Recently, I’ve had a fair few conversations with different retailers and coming from a retail background myself, many of the challenges we discussed weren't new to me.

They need to deliver highly personalised experiences to customers to build loyalty. They need to empower employees by democratising data and enabling better decision making. At a time when customers demand more for less, they also need to optimise inventory, ensuring the right products are in the right place at the right time.

And yet, despite having so much data and the tools to analyse it, one truth remains: Technology alone doesn’t create a data-driven culture. Retailers still fail to convert data into meaningful insights that lead to better informed decision-making.

In this blog, I will be sharing some core elements that are fundamental to establishing a data-driven culture in retail. All examples I will give are purely from retail organisations that I have worked with in the past.

What a Data-Driven Culture Actually Looks Like in Retail

When we talk about building a data-driven culture in retail, it’s not just about creating more dashboards, rolling out new tools or providing more data. It’s about changing behaviours, the way decisions are made, how teams work together and how the business sees data. From my experience, here are a few ways a data-driven culture looks like:

• Driving the culture isn’t the job of one team, it’s a business-wide effort, backed by the exec team to foster a data-driven environment.
• Data guides key decisions across pricing, promotions and buying, because its value is understood at all levels.
• Store managers can access in-store footfall and sales data to make rota decisions, without chasing Head Office.
• Weekly trade meetings focus on action, not debating which KPIs are right or arguing over different numbers.
• Senior leaders trust the data even under pressure, don't default to "I have 10+ years of experience, I'm right."
• Teams work cross-functionally, break down silos and share core metrics to align on common goals.
• People feel more empowered, as they have access to the data they need to make better day-to-day decisions.
• Customers benefit from more personalised experiences, because the data is actually used, not just stored.
• Not starting with generic reports, but questions like "What SKUs are in Warehouse A for Spring-Summer 2023?"

It’s worth calling out that none of this happens overnight. It takes time, consistent effort and a steady push, but with the right steps (and plan), any retailer can start moving in this direction.

The Top Blockers to a Data-Driven Culture

Over the years, I’ve worked with several well-known retailers. While I won’t name names here, I can confidently say that each of the examples below comes from a real organisation I’ve supported in their journey toward a more data-driven culture.

Here's the thing, in almost every case, the biggest blocker wasn’t technology. It wasn’t a lack of dashboards. It wasn’t even data quality. It was people or more specifically, a lack of top-down commitment to change.

Here’s what I’ve seen first-hand:

• Retailer 1: Despite clear appetite at the core data team level, there was no top-down support. Senior leaders saw data as "the BI team’s job" not a shared responsibility. Nothing changed.
• Retailer 2: Same story, leadership loved the idea of being data-driven, but didn’t change how they made decisions. Reports were created, but ignored. Data culture never took root.
• Retailer 3: Had tons of data, but it was scattered, fragmented and locked down. Teams couldn’t access what they needed and when they could, they didn’t have the support to interpret it confidently.
• Retailer 4: Everyone was reactive. The data team spent most of their time producing last-minute reports to "check the numbers" with no time left for insight or forward planning.

In all four of these (and others I’ve worked with) the main root cause was the same: a lack of executive support. Honestly, without leadership backing and real behaviour change at the top, it will all remain at surface-level. It will be an uphill battle, and one that won't be won. They might deliver some early dashboards, automation, even build a data platform, but the cultural shift never truly happens.

So, what is the take away from this? Culture change needs commitment, not just from your BI team, but from your leadership team. If the most senior voices in the business aren’t promoting the use of data, no amount of dashboards will drive lasting change. The way I like to put it is this, we need to show the entire organisation that this isn’t an IT or BI objective, it’s a business objective.

How to Start Building a Data-Driven Culture in Retail

I’ve seen retailers hire new data professionals, roll out tools, implement data platforms or launch "data transformation" projects, only to stall within months. Why? Because they start with technology, not behaviours and people. If you're serious about becoming data-driven, it's fine to start small. Below are some practical, proven steps I’ve seen work, not theory, not buzzwords, but things that have genuinely moved the needle inside real retail organisations.

1. Get Executive Support (Or Nothing Else Will Stick)

Let me be blunt, if there’s no top-down support, it’s an uphill battle. I’ve seen this first-hand (many times - beyond retail too) and it’s one of the reasons I’ve walked away from roles in the past. You can make great progress inside your data or BI team. You can deliver brilliant dashboards, get glowing feedback and build momentum within a few pockets of the business. You can even build a data platform. But without leadership backing, it never lasts. Eventually, the excitement fades, the requests pile up and the culture stays the same.

I’ve also come across very different types of executive and leadership teams. Some are already quite data-driven, others much less so. It’s in those less mature environments where we need to shift our messaging. Instead of pushing technical benefits, we need to speak the language of business value to leverage data effectively. That’s a trap many in data and tech fall into, we talk about models, platforms, performance, so technical benefits. But what we should be doing is linking our efforts directly to outcomes like: increasing revenue, reducing costs, minimising risk, improving compliance, delivering more personalised customer experiences and driving retention and loyalty.

If your leadership team understands how data supports these outcomes, everything changes. This is also where a data strategy matters for explaining how you'll embed this culture shift into the business. I know I mentioned this earlier, but it’s worth repeating because it’s absolutely critical to this point. Becoming data-driven is not a BI objective. It’s a business objective. It also need to be communicated like this. Why? Because if it’s just the data team promoting a new way of working or asking users to attend a monthly forum, there’s often resistance. But when that message comes from the top, when leadership sets the tone, people are far more likely to engage, show up and be open to understanding the change.

I once worked in a retail organisation where the BI team, myself included, was literally based in the basement. We were expected to lead data adoption across the business, yet we had no voice, no visibility - NO EXEC SPONSOR. Despite our best efforts in building the right tech, driving initiatives from the ground up and trying to rally people, it never gained real traction. Without that senior-level backing, it was always going to hit a ceiling.

But when execs lead from the front, when people see directors using data to make decisions, asking questions in meetings, communicating the change in the monthly all-hands meeting, that changes everything. People pay attention. They follow suit. On the flip side, if leadership stays silent or disconnected, then no amount of dashboards, wins or passion from your data team will make a lasting difference.

2. Overcoming Resistance to Change (Beyond Just Exec Buy-In)

This ties closely with the need for executive sponsorship, but it goes further. Even with the most senior leaders setting the tone, resistance to change is still a big blocker to a data-driven culture. I’ve seen it across many organisations where we had various influential individuals, often not at exec level, who have been with the business for many years or had a lot of influence. They may be more vocal, carry team credibility or simply be the people others follow.

Getting these key individuals on side is important. This isn’t just about top-down sponsorship, it’s about lateral influence too. You need to proactively engage these people, showcase how data can help them personally and make them advocates.

Just as important is putting yourself in the shoes of wider end users. Often, they’re not resistant to data, they’re resistant to disruption, inefficiency or more so, fear of being replaced. So bring them with you. Listen to their concerns, understand their realities and show how the change will make their jobs easier, not harder. This applies just as much to governance initiatives. Rolling out processes in isolation isn't great. You need broad-based support. That starts by addressing real worries and offering real alternatives.

3. Start With the Business Objective, Not the Tech

Too often, data initiatives begin with a tool, a new dashboard, a platform or a request to "build a report". But establishing data-driven culture isn’t about launching tech or just building solutions. It’s about solving real business problems.

If we don’t start with a clear business objective (or challenge), even the best technical solution will miss the mark. I’ve seen teams build various solutions but offer little value because they weren’t tied to an actual business objective or challenge. By the way, the same applies to AI initiatives. I often see organisations investing heavily without a clear understanding of the problem they’re solving or how to measure the ROI.

So, what should we be doing? Start by asking more tailored questions that allows us understand the "where are we trying to go?". We need to derive the value we are hoping to get, showcase how it ties back to a business objective or challenge. Once you’ve defined the objective and the true purpose, only then should you move to what data do we need, what features should we use, etc.

This approach not only leads to more relevant solutions, but also makes it easier to win business buy-in. Because when people see that the work is directly helping them do their job better, and helping to unlock insights that matter, not just ticking a tech box, engagement follows. But it’s not just about the starting point, it’s about shifting the mindset across the organisation. We need to move from being tech-focused to being value-focused. That means communicating differently, embedding the right processes and upskilling people to focus on outcomes, not outputs. We’ll explore each of those in more detail later, but it begins with stop starting with the tech. Start with the business. One more thing I want to add, if we start with the business objective, it's easier to explain the ROI or the value gained which is important to showcase this is working. How? Well, if you set an objective it should be somewhat measurable, and you can track if you are moving towards it.

If you want to see what a value-driven solution can actually look like, take a look at this blog on Data Storytelling in Power BI.

4. Empowering and Upskilling People to Use Data, Not Just Access It

Technology doesn’t stand still, we all know that. With AI advancing at speed, monthly Power BI updates and the rise of MS Fabric, it can feel like you're trying to drink from a firehose. But no matter how good the tech is, if your people don’t feel confident using it, your culture won’t evolve alongside it.

And let’s be clear, building a data-driven culture doesn’t mean turning every employee into an analyst. That’s neither realistic nor necessary. It’s about helping people feel confident using data to make better everyday decisions. It’s about laying a foundation they can build on.

When I rolled out a large-scale training programme at a major fashion retailer, I remember someone asking me "Are we meant to become experts by the end of this?" Absolutely not. What we were aiming for was data literacy, the ability to understand, question and apply data in a meaningful way. Not just reading a chart and using Power BI, but using it to spot trends, have better conversations across teams and ultimately make more informed choices.

Because without that literacy, even the slickest dashboard gets treated like a PDF - glanced at, not acted on.

To truly embed a data-driven culture in retail, training needs to go beyond tools. One of the most effective ways? Anchor it in real scenarios. Don’t start by teaching filters, start with business questions like "why is footfall up but sales down this week?" or "which SKUs are most often bought together in clearance?" When you begin with a real retail challenge and then show how data helps answer it, adoption increases.

If you want to learn more on how to implement Power BI training the right way, take a look at this blog here: What Is Power BI Training? You will also find information about building a Power BI Community Portal, gathering user feedback, establishing a Power BI Centre of Excellence (CoE), the different Power BI personas and more.

5. Making Data Accessible: Self-Service with the Right Support

In many of the retail organisations I’ve worked with, the approach to data access usually falls at one of two extremes, either everyone has access to everything with zero guardrails (a wild west of reporting) or everything is locked down so tightly that no one outside IT can do anything without raising a ticket. Neither of these extremes work.

If we want to become data-driven, we need to trust our people with data. That doesn’t mean handing over the keys to the castle and hoping for the best - don’t do that. It means enabling access with the right governance, the right training and ongoing support.

Because here’s the reality: you can’t be data-driven without democratising your data.

In retail, this is especially crucial. Store managers, merchandisers, buyers and area leads often have the best context to interpret patterns and act fast… but only if they can access the insights themselves.

So, what should we be thinking about?

Start with building a strong semantic model (or multiple models), that are performant, aligned with your business language and easy to use. Don’t just expose raw tables and hope for the best. Then, train people properly. If someone couldn’t build a report yesterday, simply pointing them at a large self-service model won’t change anything today. They need enablement, confidence the support channels when things get confusing.

I always recommend identifying Power BI champions early. These are individuals within each department who become go-to figures for all things reporting. They help advocate for Power BI, encourage adoption, support peers and feed back to central teams. They can also form part of a Power BI Centre of Excellence (CoE), not just as a data governance body, but as a business-led forum focused on helping teams get value from data. The CoE should absolutely not be an ivory tower of data professionals setting rules no one understands, it should be grounded in the real challenges and needs of the business.

One final point, I am not suggesting to go with a big bang approach, self-service should be done in phases. Start with a single business area, for example, operations or finance, and use it as a pilot. What worked? What didn’t? Did your workspace setup and security model hold up? Use this feedback to adapt before expanding to other areas. That’s how you build momentum and avoid chaos.

6. Setting Governance (and Empowering)

We’ve already touched on several core elements above that fall under governance and which I find to be important for establishing a data driven culture. Things like access, training, self-service boundaries and security. But in this section, I want to call out some additional elements.

It’s all about finding the right balance between control and enablement. Too much control and your users disengage, they wait on IT, give up on self-service and go back to spreadsheets. Too little control and you end up with chaos, duplicated reports, inconsistent metrics and serious risk to data quality, privacy and compliance.

Good governance doesn’t block people. It empowers them, with the right guardrails. In Power BI specifically, there are several features or settings that every retailer should spend some time on:

• PBI Workspace and Apps: Who can access what? Who can publish? Are you separating dev, test and prod environments? Is RLS (Row-Level Security) being used properly?
• Semantic Models: Are you centralising reusable models so teams don’t build conflicting versions? Are those models certified, documented and discoverable?
• Data Privacy and Classification: Are you using sensitivity labels? Is personal or restricted data clearly marked and protected?
• Content lifecycle: Do you have processes for version control, solution review and deprecation of old or unused reports?
• Endorsements and discoverability: Can users easily tell what’s trusted (enterprise) vs ad hoc (self-service)? Are you using Promoted and Certified labels correctly to drive adoption of the right content?
• Retention Policies: Are stale models and reports automatically cleaned up? Do you know what’s active vs forgotten about?
• Power BI (MS Fabric) Tenant Settings Have you reviewed and configured your organisation-wide settings for sharing, exporting and more?

I’ve seen many retailers get stuck because they either over-engineer these controls too early or ignore them altogether. The key is to start simple, with what’s needed now and evolve your governance as adoption grows.

One practical tip? Involve your users. When you set up a PBI Centre of Excellence (CoE), don’t just fill it with BI developers and IT. Include your store managers, merchandisers, area leads, the very people you’re trying to empower. When governance is seen as a blocker, it fails. But when it’s shaped by those closest to the action, it becomes an enabler.

7. Fixing Data Quality to Build Trust

We can talk all day about dashboards, semantic models, governance, training, people over tech - but none of it matters if people don’t trust the data.

Poor data quality is one of the most common blockers to a data-driven culture. It doesn’t just create reporting errors, it erodes confidence, leads to rework and pushes teams back into instinct-driven decisions. I’ve seen it many times in retail, the FP&A team finds inconsistencies in a weekly report, raises concerns and from that moment on, the entire team (and other teams) stop trusting the data. Once that trust is broken, it’s hard to win people back.

To avoid this, data quality needs to be tackled head-on. Start with clear ownership. Who’s responsible for the data at each point in the process? Whether it's product data, store sales or supply chain, make it explicit. Don't assume IT owns everything, data quality is a shared responsibility. That’s where a Power BI Centre of Excellence (CoE) can play a major role, helping define standards, assign data owners and track issues across departments.

In parallel, consider setting up a network of Power BI Champions, individuals embedded within teams who advocate for data quality, highlight issues early and drive cultural change from the ground up. This approach ensures it’s not just the BI team pushing for better data, but something that’s modelled and reinforced by the business itself.

Conclusion: Data Culture Is a Business Priority, Not a BI One

Creating a data-driven culture in retail isn’t about rolling out more dashboards or investing in the latest tools, it’s about changing how decisions are made, how teams work together and how leaders lead. The shift requires far more than tech. It demands executive support, changes in behaviour and a clear alignment between data initiatives and business goals.

According to BARC’s 2025 Data, BI and Data Analytics Trend Monitor, data-driven culture has now climbed to third place, clear evidence of how critical it has become for modern organisations. And yet, for many retailers, it remains more of a buzzword than a business reality. Retailers must dedicate the needed time.

Throughout this blog, we explored what a data-driven culture actually looks like on the ground, from executive sponsorship and overcoming resistance, to empowering people, supporting self-service, setting effective governance, and fixing data quality. All of it points to one truth: data culture starts with people, not platforms. If teams don’t trust the data, can’t access it or don’t feel confident using it, then adoption will always stall.

And like any strategic initiative, culture needs to be measured. Adoption and behavioural change are not abstract concepts, they can (and should) be tracked. Whether it’s the percentage of users engaging with insights, the quality of decisions made in weekly trade meetings or how many teams are actively part of your Centre of Excellence, these indicators help you understand if your data culture is maturing or simply stuck at surface level.

Want to See What 'Data-Driven Retail' Really Looks Like?

Want to check out our Power BI Solution focused on organisations in the retail industry? We’ve built a Power BI Retail Solution that helps regional managers make informed decisions, monitor performance across 100+ stores, complete with store-level drilldowns, trade meeting KPIs and team-specific views. The way it's built enables data-driven retail decisions to be made. Check out our Retail Dashboard Page here.