How Does Predictive Analytics Benefit Big Data?

How Does Predictive Analytics Benefit Big Data?


Predictive Analytics uses Big Data to anticipate future trends, combining historical data with consumer insights. It's gained importance lately due to precise, accessible predictive tools. Businesses benefit from improved decision-making, optimized processes, and enhanced customer interactions. Top providers like IBM, SAP, and SAS offer strong solutions, enabling data-driven insights and end-to-end analytics.

Predictive Analytics is a Big Data enabler. MNC’s and business houses today collate colossal real-time consumer data. Predictive analytics utilizes historical data and combines it with consumer insight to forecast future occurrences. It helps leading companies to use both the real-time and stored Big Data and shift from a historical to a forward-thinking customer perspective (also consider checking out this perfect parcel of information for data science degree). 

Predictive Analytics: The Game Changer in the Big Data Industry

Predictive Analytics has gained more prominence and relevance recently than before. Big Data drops the compute capacity. New age tools are developing predictive modules that are precise, effective and accessible to organizations.  What is the need for it? According to Gartner, companies that attempts to predict customer insights are likely to perform better, win the competitive race and ensure customer delight better than others. Therefore, to survive, function effectively and gain a competitive edge over competitors, organizations today need to possess predictive powers in 3 core areas:

  • Being able to offer direct insights regarding consumers and business procedures: Reports and dashboards are common predictive analytics tools within a company. Highlighting the data on causative trends along with future projections, the customary BI vendor tools provide generic predictive modules. Though these tools provide vital data to business executives and managers, they prove inadequate in relating process optimization, and consumer experience with crucial business decisions. 

  • To enable adaptable, intelligent and perceptive consumer interactions and business procedures: Companies that are not utilizing predictions to shape the future are making their data scientists run useless. The best predictive analytics tools can implement their scoring engines or models into applications where insights are required. Furthermore, leading and emerging companies are also resorting to predictive analytics for improving business processes and performance by identifying the forgeries taking place whilst swiping at a point-of-service, starting customer service for the “at-risk” revenue sources, adjusting digital content depending on user perspective, and many more. 

  • To re-design consumer engagement and impact new-age digital products/services: Utilities of new-age predictive analytics tools outstretches generic use. The building of a new business model and its deployment is on the rise, allowing application developers to leverage predictive analytics tools for implemented applications. The developers can thereby concentrate on bugs and features that forecast the biggest consumer value and estimate the influence of a new app utility (also consider checking out this career guide for data science jobs). 

Advanced Predictive Analytics Tools – The Top 3 Service Providers

According to “The Forrester Wave: Big Data Predictive Analytics Solutions, Q2 2015”, a report compiled by Rowan Curran and Mike Gualtieri, advanced predictive analytics tools are here to stay. However, amongst a bouquet of leading service providers, there are 3 brands that have clearly scored high owing to their service offerings and performance. These 3 trusted service providers include IBM, SAP, and SAS. 

IBM- The brand scored high with features like Usability and Tooling, Data, Ability to Execute, Model Management, Solution Road Map, Implementation Support, and the Go-To-Market Growth Rate. Since consumers today obtain insights from the data sets, the predictive analytics tools by IBM can leverage big data and arrive at vital insights. The areas that could be improved upon are Pricing and Acquisition.

SAP – According to Forrester, SAP wins it over with its end-to-end analytics investments. The service offering includes revolutionary tools that are easy to use. There are advanced visual tools that users can use to evaluate multiple databases at a go.

SAS – Similar to IBM, even SAS predictive analytics tools scored high on most features and is considered as a leader among the best names in predictive analytics vendors. It, however, needs to update features like Pricing, Acquisition, and Go-to-Market Growth Rate, which according to Forrester, is not a major concern, as SAS is already working towards it.

Finally, the great news is that enterprises willing to leverage Predictive Analytics have it within their reach. They simply have to select the appropriate big data predictive analytics solutions that cater to their business requirements. 

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