How to Create a Data Strategy That Drives Business Growth

"We want data to be a real competitive advantage. [We want to] take transactional data, enhance it with contextual data, and then understand if we're offering the best products, having the right conversations with customers, or using our marketing budget effectively." 

This is the mission of OFX as shared by Adrian Sheather, Global Head of Data Insights, and they echo the goals of ambitious, future-forward organizations today. Data strategy is the key to accomplishing such objectives and turning scattered data streams into a unified engine for growth and efficiency. 

If your organization is also seeking to develop a more sound strategy, this guide will provide the roadmap you need. 

What is a data strategy?

A data strategy is a comprehensive roadmap that defines how your organization will collect, manage, secure, share, and use data to drive business value. Unlike point solutions, a data strategy ensures every data investment drives value tied to business objectives.

At a high level, a data strategy serves three critical functions:

1. Establishing the business case for data investments by clearly connecting data capabilities to revenue growth, cost reduction, and competitive differentiation

2. Providing the operational framework for effective data management, which entails ensuring data quality, accessibility, and data security at scale

3. Creating the organizational alignment necessary to break down data silos and foster collaboration and innovation across departments and functions

Next, you’ll see how a robust data strategy directly fuels revenue growth, cost optimization, and market responsiveness.

How does a data strategy power business growth?

Advanced analytics and AI unlock new revenue streams, boost operational efficiency, and enhance customer experiences. By embedding data strategy into every function, you accelerate time-to-insight. In turn, this fuels data-driven decision-making and sharpens customer intelligence with personalized offers and proactive service.

A mature data strategy also:

• Drives efficiency at scale: Automate manual processes (e.g., reconcile disparate inventories or optimize supply chains) so your team spends less time on routine tasks and more on innovation.

• Elevates customer experience: Leverage unified 360° views of customers to anticipate needs, tailor interactions, and increase satisfaction and loyalty.

• Enables agile market response: Detect trends early with predictive models, pivot product roadmaps quickly, and stay ahead of shifting competitive landscapes.

Over time, these capabilities create durable advantages—faster insights, deeper customer understanding, and an innovation engine that’s hard for competitors to replicate.

What are data strategy best practices?

The most successful data strategies share four foundational pillars that work together to create a mature data culture. Each one represents an essential component of organizational capability—think of them as guiding principles to gradually integrate into your business processes.

Based on research across hundreds of organizations, including 35% of the Fortune 100, the Alation Data Culture Maturity Model identifies these strategic pillars as: 

1. Data search and discovery to ensure that valuable information remains accessible rather than hidden in organizational silos. 

2. Data governance, which establishes the framework for quality data, compliance, and responsible use of data. 

3. Data leadership, which provides the vision and accountability necessary to drive transformation. 

4. Data literacy that builds the human capabilities necessary to interpret and act on data insights effectively.

All four pillars build momentum across your organization—improvements in one area boost results in others. For example, by strengthening search and discovery, you empower users to locate and trust the right datasets. This then accelerates data literacy as more employees confidently leverage data to make informed decisions. 

Consider some best practices for each pillar that will help you move beyond scattered, tool-focused approaches toward a well-rounded data strategy.

Pillar 1: Data search and discovery best practices

Effective data search and discovery practices help break down silos and give every user the tools to find, trust, and act on data without friction.

Enable self-service data access

To break down silos and speed up decision-making, employees should be able to find and use data without IT support. Seek out products that offer “chat with your data” features which enable employees to ask queries like “Show me Q1 sales by product line” and get instant, visual answers—mirroring the simplicity of consumer tools like Google or Amazon.

Pair this with natural-language search, intuitive filters, and the ability to explore datasets or validate hypotheses, so even teams working with data scientists can work faster with less engineering help.

However, it’s wise to balance ease of access with governance. This will likely mean implementing role-based access controls for security, automated data classification, and audit trails. It’s critical to protect sensitive information while ensuring the right people can access what they need when they need it.

Build an effective data catalog and curation plan

A well-designed data catalog is the backbone of discovery—it combines metadata, lineage, and collaboration to make data trustworthy and accessible. 

The image below illustrates the pitfalls of not using a data catalog, which include significant waste and rework. By contrast, leveraging a data catalog enables your team to find data efficiently and evaluate its quality before use. 

Look for a user-friendly catalog that connects seamlessly with your existing data stack. Key features should include intuitive search and filtering, automated metadata capture to keep documentation current, and lineage tracking that builds trust by showing data sources and flows. Once you’ve chosen a catalog, move on to the next steps: 

1. Enrich your documentation with meaningful context, such as each dataset's source, purpose, and known limitations. A modern data catalog can automatically capture much of this information, surfacing top users, upstream sources, common joins, and popular queries.

2. Designate dedicated stewards to maintain current records and be go-to experts for questions (or select a tool such as Alation that can automatically identify ideal stewards). 

3. Implement automated monitoring to catch data quality issues like missing or inconsistent values before they derail critical decisions.

With your data catalog and curation plan firmly in place, you’ll have visibility into how your data assets are used. You can then measure data consumption, analyze usage patterns, and iterate on your strategy to drive even greater adoption.

Measure and improve data usage

Understanding how your organization uses data is key to making smart strategy investments. Use analytics tools to monitor which data assets get the most engagement, how teams interact with them, and where data directly supports business outcomes. 

Review usage every three to six months and adjust accordingly. Usage metadata naturally surfaces your highest-value data products—much like top-viewed articles rise in search rankings—so data product managers (DPMs) can focus on refining and scaling what already drives impact. These insights help DPMs prioritize enhancements based on usage patterns, business value, and stakeholder feedback to ensure resources go where they deliver the greatest ROI.

Pillar 2: Data governance best practices

Effective data governance helps keep your data accurate, secure, and compliant, but those aren’t the only benefits. When properly implemented, it also reduces operational risk, such as by helping to detect data inconsistencies and prevent downstream process failures. It can also accelerate decision-making and even uncover new business opportunities. 

Consider some of the top data governance best practices that lead to these outcomes:

Establish governance frameworks and policies

Data governance starts with clear, actionable policies. It’s critical to define who owns what, their responsibilities, and how to manage data across systems and teams. 

Go beyond check-the-box compliance with policies that address quality, security, privacy, and regulatory needs in ways your teams can actually use. For instance, segregating data governance by sensitivity helps you allocate controls and resources where they’re most needed. This tiered approach reduces risk without creating unnecessary barriers: 

• High sensitivity (e.g., financial data and PII) requires the strictest controls, with access granted on a need-to-know basis and a complete audit trail maintained.

• Medium sensitivity (e.g., customer analytics and sales data) benefits from role-based access and regular reviews to balance security with functional use.

• Low sensitivity (e.g., public datasets and general reports) can remain broadly accessible with minimal oversight to encourage self-service and innovation.

In doing this, you’ll not only safeguard critical assets but also accelerate trustworthy data access.

Ensure compliance and data quality

Data quality exists on a spectrum—executives and financial analysts may need pristine, fully curated datasets for forecasting, while data scientists and self-service users can work with moderately cleansed data for exploration. Prioritize data quality initiatives by urgency and business impact so you’re tackling the most critical use cases first.

Then, set clear standards for each of the six elements of data quality shown below. Namely, completeness, timeliness, validity, integrity, uniqueness, and consistency.

Ideally, you’ll also implement a tool that automates early error detection—for example, flagging missing fields in customer records before they hit dashboards. That platform should then ingest the error logs and user feedback in real time, surfacing recurring issues and auto-tuning your data pipelines to prevent future problems. Such continuous, automated feedback loops maintain the reliability of your data and ensure your quality program continues evolving as your business needs do.

Remember, though, that compliance isn't one-size-fits-all. For instance, multinational organizations need a unified governance strategy that accounts for regional laws. This means calibrating privacy settings, retention policies, and cloud-based access controls to satisfy data sovereignty requirements. 

Numerous enterprises find Alation’s Data Intelligence Platform invaluable for managing such complex audit and compliance needs. By centralizing metadata, automating policy enforcement, and providing real-time audit trails across regions, Alation helps demonstrate compliance to regulators.

Monitor and enforce governance

Governance should adapt to your evolving data landscape and new use cases. Leverage automated systems to flag quality issues, monitor compliance, and alert the right teams. Also, set clear resolution timelines (e.g., 72 hours) and use dashboards and audits to track progress and close gaps quickly.

Prioritize audit preparedness by ensuring your critical data elements (CDEs) and data products are fully documented, versioned, and traceable. This is especially key in highly regulated industries like banking and healthcare. Overall, leveraging CDE frameworks and modular data products not only streamlines evidence collection but also demonstrates a robust governance posture to auditors and regulators.

After each review, recognize employees who demonstrate good data stewardship to encourage others to follow governance policies in their daily work. As needed, clarify or update your policies and escalation procedures for handling violations.

Pillar 3: Data leadership best practices

Every effective data strategy has one thing in common—the support of leaders who establish and demonstrate measurable business outcomes. Consider the various data leadership best practices you can implement within your organization to drive such results:   

Activate champions and deliver early wins

To drive data adoption and engagement, take these steps:

• Find your champions. Map departments already using data, meet with them to understand pain points, and enlist them as advocates.

• Start small, show wins. Fix one pain point—say, fractured pipeline data—by building a dashboard with Sales Ops. Improve based on feedback, then scale.

• Tie projects to outcomes. Define the problem, target outcome, and success metric for every initiative, such as “reduce churn 5% in Q3.”

• Communicate often. Keep momentum with monthly leadership updates, Slack highlights, town halls, and post-launch check-ins.

How you recognize wins matters as much as the wins themselves. By providing timely, authentic acknowledgment tied to clear performance metrics and business priorities, you’ll foster genuine momentum that gradually weaves your data strategy into everyday operations and sustains cultural change.

Align data strategy with business goals

Whether your objective is to accelerate customer acquisition, reduce operational costs, or drive product innovation, more is needed than generating reports and dashboards. Consider a few ways to align your data strategy and execution with key business goals and deliver measurable outcomes: 

• Focus on business pain points. Work with business leaders to identify their top three to five challenges—like customer retention or supply chain delays—and prioritize data projects that address them directly.

• Define KPIs and ownership. For each goal, pick key metrics (e.g., churn rate, NPS) and assign an owner to track and report progress using dashboards or regular updates.

• Foster cross-functional collaboration. Hold regular sessions where data and business teams review progress, tackle roadblocks, and adjust priorities as business needs change.

Ultimately, success depends on engaging and securing commitment from people at every level, not just the C-suite. 

Secure broad engagement and buy-in across your organization

Employees must see how data supports their goals, understand what’s in it for them, and feel heard throughout the adoption process. This is why Jennifer Belissent, Principal Data Strategist at Snowflake, recommends that CDOs go on a “listening tour.” Such tours uncover hidden obstacles and surface stakeholder priorities, building trust and shared ownership of data initiatives.

Stan McChrystal, Founder and CEO of McChrystal Group and former commander of US and International Security Assistance Forces in Afghanistan, further illustrates the need for this approach. During a base visit in Baghdad, he noticed garbage bags full of confiscated laptops and documents—untapped intelligence simply left to rot. In his words, “Intelligence is like fruit. It goes bad very, very quickly.” 

Realizing the importance of empowering teams “to act on what they saw,” McChrystal overhauled the current process. This involved breaking down silos, accelerating data analysis, and pushing actionable insights to teams within hours rather than letting them languish.

Ultimately, when employees don’t understand how data connects to their work, that data is ignored or misused. Engagement starts by showing them why it matters. You can do this in several ways:

• Invest in training and support. Workshops, mentoring, and role-specific learning paths build confidence. Example: Train sales leaders on CRM dashboards tied to pipeline goals.

• Create feedback loops. Use Slack channels, quick polls, or listening sessions to gather and act on your team’s input. Example: Add frequently requested fields to the catalog and highlight updates in your newsletter.

• Make data part of performance. Include data literacy in job descriptions, reviews, and promotions. Example: “Leverages data to inform business decisions” becomes a core competency for all managers.

Speaking on the impact of the process changes, General McChrystal, said: “Suddenly not only [did] the value of data rise, [so did] the appreciation of it across the organization.” This is proof that when you embed data’s relevance at every level, momentum and culture change follow.

Pillar 4: Data literacy best practices

Data literacy enables employees to understand, interpret, and confidently act on data.

Promote data literacy through education and reinforcement

Many organizations overestimate their teams’ ability to work with data, creating a hidden drag on analytics initiatives.

Wendy Turner-Williams, former CDO at Tableau, pointed to a critical gap in data literacy: Only 39% of organizations offer broad training, yet 82% expect baseline literacy.

Turner-Williams summarized, “There’s over a 50% gap between the amount of literacy and training that’s actually being provided to the employees versus the employer expectation about these employees actually having basic literacy skills.” 

Closing this gap isn’t optional—without investing in scalable training and ongoing reinforcement, you risk underutilized tools, flawed analyses, and stalled data-driven transformation. To make training stick:

• Design job-specific paths. For instance, marketers should focus on dashboards and basic SQL, while finance leaders need to be more familiar with data quality and Excel. 

• Pair up mentors. Match data-savvy employees with peers who need support. Mentors help apply tools to real tasks and encourage adoption.

• Create learning communities. Host recurring forums, like a monthly “Data Hour,” where teams share how they solved a challenge using data.

Once you’ve equipped your teams with the skills and confidence to work with data, the next step is to amplify its impact across the organization.

Communicate and evangelize the value of data

Employees who build data literacy and see real-world impact engage more and can use data more effectively. Showcasing success stories and clear examples inspires teams, turning skeptics into advocates by demonstrating real-world impact and possibilities:

• Share regular success stories. Highlight specific wins, such as how the sales team used data to identify a new customer segment or how operations reduced costs by streamlining a process with analytics.

• Provide interactive demonstrations. Show employees how a dashboard or data analytics tool can speed up daily tasks, like quickly pulling real-time inventory numbers or tracking campaign results. Brief, hands-on sessions are best to make the information digestible and memorable.  

• Segment communications by role. Tailor messages so, for example, finance teams get updates on compliance dashboards, while marketers see case studies on campaign optimization.

Implementing these recommendations will spark enthusiasm within your organization. However, maintaining it will require a long-term investment in relationship-building. 

Build trust and ongoing relationships that support change

Successful data leaders know that building a data culture is a change management challenge. It takes empathy, trust, and strong relationships—not just top-down mandates. There are a few keys to overcoming this challenge:

• Learn how your teams really work. Shadow employees or ask questions before recommending changes. For example, ask why sales uses spreadsheets instead of assuming they resist new tools.

• Be a partner, not a policy enforcer. Host open office hours or Q&A sessions so employees see your company’s data leaders as approachable advisors.

• Keep support accessible. Offer helpdesks or peer groups for quick answers, and run short, ongoing training sessions to make learning easy.

By doing the above, data leaders lay the groundwork for lasting buy-in and momentum.

How data products support a data strategy

The various best practices across the four pillars above will help you develop an effective, long-term data strategy. For even better execution, though, consider data products with these four key attributes: 

• Accessibility: Data products should be easy to find and use, so teams spend less time searching and more time acting.

• Reusability: Designed for multiple use cases, data products prevent duplication and accelerate new analytics projects.

• Trustworthiness: Validated, governed, and compliant data products enforce quality standards and reduce risk.

• Purpose: Data products that are aligned with real business needs drive quantifiable, high-value outcomes.

Together, these characteristics turn fragmented data into a strategic asset—enabling fast discovery, consistent quality, and direct linkage to your overall business strategy.

To learn how to effectively implement your data strategy and kick off your data governance maturity journey, attend Alation’s 45-minute webinar covering top use cases and strategies.

We continue to discover why organizations with a top-tier data culture lead their competitors. In a statement that captures this point, Gartner predicts that by next year, “organizations that promote data sharing will outperform their peers on most business value metrics.” Alation’s Q1 2022 State of Data Culture Report further found that 90% of organizations with top-tier data cultures met or exceeded their revenue targets over the last 12 months.

As any c-level executive will tell you, outperforming your peers and exceeding revenue targets is a sure ticket to success. But, building a top-tier data culture that encourages enterprise-wide data literacy isn’t an easy task. Unfortunately, many organizations see the role of Chief Data Officer (CDO) as a silver bullet that can magically manifest a strategy and create a data culture. But, if the conversations with seasoned data leaders on the Data Radicals podcast are any indication, good CDOs—and good leaders—know that building a data culture requires a strategy that looks far beyond just the data.

Tip #1: A Good Data Strategy Starts With People

A data culture is one that thrives on data-driven decision-making. Data leaders don’t expect the data to make the right decisions, but they do expect insights gleaned from data to empower people to make the right decisions. That’s a fundamental concept trailblazing CDOs understand and use to be successful.

It’s important to begin with the premise that everyone in your organization works with data, so everyone must be committed to building a data culture. In a podcast conversation with Jennifer Belissent, Principal Data Strategist at Snowflake, she shared an interesting story about working with a food service management company.

Analysts were befuddled to find a spike in sales of breakfast sausage at a cafeteria. As they dug deeper, they found that a newly installed point-of-sale system somehow made the sausage button easier to press. So, cashiers used it as a catch-all, again and again, to make their jobs easier. It wasn’t that more sausages were actually being purchased—but the sausage button was being pressed far more often.

“These cashiers probably didn’t know they were working with data,” Belissent explained. “They probably didn’t know how their companies were using that data, the value data potentially brought to their company, their particular role with data. That brought home the fact that there are huge gaps in data literacy.”

Many CDOs and data leaders forget about the role we all, at every level, play in a successful data culture. But it’s not just explaining it to the workers; listening also helps. Belissent suggests CDOs embark on what she calls a “listening tour” to engage with stakeholders and understand the issues and priorities that can demonstrate the value of data. As you make data relevant to everyone at the individual level, from cashiers to the c-suite, the concept of data culture and data literacy start to make sense and take hold.

Stan McChrystal, Founder & CEO of McChrystal Group and former commander of U.S. and International Security Assistance Forces in Afghanistan, shared a similar story on the podcast. While visiting a base outside Baghdad, he spotted a pile of garbage bags and asked about them. They contained laptops, mobile phones, and documents confiscated from captured adversaries and which no one had the time to evaluate.

“Intelligence is like fruit,” McChrystal said. “It goes bad very, very quickly. And so this stuff sitting there was literally like rotting fruit, and very quickly it has no value.”

When teams don’t understand the value of data, it goes unused. In McChrystal’s case, and for any team working closely with data, it quickly grows stale and, eventually, worthless.

Tip #2: Training for Data Literacy

So how do you get workers at every level to understand the importance of data and data literacy? You explain it to them with training and constant reinforcement. More CDOs are starting to build this into their data strategy.

Wendy Turner-Williams, CDO at Tableau, understands the value of data literacy training better than most. The company publishes a data trends report, which uncovered an important gap where data literacy training is concerned. The report found that only 39% of surveyed organizations say data training is available to all employees, while 82% say they expect all employees to have basic data literacy.

“In other words, there’s over a 50% gap between the amount of literacy and training that’s actually being provided to the employees versus the employer expectation about these employees actually having basic literacy skills,” Turner-Williams summarized. “This seems to be a huge disconnect.”

McChrystal understood the importance of data literacy and quickly addressed his pile of confiscated items—essentially data—by training those involved.

“Suddenly everybody understands how valuable data is and people out across the organization start trying to get more data because they know that they see the value from it,” McChrystal added. “Suddenly not only does the value of data rise, it’s the appreciation of it across the organization.”

Training can frequently be a one-and-done endeavour, however, so constant reinforcement is necessary.

Tip #3: A Communications Strategy for Data

As people understand the value of data and their role in data culture, and increase their data literacy, the CDO can focus on expanding and deepening the resulting benefits. People just need to know what’s possible with data and get inspired by the impact data is having across the organization.

“I’ve been talking to a number of CDOs who have been talking about their comms strategy,” said Belissent. “You’re evangelizing to your peers, getting them excited about current projects, and proposing future projects. You’re also segmenting your employee base—as you would a customer base for a marketing campaign—and pitching the idea of the value of data.”

Communications and transparency matter, as they help drive accountability while ensuring more people and teams see that data culture is taking hold. To keep people accountable, McChrystal hosted a daily video call. This meant that, every day, he illuminated the work others were doing, to showcase and share the value of data—but also call out those who weren’t keeping up.

“We were sharing information more aggressively than ever before,” said McChrystal. “We solved a lot of our leadership problems there, not in the way traditional, but we created this normative pressure. Everybody could see what everybody else was doing and not doing. So that solved more of it than I expected.”

Tip #4: A Data Strategy Is a Change Management Strategy

Successful data leaders understand that creating a data culture requires a shift in the organization’s culture. It’s a massive change management project, and it requires CDOs to win the hearts and minds of workers on every team and at every level. Referring back to where we started, it’s all about people, and people desire empathy and communication.

“When you’re talking about changing a culture, you’re talking about changing the way people do something that has an underlying logic to it,” McChrystal said. “And so just coming in and saying, ‘What you do is stupid,’ is really dangerous if you don’t understand the underlying logic that it’s based on.” Instead, McChyrstal encourages leaders to be curious and ask questions: “Why are they that way? Why have they made those decisions?”

Obviously, tact and diplomacy also matter if you’re looking to build support and change a culture. CDOs aren’t there to tell people how to use data but rather to show them how to reap the benefits from using data. This is a fine line that can make or break your data strategy.

Tableau’s Turner-Williams points out that the CDO should act as the glue between teams: “I really think about data and chief data officers as they’re like a supporting actor to the corporate entity as a whole. You need to be able to talk to others. You have to be able to have a learner’s mindset. That starts to build kind of a reputation as a go-to person where people start to come to you. I think when you build relationships, they can’t be one-and-done; they’ve got to be an ongoing conversation.”

Again, even though you’re building a data culture, it’s all about the people.

“Start by changing what people do,” McChrystal concluded. “People often say, ‘I’ll change their culture and then their behaviors will change.’ And I actually think you got to go the other direction. You’ve got to change the behaviors and then the culture will follow along.”

Change management is difficult, ergo creating a data culture is also difficult. Some people will resist. But, CDOs with a people-focused data strategy can create momentum by showing them how easy it is to capture and use data, how it will help them in their roles, and where others are already using data to their advantage. CDOs also have to consider everyone in the organization, listening to them, understanding what motivates them, and helping them benefit from data. In other words, it’s communication. Easy, right?