What are the benefits of predictive analytics for businesses?

The use of data analytics in business today remains fragmented for many reasons. Some of these are the sheer quantity and quality of data needed, the high level of technology investment, skill required and the fact you need a leap of faith to believe a calculated algorithmic or AI-based insight.

Despite these challenges, it is the problems that predictive analytics addresses and solves that will drive its adoption. For businesses, it serves as a guide to identifying future risks and opportunities providing meaningful trusted insights to support the most critical decisions.

Derive value from predictive data analytics in business
Making informed data-centric decisions about future outcomes is a high-value topic for all organisations. The ongoing disruption caused by macro events to consumer habits, the economy, supply chains, and other previously stable predictable patterns has highlighted the lack of agility in the data, tools and processes many companies use to drive their view of the future.

As a result, there has been an acceleration in finding smarter quicker ways to predict the future with an increasingly limited budget and resources. Digitisation, cross-functional collaboration, unification of datasets, speed, and agility are all stated as leading ways businesses have been improving their decisions on future outcomes.

Artificial intelligence (AI) helps businesses find the competitive edge they’re seeking by extracting more value from data. By unifying and structuring this information across operations, AI has the power to embed innovation at the very heart of the business.

Power your brand and marketing with data intelligence
Marketing is a key player in the use of predictive insights in high-performing organisations where they form a regular part of the workflow and are instrumental to decision-making. Using data to inform mid- to-long-term data-led decisions on the opportunities and threats to awareness will power brand strategy, marketing campaigns, and annual Plans. As organisations strive to improve decision-making around future outcomes in an increasingly unpredictable environment, there will surely be an evolution of the predictive analytics market and its rate of adoption.

Even when the right tech, data, and people converge there is often a psychological barrier to adopting predictive analytics, particularly fully automated solutions with no human intervention. It is crucial to get the right monitoring, alerting and ability to constantly review performance to ensure you build trust in these products and also can fix them when things go wrong.

In addition, seeing the opportunity beyond a stand-alone product, many have developed partnerships to enrich predictive software in other spaces like marketing clouds, supply chain clouds, etc. At the same time, there are an increasing number of start-ups, particularly in the consumer analytics space attracting venture capital funding and striving to disrupt the market as privacy regulations tighten.

Create business value with data centricity
Data and analytics can reduce bias in decisions in planning and strategy that would be otherwise based on intuition. Data decision-making helps us to guard against our biases and unearth opportunities that might remain otherwise hidden.

The starting point is making your data accessible to the decision-makers. Often it is held in multiple systems and requires expertise to question and analyse the data. By having a way decision-makers in a company can interact and query the data you make sure your decisions are fact-based. Making core decisions based on anything other than solid data is a high risk how do you handle and make sense of the extreme amounts of data available and how do you make the right decision.

The successful businesses of tomorrow are those that can harness data in the right way and have the conviction to make radical changes based on early insights. Scenario simulations help to identify challenges at an early stage and to plan strategically. It connects all the relevant sales and consumer data across their global product portfolio into an easy-to-use, customized, intelligent solution that enables them to react, plan, predict, pivot, and win.

Data quality is a key component, the old saying ‘garbage in = garbage out’ will ring true to people who have tried to train AI models on data that is not of good enough quality for the objective. Here the necessity is to fix this first to enable the use of AI. Without this, with the most talented team in the world, you will not succeed.

Shape the future with a data-driven mindset
Finally, data has begun to look beyond the present and recent past, stretching its view into the future – with ever-increasing accuracy. Thanks to algorithms and AI companies can reduce the ambiguity and start to “predict” tomorrow increasing their degree of confidence and minimising the risk. Leaving those without sophisticated data analysis to guess and react with increasing difficulty, sometimes when it’s too late as we have seen many times in the past with huge companies disappearing overnight when a new unseen competitive threat emerges.

In a world where fact-based business guidance is now a given, what does the next generation of data-driven decisions – and decision-makers – look like? Technology will continue to evolve; our use and understanding of AI will become increasingly sophisticated. So what will separate the smartest decisions from the simply mundane in tomorrow’s world? How will the wisest brands break through the “success barrier”.

We have observed that businesses have started having conversations with employees about being more data-centric and using AI. It’s a cultural shift and has the ability to move logic and rationale ahead of charisma and the loudest voice in a room. One of the great things about using data is the ability to democratise that data across the business & rather than having data being controlled by a gatekeeper within the organisation you empower more people to make good decisions and unearth insights that would have previously remained hidden.

The writer is Daniel Shearly, VP of Products, GfK