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All through 2023, a majority of the IT and data analytics companies were busy jumping on the generative AI bandwagon. However, a handful of companies advocate for extensive due diligence. One among them is Bangalore-based data analytics firm Tredence.
“Generative AI is a key investment area and innovation focus for us for over 3 years. However, before incorporating emerging tech, especially in AI, it’s crucial to evaluate if there’s a clear path to generate value for specific business problems,” Pavan Kumar, senior manager at Tredence Studio, told AIM in an exclusive interaction.
Kumar further elaborated on the importance of due diligence when it comes to generative AI. “Many AI models are trained on generic datasets, so adapting them to address specific issues requires incorporating business and process context to these models. Stability is another consideration; generative AI, in particular, needs to provide repeatable responses over time,” he said.
“Answering these questions helps us ensure that the technology aligns with the specific business challenges that we’re addressing. From our research, we were able to create a stable extractive AI model and meaningfully incorporate it in our solutions. We are currently tinkering with Gen AI models to improve stability and product fit before they are integrated in our solutions.” he added.
Kumar works with the Studio team, an innovation pod within the Tredence organisation. “Our role involves collaborating with various teams – strategy, domain and technical – to design and deliver products for our customers,” he stated.
At the organisation, Kumar started with analytics and BI, transitioned to data engineering, spent a couple of years in data science, and has been doing product management for the last three years.
The Need for Data Product Management
For Tredence, data product management has become crucial in recent years. “After establishing a practice on the services side, we identified opportunities for innovation in building reusable assets for clients. This realisation stemmed from our consulting experience over five years,” recalled Kumar.
A pivotal change for the organisation was bringing in industry leaders in key strategy roles. Kumar explained that the three key pillars for establishing the data product management process are executive sponsorship, domain expertise, and dedicated technical expertise for innovation.
The data product team is cross-functional, with members specialising in marketing, supply chain, RGM, and one of the tech domains—data engineering, data science, or analytics.
“Our approach involves collaborating with the strategy team to identify gaps in the industry. We then work with these teams to build prototypes, validate them with customers, and design a go-to-market strategy,” he said.
He further said that within Tredence, their data quality practice addresses anomalies in transactional data, master data, and platform data. “We have designed accelerators for each scenario, such as addressing anomalies in transaction data, leveraging custom AI algorithms for master data, and utilising the platform Atom.ai to manage data platforms,” he said.
“These accelerators are integral to our core DNA,” Kumar explained. For client engagements, the company incorporates these accelerators and conducts educational sessions within Tredence to emphasise the importance of data quality across all products.
Data Quality and User Feedback a Priority
Visionaries in the industry have long preached the ‘garbage in, garbage out’ approach for data. And Tredence aligns with the same.
“Data quality is the biggest focus for us,” emphasised Kumar, explaining the foundational nature of the approach. “If a scenario arises where a feature or data quality is in question, data quality takes precedence. This is because businesses cannot function optimally without the right quality of data. Providing accurate data at the right time is crucial for effective decision-making,” he added.
Kumar and his team have observed that enterprise business process management products often lack a user feedback mechanism – an important element in creating a data product.
“Let’s take the example of a data stewardship product we have. Initially, our value proposition centred around consolidating master data in a clean way, which was relevant to the business users. However, when deployed for clients, we realised that different teams like merchandising and supply chain had distinct harmonisation requirements, and keeping a human in the loop was necessary to address this.” he said.
He further elaborated that this step led them to integrate a user feedback loop into their products so that teams could provide feedback on the harmonised data.
To enhance user feedback adoption, Tredence has implemented prompts with multiple options, reducing ambiguity for users. They also analyse product logs to better understand usage patterns and improve the user experience of their products.
“Domain experts within our company validate the features by using our products as end-users, identifying any blind spots we might have missed,” he revealed.
Keeping Up with the Market
Keeping up with trends is relatively straightforward, says Kumar. He attributes this ease to the prevalence of social media content creators on platforms like LinkedIn, Spotify, and YouTube, where leaders from leading tech companies create content tailored for professionals. Another area is industry certifications, certifications help in keeping the tech skills up to date.
“Additionally, hyper-scalers like AWS, GCP, Azure, Snowflake, and Databricks hold annual summits, publishing notes on the latest industry innovations. We also conduct educational sessions within our organisation, discussing research papers, successful business practices, and product management practices,” he concluded.
