5 Steps of Evolution in Predictive Analytics

Rates of change in all industries, coupled with enormous macroeconomic pressures, mean stagnation and complacency can be the death of any business. It is important that leaders think big and long term. An essential challenge in this is to use analytics at its best to truly understand what lies ahead. This means a careful approach amid tight resources and stringent regulatory expectations.

Based on our extensive interactions with businesses worldwide, here are five of the foremost changes happening in companies that are advancing with predictive analytics:

1. Businesses will move from data to science. For years, businesses have focused on identifying and wrangling credible valuable data sources. This is resulting in an overabundance of data. Over time, almost anything will be measurable, depending upon the attitude and consent of consumers and regulators. For businesses, success will depend upon moving away from a focus on data alone, and instead towards the sophisticated science and analysis that distills data into timely, relevant insights and direction.

2. The focus will shift from output to curation. Ever cheaper and more readily available cloud computing, and infinitely higher processing power, will combine to make it possible to run thousands of scenarios simultaneously for modelling purposes. Over time, that computing power will be used to hone down thousands of possibilities into an auto-curated, best-performing few, which will turbo charge decision-makers in their pursuit of desired business outcomes.

3. Usage will expand from the evangelists to the masses. While data scientists will become ever more important to businesses, core predictive analytics will also become accessible to all teams. This “data democratization” is being driven by the emergence of a powerful solution set, focused on decision support intelligence, that automatically provides the right information to teams in their various domains. By understanding decision-makers’ business objectives, these systems create easily comprehensible, specifically relevant, and properly actionable insights, that can be prescriptive.

4. Attitudes towards AI will go from rejection to appreciation. From music streaming or restaurant recommendations to weather warnings and style advice, predictive analytics is becoming omnipresent in our daily lives. A growing general acceptance of algorithms is gradually turning into enthusiasm for the technology. In turn, this trust rubs off on life at work, where algorithms will become a normal – even essential – part of decision-making. This change will be underpinned by the growth of responsible and explainable AI that closely incorporates human intelligence at certain critical stages.

5. Data-dependent businesses will switch from taking to giving. Consumers are increasingly aware of how extensively their personal data is used, for what, and where it ends up. This has led to many people turning up their device privacy controls. Combating this, though, are instances of companies delivering obvious value to consumers through good use of their data to provide personalised and relevant services. This is the key to the personal data economy. Businesses must focus on delivering visible, desirable value in exchange for their customers’ data. They must also demonstrate governance and compliance with swiftly evolving data privacy rules.

The scale and pace of global change has wrongfooted a great many companies in the last few years. Amid the fast-changing conditions, businesses will need to be both boldly forward-thinking and respectful in their use of predictive analytics. The quality of businesses’ analytics adoption will determine their ability to react effectively and at speed to change, allowing them to remain resilient and identify powerful opportunities to step ahead.

What to see more? Watch GfK’s Warren Saunders answers questions around the barriers of adopting AI, and how to solve them.