

One thing is certain: the adoption of predictive analytics will continue and those that don't invest now will be overtaken by competitors that do. This is indisputable, given executives’ insatiable appetite for fast, efficient systems that allow them to identify future risks and opportunities and the actions that will push their businesses ahead of competitors.  

3 factors to running successful and powerful predictive analytics

What separates the businesses that are successfully running powerful predictive analytics, from those that are stumbling? Here is what we have observed, in working with major brands across sectors, worldwide: 

  1. 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, via platforms that deliver results in an easy-to-view format, ensure consistency and limit errors. They seek superior quality of information, and they put in place the right tech stack. To augment how data drives business intelligence and 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, perfectly placing them to make powerful predictions.

  2. Develop a data-driven culture: The most effective predictive analytics projects are those led by execs who recognize the need to start with a cultural revolution within their organizations. 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 across the entire organisation with direct access to the same, shared source of intelligence. This unlocks the ability for knowledge to be applied consistently across all teams - allowing all teams to take better decisions based on the same, unifying knowledge, and accurately measure outcomes. This cultural transformation can never be forced. The best way for leaders to achieve data democratization is by appreciating cultural sensitivities. Continually invest in developing the right skillsets across the organisation. Tackle any shortage of in-house data science capabilities with a multi-pronged approach of new hires combined with re-skilling and upskilling existing teams. 

  3. 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 negative reactions to fully-automated solutions that require no (apparent) human intervention. Research shows that many individuals are instinctively averse to algorithms, even when they are 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 confidence in the technology that then drives increased adoption 

Creating predictions for business success

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 create predictions for business success. 

Like to see more? Watch our video:

AI adoption barriers across organizations: How to solve them & implement a  data-driven strategy