Lenskart is an eyewear brand that operates in a unique D-C omnichannel category and aspires to go full-stack, controlling manufacturing, assembly, and supply of eyewear across all customer segments. Data & Analytics are crucial to driving Supply-chain and Marketing initiatives for this market-leading retailer.
At the helm of affairs at driving Analytics at Lenskart is Saurabh Agrawal SVP, Analytics, and CRM Lenskart.com, (at the time of interview and has since moved on to another organization), who is passionate about changing lives with all things Data & Digital and is identified as a Top -100 leader in India.
Prof. Madhu Viswanathan, (MV) Associate Professor and Research Director atIIDS, interviewed Saurabh Agrawal (SA) to capture learnings from his experience as a Data Science Consultant and a Data Science Practitioner. Sharing snippets of the conversation that ensued..
MV: Saurabh, you have worked in Digital Analytics in companies across varied industries – Fintech, Automotive parts supplier, and now Eyewear Retail. How similarly/ differently is analytics practiced across sectors?
SA: Contrary to popular belief, there are more similarities than differences in how varied industries conduct data analysis. There is a pattern to data analysis that follows a common framework – Organizing the data, Drawing Insights from it, Building Models, and Applying the insights to solve Business Problems.
A crucial step towards solving Data-driven problems, he observed, is a deep understanding of the product & the customer. Also, driving data-driven solutions can be a long, exhaustive iterative process. Often, solutions appear in more than one go. Hence, he emphasized the need to acquire under-credited soft skills like passion, patience & endurance, along with the hard skills of Data Engineering and Coding required to solve them.
MV: We all know each organization has an embedded ‘Culture.’ Can the Culture of a company encourage data-driven solutions?
SV: A company must have the belief and a mindset that data can offer business solutions. A unified spirit across all domains to willingly look at data in everything can be transformative for the organization. The process will also be riddled with challenges and failures, but the common mindset will enable the organization to look at data-driven solutions. I urge employees to experiment with data to get meaningful insights. You’ll be surprised by the exciting & surprising insights that spring up serendipitously.
MV: So, what do you do when posed with a statement like “Iss data se kuch karo?” (Please do something to get valuable insights from this data)
SV: It is an excellent strategy to undertake a mental checklist of “what do I want to do with the data?” Establishing clarity around what can and cannot be done with the available data is essential. Data Cleaning is another significant step that needs to be undertaken. It is crucial to bring more people on board with data-driven decision-making across the organisation. To create an organisation ecosystem that allows data to prove itself right rather than trying to prove people wrong. It is beneficial to find champions within the organisation by enabling all employees to work towards offering a better customer experience at minimal economic costs. I believe in doing analytics for shop floor managers and Business Unit Heads rather than for the CEO/ CXO to have deeper entrenchment that will drive real change.
MV: Any significant impediments in the process, Saurabh?
SA: Convincing the non-believers is a mammoth task. A good start is identifying the believers early on and forging partnerships with them. Also, anyone struggling to grow will be easier to convince and is more likely to take help from data sciences to develop actionable insights. The possibilities that emerge with Data will then create a FOMO with the non-believers. Funny as it sounds, it has worked in some cases.
So, it is crucial to get started first. Focus on what is doable, even if it has a small impact. Small wins help build credibility to take on larger projects subsequently. It may not be a great idea to take on a challenge that’s a holy grail. Sometimes, data may not be available, or data may not be of good quality. Other times, senior management may not have the patience to combine the data collection system. In such cases, waiting it out and collecting data for analysis is a good strategy.
MV: Given the rapid digitization & automation, more roles & jobs are getting analytical. Should then everyone aspire to be a Data Scientist?
SA: Not really. I cannot emphasize enough the need to adopt a data-driven mindset to solve business problems and to equip oneself with some necessary tools imperative to make astute data-driven decisions- Knowledge of a basic computer language though not an essential skill, is a handy tool for a better understanding of data. Analytics is unique in that it combines both hard-core technical skills and business understanding.
MV: Data-driven organizations have traditionally hired Data Engineers and Business Intelligence Developers. There exists a gap between the people who translate business needs and what the technology team needs to deliver. Your thoughts?
SA: Yes, I agree. Business Translators can fill this void. While the role of Business Translators does not necessarily require hard-core coding skills. However, it is helpful to understand how to code. Being open and flexible to learning and staying agile with changing skills of the future will help you stay abreast with the latest. As I see, the world will soon see more Citizen Data Scientists.