Unsure About Your Next Crucial Decision? Try Predictive Analytics
In the ever-expanding universe of Big Data, predictive analytics is an approach which can leverage the diverse data an organization accumulates, to model future business scenarios and outcomes. The value of predictive analysis lies in the fact that it sheds light on the right path, leading the way to better decisions making. Hence, businesses can look beyond the haziness of uncertainties to predict a crucial business outcome.
Predictive analysis tools serve as a base for successful business strategies and operational elements. The converging trends like the big data phenomenon, evolving data analysis tools and a wide range of successful implementations, are all fueling the adoption of predictive analysis. Advanced and Predictive Analytics (APA) tools offer less flexibility compared to standalone statistical models, yet the intuitive graphical user interfaces and easier-to-use features have struck a chord with business analysts.
Predictive models can deliver greater value when a model calculation is inserted into the business process, which has a significant business impact. Here’s how:
- Credit risk assessment models leverages the loan information to derive a credit score, which plays a crucial role in credit decisions
- Churn models based on customer behavior helps set strategies for reducing churn rates
- Mapping of demand and price to arrive at the optimized pricing strategy is extensively used in industries like aviation, hospitality and banking
- Healthcare models connects symptoms and treatments to outcomes
- Fraud identification models predict possible fraud, based on previous patterns
Enterprises getting the most value out of analytics and BI, have leaders that concentrate more on collaboration, instilling confidence in their teams, and creating an active analytics community, while laggards focus on technology alone.
“Resisting new ways of doing things is the reason most projects fail”
John Lucker, head of Deloitte Consulting LLP’s advanced analytics and modeling practice
Organizations need to choose and deploy predictive analytics tools that best fit the job at hand, followed by developing appropriate models. Once this is in place, the analysis process begins with questions that probe deeply into data, to unearth findings with high operational value. However, with all this in place, the analytics strategy needs to be revisited and reworked as the business need evolves.
Businesses are recognizing the potential of predictive analytics, yet there lies a significant gap between those who see it as important and those who actually use the technology. Predictive analysis can prove to be an effective way of deriving more value from data, and also presents a wide range of application possibilities which can be exploited.
The starting point is setting the right mindset right amongst the stakeholders, before adopting analytics for your organization. Are you ready to make this digital transformation?
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