What is Data Strategy?

Data is your most valuable strategic asset, but it’s not always easy to get to that value. In fact, 98% of business leaders in India feel that their organisation should be extracting more value from their data.

So, how do companies mine the riches that are buried in their data, and achieve better business results? Simply put, with a data strategy. With 78% of analytics and IT leaders worldwide acknowledging that their organisations struggle to drive business priorities with data, it’s clear that there’s work to do — and a winning data strategy can set your organisation apart from the rest. Read on to learn how.

What you’ll learn:

• What is data strategy?
• Why is a unified data strategy important for Indian businesses?
• Five reasons why you should prioritise data strategy
• Steps to build a data strategy
• Key components of a data strategy
• Data strategy approaches: centralised versus decentralised
• Five best practices for implementing a data strategy
• How can Data Cloud help your data strategy?

A data strategy is a comprehensive plan that addresses how data will be used to support the goals of a business. It’s not just another IT initiative — it’s an enterprise-wide approach to data management that makes it easier for everyone to trust their data, and use it effectively.

At the minimum, your data strategy framework will include:

• Processes for collecting, storing, managing, sharing, and analysing data, to ensure that high quality data is being used to support decision making and accomplish business goals.
• Technology infrastructure that includes the business intelligence tools, advanced analytics software, databases, cloud storage, and a data platform to support your organisation’s goals.
• Dedicated people resources to implement and support your data strategy—and ongoing training and development opportunities to grow their expertise.

India has the second-largest digital population in the world. More than 800 million users use the web every day to shop, study, invest, and seek healthcare. This striking development can only mean that no Indian business, even if it's not customer-facing, can afford to ignore data and the insights it offers.

And Indian businesses are stepping up. Around 59% of Indian companies have a data strategy as a major pillar to unlock value from the growing data with advanced techniques like AI. Yet, 52% are struggling to get meaningful insights from fragmented data.

Why is this? It’s because it is difficult to process the vast volume and complexity of this wide variety of data spread across disconnected silos. There is the external data which is growing at lightning speed, third-party data such as KYC which is increasingly critical in a rapidly digitising economy, and the data generated by the business’ operations, which presents its own challenges, since the average enterprise runs over a 1,000 applications.

In this scenario, a strategy that brings your data together and manages it effectively, so you can use it to its fullest potential, will help you pull ahead of your competitors.

Everyone from the CEO to the customer service manager needs to understand the strategy, their role in supporting it, and how it relates to business objectives.

Here’s a closer look at four reasons why data strategy should be a top priority for every business.

Data strategy unifies data and enables action:

Given the complexity of managing an ever-growing amount of data, it's critical that your data strategy not only includes a plan for unifying all your data, no matter where it resides—but also putting it to good use across your business.

The key to bringing all of this data together is a technology solution that gives you a 360-degree view of all your enterprise data. With a platform like Salesforce’s Data Cloud at the centre of your data strategy, you can bring data from all of your internal systems and apps, like your ERP, and external sources, such as your data lakes or warehouses. Data Cloud and the Salesforce Platform make it possible for your organisation to build a strategy that enables everyone to use data to its fullest—for personalised customer experiences, automated processes, advanced analytics, AI innovation, and more.

Data strategy fuels informed decision-making

When your organisation uses data as the basis for decision making, you can be confident that decisions are based on facts about your business and customer needs instead of relying on intuition alone. Data insights are critical for improving products, helping your operations run smoother, and gaining an understanding of your customer that translates to bottom-line results.

Data strategy builds competitive advantage

Unifying data to build a 360-degree view of customers helps organisations build competitive advantage. At Sai Silks, a top fashion retailer, rich and unique customer 360 views help marketing teams design, target, and monitor campaigns for highly specific customer segments — all on Salesforce. Meanwhile, store managers keep a close eye on customer satisfaction through Tableau dashboards. This data-driven approach has led to stronger sales and customer loyalty.

Data strategy brings AI initiatives to life

Research shows that the single most important thing you can do to get ready for AI success is to prepare your data. AI models depend on quality data — and without it, your results will be inaccurate. Take sales, for instance. A generative-AI solution for sales that is based on incorrect or incomplete data will generate irrelevant leads or recommend upsell opportunities that are off the mark. In contrast, AI that’s built on well-managed, reliable data makes it easy to use CRM data and external data to create accurate customer profiles. With trustworthy AI, you can identify high-value customers and prospects, discover cross-selling opportunities, and build reliable sales forecasts that help you make accurate revenue projections.

Data strategy builds data culture

When you implement a data strategy, you’re also reinforcing data culture—the organisational mindset that puts data in the centre of every decision and empowers everyone in your business with the insights they need to be data-driven. What do data cultures look like? Successful businesses, with outcomes such as:

• 41% greater improvement in production time to market
• 89% improvements to customer retention and acquisition
• 45% greater improvement in employee retention

For fintech leader Angel One, a unified customer view underpins everything they do. The entire organisation - including service, KYC, risk management, operations, and product teams - refers to the real-time 360 view for a strong, shared understanding and collaborates to provide frictionless customer experiences.

Anup Sarma, Senior VP and Head of Customer Experience, notes, “We build better products and experiences by harnessing customer feedback and insights. The fact that those insights are relayed so smoothly and securely across teams makes all the difference.”

Angel One teams, right up to the leadership, also turn to numerous Tableau dashboards for valuable insights to support agile decision-making.

“Today, we’re the second-largest in terms of incremental monthly SIPs created in the industry. That’s the result of a data-driven approach enabled by Tableau,” remarks Jyotiswarup Raiturkar, Group Chief Architect and CTO.

Identify your business objectives, success metrics, and align them with your data goals

It sounds simple, but 41% of line-of-business leaders say their data strategy has only partial or no alignment with business objectives, according to our State of Data and Analytics Report. This disconnect works against any effort to build a data-driven organisation.

To start on the right footing, you’ll need to understand your organisation's overall goals and objectives. And as a best practice, start with lines of business that directly interact with customers, such as sales, customer service, marketing, and commerce–as those are areas that impact your organisation’s success the most. For example, if your business objective is to boost sales by 10% year over year, your data goal may be to create more targeted offers based on customer engagement across all your ecommerce and social channels.

Assess your current data infrastructure and invest in solutions that support your goals

Is your data architecture able to support your organisation's evolving needs and business goals? Does it have the capacity to scale, store, unify, and analyse your growing data assets? By assessing the current state of your data ecosystem and identifying gaps, you can clarify what data platform investments need to be made to support your long-term business needs.

Your data platform plays a central role in how well you can deliver on your data strategy. To provide a superior customer experience, you need the ability to bring all of your data together to build a 360-degree view of your customer. That includes web and mobile engagement data, machine learning data, data in your external data lakes and warehouses, your CRM data. Unfortunately, nearly two-thirds of business applications are disconnected, leaving data “trapped” in back-end solutions, as opposed to the everyday applications business teams use to engage with their customers.

Platforms such as Data Cloud solve these problems by making data from any data source, warehouse, or data lake easier to use, so organisations can realise the true potential of all their data. Data Cloud will also make sure you are compliant with evolving Indian data regulations (including data localisation requirements) and have the integration capabilities with India-specific digital infrastructure (Aadhar, UPI, ONDC, India Stack, etc.)

Create a roadmap for data strategy implementation and follow-up

Develop a detailed roadmap that outlines the goals, steps, and timelines for implementing your data strategy. This roadmap should include milestones, KPIs, key initiatives, challenges and the resources required. It’s essential to bring key stakeholders into the process, so viewpoints from across the organisation are incorporated into the final plan. Additionally, you should review your strategy on a regular basis, benchmarking its performance against your business objectives, and making adjustments as needed.

Your data strategy will be unique to your organisation and the priorities you are focused on. But by following these steps, you’ll be able to begin the process and start to see the synergy that happens when your data and your business are working in support of one another.

Just like for any important infrastructure project—say, laying down a high-speed rail line or setting up a smart city—your data strategy also requires specific components for strength and resiliency. As we discussed above, it’s essential to begin with business alignment—ensuring that your data strategy is directly tied to your business’s goals and objectives. You’ll also need clear KPIs that provide you with the information you need to measure success and to identify any areas where your processes can be improved.

Data governance and data management: Simply stated, data governance refers to the policies and procedures that determine how you handle your data, and who can access it. Data governance policies include clearly defined standards for data quality (the standards for accuracy, completeness, and consistency of your data), and data integrity (reliability, and consistency of your data).

Data integration: Data integration is what it sounds like: it’s how you combine data from different sources to create a unified view, or a single source of truth. It helps your business eliminate the data silos that prevent your organisation from understanding the big picture about your business and your customers.

Data security: Data security includes all the measures (including encryption and multi-factor authentication, event monitoring, identity and access management) that prevent security breaches and protect your customers’ privacy.

Data compliance: Compliance policies require everyone to follow legal, industry, and internal requirements to mitigate the risk of potentially serious data breaches and penalties. Your data has to comply with the Digital Personal Data Protection Act (DPDP Act) for your operations and customer interactions in India. In addition, if they are related to your activities, you may need to look at RBI guidelines for payments data and TRAI rules for telecom data. If you have a presence abroad, compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) needs to be kept in mind.

You can take a centralised or decentralised approach when it comes to data strategy. There are advantages and disadvantages for each, and understanding the differences between the two will help you determine which way to go.

What is a centralised data strategy?

A centralised data strategy means that you’re consolidating all data management responsibilities and processes under a single entity or team. With this approach, everyone in the organisation is using the same data platform, and data access is controlled by a central authority.

Advantages and disadvantages of a centralised data strategy:

• Ensures data consistency and integrity, as all data is stored in a centralised location and managed by a dedicated team.
• Enhances data security, making it easier to implement and enforce data protection measures.
• Facilitates better decision-making, because leaders can access comprehensive data from across the organisation.

On the other hand, centralisation has its challenges, including:

• Bottlenecks and delays. Since all data-related decisions and processes need to go through a central authority, it can slow down the decision-making process and hinder agility.
• Significant upfront investment in infrastructure and resources. This approach requires unified data systems across the organisation, along with secure data storage, and extensive training for all employees to ensure compliance with policies and procedures.

What is a decentralised data strategy?

A decentralised data strategy distributes data management responsibilities across different departments or business units. It lets every department handle data in the ways that work best for them.

Advantages and disadvantages of a decentralised data strategy:

• Promotes autonomy and flexibility, enabling teams to make decisions quickly.
• Allows departments to focus on specific data needs and tailor their strategies accordingly.
• Improves responsiveness, because departments can react faster without having to align with centralised processes or wait for approval.

However, a decentralised data strategy can:

• Lead to data silos, where information is fragmented and inaccessible to the organisation as a whole. Without a single source of truth, you run the risk of making decisions based on inaccurate or inconsistent data. Silos can also lead to data duplication, difficulties in sharing information, and the inability to integrate data across different business units.
• Create challenges in ensuring data quality, privacy, and compliance. If your business lacks a unified data strategy, every team and business unit is playing by their own rules—leading to a disjointed approach that can lead to a lack of trust in the data.

Finding the right balance between the two approaches is critical—you may even want to adopt a hybrid approach that combines the strengths of both. You can centralise core data management functions, and at the same time, allow decentralised teams to manage their own specific data needs.

Data strategy is iterative: it evolves as your business evolves. But by following the five best practices below, you can begin building a foundation for long-term success.

Engage your stakeholders and obtain buy-in

Your key stakeholders must be involved from the beginning. By communicating the goals and expected benefits, leadership will understand what you’re trying to accomplish, and they’ll be more inclined to provide the needed support and resources needed to bring your strategy to life.

Establish clear data ownership and accountability

Your data strategy needs to include clear roles and responsibilities for data management and maintenance. Throughout the data lifecycle, you need to ensure that your teams are overseeing data quality, addressing data issues, and following data governance policies.

Continuously monitor and evaluate effectiveness

Like we mentioned above, your data strategy isn’t static—you’ll need to make adjustments based on ongoing monitoring and evaluation. Tracking key performance indicators (KPIs) and analysing data outcomes on a regular basis will help you identify what should be improved, and how to optimise your data strategy for better results.

Data literacy and data culture

Implementing a successful data strategy requires an organisation-wide commitment to data literacy: the ability to explore, understand, and communicate about data.

Promoting data literacy—and ultimately, establishing a data culture—is important for every organisation. According to a 2022 Forrestor’s Data Literacy Report, 87% of respondents, including decision makers and employees rated data skills as very important for their day-to-day operations.

Change management

For any large-scale change to succeed—and implementing a data strategy certainly qualifies—your organisation needs to apply change management principles. Introducing and reinforcing the importance of being data-driven, offering training, and ongoing communication will help your employees understand why data literacy is a fundamental competency for your organisation.

As one of India's flagship airlines serving millions of passengers, Air India recognised that creating truly exceptional customer experiences depended on a unified data strategy. To achieve this, it implemented a powerful customer data platform so teams could succeed with data.

Air India now connects siloed customer data across its brands, bringing together information from call centre systems, passenger service systems, and enterprise data lakehouses into unified customer profiles. Service agents refer to a holistic customer view, including loyalty data, web browsing data, and unified case management across brands, for contextual and personalised service when a call comes in. The airline processes 550,000 service cases monthly, and with AI-powered reply recommendations and predictive capabilities, agents always know what to do - like offering seat upgrades during flight delays. Pursuing this data-led digital transformation strategy, Air India continues to deliver fast, personalised service for a large, ever-growing customer base.

India’s BFSI sector is witnessing a digital shift in customer behaviour that is pushing firms to leverage their data to serve with greater agility. Meanwhile, manufacturing, which is a focus sector, will have to digitise rapidly to achieve its aspirations. And the retail and ecommerce sectors, which can be considered digital trailblazers in India, are increasingly seeking to deliver stand-out, omnichannel experiences.

These are just a few examples of businesses that will gain from a unified data strategy underpinned by Data Cloud. Data Cloud makes it possible to access data from your entire data ecosystem. From structured data, like customer sales records, to unstructured data, like call centre transcripts, Data Cloud enables you to bring all of your data together in a unified view, and activate your data using low-code or no-code tools within all your applications and workflows. And with Data Cloud at the centre of your data strategy, it’s easier to align your data strategy with your business goals—and deliver a better experience to your customers.