What is predictive analytics and what are the business benefits?

In a competitive and often tumultuous marketplace, your organization should be seeking to squeeze every competitive edge it can – which is where age-old predictive analytics can prove itself a major business benefit.

Considered a subset of business analytics or data analytics, predictive analytics uses technologies like machine learning (ML) and deep learning to create a predictive model that analyzes past trends to project how things might change in the future. This approach could be used across a range of applications from forecasting future cash flow to preventing malfunctions in operational technology (OT) machinery deployed on the factory floor.

Although businesses have been enamored with innovations in the AI space – especially given the rise of generative AI – good old predictive analytics may yet prove more advantageous than incorporating a flashy new chatbot.

But implementing a predictive analytics regime is no easy feat. Most modern organizations are likely inundated with mountains of business data that need to be cleaned and refined for use in predictive modeling, so insights can be fed back into strategic decision-making.

What is predictive analytics and how does it work?

Predictive analytics lets businesses use the data at their disposal to analyze past trends and determine how different moving parts may look in the future. These elements could include how markets are moving, how revenue and expenditure may change, or when a website can be expected to experience much higher traffic flow.

Broadly, businesses will have access to a toolset that comprises statistical algorithms, analytical queries, and ML models to determine the probability of a future projection based on historical data. Through these tools, businesses can draw a line through commonalities and patterns to raise insights that might otherwise go unnoticed. These can then be used as additional intelligence when decisions need to be made, whether about a sales strategy or better ways to reach customers.

There are a few typical workflows for adopting predictive analytics in businesses. Google defines five steps to devising a model that can lead to useful takeaways. These are:

• Defining the business problem (or forming a thesis)
• Acquiring and organizing the business data
• Pre-processing raw data by cleaning it and stripping away anomalies
• Developing the predictive models (by tapping into technologies like ML)
• Validating and deploying the results.

Which technologies does predictive analytics use?

Because businesses are inundated with all kinds of data from images to PDFs, predictive analytics is often associated with data science and big data, according to IBM. Data mining is used in predictive analytics to scrutinize large sets of data to discover patterns or irregularities – allowing an organization to sift through the streams of raw data that a business has collected through the years to glean genuinely useful information.

Gaining insights usually also relies on various subsets of AI technology including ML and deep learning, with predictive analytics regimes using statistical techniques associated with neural networks, including linear regression models. Neural networks in predictive analytics comprise three layers – an input layer, a hidden layer and an output layer. The input layer feeds historical data into the hidden layer layer, which performs computation functions that create predictor functions, which in turn modify the input data and produce predictions on the final output layer.

Cloud computing is also a huge part of predictive analytics, with many small and medium sized businesses (SMBs) particularly well-suited to access the power of this tool via cloud subscriptions from various vendors. The expansion of the cloud and infrastructure as a service (IaaS) models has democratized access to tools like predictive analytics, meaning smaller organizations can glean valuable insights in much the same way that only larger enterprises once could.

Predictive analytics vs AI: what's the difference?

As we've come to understand, there is no singular technology called 'AI' – rather, it's a vast collection of different technologies that complement one another and operate under the same fundamental basis. Predictive analytics can be described as a subset of AI, as it combines different technologies and functions from within the AI family to parse data and predict outcomes.

ML, for example, is the subset of AI that uses statistical techniques to extract algorithms and models for learning. It doesn't need direct programming by humans, with ML systems able to learn from experience instead based on the data and the act of processing it over time. This is one of the key technologies used in predictive analytics, which is the process of projecting outcomes based on legacy data points.

Generative AI tools such as large language models (LLMs), meanwhile, can be used in use cases such as content generation – or summarizing documents – but wouldn't required for analytical purposes when ML and similar technologies would be more than enough. An LLM could instead draw on the data produced by a predictive analytics model to provide context for natural language output – with no direct overlap of the two branches of AI happening during the actual analytics process itself.

What are the benefits of predictive analytics for business?

Principally, predictive analytics offers strategic insights that are otherwise extremely difficult – or impossible – to obtain. Many different industries use predictive analytics to gain advantages and have been for more than a decade. As far back as 2011, IDC published a white paper highlighting the business value of predictive analytics – citing a few concrete examples. These included an insurance company identifying fraudulent claims 30 days faster than before, and an asset management firm increasing marketing offer acceptance rate by 300%.

In today's age, predictive analytics can be used in a variety of ways, including in modeling revenue growth, mitigating risk, increasing efficiency through automation, reducing costs, improving customer retention, and boosting employee satisfaction and morale. Businesses can use predictive analytics to improve decision-making, for example by projecting the possible outcomes when adding to a product line or weighing up the prospective benefits and risks of investing in a particular area, per IBM.

How can an organization implement predictive analytics?

According to academic research published in 2024 in the Journal of International Technology and Information Management, making the most of a predictive analytics implementation is more than just importing new technologies into the business. There are seven key steps organizations should take to implement predictive analytics in their organization. These include the following:

• Emphasizing non-technical skills including communication and business understanding. This ensures teams can covey the benefits of predictive analytics to stakeholders and ensure analytics are aligned with business needs.
• Fostering a data-driven culture in which management provides support for decisions actively informed by data, including changing mindsets from "institution-based" thinking to "data-informed" thinking.
• Managing resistance to change among IT staff and employees directly affected by a new predictive analytics regime, including demonstrating career advancement opportunities and integrating it into strategic initiatives.
• Aligning predictive analytics with organization goals in order to help secure investment from senior management and support and ensure predictive analytics approaches delivers meaningful benefits.
• Adapting the business' organizational structure to support both centralized and decentralized analytics. This allows ideas from both the grassroots and from the top. Resources – like staffing or funding – may be centralized, but they should be flexible so other business units can take advantage.
• Establishing policies and procedures for data accessibility and maintaining high-quality data, which ensures that outputs are reliable and there are fewer roadblocks in using data effectively.
• Adapting to innovation and new technologies while ensuring the right tools are chosen for the particular business needs highlighted. It's key not to get distracted by the newest technologies and instead focus on the tools and systems that are proven to produce meaningful results.