By: Mayank Baid, Regional Vice President, India, Cloudera
Since data is the new currency in today’s digital context, Indian businesses are in a rush to find ways to monetise treasure troves of data. They are applying AI-driven analytics to derive insights and extrapolate everything they can on the customer, in the hopes of uncovering new audience profiles and markets, optimising operations, and reducing marketing costs.
To do this, Indian enterprises operating in industries including e-commerce, telecom, and BFSI (Banking, Financial Services, and Insurance) feed personal and sensitive consumer data into these AI systems, and therein lies the kicker. While data is used to enhance the customer experience, companies also face an added responsibility to keep this information safe and guarded.
The stakes have never been higher– the reputational, financial, legal and customer retention risks of mishandling data are too great to ignore. Any large organisation that has significant brand value is extremely cautious about reputational risks if data is not properly managed. This is also true for highly regulated organisations such as financial institutions. Falling short of compliance or adhering to regulations can result in lawsuits and long-term loss of brand loyalty.
Yet, the promise of new Gen AI applications and their potential value, coupled with the massive amount of personal data that organisations are looking to tap on, seem to be at odds with this privacy mandate. Businesses struggle with what appears to be a zero-sum game – whether to utilise the available data to elevate its offerings or dial back to avoid any risk of infringing on data privacy.
Based on what we hear from IT decision makers, some of them feel that their organisations can afford to push the envelope further when it comes to data utilisation. The good news is businesses do not need to treat this as an “either-or” conundrum when data privacy is made a core business process. India’s rapid digital transformation and expanding data landscape make it a critical growth market. By prioritising hybrid cloud and AI investments, companies can better navigate the distinct challenges and seize the abundant opportunities in this dynamic environment.
Privacy by design
The first step in elevating data privacy to be a core business function is pinning down a codified approach to the people, processes, and technologies involved in managing data. This encompasses baking privacy measures into the design of IT systems and business practices, not bolting them on as an afterthought or in reaction to a breach.
Whether they purchase, sell or gather that data, businesses should know what information they have on consumers, how it has been retrieved, where it is stored, how and when it is used or processed, who is using it or has access, how it’s secured, or when it gets removed or deleted upon request.
Simply put, it is about taking ownership of the entire data lifecycle, and articulating the guardrails governing the collection, management, and utilisation of data. This strategy then needs to be evaluated for compliance with privacy regulations in the markets that the business operates in.
In such an IT environment, all personal data is safeguarded by default throughout the lifecycle, from its collection and use, until its destruction.
This principle is applied across any data architecture involving the use of personal data to support business use cases, ranging from marketers looking at a 360-degree view of customers to inventory teams overseeing supply chains.
India’s data centre capacity is growing at an impressive 50 percent compound annual growth rate (CAGR), with projections to increase from 1,400 MW to 17,000 MW by 2030, according to a new report by Jefferies. As digital transformation accelerates and data volumes surge, the need for advanced data management and analytics capabilities becomes more critical. Modern data management platforms enable enterprises to reduce the costs and risks of data breaches by streamlining user access, enforcing data permissions, and securing encrypted data transfers. By embedding data privacy into core business functions, Indian companies can confidently navigate this evolving landscape, ensuring compliance and strengthening consumer trust while charting the way forward.
Wrestling back control of data
To bring to life a data strategy with privacy at its core, businesses should deploy a modern data platform that applies consistent security controls across all of an organisation’s data. With a consistent data context seamlessly applied everywhere, business users can rest assured that the sensitive data they are working with is kept secure across environments, creating more freedom for innovation without being limited by security concerns.
Deploying modern data architectures like an open data lakehouse, eliminates data silos, applying an integrated set of security and governance technologies and policies across all data, structured or unstructured, no matter where they reside. With volumes of complex data housed in cloud or on-premises environments being accessed by various business functions, consistent data security policies is key. This underlying layer includes the encryption or tokenisation of data, as well as the management of access, privilege and audits. They also detect anomalies and send timely alerts to IT leaders if a breach has occurred, which boosts threat prevention across the entire enterprise data and AI landscape. This is critical because regulation covers all personal data stored, not just the data that is readily available.
In the area of governance, security controls on such a data management platform provides the right visibility, audit trail and access controls to determine who has access to what data, why, and how and what they have done with it. This includes knowing how data moves through the system—also known as data lineage or data provenance—from where it was collected, how it was distributed, who changed it and why and when, where they moved it.
As a result, most Indian businesses get to operate AI models where their most secure data resides, while maintaining consumer privacy, ensuring responsible AI deployments, and adhering to ever-expanding data regulations. Ultimately, a data management platform that is secure by design cedes control of data back to businesses.
No either-or dichotomy
When data privacy is entrenched as a key tenet of the business, enterprises no longer face a toss-up between benefitting from data analytics and AI, or respecting individuals’ privacy – it is possible to have both.
The data privacy dilemma is not a dilemma, but an opportunity for enterprises to demonstrate that they prize personal data privacy, not just to comply with regulations, but because it is the right thing to do.