Need for specialisation far greater for new entrants in data analytics domain: Tredence’s Shub Bhowmick

Bengaluru and San Jose-based data analytics company Tredence is tapping into generative artificial intelligence to offer more contextual solutions to its customer. This year, the company introduced Atom ai, an end-to-end ecosystem of AI and machine learning accelerators to help enterprises solve their data science challenges.

TechCircle interviewed Shub Bhowmick, the co-founder and chief executive officer to know more about their AI strategy and how the data analytics industry is projected to grow. Edited excerpts:

What has been your business strategy for 2023?

From a business model perspective, we have launched two new verticals, banking, financial services, and insurance (BFSI) and the second one is healthcare and life sciences (HLS). We have been a predominantly retail and CPG company so far. Now, with these verticals, we'll start seeing more diversification. So these are from a vertical perspective. From a horizontal or from service line perspective, we have started the MarTech practice. 

We have set up a merger and acquisition office and are aggressively scouting companies, identifying them for potential acquisitions or strategic partnerships. We've defined the playbook, which is in execution. It's still in the early stages but there are about three to four companies at this point that are in more mature stages of conversations.

What is the hiring plan for this year?

We have a headcount of about 2,300 people. So, proportionately we keep hiring into the organisation. Having said that, we are significantly up-levelling our training engines on becoming more and more specialised. Some of these generative artificial intelligence (Gen AI) lead benefits will also hopefully make us more efficient and productive as an organisation. We continue to hire and I don't anticipate the rate of hiring to slow down in any meaningful way, but we may be able to do more with less in the future. 

How has Gen AI impacted your offerings?

In general, with the industrialisation of AI, we're starting to see a lot more pervasive presence of AI across many use cases. AI is no longer a lab experiment, companies are starting to expect and invest in use cases where AI will be the core.

We are training and fine-tuning a set of large language models based on the contextual data that we have, to offer responses specific to a particular domain — for instance, retail. Additionally, on top of this context, we further fine the models to customer-specific information to improve accuracy and reliability.

Due to open-source models and the general democraticisation of AI, we see that the cost of training has also come down. One of our partners, Databricks recently acquired AI company Mosaic ML which basically shrinks the cost of training large language models significantly. By virtue of our partnership with Databricks, we have benefited in terms of creating a large corpus of knowledge to train our models.

What is the current state of the data analytics market?

It's a very thriving market. The entire world is now going to lean more and more into AI and unlock the value of AI in a meaningful way to drive business outcomes. In the next five years, the industry may grow 25-30%. We will have more startups in this field. The only difference for these entrants now would be that the need to specialise would be much greater, than what was a decade back.