Despite the current wave of excitement around the importance of analytics within the modern enterprise, BI and analytics have dominated analyst hype lists for more than a decade.
In fact, analytics has been the top priority in Gartner’s annual CIO survey for ten out of the last twelve years. Similarly, predictive analytics is not a new concept that has emerged out of recent technological advances – financial services companies have used predictive analytics in one form or another to determine consumer credit scorecards and repayment terms for decades.
What is new, though, is the in-memory capability that enables business leaders to make critical strategic decisions as they emerge.
Analytics is not a passive tool used to develop reports from historical data. Smart business leaders are combining data with multiple machine learning algorithms and in-memory computing capabilities to gain real-time and predictive insights that can assist with quicker and more accurate decision-making.
Analytics is such an important component of today’s technology mix that it is impossible for technologies such as AI and machine learning to be of strategic business value without effective analytics at their core.
In a recent Oxford Economics study of 1,500 global CFOs, business performance analytics was paramount for leaders in the EMEA region.
Smart CFOs are using advanced analytics to understand the market and leveraging insights to generate growth. Financial services companies are particularly well-suited to employ analytics as a value-generating tool.
By analysing the huge volumes of customer data available today, financial services companies can for instance develop and offer personalised products and services, and unlock new revenue opportunities – and even business models – in real time, based on actual customer, situational, and environmental data.
This changes the game dramatically: insurance companies can now leverage analytics by employing machine learning systems that detect anomalies and unusual patterns to identify potential cases of fraud.
In addition, by extracting insight from customer data, social media, and partner and supplier networks, insurers are able to unlock greater efficiency and improve their competitiveness as they make better decisions more quickly and accurately than before.
Predictive analytics, AI and machine learning are also empowering businesses to better understand client needs and provide the personalised services and offers that are the hallmark of modern customer expectations.
With customer data sets growing exponentially, businesses can gain key insights into individual customer behaviour drawn from the types of purchases they make, the car they drive, the life events that impact them, and more.
By analysing these data sets, businesses are developing more accurate and personalised offers to customers and equipping all levels of staff with key customer insights that can improve customer service, brand perception, and customer loyalty.
Businesses are also moving away from “customer processes” and implementing analytics platforms that enable them to create thousands – or millions – of personalised processes in real time based on the needs of each customer. Since these processes are powered by analytics, they can be far more agile and responsive to change, even updating and improving themselves provided there is effective machine learning capabilities imbedded.
Today’s successful businesses run “live”; in other words, they anticipate, simulate, and innovate new business opportunities with a future-minded approach instead of just reporting past success.
A live business strives to know about problems before they affect the bottom line, or damage customer relationships. Smart business leaders are investing in in-memory operational and analytics solutions that can produce the real-time and predictive insights that helps their businesses run live.