Companies have been integrating analytics technology into their organisation to streamline operations and improve risk management for many years now. Compiled data becomes crucial for Churn Prevention – it identifies customers in high risk of churning, enabling companies to deploy preventive measures to maintain their customer base. Data generated is then used to drive the direction of the business.
Artificial Intelligence and Machine Learning brings out many opportunities, but in particular from the perspective of marketing. Over recent years, companies geared up towards targeted advertising, where it becomes more like a service or personal assistant to the audience; analytics technologies help propel us further in that direction.
With cloud’s integration and quicker processing power, data becomes easily accessible on a scalable platform for intensive calculations. It eliminates the need for basic hardware maintenance, freeing up resources to focus on data analysis. Meanwhile, machines are becoming increasingly better at identifying anomalies and spotting trends to a higher accuracy than a human can. It encourages both human and machine to focus on their strength to collaborate in making a more informed decision beneficial to an organisation.
However, the challenge in implementing analytics technology lies in getting an organisation to adopt a data-driven mindset; letting data and analytics steer on the right business’ path. There are legacy issues needing an address and with this, to bring in unfamiliar processes and culture assimilated into the current workforce. An overload of data might also lead to analysis paralysis for decision makers.
The focus of analytics has traditionally been in descriptive analytics where it describes the current ongoing situation. The technology has gradually shifted into predictive analytics to predict upcoming trends and soon, heading towards prescriptive analytics, where data gets translated directly into actions.