Luka Jasionyte, in the marketing team at Snap Analytics, catches up with Deepam Biswas, our Head of Technology and Delivery in the India office, to get the low down on his journey through data and analytics, and to share insights on what he thinks are the most important trends, Generative AI in business, and challenges facing enterprise businesses today.
What inspired you to get into data and analytics, Deepam?
Data has always fascinated me, not just as numbers, but as the foundation of every decision, strategy, and transformation. What truly drew me into the field was the recognition that data is only as valuable as its accuracy, structure, and data governance. Without trust in the data, businesses can’t make informed decisions, drive efficiency, or unlock meaningful insights. My curiosity for uncovering patterns, validating assumptions, and refining raw information into something truly usable has been a constant throughout my journey.
What is the most exciting trend right now?
In my opinion, the most exciting trend in data and analytics right now is the integration of Generative AI in business. With Generative AI, we can leverage natural language to both analyse data and generate actionable, prescriptive strategies. This means that we can ask complex questions in plain language and receive insightful, data-driven responses that guide decision-making.
Additionally, modern BI tools have evolved to automatically identify patterns, anomalies, and correlations in data that might be missed by human analysts. These tools present insights in an easily digestible format, making it simpler for businesses to understand and act upon the data. Another fascinating development is edge computing, which allows for the processing of sensor data almost in near real-time. This capability enhances efficiencies in business processes such as warehouse management and production management by providing timely insights that can significantly improve operations. Overall, these advancements are pushing the boundaries of what we can achieve with data and analytics, making it an incredibly exciting field to be a part of.
What are some common data-related challenges that large enterprises typically face?
One of the most significant challenges I’ve seen in large enterprises is dealing with data silos. Data is often scattered across various systems like CRM, OMS, Billing & Invoicing, and legacy systems, making it incredibly difficult to get a comprehensive 360-degree view for data analytics. This fragmentation can lead to inefficiencies and missed opportunities for insights. Another major issue is the persistent skill shortage in the data field. There’s a high demand for skilled professionals such as data engineers, data scientists, data analysts, and data governance specialists, but the supply just can’t keep up. This gap can hinder the ability of enterprises to fully leverage their data. Additionally, many large enterprises still rely heavily on batch processing of data, which results in considerable latency in generating insights. This often forces upper management to make business decisions based on outdated data and gut feeling, rather than real-time analytics.
Near real-time data analytics is still complex and costly, but it’s crucial for making timely and informed decisions. Generative AI in business is helping bridge this gap by automating data analysis and providing real-time insights without requiring deep technical expertise, enabling enterprises to make smarter, faster decisions. These challenges can significantly impact the efficiency and effectiveness of data-driven strategies in large enterprises.
What are the top client priorities for those looking to drive successful outcomes in data and analytics?
When it comes to driving successful outcomes in data and analytics, clients have some top priorities that are pretty clear. First and foremost, they want to know how this data is going to make them more money or save them money. It’s all about ROI and tangible business values. It’s no longer enough to just get reports or dashboards that tell them what happened. Clients want to understand why it happened and, more importantly, what they should do next. They seek prescriptive analytics that guide decision-making. And let’s not forget about protecting sensitive data. With the growing number of data privacy regulations like GDPR and CCPA, adhering to these rules is non-negotiable. It’s all about making sure their data is secure while still being able to leverage it for business success.
Deepam, why Snap Analytics?
Snap Analytics has extensive experience with diverse and complex client projects across industries such as FMCG, Finance, and Supply Chain. This means clients benefit from the exposure to a wide range of real-world business problems and data challenges, and we provide effective solutions to tackle them. Our core mission is to help clients ‘make sense of data’, by connecting their data, technology, and teams to drive more effective decision-making. This approach ensures we deliver tangible business value, not just technical solutions.
Also, we proudly work with the most progressive technologies and leading cloud vendors like Snowflake, Databricks, and Matillion, providing clients with hands-on experience with in-demand tools and platforms. I would say that one of our key differentiators is our specialisation in SAP Data. We excel in connecting complex SAP landscapes to modern cloud data platforms, providing seamless and efficient data integration.
