Data Strategy Development

A “data strategy” that starts with picking tools... ... is like a fitness plan that starts with buying a smartwatch. Sure, the gear is shiny. But you haven’t broken a sweat yet. Here’s what I keep seeing: → “Our data strategy is Databricks + dbt + Power BI” → “We’re migrating to XYZ Lakehouse, that’s our strategy” → “We hired a Staff Data Engineer, LET's GO! 🤘” No. That’s not strategy. That’s a shopping list. Strategy is: → What are the business goals? → What decisions do we want to improve? → What problems do people actually have? → How do we distribute data products to stakeholders? → What systems to we put in place to create value? → What outcomes (not outputs) will we deliver? → How do we hire, grow and retain talent? Only then should we ask: What’s the simplest stack that gets us there? Not the most modern. Not the one that makes your team feel smart. The one that lets your org move fast and focused. Because a $200K tool won’t fix the fact that your marketing team doesn’t trust your data. And a new data observability platform won’t save you if no one knows what actions to take after seeing the dashboard. Want to build a data strategy that creates real business impact? 👉 Join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/ghg5-5U7 ♻️ Repost if your “data strategy” once started with a vendor pitch deck

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.

Building a Future-Ready Data Integration & Automation Strategy To address data quality and integration challenges, enterprises must shift from reactive, patchwork solutions to a holistic, long-term strategy. A well-defined data integration and automation approach ensures consistency, accuracy, and efficiency. Adopt a Centralized Data Integration Layer – Implementing a cloud-based integration platform allows seamless data exchange between disparate systems. Leverage AI & Automation for Data Governance – Machine learning algorithms can detect anomalies and improve data accuracy, while automation reduces human intervention. Standardize Data Across the Enterprise – Establishing data governance policies and master data management (MDM) ensures consistency across all business functions. Recommendation for CDOCIO/: Develop a roadmap for enterprise-wide data integration, aligning technology investments with business goals. Start with a phased approach—integrating high-impact systems first while ensuring scalability for future needs.

Why Your Data Strategy Fails at the Handoff Points Data flows through your organization like water through pipes – and the leaks happen at the joints. When marketing, sales, and customer success operate from different data realities, the result is missed opportunities and fragmented customer experiences. Traditional approaches treat data challenges as isolated technical problems: CRM implementation, data cleansing, lead routing, and marketing automation. But these elements form an interdependent ecosystem where actions in one area cascade throughout your entire go-to-market motion. The breakthrough comes when you shift from siloed optimization to building a connected data ecosystem. Start with these practical, cross-functional steps: Map your data value streams – document how customer information flows through your organization and identify critical handoff points where integrity deteriorates. Implement closed-loop feedback mechanisms that track not just data volume but quality indicators at each transition point, automatically triggering refinement when leads fail to convert. Consider strategic partnerships with third-party data providers who offer immediate quality baselines and cross-system standardization, creating momentum for broader transformation efforts. Success doesn't go to organizations with the most data or flashiest tools – it belongs to those turning information into a connected, enterprise-wide asset that delivers smarter decisions, stronger customer experiences, and measurable revenue impact.

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

𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗲𝗰𝗿𝗲𝘁𝗹𝘆 𝘀𝗮𝗯𝗼𝘁𝗮𝗴𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗥𝗢𝗜? (𝗧𝗵𝗿𝗲𝗲 𝗵𝗮𝗿𝗱-𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜) A data strategy without alignment is just a budget drain waiting to happen. In my work with data-driven companies over the years, I’ve experienced firsthand pretty tough lessons on scaling with data and AI. Today I want to share three of these hard-earned insights with you, along with one actionable strategy you can use to accelerate your growth trajectory. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟭: 𝗗𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗶𝘀 𝗿𝗶𝘀𝗸𝘆 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 I’ve seen companies invest heavily into data infrastructure only to realize that their KPIs and data initiatives weren’t aligned with core growth objectives. To make sure your data strategy is a growth driver, you have to map back every data project to a specific business outcome and assign ownership across departments. This not only maximizes ROI, but it also builds essential cross-functional accountability. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟮: 𝗬𝗼𝘂𝗿 “𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲” 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂𝗿 𝗴𝗿𝗼𝘄𝘁𝗵 𝗴𝗼𝗮𝗹𝘀 In many cases, leaders become paralyzed by the overwhelming number of options that are available when looking to move forward on an AI implementation. The most effective approach: Vet potential use cases and select the one with the highest ROI potential — what I call your "Winning Use Case." It’s focus like this that truly empowers leaders to allocate resources wisely and drive measurable results. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟯: 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗺𝗮𝗻𝗱𝘀 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 In today's regulatory climate, overlooking ethical AI isn’t just risky — it’s downright unsustainable. As early as possible, set benchmarks for data privacy and bias checks. This commitment will pay off in both risk mitigation and brand equity over time. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗰𝘁𝗶𝗼𝗻: 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝗮 “𝗴𝗿𝗼𝘄𝘁𝗵 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝘂𝗱𝗶𝘁” 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 If you’re investing in AI or planning to do so, try this: audit your AI initiatives by evaluating their alignment with three criteria — scalability, strategic impact, and ethical compliance. Prioritize projects that meet all three and realign or rethink those that don’t. This approach ensures that your data- and AI- investments contribute directly towards reaching your growth targets, without unintended consequences. In 𝘛𝘩𝘦 𝘋𝘢𝘵𝘢 & 𝘈𝘐 𝘐𝘮𝘱𝘦𝘳𝘢𝘵𝘪𝘷𝘦, I share every nook and cranny of my signature STAR Framework in order to help you identify high-impact AI initiatives, align data strategies with growth, and maximize your ROI. Pre-order your copy now to get the full blueprint: https://lnkd.in/gMGraK32 #datastrategy #growth #AI #ROI #data #kpis #alignment #business

The foundation of a strong Data Strategy is an organisation's strategy A data strategy isn’t just about data for the sake of data or technology for the sake of technology, it’s about business impact. One of the biggest mistakes organisations make is treating data strategy as a standalone initiative rather than aligning it with strategic business objectives. ✅ Are you using data to drive growth and revenue? ✅ Is your data strategy improving efficiency and operations? ✅ Does it enhance customer experience and personalisation? Those are all examples of alignment. Without business alignment, even the most advanced data capabilities won’t deliver real value. So, How to Ensure Business Alignment in Your Data Strategy: - The "Why": What business problems are you solving? - Key Metrics: How will success be measured and agreed cross-functional? - Collaboration: Data shouldn’t sit in silos; it must support company-wide goals. - Enable Decision-Making: Ensure leaders have access to the right data, at the right time, in the right format. 💡 Your data strategy should be a business strategy—powered by data. Would love to hear how your organisation ensures business alignment in its data strategy in the comments! #DataStrategy #Data #Leadership #AI #Innovation

At Eckerson Group, we’ve guided companies through many twists and turns in their data journeys. Some race ahead, others veer off course, and many find themselves looping back to familiar starting points. But through it all, one path stands out as the reliable route to a truly data-driven organization. With a well-mapped strategy and clear guidance, companies can steer clear of costly detours and find their way home. Here’s a brief look at each stage—and how to ensure your organization is on the right track: 1. Flying Blind - State: Decisions rely on intuition and fragmented spreadsheets, increasing risk due to unreliable data. - Pathway: False Starts – Many stay in this quadrant, as data strategy efforts often lack the funding or leadership needed for real progress. To advance, a solid strategy and dedicated investment are essential. 2. Pockets of Analytics - State: Departments are active with data initiatives, but isolated efforts yield limited, inconsistent insights. - Pathway: High Risk – Without an enterprise approach, these projects lose momentum. Building a unified data platform and governance framework can drive sustainable, integrated efforts. 3. Analytic Potential - State: Advanced infrastructure is in place, but lack of business alignment hinders its potential. - Pathway: High Cost – This setup can be expensive, often leading to dissatisfaction if business and IT remain siloed. Cross-functional alignment can help unlock the full value of data resources. 4. Analytic Competitor - State: Data drives decision-making at all levels, enabling agility and competitive advantage. - Pathway: Straight and Narrow – Sustaining this ideal state requires disciplined governance, data literacy efforts, and alignment with strategic objectives to meet evolving business needs. Wondering which path your organization is on? Our hands-on experience in navigating these stages allows us to help companies develop, refine, and execute data strategies that align with their unique goals. Let’s put your organization on the right path. Contact us for a free consultation: https://lnkd.in/e242jCMQ

Your AI strategy will fail without a solid data strategy.   The majority of the past machine learning projects, data lake to analytics transformations initiatives have failed to deliver. Now, countless AI efforts risk repeating that history for one simple reason: they didn’t start with the right data strategy.   A data strategy isn’t just another technical feature. It’s the foundational supply chain that ensures your AI efforts create real, lasting business value. Think of it as a balance between:   Supply - How you gather, transform, and store data Demand - How executives, analysts, data scientists, and AI agents access and use it   A strong data strategy isn't about dumping everything into one massive database. It’s about thoughtful decisions around:   🔹 Freshness & Frequency - How often your data is refreshed to stay relevant 🔹 Centralization - Logical organization and integration (not just physical consolidation) 🔹 Adaptable Storage - Lake, Lakehouse, Warehouse, or a hybrid, depending on needs 🔹 Data Types - Structured (tables) and unstructured (text, documents, media) 🔹 Empowered Users - Ensuring seamless access for SQL users, notebooks, Excel, Power BI, and beyond   At its core, your data strategy should fuel smarter, faster decisions and turn your data from a cost center into a strategic advantage. Because without it, even AI, especially Agentic AI, just helps bad data generate more garbage, faster.   How is your organization approaching this critical step?   #DataStrategy #AIStrategy #DataLeadership #Analytics #AI #BusinessIntelligence

Data strategy often flies under the radar, overshadowed by AI's allure, yet it's the critical backbone that determines whether AI delivers real value. To truly unlock AI's potential, data and AI leaders must prioritize a robust, long-term data strategy aligned with business goals. Data strategy is a marathon, not a few sprints of quick wins; it’s about laying the foundation for sustained success. Leaders should focus on: ✅ Understanding Business Needs: Align data initiatives with strategic business objectives to ensure relevance and impact. ✅ Building Scalable Data Infrastructure: Invest in scalable, flexible data architectures supporting growth and innovation. ✅ Fostering a Data-Driven Culture: Encourage data literacy and collaboration across the organization to empower teams to make informed decisions. ✅ Prioritizing Data Governance: Ensure data quality, privacy, and compliance are at the forefront of your strategy. As an instructor for the Berkeley Haas AI Business Strategies course, I reinforce that data strategy is a deep field in its own right and requires a holistic approach to power AI products and services and data-driven business processes. See the comments for a few data strategy resources: 🛠️ EDM Council CDMC Framework - Data Strategy is the first capability 🛠️ Berkeley Haas Data Strategy course 🛠️ AWS - What is Data Strategy? 🛠️ HBR data strategy article ♻️ Repost if you found a valuable insight #datastrategy #aistrategy