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The Story of Prescience, A Boutique Data Science Startup Which Is Industrialising Analytics With AI & ML

The Story of Prescience, A Boutique Data Science Startup Which Is Industrialising Analytics With AI & ML

As the name of this startup suggests, Prescience is in the business of creating products for companies to allow them to foresee events in the future and take preemptive actions. Offering services in analytics consulting, prescriptive analytics, visualisation and business intelligence, among others, this Bengaluru-based startup believes in helping executives improve their anticipation by analysing the past data and deciphering trends, patterns and linkages for better output. “This allows the organisations to get business insights which add to their experience and knowledge, and helps them to be ‘prescient about their business”, the startup founders say.

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Analytics India Magazine got in touch with the founding team to get a deeper insight into what the startup offers, the idea behind its conceptualisation, and more.  

The Beginning

Shivkumar Krishnamurthy, co-founder of Prescience notes that many companies still struggle to figure out how to leverage the power of data analytics due to the complexities involved in it, and the lack of business outcome-driven analytics. “The proliferation of various analytics products and platforms have contributed to the confusion”, he says.

As the entire team believed in the power of data analytics and the help it could offer businesses to quickly make decisions based on insights and alternatives assessed, the founding team came up with the idea of Prescience. “We believe it is not just data and technology but a combination of software power and human intelligence that will deliver the best solutions for the businesses. And Prescience is focussing on just that”, he adds.

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As of now, the startup is completely bootstrapped. But the team intends to industrialise analytics by providing solutions that weave machine learning and artificial intelligence into every aspect of the data value chain, and help executives become more prescient.

Co-founded by Anirban Majumdar and Krishnamurthy, who have varied complementing experiences in analytics, enterprise technology and domain expertise in setting-up and running business, the duo is now achieving their aim with the help of their 16-member team.  

The Journey

As organisations are capturing the right data and blending technology get benefit from analytics, the startup is helping their customers to do this by getting information about the various aspect of the businesses as well as possible solutions through careful analysis of the data. “We combine the triad of latest technologies, our skills and experience, and the business knowledge of our customers to create tangible solutions”, Krishnamurthy shares.

They help companies to go through the full cycle of data journey:

  • Understanding the available data
  • Improving its quality
  • Formulating the right set of questions and models
  • Delivering the right analysis about trends
  • Finding causes and effects of the various critical business issues

“We look at delivering a holistic solution instead of offering a multitude of point-solutions for the various aspects of the business”, adds Krishnamurthy.

Some of the services provided by the startup are:

  • Analytics Consulting: It includes services such as analytics maturity assessment, data program assessment, data monetisation consulting, data for regulatory compliance, data program management, among others.
  • Prescriptive Analytics: Prescience’s ML and AI helps in improving the speed and accuracy in large-scale deployments. It solves classification and regression problems to ensure better profiling of customers and understanding their needs. It can analyse voluminous, unstructured data, handles a variety of data, among others.
  • Visualisation And BI: It allows visualisation of connected data from various structured and unstructured sources to generate standard and ad-hoc reports, and analyse trends.

Analytics Use Cases By Prescience

The startup is currently focused on retail and CPG/FMCG segment, and is extensively looking to expand in the BFSI space. Krishnamurthy further shares few use cases as follows:

  • They are currently ensuring deal effectiveness measurement and optimisation for one of the largest global e-commerce players
  • Automated data governance and data quality enhancement process setup for a large CPG company
  • Business performance analytics for a large global e-commerce retailer
  • Development of a micro-level demand-sensing model for pharmaceutical companies by using their in-house solution which combines structured and unstructured data

Growth Story And Challenges

Krishnamurthy proudly shares that since the first year of operations, they have grown to a team of 16 with data scientists, big data engineers and visualisation designers, who are all working in multiple active engagements. “We are hiring an average of two employees every month, and expect to grow our team size to at least 40 by the end of FY 2018-19”, he said. Talking about the skills that they look for in these candidates, he said that they look for people with strong technical skills combined with business acumen and an inherent curiosity.

“We mostly induct seasoned professionals with experience in data engineering, data sciences, and business analysis. Our current projects include technologies like SQL, NoSQL, Hadoop, Teradata, Python, R, Deep Learning, Power BI, D3JS, Tableau and Excel, among others”, he added.

Though the startup shows a remarkable progress report, it isn’t averse to challenges. “Due to the buzz created about data analytics, ML and AI, we find that customers have very high hopes of getting quick and great results. This hype neglects the hard work needed to understand the data, ensure it is of good quality, and to apply the right data analytics technique to solve the critical issues. Hence, it becomes difficult to set realistic goals and timelines for such initiatives”, he said.

The startup overcomes this challenge by taking a business-centric approach. “We look at the business questions and issues which need to be answered by data analytics. We then go through the process of analytics model, data collection, predictions generation and recommendations. This process helps us to always keep the end goal in mind and also avoid force-fitting any product or solution in our output”, he said on a concluding note.

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