Zion market research estimates that the Predictive Analytics market is expected to touch 10.95 Billion by 2022, recording a CAGR of approximately 21% between 2016 and 2022. As the volume of data in the hands of organizations continues to grow, companies are placing more importance than ever on data to gain intelligent insights. It won’t be off the mark to say that data has effectively moved out of the hallowed walls of the IT department and has successfully made its presence permanent in almost all departments within an organization. The need now, however, is for data to do more than just decrease expenses and improve revenue. Organizations today, are leveraging data to gain predictive intelligence for competitive advantage and this information can only be achieved with the help of predictive analytics.

What is Predictive Analytics?

Predictive Analytics is a branch of advanced analytics that uses data mining, statistical models, machine learning, and artificial intelligence techniques to identify future outcomes based on the historical data. More organizations are turning towards predictive analytics today as the volume and velocity of data are increasing, computing costs have decreased, software to run the analytics have become readily available, and there is a greater need for competitive differentiation.

By employing text analytics, data mining along with statistical algorithms, organizations today can create predictive intelligence by discovering patterns and relationships that lie hidden within structured and unstructured data. The seventh annual IBV analytics research study reveals that 74% of organizations are using at least predictive analytics in at least one of their departments.

The main application areas of predictive analytics lie in customer management (from acquisition to retention), fraud management, risk management, operation improvements, sales, and marketing etc. amongst others. Amongst other companies, predictive analytics is used extensively in banking and insurance, retail, manufacturing, healthcare, pharmaceutical, travel, oil and gas and financial services companies to work out their strengths and weaknesses, improve operational processes, reduce risks, detect fraud and prevent network breaches and optimize marketing campaigns by capturing customer usage patterns. Forecasts made using predictive analytics make the strategic decision-making process streamlined and more fact-based and hence, more accurate.

Implementing Predictive Analytics

At a high level, here are the steps to follow to implement predictive analytics.

Identify the Business Problem

The success of predictive analytics begins with ensuring that the business problem that it needs to solve is well understood. Identifying who will use the model, what problem it will solve, how will the success be measured, what decisions will this model influence etc. are few of the things to account for. To do this, the analytics team has to work closely with business partners and IT teams to develop a common vocabulary that boosts collaboration and understanding.

Build Analytical Models

For predictive analytics success, it is essential to create a scalable data model by taking a systematic approach to data management and creating data models that support reusability, repeatability, and automation. The aim is to create a high-performance analytics architecture that supports high workloads and allows faster turnaround by mining both structured and unstructured data faster.

Statistics and Analysis

Data analysis helps in identifying important and influential variables to uncover information that can have a business impact. Statistical analysis validates the assumptions and hypotheses and tests them using statistical models using different analysis processes.

Predictive Modelling

Using machine learning, statistics, and artificial intelligence predictive models are created to evaluate and validate the probability of a result for a given data set.

Model Deployment

Once the models have been created, these have to be deployed. Ideally, these models should be deployed into the operational systems to generate business value, prevent time loss, and improve the effectiveness of decision-making by integrating decision logic with analytics. The deployment architecture has to have the capability to remain in sync with the changing business environment and should enable organizations to leverage the results of these predictive models on a real-time basis.

It is a fact that the core of the business intelligence market is getting increasingly crowded. As organizations leveraging BI solutions search for new business differentiators, predictive analytics presents itself as that natural differentiator that helps in the evolution of BI solutions. As predictive analytics tools become more user-friendly, they can become more ready to play a pivotal role in everyday business functions. These tools can deliver insights that can resolve business uncertainties into profitable probabilities and can increase decision effectiveness and identify opportunities for growth.