Predictive analytics - one dimension to tap max potential of your Big Data

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. When we say “predictive analytics” we mean   application of a statistical or machine learning technique to create a quantitative prediction about the future.
For example False Alarms from electrocardiographs and other patient monitoring devices are a serious problem in intensive care units (ICUs). Noise from false alarms disturbs patients’ sleep, and frequent false alarms desensitize clinical staff to genuine warnings. Metlab has developed an algorithms that can detect QRS complexes, distinguish between normal and ventricular heartbeats, and filter out false QRS complexes caused by cardiac pacemaker stimuli. The algorithms produced a true positive rate (TPR) and true negative rate (TNR) of 92% and 88%, respectively. What it means is data of ECG, ABP & PPG devices are now being processed to identify a false alarm and it is improving life of patients as well as hospital staff.


Predictive analytic's has become the key to helping businesses create differentiated, personalized customer experiences and help customer make better decisions. As we know predictive analytics strategy and architecture are very custom to the client landscape & requirements.

Key considerations while architecting for Predictive Analytics-

1. Predictive analytics must cover the full customer life cycle                                                       . Organizations require predictable insights into customer behaviors and business operations. Design idea should be to deliver value to customers throughout their life cycle to differentiate their customer experience and sustain business growth. The business stakeholders input is key and it will help identify effective mechanisms for translating the business knowledge to predictive algorithm inputs thereby optimizing predictive models faster and realizing deeper customer insights.
2. Build your Big Data architecture around predictive analytic's                                                    There is no dependency of predictive analytic's on big data, Only thing that has changed is with Big Data processing e have access to larger data sets that can be used for predictive analytic's.  By putting predictive analytics solutions at the core of your platform we create synergies between the analytic's layer and big data processing.
3. Learn from leading companies embracing digital transformation.                                         A large Australian telco improved customer discovery for precise targeting of ads; Another US telco optimized its marketing mix for effective channel interaction; A UK telco major deepened its insights into the voice of the customer to improve customer satisfaction; and many companies are providing personalized digital experiences to maximize customer loyalty.