How to Build a BI and Analytics Strategy

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.

📌 The Data & BI Strategy Playbook Everyone wants to be "data-driven." But most companies get stuck halfway. They start by buying tools, setting up data platforms, or hiring data consultants believing that technology alone will make them data-driven. And then, months later, they wonder why adoption is low, why leaders still make decisions in Excel, and why the dashboards they worked so hard to build barely get opened. The truth is that your data strategy is not failing because of the tools but due to lack of strategy. That’s exactly what the playbook below is about. It shows the 3 levels every organization needs to move through if they want BI to truly drive decisions. 1️⃣ 𝐋𝐞𝐯𝐞𝐥 1 - 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 This is where everything starts. Before a single dashboard is built, you need clarity. → What are the business needs? → Who are the decision-makers? → What key problems are we solving? From there, you shape your data strategy: It’s not just about collecting data. You have to define how data will serve the business. That means setting governance rules, choosing reliable sources, and aligning every KPI to an actual decision. A strong data strategy also includes: ⤷ Ownership (who maintains what) ⤷ Accessibility (who gets access to which data) ⤷ And long-term vision (how today’s decisions scale tomorrow) Finally, you establish solid data foundations including semantic models, consistent metric definitions, and a shared language of business performance. Without this level, everything that follows will be shaky. 2️⃣ 𝐋𝐞𝐯𝐞𝐥 2 - 𝐓𝐚𝐜𝐭𝐢𝐜𝐚𝐥 Once strategy is clear, you can move into execution planning. This means building a data project plan (sources, tools, roadmap, budgeting, KPIs) and setting up the data system (pipelines, processes, data warehouses, automations). But here’s the catch: if you cross into this level without finishing Level 1, you’ll end up with technical work that doesn’t connect to real business problems. And that’s the fastest way to lose adoption. 3️⃣ 𝐋𝐞𝐯𝐞𝐥 3 - 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 This is where the rubber meets the road. Data teams move from design to execution and adoption. The strategy comes alive. Business users start to rely on insights for daily decisions. And BI shifts from being a reporting tool to becoming a decision engine. The biggest mistake I see? Companies skipping straight to delivery. It’s tempting to believe that implementing tools or building reports will automatically create adoption. But without business alignment, governance, and clear KPIs, you end up with outputs that look complete on the surface yet fail to influence real decisions. The organizations that succeed with BI respect the sequence: Strategy → Tactics → Execution. Data strategy isn’t optional. It’s the foundation of trust, adoption, and real impact. 👉 Where do you think your company is today in this playbook? #BusinessIntelligence #DataStrategy

Most “data strategies” are just tool shopping lists. → New warehouse. → New BI tool. → Maybe sprinkle some AI on top. And somehow… still no impact. 15 years ago, in my first data leadership role, I learned an important lesson: A real data strategy has very little to do with tools. It’s about clarity. Clarity on: - Who you’re actually helping (and what they struggle with today) - Who owns what (so your team doesn’t become a ticket machine) - What value you promise (beyond “better dashboards”) - How your work gets used (distribution > perfection) - And whether you’re driving outcomes… or just producing outputs Notice what’s missing? → “We need real-time” → “Let’s hire 3 more data engineers” → “Maybe AI will fix it” I NEVER failed because of bad tech. Whenever I failed it was because: - I didn't understand the business problem deeply enough - I optimized for dashboards instead of decisions - I built things… no one asked for (or uses) Today, I: → start with humans. → design for adoption. → measure impact. And only then… I pick the simplest possible tools to get there. ♻️ Repost if you’ve ever seen a “data strategy” that was just a rebranded tech roadmap 👉 And follow me, Sebastian Hewing, for daily posts on data strategy.

It made perfect sense on paper. I was supposed to consolidate over a dozen analytics teams across five divisions into a single shared service under the CIO. Analytics resources were expensive and hard to hire. We had duplication across business units and consolidation would drive efficiency, enable knowledge sharing, create a “Center of Excellence.” The business case was solid. But in the six months we spent making it happen, I learned something critical: consolidation without democratization destroys velocity. When you pull analysts out of business units and centralize them, you create a service organization. Service organizations need intake processes. Prioritization frameworks. Project governance. All reasonable. All necessary for a shared service. But if you don’t simultaneously give business teams the tools and training to answer their own questions, you’ve just traded distributed capability for a perpetual backlog. This isn’t a one-time lesson. The same pattern keeps surfacing—especially at airlines. Analytics gets centralized for cost savings. Talented people get pulled from Revenue Management, Network Planning, Marketing. The analysts end up in a central team without deep business context. The business teams lose their analytical capability. Backlog grows. Shadow systems emerge. Nobody can move fast. And it’s not because anyone made a bad decision. The logic is sound every time. The flaw is in the system design—centralizing resources without democratizing access. We succeeded because we implemented a BI platform that put data, reporting and analysis directly in business hands. The central team built infrastructure and capabilities. The business teams used them. Both sides owned the outcome. I believed in it so much that once democratization was in place, I spun myself out of the shared service and back into the business—forming a new sales and marketing analytics team on top of the infrastructure we’d just built. Making data work for organizations means recognizing that centralization is a cost play—not a capability one. Consolidate your data. Democratize your analytics. Keep problem solving close to business problems. — Good morning, New York. 👋

"You need a data strategy" is sound advice. Yet it tends to land in the boardroom with the elegance of a lead balloon. The problem? It’s often confused with an operational IT plan. Say "data strategy" in a meeting and watch executives squirm. While everyone will acknowledge that it's an important topic, the term conjures up images of confusing technical diagrams, visions of tedious roles and responsibility alignments, and a deep fear of creating the next armada of soul-crushing governance committees. The core problem? Treating data strategy as an operational deep-dive exercise, and not as devising the engine that powers every business decision that matters. The good news? Effective data strategy is simple. All it takes are three questions that cut through the noise and drive action: First: Where does data actually matter to your business? If the answer is "everywhere", that's probably correct, but it’s not a strategy. Stop trying to boil the ocean and focus. The strongest data initiatives start with precise pressure points – specific problems where better information drives immediate value. Treat data like a scalpel, not a sledgehammer. Don't analyze everything. Analyze what matters most. Second: What's really blocking progress? New flash: It's rarely a lack of data, technology or data governance frameworks. The real culprits are usually organizational silos, hastily grown tech stacks, and–most tellingly–leaders who treat analytics as validation for decisions they've already made. Valuable data, however, creates change. If your data isn't making anyone uncomfortable, you're doing it wrong. Third: How do we turn insight into action? Too many dashboards and fancy reports are where insights go to die. Give your teams clear guidelines and air cover to act on data – and expect them to wield this power. When teams and managers can act on real-time signals – and aren't punished for data-driven failures – you'll see undeniable results. Remember: Most (data) strategies fail because they avoid organizational conflict. Like any good strategy, success lives in clearly making the hard decisions of what not to do. The most effective data strategies aren't the most complex. They target critical business needs, are clear on how to knock down barriers, and enable quick action. This requires understanding how data powers the business to win. Start small, test fast, iterate at lightspeed and scale what works. In a market where everyone claims to be "data-driven," the winners aren't the ones with the thickest strategy documents – they're the ones making better decisions, faster, every single day. They're not writing their data strategy. They're executing it.

As analysts, uncovering valuable insights is just the first step. The real magic happens when those insights drive action and results. Here’s how I approach turning analytics into decisions that matter: 1️⃣ Start with the End in Mind Always tie your analysis to a business objective. Whether it's increasing user retention, reducing churn, or improving operational efficiency, knowing the "why" behind your data ensures your insights are actionable. 2️⃣ Frame the Narrative Insights are only as powerful as the story behind them. Craft a narrative that’s: Clear - Avoid technical jargon; explain what’s happening and why. Concise - Highlight the key takeaways in a few bullet points or visuals. Compelling - Use data visualizations or analogies to make your insights memorable. 3️⃣ Collaborate Early and Often Actionable insights often require buy-in from multiple stakeholders. Engage key decision-makers, product managers, and engineers early in the process to align on priorities and understand constraints. 4️⃣ Provide Recommendations Data alone doesn’t drive action—recommendations do. Pair every insight with a clear next step, such as: A/B test this feature for higher engagement. Adjust pricing strategy to improve conversion rates. Focus marketing efforts on underpenetrated customer segments. 5️⃣ Quantify Impact Leverage forecasts or historical comparisons to show the potential upside of acting on your recommendations. For example, “Implementing X could increase revenue by 10% over the next quarter.” 6️⃣ Follow Through Action doesn’t end with delivering insights. Stay involved: Monitor implementation progress. Measure outcomes against your forecasts. Share success stories or lessons learned. 7️⃣ Build a Culture of Action Encourage data-driven decision-making across your organization. Host workshops, create dashboards, or share case studies of how analytics has driven impact. Insights are powerful, but actionable insights are transformative. What steps do you take to ensure your analytics drive real-world change? #data #dataanalytics #datainaction