How to Build a BI and Analytics Strategy

Your data platform is live. The dashboards are running. The reports are being generated. And yet the decisions feel exactly the same as before. This is more common than most organisations acknowledge. And the reason is almost never the platform. A large enterprise came with this exact problem. Snowflake was live. Microsoft Power BI was deployed. Six months in leadership was still making decisions based on gut feeling and outdated spreadsheets. The technology was working. The organisation was not using it right. Three things were broken beneath the surface. Data was being collected. But nobody had defined what decisions it was supposed to support. Every business unit had its own metrics. Same numbers. Different definitions. Zero alignment. Reports were being built for visibility. Not for action. The platform was answering questions nobody was actually asking. The fix was not technical. It was strategic. We started with the decisions first. Mapped every key business decision to a data output. Standardised metric definitions across every business unit. Rebuilt reporting around actions not observations. Connected every dashboard directly to a business outcome the leadership team owned. Within three months decision cycles dropped by half. The board had clarity. The teams had direction. The platform had not changed. The thinking behind it had. Data platforms do not improve decisions. The strategy behind them does. If your organisation is sitting on data and still not moving faster that gap is solvable. Let Discuss in the comments. #DataAnalytics #BusinessIntelligence #DigitalTransformation #DataStrategy

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

Building a Data Analytics Team for a Mid-Sized Fashion & Beauty E-Commerce Brand! Continuing from my previous post on building a data analytics team, I received many DMs asking for real-world examples. So, in this post, I’ll try to wear the hat of a mid-sized fashion & beauty e-commerce brand and build their data team from scratch. -> Challenge? Scaling an analytics team that drives growth, retention, and profitability while solving key business problems First, What Problems Do We Need to Solve? Before hiring, let’s define the top challenges a data team should tackle: 1) Marketing Attribution & ROI – Are our paid ads actually bringing new customers? 2) Customer Segmentation & Retention – Who are our high-value customers? How do we keep them engaged? 3) Demand Forecasting & Inventory Planning – What should we stock, and when, to minimize dead inventory? 4) Personalization & Conversion Optimization – Can we recommend the right products at the right time? 5) Fraud Detection & Order Cancellations – Are we losing money due to fake COD orders or excessive returns? #Year 1: How to Build the Right Data Team & Solve These Problems? A) Phase 1 (0-3 Months) – Laying the Foundation ->Key Hires: 🔹 1 Data Analyst – To track key KPIs, build dashboards, and analyze marketing performance 🔹 1 Data Engineer – To set up ETL pipelines and connect multiple data sources 🔹 1 BI Developer – To automate reporting and create self-serve dashboards -> Quick Wins: ✔️ Centralize data in a data warehouse (Snowflake, BigQuery, or Redshift) ✔️ Automate daily sales & marketing reports for better decision-making ✔️ Implement UTM tracking for paid ads & influencer campaigns B) Phase 2 (3-6 Months) – Scaling Insights & Retention Strategies ->Next Hires: 🔹 1 Data Scientist – To build customer segmentation models & predict churn 🔹 1 CRM Analyst – To optimize retention campaigns, loyalty programs & lifecycle marketing -> Key Initiatives: ✔️ Identify high-value customers vs. those likely to churn ✔️ Optimize ad spend & ROAS – Cut waste, double down on high-performing channels ✔️ A/B test pricing & discounts – Find the sweet spot for conversions C) Phase 3 (6-12 Months) – AI-Driven Decisions & Advanced Analytics -> Final Hires: 🔹 1 Demand Forecasting Analyst – To predict inventory needs & optimize supply chain 🔹 1 AI/ML Engineer – To implement recommendation engines & dynamic pricing -> Big Impact Areas: ✔️ Build AI-powered product recommendations to increase AOV (Average Order Value) ✔️ Implement predictive demand forecasting to reduce stockouts & excess inventory ✔️ Set up fraud detection models to minimize return abuse & fake COD orders What challenges have you faced in scaling data teams for e-commerce? Let’s discuss! #Ecommerce #DataAnalytics #AI #CustomerRetention #FashionTech #MarketingOptimization

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.

BI and analytics teams should be building apps.  Apps that integrate data, workflow, process, analytics, models, reporting and the UI. In this example I walk through an app that automates an invoicing process. It pulls billing data from multiple source systems, categorizes transactions using AI, validates the results, flags anomalies for human review, supports adjustments, generates invoices and then provides analytics on the whole process. That is the important shift. For years, BI teams have mostly built dashboards that sit beside the process. Useful, yes. But still separate from the actual work. The next generation of BI and analytics teams will build apps that sit inside the process. Not just: “What happened?” But: “What needs to happen next?” Not just: “Here is the report.” But: “Here is the workflow, the recommendation, the exception, the approval path and the action.” This is where analytics, AI and software start to converge. The dashboard becomes the cockpit. The model becomes part of the workflow. The human becomes the reviewer, approver and decision-maker. And the BI team becomes much more than a reporting function. They become builders of operational intelligence. This is the direction I think data teams need to move in. From dashboards. To decision systems. To apps that actually run parts of the business. #BusinessIntelligence #Analytics #AI

Business intelligence is more than just a buzzword; it's your competitive edge. Yet, many CTOs and CIOs find themselves swamped by data but starved for insights. You need a sharp, actionable plan. Here are six steps to turbocharge your BI efforts. Clarify your objectives. - Define what success looks like. - Align BI with business goals. - Prioritize key performance indicators. Streamline data collection. - Ensure data accuracy. - Automate data gathering. - Focus on relevant data sources. Enhance data analysis. - Implement advanced analytics. - Encourage exploratory data analysis. - Validate insights through testing. Boost data visualization. - Use intuitive dashboards. - Highlight critical metrics. - Simplify complex data. Promote a data-driven culture. - Encourage data literacy. - Reward insight-driven decisions. - Foster open data discussions. Evaluate and iterate. - Regularly review BI strategies. - Adapt to evolving needs. - Embrace feedback for improvement. The big takeaway: A strategic approach to BI reveals hidden opportunities. Trust in a structured, iterative process. What’s your biggest BI challenge today?

Building a #DataAnalytics Platform in 2026 is no longer a “warehouse + BI” project. It’s a product: engineered for trust, speed, and decision automation—while staying governable. #AI can accelerate the build, but only if the fundamentals are strong. 1) Start with outcomes, not tools Define 5–10 decision journeys (pricing, claims triage, churn prevention, fraud, forecasting). 2) Treat data like a product Create domain-owned datasets with: • Clear contracts (schema, SLAs, freshness, lineage) • Ownership (stewards + accountable teams) • Quality gates (completeness, accuracy, timeliness, uniqueness) • Documentation and discoverability #AI helps generate documentation and validate contracts—but cannot replace ownership. 3) Build an architecture that supports both analytics and AI A practical pattern: • Ingestion & CDC for operational sources • #Lakehouse / modern warehouse for scalable analytics • #Semantic layer to standardize metrics and definitions • Feature / entity layer for ML and personalization • Serving layer (BI, APIs, reverse ETL, apps) 4) Make semantics the “control plane” Implement: • A canonical entity model (#MDM where required) • A governed metric store / semantic model • Business glossary tied to #datalineage #AI accelerates mapping and suggests harmonization 5) Engineer data quality and observability by default #AI is particularly strong here: it can detect drift, propose likely causes, and recommend fixes. 6) Design security, privacy, and compliance up front #AI can assist in PII classification and policy recommendations, but enforcement must be deterministic. 7) Use #AI to accelerate delivery (safely) • Automated ingestion mappings and schema evolution suggestions • #SQL / transformation generation with validation tests • #Metadata enrichment (catalog, descriptions, tags) • Automated #dataquality rule recommendations • #Semantic model and metric definition drafts • Faster incident triage via #lineage-aware copilots 8) Operationalize with “platform SRE” discipline Adopt: • CI/CD for data pipelines and semantic models • DataOps runbooks, rollbacks, and release gates • Cost management (FinOps) with usage chargeback/showback • Clear RACI between platform, domain, and analytics teams ⸻ Where EXL Data Management fits EXL Data Services helps organizations build analytics platforms that are #AI-ready, governed, and production-grade—not just “standing up a tool.” We bring: • Reference architectures for banking, insurance, and healthcare • Accelerators for ingestion, data quality, semantic modeling, and MDM • Operating model + governance design (so the platform stays trusted) • Rapid modernization paths from legacy #DW to #lakehouse. https://lnkd.in/eJX_GKef