What is Data Strategy?

Data is your most valuable strategic asset, but it’s not always easy to get to that value. In fact, 94% of business leaders feel that their organization 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 saying their organizations struggle to drive business priorities with data, it’s clear that there’s work to do — and a winning data strategy can set your organization apart from the rest. Read on to learn how.

What you’ll learn:

• Data strategy defined
• Why is data strategy important?
• Steps to build a data strategy
• Key components of a data strategy
• Data strategy approaches: centralized versus decentralized
• 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 analyzing 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 organization’s goals.
• Dedicated people resources to implement and support your data strategy—and ongoing training and development opportunities to grow their expertise.

With nearly 100% of the world’s analytics and IT leaders wanting more value from their data, it’s essential that businesses have a uniform approach—a data strategy—to establish guidelines for how data is managed and used.

And the volume and complexity of data continues to grow, with no signs of slowing down . Managing all of this data is an ongoing challenge for every business. It’s difficult to process the vast volume and variety of data, and most data is spread across many, many, disconnected silos. The average enterprise is running over 1,000 applications, making it impossible to have a unified, coherent data ecosystem that people can actually use.

By creating a plan for bringing your data together and managing it effectively to drive business success, you ensure that your data is used to its fullest potential.

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

Data strategy ensures that you have a plan for bringing all your data together, and have the ability to act on it. 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 center 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 organization to build a strategy that enables everyone to use data to its fullest—for personalized customer experiences, automated processes, advanced analytics, AI innovation, and more.

Data strategy fuels informed decision-making. When your organization 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 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 organizations build competitive advantage. For John Lewis Partnership, a comprehensive data strategy powered by Salesforce and Tableau provides their food shops with the real-time, actionable insights to manage their floor inventory and better meet their customers’ expectations. Stores can anticipate the needs of their customers and ensure the right products are available at each location, contributing to the perfect order rate.

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.

Eighty-six percent of analytics and IT leaders agree that AI's outputs are only as good as its data inputs. A comprehensive data strategy helps organizations have the rock-solid data foundation you need to succeed with AI.

Data strategy builds data culture. When you implement a data strategy, you’re also reinforcing data culture —the organizational mindset that puts data in the center 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 the Seattle Seahawks , data is incorporated into everything they do, and building a strong data culture is part of that focus. The entire organization understands that data insights are tied to improving fan experience and engagement, and in turn, building a loyal customer base. According to Paimon Jaberi, Director of Business Strategy and Analytics, “We want everyone in the organization to see as much as they can from data. I want someone in ticketing to know what's going on in sponsorships or stadium operations. It's hard to do that unless you create great visuals and use data to inform people.” And with a data strategy that encourages data fluency, people are excited to learn and grow. “Once they start building dashboards, they want to dive deeper with new ones,” remarks Jeff Dunn, Vice President of Business Strategy and Analytics.

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 organization.

To start your data strategy on the right footing, you’ll need to understand your organization's overall goals and objectives. And as a best practice, start with lines of business that directly interact with customers, such as sales, customer, marketing and commerce–as those are areas that impact your organization’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 the evolving needs of your organization, and your business goals? Does it have the capacity to scale, store, unify, and analyze 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 organizations can realize the true potential of all their data.

Create a roadmap for data strategy implementation and—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, and the resources required. It’s essential to bring key stakeholders into the process, so viewpoints from across the organization 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 organization 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 steel, reinforced concrete, and glass are required to form the structure of a building, 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’ 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.

To continue our building analogy, the ‘brick and mortar’ of your data strategy includes:

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. 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 single source of truth. It helps your business eliminate the data silos that prevent your organization 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 customer’s 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. Examples of data compliance regulations include the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPPA).

You can take a centralized or decentralized 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.

Centralized data strategy

Definition: A centralized 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 organization is using the same data platform, and data access is controlled by a central authority.

Pros and cons:

• Ensures data consistency and integrity, as all data is stored in a centralized 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 organization.

On the other hand, centralization 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 organization, along with secure data storage, and extensive training for all employees to ensure compliance with policies and procedures.

Decentralized data strategy

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

Pros and cons:

• 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 centralized processes, or wait for approval.

However, a decentralized data strategy can:

• Lead to data silos, where information is fragmented and inaccessible to the organization 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 centralize core data management functions, and at the same time, allow decentralized teams to manage their own specific data needs.

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

Engage your stakeholders and obtain buy-in

Your key stakeholders need to 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 analyzing data outcomes on a regular basis will help you identify what should be improved, and how to optimize your data strategy for better results.

Data literacy and data culture

Implementing a successful data strategy requires an organization-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 organization. In a recent survey , 87% of respondents 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 organization 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 organization.

As a leader in multi-cloud application services, F5 knows the importance of using data to drive their business forward. To achieve this, they implemented a company-wide strategy that included adopting a powerful analytics platform, Tableau , to ensure that teams had the support they needed to succeed with data.

“Data has been transforming our corporate culture, right in front of our eyes. I feel like every morning, I wake up, and I’m learning something new about data.” — Amie Bright , RVP of Enterprise Data Strategy and Insights at F5

Customer success, product, global services, finance, IT, manufacturing and HR teams are now getting more value from their data. The marketing team is effectively using data to determine how their campaigns are driving leads, and how best to engage with sales. Customer success is able to analyze purchase patterns and histories and use data insights to understand how to better serve their customers. According to Amy, “That’s what it’s like here now — everyone gets to create their own magic.”

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 center 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 center of your data strategy, it’s easier to align your data strategy with your business goals—and deliver a better experience to your customers.