Written by Dan Knott, Data and AI Practice Lead
Technology alone won’t transform your organisation. If it did, every business with a reporting tool, a data warehouse or an AI pilot would already be an industry leader.
You can’t buy a data culture. And that’s exactly where most organisations go wrong.
They expect that a platform, like Microsoft Fabric (powerful as it is), will magically create alignment, consistency, and better decision making. But real cultural change doesn’t come in a box. It comes from people: how they work, how they think, and how they use data to drive the business forward.
In this follow-up to our Blueprint of a Great Data Culture, I’ll delve into the practical steps IT leaders can take to embed data into the fabric of their organisation and highlight the common pitfalls that stall so many initiatives.
The 3 Key Mistakes That Hold Businesses Back
Many organisations that think they’re becoming data driven, but in reality, never quite get there. There are a few common pitfalls that I’ve seen time and time again.
- Treating data culture as a technical project: Data culture isn’t a reporting rollout, a BI project, or an AI pilot. Those are outputs. Culture should be embedded in the way of working.
- Missing business involvement from the start: Building a data culture will not be possible unless everyone in the business is bought in. Without cross department input, teams are not invested and it can feel like another unnecessary process.
- ‘This is how we’ve always done it’ mentality: Gutfeel decisions. Spreadsheets saved on desktops. Siloed versions of the truth. Culture change means letting go of old habits, and that can’t happen without intentional support.
Practical Steps to implement a strong data culture
Creating a data culture begins long before platforms and dashboards. IT leaders must shape and secure a business-wide mandate for change. Here’s how:
Step 1: Start with Leadership and Vision
Senior leaders must be the ones driving and backing the cultural shift. Leadership support empowers IT teams by enabling them to work seamlessly without resistance. They also encourage the company-wide adoption that is critical for success.
Key actions to take:
Build a shared vision with the C-suite
- Run a workshop with key business leaders to help align data initiatives with business goals. Consider cost reduction, customer growth, compliance and operational efficiency.
Translate the vision into a business-backed roadmap
- Build a structured roadmap that demonstrates key milestones, from quick wins to long-term business outcomes. Each milestone should be measured and tracked to a KPI to ensure successful review.
Secure executive sponsorship
- Dedicated exec sponsor(s) should champion the change publicly in various forums to reinforce expectations as well as model the intended behaviours you want employees to adopt. They should also ensure that there are adequate resources allocated to the initiative.
Step 2: Establish a Single Source of Truth
Every organisation struggles with spreadsheet chaos. It’s easy for different teams to manipulate the same numbers in different ways, potentially coming to wrong conclusions. Most critically, having a trustworthy data platform lays the groundwork for AI readiness. If the data isn’t accurate and trustworthy, neither will the AI be. It’s that simple.
Key actions to take:
Identify and prioritise core data domains
- Start with areas where inconsistent data causes the most friction. This could include customer master data, finance/forecasting, marketing attribution and service delivery metrics. Then run a data audit to understand where the data lives, who owns it, how many versions exist, and how it’s currently used (and misused).
Design a governed, accessible architecture
- This typically includes implementing a unified data platform such as Microsoft Fabric, defining ownership and ingestion processes, as well as controlling access where necessary. There should also be a built-in data quality and validation processes. But governance doesn’t have to be heavy-handed. You need to start with core principles, not long policy documents.
Clean the data before surfacing it
Before exposing dashboards, you should…
- Fix critical data quality issues
- Validate key metrics with business users
- Document known limitations
- Test data accuracy with “friendly sceptics” in the business
Step 3: Build Data Literacy Across the Business
A sophisticated data platform is meaningless if people don’t know how to use it, or don’t share the same language. Data literacy isn’t about teaching everyone Python or SQL. It’s about ensuring people can understand the metrics, interpret the data, and apply insights in their role.
Key actions to take:
Create a business glossary
Start with essential, high-impact terms such as:
- What does “Gross Margin” mean here?
- When does an Opportunity become Qualified?
Co-create definitions with each business function. Host them in a central, easy-to-access place.
Run role specific training
Avoid generic “Power BI training for everyone,” And instead design learning journeys by role:
- Exec team: reading dashboards, challenging assumptions
- Sales: understanding pipeline metrics
- Marketing: attribution logic, campaign performance
- Finance: forecasting and scenario modelling
- IT: governance, access, troubleshooting, lineage
You should also make data literacy part of your company’s induction process. This makes data driven behaviour a default, not a bonus.
Encourage “data ambassadors”
Identify individuals who naturally ask good questions and understand the tools. These are people who can influence their peers and support with questions.
Step 4: Embed Data into Everyday Behaviour
A culture only forms when new behaviours become habit. It needs to be embedded in employees’ regular ways of working to be effective.
Key actions to take:
Integrate data into existing rituals
Rather than creating new processes:
- Use dashboards for monthly performance reviews
- Review KPIs at weekly team meetings
- Incorporate data into quarterly planning
- Make reports the default starting point for decision making
Standardise on agreed tools and dashboards
- Nothing kills culture like fragmentation. Ensure your entire organisation is aligned by making it clear which dashboards are “the single source of truth” and decommission redundant spreadsheets and shadow systems.
Use Power BI and Fabric to democratise access
- Give teams self-serve analytics where appropriate, but with standardised definitions, pre-built data models, and simple, clear visualisations. Self-serve only works when the foundation is governed.
Step 5: Start Small, Celebrate Wins, and Iterate
Cultures shift gradually. Start small, and celebrate where it is going well.
Choose small, visible use cases first
Good examples:
- Reducing forecast discrepancies between Sales and Finance
- Improving campaign reporting accuracy in Marketing
- Reducing manual spreadsheet reconciliation in Operations
Quick wins build belief.
Create a feedback loop
You need that feedback loop… a great data culture never stands still.
Implement:
- Monthly data council meetings
- Dashboards tracking data quality
- Suggestion channels
- Regular retrospectives after report launches
Publicly celebrate success stories
People adopt what they see being rewarded. You should showcase wins including: Who used data to solve a problem, Where a decision changed because of insight and what the key outcome was.
Iterate continuously
Your data culture is a living system, not a project:
- Update definitions as the business evolves
- Add new use cases
- Improve data models
- Retire reports that no longer serve a purpose
- Continually refine governance
Culture compounds through iteration.
Ready to strengthen your data culture?
If you’re looking to build a data culture that drives decisions, unlocks AI readiness, and aligns your entire organisation, we can help you. Cloud Direct works with leaders who want to break through old ways of working. To build something stronger, smarter, and more sustainable. Reach out to us using the form below.
If you’re searching for more information to help plan your AI journey, our data and AI playbook can help to guide you.
