By: Chris Pantelidis, Deputy Head - Sales and Business Development, LTIMindtree Canada

While enjoying a Greek salad drizzled with olive oil, one of my kids who is enrolled in a high school Philosophy class shared  an interesting story about one of the philosophers her class was studying. Until hearing this story, I had no idea that Philosophy would have any ties to our digital age or Greek salad.

I discovered that Thales of Miletus, a Greek philosopher, astronomer and mathematician applied  what some consider an early form of predictive analytics to predict a promising olive harvest season. Based on his analysis of weather patterns and the upcoming harvest season, Thales reserved all the olive presses in Miletus well ahead of the harvest season to monetize his data analysis into significant wealth for that day and age.

In a 2017 report published by the Zion Market Research group, the predictive analytics market was expected to reach approximately USD 10.95 billion by 2022, growing at a compound annual growth rate of 21% between 2016 and 2022.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. In today’s digital economy, businesses generate a lot of insightful data on a daily basis through the transactional basis of their customer relationships. The basis of predictive analytics centers on identifying relationships between patterns and future trends based on these past events and like Thales, leveraging them to predict what  the masses are unaware of.

Leveraging predictive analytics, more and more businesses are hiring Data Monetization Analysts to uncover the true potential of data by providing decisions makers with actionable insights,   and  supportive data to assist in driving strategic initiatives and delivering improved business decisions. Such business decisions then foster improved technology investment and a superior client experience.

Five of the most common techniques of predictive analytics involve:

• Data Modelling
• Machine Learning
• Artificial Intelligence (AI)
• Deep Learning Algorithms
• Data Mining

One frequent commercial application of predictive analysis can be found in the adoption of CRM tools in the business financial services industry. The common techniques listed above are applied to customer data to construct a complete view of an organization’s clientele.

CRMs utilize predictive analysis embedded within their applications to deliver targeted advertising, promotions, and product offers to customers. Analytical CRM can be applied throughout the customers’ lifecycle to develop close relationships with said customers based on the understanding of their behaviors and needs.

With its enormous repositories of transactional and customer profile data, the banking industry is rich with potential for the application of predictive analytics. There’s no better example of applied predictive analytics in banking than Pega’s business process management (BPM) and customer relationship management (CRM) solutions for the financial services sector. Eight of the top 10 global banks use Pega technology for its unique ability to  implement  operationalize predictive analytics in banking.

Why does this matter? In contrast to Thales, numerous leaders of organizations historically have made management decisions based on their instincts, rather than data-driven insights. In our expanding digital economy, data-driven modernization and  monetization should be a top priority for all leading organizations to empower their decision-making process. The decision makers of today desire to draw greater value out of their technology investments. Hence, it becomes imperative to have a sound data and  analytics strategy aligned with  the company’s vision and  mission. This also needs to strategically map with the value realization framework, delivering quick and complex wins.