Big Data Strategy Development

📌 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

I built the data and AI strategies for some of the world’s most successful businesses. One word helped V Squared beat our Big Consulting competitors to land those clients. Can you guess what it is? Actionable. Strategy must clear the lane for execution and empower decisions. It must serve people who get the job done and deliver results. Most strategies, especially data and AI strategies, create bureaucracy and barriers that slow execution. They paralyze the business, waiting for the perfect conditions and easy opportunities to materialize. CEOs don’t want another slide deck and a confident-sounding presentation about “The AI Opportunity.” They want a pragmatic action plan detailing strategy implementation, execution, delivery, and ROI. They need a framework for budgeting based on multiple versions of the AI product roadmap that quantifies returns at different spending levels. They need frameworks to decide which risks to take. Business units don’t want another lecture about AI literacy. They need a transformation roadmap, a structured learning path, and training resources. They need to know who to bring opportunities to, how to make buying decisions, and when to kick off AI initiatives. Most of all, data and AI strategy must address the messy reality of markets, customers, technical debt, resource constraints, imperfect conditions, and business necessity. Technical strategy is only valuable if it informs decision-making and optimizes actions to achieve the business’s goals.

Everybody believes they can do strategy, especially data leaders The truth? These leaders can do it, but a lot don’t have the time, context or separation needed to do it well And, like in most organisations, this is 𝘄𝗵𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗲𝘅𝗶𝘀𝘁 Obviously I'm a proponent of getting outside help. Why? 💡 𝗙𝗿𝗲𝘀𝗵 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 – Don’t aim low because of company politics or historical "we've always done it this way" mentality 𝗖𝗿𝗼𝘀𝘀-𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 – Consultants have seen what works (and fails) across multiple organizations. That perspective (as long as they are telling the truth) can help save millions of dollars and a ton of time 𝗗𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗙𝗼𝗰𝘂𝘀 & 𝗧𝗶𝗺𝗲 – Data leaders don’t have time to think and build strategies on their own, especially with all the day-to-day fires they get pulled into 𝗛𝗼𝗻𝗲𝘀𝘁 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 – A huge part of strategy is delivering difficult truths without worrying about career implications. A good strategy is not all sunshine and rainbows 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – A strategy project is more about getting buy-in and support than the actual ideas that come from it So if you do decide to spend money on a strategist, what should you ensure? 🤔 𝗧𝗵𝗲𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 – Even a data strategist should understand your business; generic strategy templates (and I’ve seen a lot of them) rarely deliver value 𝗧𝗵𝗲𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 – Oh yeah, and don’t hire a general strategist for data work, otherwise you will get unrealistic ideas without the technical underpinning 𝗧𝗵𝗲𝘆 𝗲𝗺𝗯𝗲𝗱 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 – The best strategies are built with your team and it is an ongoing process of development/ buy-in 𝗧𝗵𝗲𝘆 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 – This should be something you leave there, so your strategy partner should work to teach your team how to think strategically and execute, not just deliver recommendations 𝗧𝗵𝗲𝘆 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 – Speaking of execution, always have a roadmap; great strategies focus on the realities of execution from day one 𝗧𝗵𝗲𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 – This is your chance to define what success looks like and how it will be measured; without that, six months later your strategy will unravel At the end of the day, effective data strategy isn't about having the perfect plan – it's about aligning your data capabilities with business objectives in a way that drives measurable value. What's your experience with building data strategy? Have you worked with external partners or built it internally? And if anybody wants to chat further about this, shoot me a message!

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

"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.

Everyone wants AI. No one wants to craft a strategy that actually makes it work. Here’s a reality check: A real data strategy isn’t just about what you build with AI. It’s about why you build, for whom, and how that work turns into real outcomes. ✅ So here’s a 9-part playbook that I’ve seen work again and again: 1/ Understand business problems - deeply. → Talk to users. Obsess over pain points. 2/ Define who does what. → Data teams ≠ dashboard vending machines. Clarify roles early or drown in confusion later. 3/ Craft your unique value prop. → How does your team beat the status quo of gut-feel and spreadsheet hacks? 4/ Build solutions (only after understanding the problem) → Yes, that includes dashboards. But also pipelines, experiments, automation, AI... whatever fits. 5/ Don’t skip distribution. → The best dashboard or AI tool in the world is worthless if no one uses it. Plan adoption from day one. 6/ Create a systems strategy. → Standardize. Automate. Reduce firefighting. Build a machine, not chaos. 7/ Outcomes > Outputs. → A shiny new dashboard means nothing. Show the business impact. Prove your value. 8/ Know your cost structure. → Track it. But don’t obsess. 80% of your focus should be on value creation, not cutting costs. 9/ Invest in people. → Your strategy is only as good as the humans behind it. Hire, onboard and lead with intention. This is how you build a strategy that actually works. Not a wishlist. Not a 200-slide deck. A strategy your execs understand, your team rallies behind, and your business feels. Want to stop building slideware strategies and start driving real business impact? 👉 Join 3,000+ data experts who read my free newsletter for weekly tips on building outcome-driven data strategies: https://lnkd.in/g59sqJnk ♻️ And Repost if your company’s data strategy is mostly a list of tools and buzzwords

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

Most data strategies do not fail in execution. They fail in how they are defined. Too often, what is labeled "data strategy" is a roadmap of initiatives. Platforms to modernize. Pipelines to build. Dashboards to deploy. The activity looks impressive. The alignment remains shallow. A real strategy is not a list of projects. It is a set of explicit tradeoffs about where the enterprise will compete, how it will win, and which decisions must improve to get there. When strategy is built from technology up, it optimizes for capability. When it is built from decisions down, it optimizes for consequence. ↳ The first creates infrastructure. ↳ The second creates advantage. Confusing strategy with implementation creates three risks: ↳ Fragmentation - every function optimizes locally while the enterprise drifts. ↳ False confidence - activity is mistaken for impact. ↳ Erosion of trust - leaders question whether data is shaping direction or merely reporting on it. The strongest data strategies start with one disciplined question: Which decisions define our performance, and what must be true for those decisions to improve? Revenue allocation. Market entry. Risk appetite. Product investment. Everything else flows from that clarity. Before your next transformation conversation, ask: Are you designing direction or just funding activity? Follow Arun Gamidi for data, AI, and the leadership decisions that shape real outcomes.

Most data strategies fail because they never go below the surface. On top, it’s all shiny: tools, dashboards, slide decks called “Data Vision 2025.” Underneath? Politics, mismatched KPIs, and a Google Sheet named final_final_v3. Everyone wants to look “data-driven.” Few want to do the boring work that makes it true. Because real data strategy isn’t about stack diagrams. It’s about alignment, ownership, and calling BS on things that don’t matter. Here’s the playbook: 1 — Match ambition to stage. You don’t need a spaceship to cross the street. A spreadsheet that tracks spend and churn beats an abandoned dbt project any day. Start small, build trust in data, and scale once foundations are in place. 2 — Define KPIs before dashboards. If every team has a different version of “truth,” you’re not measuring performance — you’re writing fanfiction. Set ownership early, define success metrics clearly, and make sure everyone speaks the same language. 3 — Build culture, not theater. Dashboards don’t make you data-driven. Using them in decisions does. Drive adoption, ensure scalability, and keep it alive long after launch. The real data strategy happens underwater — quiet, consistent, and unsexy as hell. But it’s what keeps the company afloat — and growing. ♻️ Repost to help more teams go below the surface. ––– I’m Nick, founder of Valiotti Analytics. We build data strategies that make decisions easier, not decks longer. DM me if you’re done confusing activity with impact.