Predictive Analytics versus Prescriptive Analytics

By Sanjay Dhakar, Vice President- Engineering, Jugnoo

Prescriptive analytics has already being practiced in startup these days. Companies have started to follow data driven approach. Most of the operational and marketing decisions are based on data analysis in startup. It helps them make decision fast and move faster. There are multiple use cases in day to day operations which are aided by predictive analysis- Which marketing campaign will perform better for user acquisition based on historic campaign results? Which incentive structure will suit best to increase supply in operations? What will be supply and demand trends over the weekend? Prediction about dynamic pricing based on demand and supply ratio etc. However, it will take some time for prescriptive analytics to become a part of standard process in enterprise businesses. In next 1-2 years we can expect its complete adoption.

In terms for data analytics requirements, business needs both BI along with Predictive analytics. It actually depends upon use case whether it’s better to just add predictive analytics in current setup or rethink the whole infrastructure keeping predictive analytics in mind. In our case, we started with BI dashboard first and kept on adding predictive data to our BI dashboard as and when required and we now have complete BI tools to aid various teams in the company be it marketing, growth, operations, finance etc. So it’s completely up to the developers how they want to proceed with this - if their current platform has capabilities to add predictive analytics then it’s easier to add it in existing setup. But in the end, the goal is to have predictive analytics as key offering of your platform. Standard BI infrastructure and dashboards will not cut it anymore. It has to have machine learning and predictive analytics toolset.

The Skill Gap in Predictive Analytics

Many colleges in India has started adding AI, machine learning and advanced mathematics to their course curriculum already. Apart from this, there are great online courses and material out there from industry veterans in these areas. Leading companies in these areas such as Google, Facebook has open sourced various tools such as Torch, Tensorflow to help you build your own AI and machine learning solutions with ease. Coursera has some really good courses on machine learning and AI and it’s accessible to everyone and is completely free. Anyone with interest can learn and enhance their skills in these areas. According to a recent survey, there is a requirement for nearly 4,000 machine learning and AI programmers in Bengaluru alone. There is a huge opportunity for India to advance in these areas. For India to get maximum out of this opportunity, it must adopt a deliberate policy to drive AI innovation, adaptation, and proliferation in sectors beyond consumer goods and information technology services. Also, policymakers should make AI & machine learning a critical component of various initiatives taken by them. India would also need to grow in terms of cloud-computing infrastructure to be capable of storing vast amount of data and massive amount of computing power required by AI and machine learning algorithms.

Self-Service BI Tools

Self-service BI tools are really great for small and medium businesses which don’t require customer solution. Self-service business intelligence demonstrates how the balance of power in BI is shifting from IT to business. It’s very cost effective as well as fast way to get started. Self-service BI tools have evolved a lot in last few years and can provide you with standard BI capabilities out of the box and are good starting point for any business. You might need to switch to your own customised solution once your data start growing and self-service tools are not able to fulfil your complex BI requirements. Consequently, the business intelligence industry is going through a transformation to meet the changing needs of many organizations, particularly large enterprises that are dealing with a huge amount of data.

A few years ago, business intelligence was only popular among large enterprises. Nowadays, mid-size companies and even small businesses are becoming data-driven thanks to BI services. It has become easy for companies to set up their BI dashboard and visualize their data in real time. BI has started active role in decision making as opposed to just passive analytics.

We should see a lot of advancement in AI and machine learning being offered along with standard BI services. Predictive analytics will become center of the Business Intelligence offerings. 

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