Data: How Industry leaders are harnessing the new superpower17 min read
Reading Time: 11 minutesThe world we live in is digital. Our day-to-day lives are now run by an intricate network of live and digital brains that are interconnected. In this evolving world, Data is the new superpower.
But unlike most superpowers, data is abundant. With every keystroke, swipe, and click, new bits of information are being created and shared daily. For enterprises that look to derive value from data, just knowing where to look is a mammoth task. Especially in the current era of transformation being brought about by AI, businesses need to know how to handle their data – it’s not an option anymore. It is a necessity.
Data is the backbone of AI Transformation. How are business leaders around the world navigating through the sea of data that is available? We asked them ourselves at REBUILD, the biggest AI Transformation event.
REBUILD is an experience designed for changemakers to come together to share their ideas and insights with the whole world. We asked leaders from across domains how they put the best use to their data while tackling challenges, and they shared some golden insights. Here’s how you can navigate your way to AI Transformation success, too! Following are the biggest ideas surrounding data that were discussed.
Data as the nucleus of ecosystems
Over the years, with the evolution of technology and diverse product and service offerings from enterprises, ecosystems have been thriving – and there are companies across domains that have stayed at the top of their game for decades through this. It’s almost a reflection of the natural world, where Data is the DNA, the atom that powers everything in the ecosystem. We saw this example come to life with the story of Tata Neu – India’s first super app.
Pratik Pal, the CEO of Tata Digital, spoke about data while telling us the never-before-told story about India’s first super app at REBUILD. “Tata Neu was built on 3 pillars – data, supply chain, and omnichannel. Data was obviously the first pillar. We had a customer base of 150 million and 4800 physical touchpoints across the country. We wanted to harmonize all that data in one ecosystem, keeping security and privacy in mind, with utmost importance – and that was how Tata Neu was born.” he added, speaking of the app’s inception.
The data the 150-year-old conglomerate had acquired over the years helped them increase its stronghold in the digital space.
Contextualizing and Transforming Data
“For data to be effective, it has to be taken in context,” remarked Sid Kargupta, Chief Data Office of Tata Digital. With an analogy based on cricket, Sid Kargupta explained a shortcoming that most leaders look over. Contextualizing data enables easier analysis and turns huge volumes of complex data into actionable insights. For context, you need to consider the trends, patterns, and correlations between them.
For example, a major event like the Football World Cup could significantly increase the sales of football-related merchandise and accessories. Still, the increase doesn’t guarantee that the same products will offer huge returns when the World Cup ends. Without context, this might seem like an opportunity that would eventually lead to a huge lack of resources.
“When the context changes, so should the actions taken on the data. Contextualizing data enables smarter and more accurate decision-making and delivers the best possible experience to the customers,” added Sid.
Contextualized data by itself can only do a little. Data by itself is like a raw ingredient in a recipe. It isn’t ready for consumption in its raw form. It needs to be seasoned and cooked (transformed) to make something that can be consumed. Here’s what Anand Chandrasekaran, Founder & CTO, Mad Street Den had to say about data transformation:
“If you can’t transform that data into understandable journeys, it can’t be useful in the end. Transformation of data is the middle ground to create something impactful.”
– Anand Chandrasekaran, CTO, Mad Street Den
Building Inventories
“Water, Water, everywhere, not a drop to drink” from the Rime of the Ancient Mariner served as a metaphor for being surrounded by a resource but not benefiting from it. This surprisingly applies to the way enterprises handle data today. Data is abundant, diverse, and of varied quality – how do you use it to your advantage?
“Having good quality data is great, but not knowing what to do with it is the problem,” says Ahmad Musa, Head of Planning, Budgeting & MIS at Invest Bank P.S.C., touching upon the challenges he faced in
Data, in 2022, is one of the most valuable resources at Enterprises’ disposal. The way you use it makes or breaks your business. But with so much data, how do you navigate that?
“Should we go from Data to Decision or from Decision to data? The latter works better – you work backward, and it gives you a clear picture of your goals and compares it with the data you have, enabling you to fill in the gaps in data, if any,” said Shailendra Singh, Chief Growth Officer at Fractal.ai. “It’s better to decide where you want to go first, and then look for the data you need; it makes the job easier,” he added.
Any digital or AI Transformation today starts with a goal that enterprises seek to achieve, starting from the goal assesses where you stand and where you need to go in the journey. It is crucial to keep your business strategy in mind.
Venkat Raghavan, Associate Director and Global Head of Data at Tesco, told us how they align the data they have with their goals concerning personalization and delivering a better customer experience. “In Tesco, we have a good grasp of what customers are doing with the data that we have. What we’re trying to do is to understand what they’re thinking and what they’re feeling. There’s an interaction between the brand and the customer – and the decision is taken only after the interaction. What do they think when they engage with us? Do they feel good after they buy from us? We get to hear what they don’t like from customer care – but what do they like? That’s the next frontier of understanding customers. We call it personalization, but for them, it’s relevance.” he said.
Retailers use the data from customer complaints to continually improve and offer a better experience, but what if the goal is to improve a good experience? Taking account of this and addressing the gaps in the data gives leaders direction to act on ways to collect the necessary data.
Timo Weis, Associate Vice President at Infosys, added to that with a solid insight into his team’s process and how they get started with the transformation journey. “It all starts with building a base – breaking down a problem into smaller mini problems, assessing what’s in place and what has to be brought together. It all starts with data – bringing all the data together from different sources in one data lake, ensuring they all have a unique identifier. Building a strong base is critical to start the personalization journey.” he said.
Deciding where you need to go with your data and then taking inventory of it helps you analyze the missing pieces of the block and points you exactly where you need to look.
Innovation in Data Collection
Aligning your business strategy with your AI transformation strategy has another benefit when handling your data – you find the room to innovate and look for newer sources and processes.
We hosted Arjun Sarkar, Senior Director of Digital Solutions at Dubai CommerCity – the first free zone to enable digital commerce in the Middle East, Africa, and South Asia region, in our panel, where we discussed building Digital First organizations.
“We look at data from multiple perspectives and sources,” he said, “We go beyond just customer data and look at the bigger picture”. These sources are crucial for companies like theirs to decide where their next big customers will come from, from both microeconomic and macroeconomic levels. The data sources might be diverse, but having a plan and clear goals to achieve will point you directly where you need to be looking and what processes you need to undertake to collect it.
In line with that, Gala Blasco, Head of Digital Experience at the Alshaya Group, mentioned how changing a day-to-day activity helped to make data collection easier. “At Alshaya, Digital Receipts have been a game changer in terms of organizing and collecting data, and also the willingness of customers to digitize it is a lot more convenient for transformation, but there’s more to do in the pre-transaction journey, and that’s what we’re working towards,” she said. Making a simple change to the existing processes can make a significant difference to the way you collect your data and also the quality of data at large.
Paras Rishi, Senior Manager of Marketing at GMG, added to the conversation with how they bridged the gap between online and offline as an omnichannel brand. “At Sun & Sand Sports, we have a lot of customers walking into our stores. They may not buy anything at that point, but we know that there is intent, and they’re a group we need to target. We use geo-fencing and geo-targeting vendors to ensure that we are running the right ads, showing the right callouts, showing the right discount messaging, and showcasing our value proposition to a relevant audience through our digital ads. That’s how we’re capturing offline store data while keeping regulations and principles in line.” he said. The data sources are varied, but an out-of-the-box approach and a bird’s eye view of the situation help drive growth, but most importantly, while keeping privacy in mind.
Privacy & Security in a digital world
Users today are incredibly aware and hyper-conscious about their privacy, with service providers taking all the necessary steps to ensure privacy is safeguarded and regulatory measures are being followed.
“We receive huge volumes of heterogeneous data. The heterogeneity gives us valuable information, but it is critical to ensure that privacy isn’t breached,”
– Sid Kargupta, CDO, Tata Digital
“The consumer expects you to, you know, know a lot about them. And that kind of gives, especially retailers, who have both offline and online businesses, a lot of data responsibility. How do we responsibly use that data?” said Samrat Sengupta, Ecommerce, Omnichannel & Product Director of GMG.
In some industries like Healthcare, the availability of data in itself is scarce due to privacy concerns.
“With regards to healthcare – people care about their privacy and who their data gets shared with. We face many challenges in convincing patients that their data is going to remain discreet and private. I think we’re in the stage of getting them to trust us. I’d like to learn more about that as we move along,” said Dr. Karthik, while discussing the concern for privacy within the healthcare industry.
“Unlike other industries, the access to data & qualitative research in healthcare is limited. In pharma, the access to data ends with secondary sales – we don’t receive data after it reaches the pharmacy. The customers come at tertiary sales, where the final consumption happens. e-Pharmacies could bring in Pincode level information on consumption, at a brand level as well. All pharmacies have data on which doctor prescribed it from which hospital. With consent and anonymity from the doctors and anonymizing patient information, we could run a data science project on which brand holds market share and why. We could sell that data to pharmaceutical companies to help them determine where they need to focus on,” he added, talking about how data can still be extracted while respecting the privacy of all the players involved.
Data Quality
Bad data can adversely affect every part of the decision-making process, with the impact felt across teams and companies. Bad data costs US companies around $3.1 trillion every year!
The quality of the data you use directly impacts the decisions you make on top of it. ‘Garbage in, Garbage out’ is a prevalent modern adage about data, and the scrutiny on data quality is at an all-time high now, to derive the best results.
Mad Street Den’s Founder & CEO, Ashwini Asokan had this to say about bad data:
“Without clean data, there’s nothing else. There’s no AI, no fancy application, and no ROI. Clean data is ground zero.“
– Ashwini Asokan, CEO and Co-founder, Mad Street Den
“We want all we all want to be data-driven. But what happens is, if that data is not correct. Then you make incorrect decisions. So this is key, and we need to be careful with that,” remarked a Senior Leader from one of the world’s biggest eCommerce marketplaces.
“We collect all the data, but we also ensure that the quality of data we are capturing is right because we know it influences the quality of our business decisions,” said Arjun Sarkar, on how the two are interconnected. Your actions are only as good as the information you have to act on.
But getting the perfect data to help you achieve your goals is not always possible – to which Timo Weis had a great insight to add. “Data will never be 100% correct. Not everything can be perfect, and that’s okay. Even if your data is just 80% perfect, you could still use it for personalization, as long as you are delivering benefits and deriving results,” he said. While it is okay for data to not be entirely perfect, you can still deliver results with a portion of the data that is of good quality.
Data might be of good quality – it could be structured, cleaned and organized to suit your needs – but your decisions will be of value only if the data is reliable. How are leaders ensuring that the data they’re using is reliable?
Within healthcare, Dr. Karthik Anantharaman highlighted data availability directly from the patients themselves. “People are rating Doctors on a scale of 1-5 online based on their satisfaction with the service provided. It’s a hard reality that healthcare providers are trying to accept, but it is necessary, because we are getting firsthand information,” he added. Data is not one-sided, it is dynamic, and any input that is received directly from the customer can be used to tailor their experience and make it more personalized.
Companies need to ensure that there are set processes in place to ensure data reliability, to ensure it is of use, and to prevent data decay, among other things. Venkat Raghavan, took us through the fundamental steps for ensuring data reliability.
Human intervention is still very much a part of processes within digital transformation, but specifically concerning preventing errors and ensuring reliability, Oan Ali had this to add –
Data that is impactful
“We like data that drives impact. Collecting data is just 30% of the challenge. It’s all about transforming it and making it useful. That’s where the mischief and delight are – when it works well,” said Con Conlon, MD & CEO of Merit Group, speaking of how data by itself doesn’t become impactful, it requires transformation.
“How much can the data tell you after you’ve transformed it? For the work we do in retail, Blox.ai helped us distill huge volumes of data and make it usable. We had crucial information about the garment – ranging from the most popular colors right down to sizes- that is good quality data, it enables impact.” he added.
In coherence with that, Anjana Reddy, CEO of USPL – the company behind one of India’s biggest retail brands, told us how data helped them not just stand out, but also helped them survive and thrive during the global pandemic.“AI is the latest kid on the block.” said Anjana Reddy, CEO of USPL, one of India’s leading consumer fashion brands. “As a fashion brand, data helped us stand out because we got to understand our consumers on a deeper level beyond just the basic metrics – we understood what our customers wanted from Sleeve lengths to colors,” she continued, on how data helped power their business and helped them tide the wave that was the COVID-19 Pandemic.
They used merchandise data effectively by tracking up to 17 attributes per style and location to help them manufacture their products, minimizing their risk of holding on to inventory and also helping them with sales and effective usage of their working capital. These turnarounds, combined with the brand value they had created over the years with their endorsements and marketing, helped them emerge as the #1 brand on the e-commerce platform Myntra at a premium price point.
Where are we headed?
With all being said, what do the next five years look like for enterprises?
“There is so much data! People will struggle to put together all of that data and make something out of it. That’s why they need good technology partners who can help them make meaning out of it, instead of breaking their own heads to figure out data. They would rather rely on someone who has the skills, the people, and the solution to help them. Finding the right partner like yourselves(Mad Street Den) will be very important to really tap into the data and drive business growth.” said Arjun Sarkar on the impending data explosion.
Will it be Data Heaven or Data-geddon? Depends on your partner! (Wink)
