Future-proofing data strategy: Perspectives from industry experts

In an era defined by data, the ability to harness its power effectively has become a cornerstone of success across industries.

Couchbase, in association with YourStory, hosted a roundtable discussion with experts to understand the key to future-proofing data strategy. Madanmohan Rao, Research Director, YourStory, led the discussion with industry-specific insights.

The discussion brought together a diverse panel of industry experts to explore the nuances of data management, the challenges faced, and the opportunities unlocked through strategic integration of data and AI-driven innovations.

The panel comprised Kaushik Mukherjee, Head, Raptorise; Swapnil Gupta, VP Engineering, Xpressbees; Arvind Aravamudhan, Director of Engineering - User Fraud & Risk, Fi Money; Sundeep Kumar, Head of Engineering, Data, AI/ML & Recommendation Platforms, VerSe Innovation; Ankur Sharma, Co-founder and Chief Product Officer, Instamojo; Jyotiswarup Pai Raiturkar, CTO, Angel One; Prashant Parashar, SVP, Head of Technology, Delhivery; Satendra Singh, CTO, Propelld; Mathangi Sri Ramachandra, Chief Data Officer, Yubi; Zaher Abdul Azeez, Director, Data Sciences & Engineering, NoBroker, and Chhaya Sharma, Director of Engineering, ShareChat.

The need for a robust data strategy

Centered on the strategic use of data in various industries, with insights shared by professionals from diverse backgrounds, key themes of this roundtable included the importance of data strategy, the challenges of data management, and leveraging AI for innovation and competitiveness.

Participants emphasised the significance of having a robust data strategy as a foundation for effective decision-making and innovation. They highlighted the need to break down data silos and democratise access to data across organisations, enabling better collaboration and informed decision-making. This approach fosters a data-driven culture where insights from different functions are integrated, leading to faster problem-solving and improved agility.

A major challenge discussed was ensuring data quality and integrity, especially in industries like finance and fintech where trust and compliance are paramount. All panellists underscored the importance of involving security and compliance teams early in the data lifecycle to mitigate risks and ensure regulatory adherence.

Innovation through AI and machine learning was a focal point, with panellists sharing how their organisations leverage these technologies to drive competitive advantage. They discussed custom data pipelines, real-time data processing, and feature engineering as critical components for building effective AI models. By investing in feature stores and real-time data pipelines, organisations can achieve faster model iteration cycles and deliver personalised experiences to customers.

The discussion also touched upon the evolving nature of data management, with insights on adopting new technologies like Snowflake, Pino, and Feast/Tecton for data aggregation, storage, and feature management. This reflects a shift towards modern data architectures that prioritise scalability, agility, and real-time analytics.

Key points from the discussion:

Data complexity in growing businesses: Rapid growth in businesses, particularly in fintech, leads to a surge in data volume and complexity. Managing this complexity becomes a significant challenge, especially in identifying relevant insights and ensuring data-driven decision-making.

Role of analytics and AI: Analytics and AI play a crucial role in managing data complexity and driving business performance. They enable functions such as marketing, sales, risk, and compliance to make informed decisions by providing insights derived from data analysis.

Data democratisation: The aim is to democratise data access across various organisational functions. This involves enabling self-service analytics to empower teams to access and analyse data independently, leading to faster decision-making and improved operational efficiency.

Challenges in data governance and compliance: As regulatory requirements evolve, ensuring data governance, security, and compliance becomes increasingly challenging. Businesses need to adapt their data management practices to meet these requirements while maintaining operational agility.

Technology stack and infrastructure: Fintech companies employ a variety of technologies and platforms for data management, analytics, and reporting, ranging from traditional data warehouses to modern cloud-based solutions. The choice of technology stack depends on factors such as scalability, real-time processing requirements, and regulatory compliance.

Focus on fundamentals: Amidst the complexity and technological advancements, there is a recognition of the importance of focusing on the fundamentals of data management. Building a solid foundation in data governance, quality assurance, and compliance is essential for long-term success.

Adapting to regulatory changes: Fintech companies must adapt to evolving regulatory landscapes, such as data protection laws and compliance standards. This requires continuous monitoring, updating systems, and ensuring alignment with regulatory requirements.

Overall, the discussion provided valuable insights into the complexities of data management, ML implementation, and operational optimisation in various industries.

The roundtable also highlighted the transformative potential of data when integrated strategically into business operations. By prioritising data quality, fostering a data-driven culture, and embracing AI-powered innovations, organisations can unlock new opportunities for growth and competitiveness in today's data-driven economy.