While GAVS Technologies have used Predictive Analytics very effectively in its automation-led infrastructure management product that cuts across industries, in this article we will look at how the customers in different industries that are potential clients for GAVS can benefit from use of Predictive Analytics and the future trends in development and use of Predictive Analytics tools that GAVS and others will leverage.

In simple terms, Predictive Analytics is the technique of using past and present data in conjunction with machine learning and other statistical algorithms to identify patterns that is then used to predict the likelihood of future outcomes. Once the nature of possible future outcome is known with a fair degree of accuracy, necessary steps can then be put in place to address these outcomes and thereby improve efficiencies of the underlying operational processes.

Let us now look at a few scenarios where predictive analytics techniques are being used in situations that we come across in our day to day life:

  • Predictive Maintenance of Equipment: Reactive maintenance of equipment in the manufacturing industry or underground/underwater pipelines in Oil & Gas industry can be very expensive because once the components have failed the cost of replacing them and the associated downtime can prove to be not only costly but may also cause disruptions in providing services to the customers. Preventive maintenance, the process followed most widely today, on the other hand may not result in optimal use of the components as these components may need to be replaced well ahead of the end of their useful life, simply to avoid potential failure in future. Predictive Maintenance using predictive analytics techniques based on data relating to stress measurements, temperature measurements, acoustic emission measurement etc., on the other hand, can accurately guide the maintenance engineers to what components need to be replaced and when.
  • Risk Modeling: As the risk can come from many sources, predictive analytics techniques used with appropriate risk management algorithms can be used to analyze huge amount of data coming from all the sources of potential risk and then predict the best possible actions to mitigate the risks. Financial Institutions are using these predictive analytics techniques very effectively for fraud detection and prevention in addition to risk modeling etc.
  • Customer Segmentation, Retention and Lifetime value: Identifying the right market segment for a product can result in substantial savings in sales cost. Predictive analytics can identify target market segments based on real data and indicators. Predictive analytics can also identify signs of dissatisfaction in specific customer segments and help in putting remedial measures in place before it is too late. In addition, Predictive Analytics can help in identifying the customers who are likely to use the company’ products and services consistently over an extended period.

Predictive Analytics is thus the ability to predict, with a fair degree of accuracy, what will happen in future by combining real time, historical and third-party data. Options are now available for scalable cloud based ‘Predictive Analytics as a Service’ offering from several specialist providers.

With such widespread use of Predictive Analytics techniques across all industries, what are the future trends that the organizations may want to prepare to embrace?

  • Artificial Intelligence: The way we look at and interact with analytics and data management are being made lot more effective and powerful by usage of AI and Machine Learning tools. Real time alerts indicating the latest condition of machines or war ships can be generated by using these powerful algorithms on the data received from IoT sensors embedded in these structures, live dash boards replacing passive reports.
  • Prescriptive Analytics Tools: While Predictive Analytics techniques are mostly based on Artificial Neural Networks (ANN) and Autogressive Moving Average (ARIMA), a model that applies the data from the past to model the current data and predict the future; Prescriptive Analytics goes further into future and tries to recommend steps to be taken to achieve the desired objective by using techniques like recommendation engines, graph analysis and complex event processing.
  • Natural language Processing (NLP): NLP is changing the human-computer relationship by removing the barrier to usage of BI by common people. While there are still significant improvements to be made in natural language recognition, speech recognition etc., NLP will clearly help in improving productivity and give a competitive edge to the business users.
  • Multi-Cloud strategy: Businesses will more and more embrace multi cloud environment – combination of multiple private and public clouds, to reduce risks and increase flexibility, especially to cater to peak volumes, with predictive analytics playing a major role in this decision-making process – when to use which cloud environment.
  • Embedded and Collaborative Business Intelligence: Embedded BI and Collaborative BI enable “Self Service BI” for the end users where the end users can analyse and interpret data themselves without having to depend on IT teams. Here an intelligent BI tool or its features are either embedded into another application or multiple BI applications work collaboratively to enable the end users to set up “Intelligent Alerts” or share dashboards across the team etc.

Over the next few years, the predictive analytics tools will not only become more accessible, flexible and user friendly, but also context sensitive. We will see lot more tighter integration between Predictive Analytics tools and IoT, specific BI applications that cater to specific industries and niches, more data visualization, and more flexible customization that will enable not only more flexible customization but also individualized attention.

Arup Gupta, Strategic Advisor and Business Partner at GAVS

This article is reproduced from GAVS’ enGAge magazine, May 2018 edition.