In 2018, Rajit Bhattacharya, Aisik Paul and Ankit Das founded the location intelligence company Data Sutram while pursuing BTech at the Jadavpur University, Kolkata. Data Sutram is an AI-driven platform providing intel on people and places with data from over 250 sources.
“We had joined a high growth startup that was designing analytics solutions for a variety of enterprises including banks, retail brands and others. During this stint, we observed that the biggest challenge to businesses looking to adopt data driven processes was the availability of the right data.Today, unless the business is an Amazon, Facebook or Google, it does not have enough information to know more about its customers,” said CEO Rajit.
When the organisation filed for an IPO, the trio decided to call it quits and work on sustainable solutions to address the data problem. Analytics India Magazine spoke to Data Sutram’s co-founders to understand their entrepreneurial journey and the solutions the company has designed to tackle data challenges in AI and analytics.
Tech stack
The proprietary AI and ML driven data engine automatically converts raw unstructured data from satellites, mobile phones, POS machines etc to actionable insights.
“Data Sutram’s AI and ML driven data engine is a proprietary technology (built in-house) that automatically processes, cleans and catalogues raw and unstructured data in any format, geotags it and combines it together to churn out useful trends and create insights that capture how people live and behave, available for any location upto 100m granularity,” said CTO Aisik.
The platform also allows injecting internal data of businesses to enrich the location intelligence: The data is protected in secure servers, managed and tracked by APIs, and regularly updated. “This automated pipeline has a unique, state-of-the-art architecture with visualisation screens at par or superior to other GIS data competitors in the market,” he added.
Challenges
- Businesses are not data savvy: The biggest challenge in product development is to build a viable Product Market Fit (PMF). Indian enterprises are not data savvy, despite the progress the country has made on the IT front. As a result, Data Sutram had to work on a use-case specific product and also develop processes to aid a data driven approach.
- Finding the right sector
- Hiring: Data Sutram required pragmatic candidates with exposure to different business processes, and experienced data scientists were in short supply.
Data Sutram has deployed a bunch of initiatives to overcome such challenges, including:
- Building processes and related enhancements to support the product pipeline to function and operate, providing incremental value to businesses as a whole. The company has added a service component to complement their use case specific products.
- As most enterprises in India are not data savvy, Data Sutram has decided to target the BFSI sector, which is the closest to being data inclusive and is constantly evolving.
- The company is conducting extensive training sessions for the workforce to become more empathetic towards the clients, understanding their requirements, and developing high quality, relevant solutions for them.
Fundraise
“We are trusted by over 50 companies across different sectors in the country, which has attracted many more clients to consider our products and services,” said Aisik.
The company has recently raised USD 2.07 million from top drawer investors from the finance space.
The north star of Data Sutram is to consolidate its position as a market leader in the “data as a service” domain in emerging markets like India, South East Asia (Indonesia/Singapore/Vietnam), Middle East and North-East Africa.
Early in their entrepreneurial journey, the co-founders understood the importance of taking calculated risks and power through key decisions. Striking a balance between good customer service, superior product quality and building a robust team is the key to sustainable growth, said Aisik.
Hiring
“Skills can be mastered with experience and practice. To become a good data scientist, what one really needs is empathy–to understand the pain points of the customer and build legitimate solutions around it,” said CDO Ankit.
Learning ML models and running Python scripts accounts for less than one percent of the job. Ideating the solution, determining usable data, building data-driven processes, creating steady pipelines etc have more importance.
“Everyone in the industry is taking tangible steps towards being data intelligent, which is going to be helpful for companies like us to add more value to the ecosystem,” he added.
