An intelligent data and analytics strategy enables businesses to break down data silos and manage data with the utmost security and governance controls. The panel discussion at DES 2022, ‘Powering business growth with an intelligent data strategy’, revolved around effective data strategy for businesses and the associated challenges. Research suggests only 30 percent of companies worldwide have a good data strategy.
The panellists included Kunal Sakariya, VP, head of analytics at Fino Payments Bank; Yash Dayal, head of engineering at Zepto; Saurabh Odhyan, CTO – consumer at FreshToHome; Aditya Bhandari, decision science practice head at Axtria; Puneet Tripathi, head of data science at Wakefit; Ravi Kaushik, Sr. VP data science at Near; Deependra Singh, VP and head data science and analytics, Junglee Games; Rajesh Ramdas, head of customer engineering, corporate at Google Cloud, and Manoj Gupta, founder and CEO of Craftsvilla.com.
How to craft an effective data strategy
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Moderator Paromita Chatterjee kicked off the discussion highlighting the struggles business leaders face in creating an effective data strategy with high ROI. “From Google’s perspective, we see a few important trends. First is, everybody wants to run analytics at scale. And most of the time, instead of managing analytics, we tend to manage infrastructure. Once you achieve analytics at scale, the next thing customers want to do is build predictive capability around their large data set and try to achieve it in real-time. Once that is done, and as the organisation grows, the data sometimes might not be in one place. So the challenge here is, can I have the ability to look at data across multiple clouds? And finally, it’s around the democratisation of data,” said Google’s Rajesh Ramdas. To address these challenges, Google provides a platform that scales transparently, irrespective of the data and data load. This lets the customer completely focus on the analytics, leaving the infrastructure and management related work to Google.
Paromita cited the rapid growth of Fino Payments Bank and asked Kunal Sakariya how they built the right data infrastructure. “IT plays a vital role in the data integration processes. Once you have a strong data foundation, then tapping into it and deriving value and insights become easy,” said Kunal.
The moderator also asked how companies can break siloes and integrate the decision-making process to build a data-first culture. “Data, if leveraged correctly, can be used as a weapon to grow. You need to show your team that it can increase your sales, reduce costs or make you more operationally effective. For this to happen, you need to follow the right steps for data setup, collection, processing and presentation,” said Craftsvilla.com’s Manoj Gupta.
Challenges & solutions
Given the magnitude of data pouring in every second, following these steps is easier said than done. So how should companies go about it? “At Wakefit, we have in-house capabilities with a reasonably sized tech team to leverage the data and get to know our customers better. And with these data insights, we were able to pivot from a sleep solution to a home solution company,” said Puneet Tripathi of Wakefit.
Saurabh Odhyan of FreshToHome concurred with Yash and said data has helped scale their business, understand customers better and seamlessly render services to users across 190 cities in India and the Middle East.
Paromita asked whether models or data strategies can be standardised across industries. “Data strategy differs from industry to industry. Compared to an online food/grocery delivery company which continuously requires real-time data, an online gaming company like Junglee games does not require frequent real-time data; we need more data on the attributes of a customer,” said Deependra Singh.
“The biggest challenge in applying data science in the pharma industry is the integration of data from various sources. Typical legacy companies ask for the value created from data analytics and are concerned about the ROI,” said Aditya Bhandari from Axtria.
Puneet said companies need to focus on both traffic to conversion and conversion to revenue while building the right strategies to get quick and effective ROIs. “Nowadays, companies want quick solutions without wanting to invest too much and wait for a fast ROI before investing in the future. Our research in AI & ML at Google makes this possible. We generate custom models for our clients and provide open-sourced solutions,” said Rajesh.
As per Gartner, nearly 50 percent of companies adopting data orchestration platforms prefer a hybrid approach. “Most data science companies follow a hybrid approach where both open sources and vendor-provided systems are used,” Deependra said.
But does it work in highly-regulated markets? “For fintech, with the additional regulations put by the RBI, there is more control over where the data is stored, but the use of data is less regulated,” said Kunal.
“Data privacy and security should be built into the data ecosystem. Cost is one of the key factors that affect a company’s technology adoption decisions. Most companies may opt for open source at the start, but as they begin to grow, scaling becomes an issue. So they tend to create in-house systems,” Yash added.
For companies looking to build in-house systems, Saurabh recommends starting with creating goals, pipelines and strategies to store, use and get the most value out of data. “To achieve high ROIs, data needs to be democratised and made available throughout the organisation,” he said.
But how could companies ensure the data they collect and analyse translates to business success? “Accuracy, high-level targeting of customer reach, end-to-end automation, and measurement are the most important factors to take into account to leverage data moving forward,” said Ravi Kaushik from Near.
In the latter part of the discussion, Deependra advised companies to focus on personalisation, automation (i.e, to merge information into actionable insights) and investing in emerging technologies like crypto and NFT gaming.