Eigenlytics, headquartered in Mumbai, is an early traction startup in data science domain, solving problems of the data ecosystem, so that organizations become data-ready to optimize RoI for their downstream data initiatives. From uplifting the quality of data to delivering desired readiness, the co-founder of Eigenlytics throws light on the ways his firm is helping clients in making more predictable and timely decisions with distinct goals and optimal cost.

In conversation with Prashant Kumar, Co-Founder & CEO, Eigenlytics

Data analytics technology trends will have significant disruptive potential over the next three to five years. As a data analytics domain player, how has Eigenlytics grown and adapted to the changes so far?
When we talk about trends in the fields of data analytics, it is critical to first focus on the trends and patterns pertaining to data which is to be processed and analyzed. Eigenlytics, being a player in this domain, is future proofing its product capabilities around these changing data trends and patterns. Data to be analyzed, which was predominantly text, document or system driven, could now be seen in forms of gestures, symbols, audio and videos. The analytics was mostly English data driven, is now paving ways of vernacular and non-English datasets. This turnaround is not just happening around enterprise data, it has reached our home too. Over time, utilities like Alexa, Siri or Google Assistance have become part of our day today life and generate rich datasets, to be analyzed. Similarly, in the industrial peripheral, gestures have taken a lot of precedence. And that’s the reason companies are now making apps, systems and tools based on gesture functionality, which will have no voice commands and no punching of buttons. So, we have been witnessing a sea change in the nature of data, which need to be processed and analyzed.

Eigenlytics, focusing on these trends, is building capabilities to handle data in voice and video format, to garner insights for the companies and help them with the needed datadriven business decisions. Be it readiness for big data initiatives, for high end analytics like fraud scoring, or persistency modelling etc., or you talk about readiness for cognitive computing, AI/ML, IoT or cloud readiness, we aim to become precursor for all critical downstream data initiatives.

Kindly tell us about your achievements which held out to be a turning point for the company’s success.
Eigenlytics was incorporated in the year 2018, and during our initial period itself, we got some good traction. One was with an Indian conglomerate. The client was into the field of business process outsourcing, banking, and auto manufacturing. An FMCG client of this conglomerate was launching a multilingual survey across India in different states, in different languages, and on different platforms. For this process, they used to deploy 15-20 people to translate those surveys in English, manually. This obviously used to increase turnaround time and cost. Eigenlytics, through its platform, enabled them to carry out multilingual data extraction across the survey platforms and helped them achieve to key business benefits; first, we made their sentiment analytics real-time, and secondly, we helped them achieve time and cost effectiveness. This has been one of the projects which has really impacted us and has boosted our morale to keep marching forward.

Where is the company headed in the years to come? What are your future innovation goals?
Aligned with the viewpoint that the nature of data itself is undergoing a great transformation, we are strategizing our future product roadmap to leverage on technological enhancements on our platform to cater to dataset in audio, video and jesters. For short- and medium-term, our focus for image processing platform is around handwritten vernacular data extraction capabilities. As far as the domain is concerned, our focus will continue to be in financial services, banking, and insurance sector. However, with this domain focus, we under stand the criticality or relevance of data security and the concerns or apprehension around data sharing for downstream analytics. At the same time, we also know that various machine or deep learning algorithms that get trained with data across the enterprises, delivers better results. Thus, there is a need to leverage learning across datasets of various companies and yet, let the company’s dataset remain within their ecosystem. To work this out, we have been investing our efforts to leverage federated learning, through our platforms and pass on the empowering benefits to our end clients. Further, we are investing in setting up partner ecosystem and developer ecosystem, which will leverage our core platform capability and help put in place a reliable use case specific local support to the end client.