How to Maximize the Value of Data

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.

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

I’ve lost count of projects that shipped gorgeous features but relied on messy data assets. The cost always surfaces later when inevitable firefights, expensive backfills, and credibility hits to the data team occur. This is a major reason why I argue we need to incentivize SWEs to treat data as a first-class citizen before they merge code. Here are five ways you can help SWEs make this happen: 1. Treat data as code, not exhaust Data is produced by code (regardless of whether you are the 1st party producer or ingesting from a 3rd party). Many software engineers have minimal visibility into how their logs are used (even the business-critical ones), so you need to make it easy for them to understand their impact. 2. Automate validation at commit time Data contracts enable checks during the CI/CD process when a data asset changes. A failing test should block the merge just like any unit test. Developers receive instant feedback instead of hearing their data team complain about the hundredth data issue with minimal context. 3. Challenge the "move fast and break things" mantra Traditional approaches often postpone quality and governance until after deployment, as shipping fast feels safer than debating data schemas at the outset. Instead, early negotiation shrinks rework, speeds onboarding, and keeps your pipeline clean when the feature's scope changes six months in. Having a data perspective when creating product requirement documents can be a huge unlock! 4. Embed quality checks into your pipeline Track DQ metrics such as null ratios, referential breaks, and out-of-range values on trend dashboards. Observability tools are great for this, but even a set of SQL queries that are triggered can provide value. 5. Don't boil the ocean; Focus on protecting tier 1 data assets first Your most critical but volatile data asset is your top candidate to try these approaches. Ideally, there should be meaningful change as your product or service evolves, but that change can lead to chaos. Making a case for mitigating risk for critical components is an effective way to make SWEs want to pay attention. If you want to fix a broken system, you start at the source of the problem and work your way forward. Not doing this is why so many data teams I talk to feel stuck. What’s one step your team can take to move data quality closer to SWEs? #data #swe #ai

"Without data, you're just another person with an opinion." — W. Edwards Deming Most leaders today are drowning in information but starving for meaning. A CEO I work with recently questioned his controller about why their facility costs were higher than competitors. The response? A detailed spreadsheet. Wrong answer! A CEO doesn’t want data dumps—they want clarity, context, and action. The real value isn’t in the numbers themselves but in what you do with them. The best leaders don’t just present data; they uncover patterns, anticipate questions, and deliver insights before anyone asks. They turn raw numbers into a roadmap for decision-making. Take a cue from Ryan Yockey’s graphic—sometimes, a single visual tells the story better than a thousand words. Data needs to be seen, not just read. Here’s how to transform raw data into action: 1. Sort it – Identify the key patterns and anomalies. 2. Arrange it – Structure it so it tells a clear, logical story. 3. Visualize it – A powerful graphic or chart conveys more than a thousand spreadsheets. 4. Tell a story – Make it compelling, memorable, and actionable. The top performers—whether executives, engineers, or analysts—don’t just collect data. They shape it, give it meaning, and translate it into a story that inspires action. Because in the end, the organizations that thrive aren’t the ones with the most data; they’re the ones that know what to do with it.

After building 10+ data warehouses over 10 years, I can teach you how to keep yours clean in 5 minutes. Most companies have messy data warehouses that nobody wants to use. Here's how to fix that: 1. Understand the business first Know how your company makes money • Meet with business stakeholders regularly • Map out business entities and interactions  • Document critical company KPIs and metrics This creates your foundation for everything else. 2. Design proper data models Use dimensional modeling with facts and dimensions • Create dim_noun tables for business entities • Build fct_verb tables for business interactions • Store data at lowest possible granularity Good modeling makes queries simple and fast. 3. Validate input data quality Check five data verticals before processing • Monitor data freshness and consistency • Validate data types and constraints • Track size and metric variance Never process garbage data no matter the pressure. 4. Define single source of truth Create one place for metrics and data • Define all metrics in data mart layer • Ensure stakeholders use SOT data only • Track data lineage and usage patterns This eliminates "the numbers don't match" conversations. 5. Keep stakeholders informed Communication drives warehouse adoption and resources • Document clear need and pain points • Demo benefits with before/after comparisons • Set realistic expectations with buffer time • Evangelize wins with leadership regularly No buy-in means no resources for improvement. 6. Watch for organizational red flags Some problems you can't solve with better code • Leadership doesn't value data initiatives • Constant reorganizations disrupt long-term projects • Misaligned teams with competing objectives • No dedicated data team support Sometimes the solution is finding a better company. 7. Focus on progressive transformation Use bronze/silver/gold layer architecture • Validate data before transformation begins • Transform data step by step • Create clean marts for consumption This approach makes debugging and maintenance easier. 8. Make data accessible Build one big tables for stakeholders • Join facts and dimensions appropriately • Aggregate to required business granularity • Calculate metrics in one consistent place Users prefer simple tables over complex joins. Share this with your network if it helps you build better data warehouses. How do you handle data warehouse maintenance? Share your approach in the comments below. ----- Follow me for more actionable content. #DataEngineering #DataWarehouse #DataQuality #DataModeling  #DataGovernance #Analytics

In a sea of possible insights, how do you know which are worth reporting? As a data analyst, there are two types of insights you will report: 1) Ones that are directly aligned to a business question or priority 2) Ones that nobody is asking for… but should be 90% of the time, you should be focusing on the first one. But when done right, the second can be very powerful. So… how do you find those hidden insights? How do you know which ones truly matter? ➤ Explore high-level trends Scan dashboards, reports, or raw data for unexpected patterns. Look for sudden spikes, dips, or emerging trends that don’t have an obvious explanation. ➤ Slice the data by different dimensions Break data down by different categories (customer segments, time periods, product lines, etc.). Where are things changing the most? Which groups are behaving unlike the others? ➤  Identify outliers Look at the extremes. What’s happening with your best customers? Worst-performing regions? Most productive employees? Outliers often reveal inefficiencies or hidden opportunities. ➤ Tie insights to business impact Before reporting, ask: Would knowing this change a decision? If it doesn’t, it’s probably not worth surfacing. ➤ Pressure-test with stakeholders Run your findings by a manager or friendly stakeholder. Ask them if the finding resonates with other trends they've seen, whether they see potential value, and whether it could influence strategy. In other words: - Start broad - Dig deep - Sense-check —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Let me share a personal story that changed my perspective on data's role in decision-making. Picture this: I'm on the New York subway platform, staring at the digital display. "Next train: 6 minutes." Useful? A bit. But I've already swiped my card and committed to this train line. All I can do is figure out how to best use the wait time. This is classic Business Intelligence (BI) - information that's useful but not action-oriented. Now, fast forward a few years. The MTA installs displays outside the stations. Seeing a 6-minute wait for the local train, I now have a choice. It's a 4-minute walk to the express station. Stay or go? This is Decision Intelligence (DI) - the power of right place, right time delivery. The same principle applies to our role as CDOs. We often pour resources into creating insights, reports, and metrics, but then neglect that crucial last mile - getting the right information to the right person at the right time. Here's how we can shift from BI to DI in our organizations: 1. Identify Key Decision Points Where in the business cycle are your stakeholders making critical decisions? That's where your data products need to be integrated and ready to use. 2. Focus on Actionable Insights Don't just report what happened. What's relevant to the decision-maker? Is your insight in the "good to know" category or the "option A is vastly better" category? 3. Optimize the Last Mile Think about how you're delivering insights. Are they embedded in the decision-making process or sitting in a separate report? This shift isn't just about technology - it's about positioning data as a profit enabler, not a support function - from data aware to data driven. This is how we move from being seen as a cost centre to becoming a strategic partner directly contributing to the core objectives of the business. *** 2500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organisational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

In the race to adopt the latest technologies, many companies are jumping on the AI bandwagon. But here's the truth: You don't need an "AI" strategy – you need a solid data strategy. + AI can only be as good as the data it processes. Without high-quality, well-organized data, even the most advanced AI systems will fall short. Start by ensuring your data is accurate, comprehensive, and easily accessible. + Invest in the tools and processes that allow you to collect, store, and analyze data effectively. This includes data governance, data quality management, and scalable storage solutions. + Break down silos within your organization. Ensure that data from different departments and sources can be integrated and analyzed cohesively. A unified data approach will provide a more complete and actionable view of your business. + A successful data strategy requires collaboration between IT, data science, and business units. Ensure everyone understands the value of data and works together to harness its potential. + With a solid data strategy in place, you'll be in a prime position to adopt AI technologies. Your AI initiatives will be more effective and deliver better results because they're built on a strong foundation of reliable data. In conclusion, before you think about implementing AI, make sure you have a robust data strategy. It's the backbone of successful AI applications and will drive long-term value for your organization. #DataStrategy #AI #DataDriven #BusinessIntelligence #DataQuality #TechStrategy #Innovation

Is your business drowning in data?! But here's the reality: data is only powerful when it's actionable. Even the best tools can't move the needle on your customer experience (CX) or business outcomes without a clear plan. Let's dive deep into how leaders can cut through the noise and extract meaningful insights from overwhelming amounts of customer data. Spoiler alert: it starts with having a clear objective. 🌟 Here's what we unpack in this Experience Action episode: ✅Clarity is Key: Before diving into tools or technology, ask: What's my objective? Knowing what you're after will help you focus on the right data. (Improve retention! Increase Referral Rates! More Repeat Purchases…get clear about outcomes.) 🔬 ✅Centralizing Your Data: Many organizations face the challenge of siloed data across departments. Implementing a Customer Data Platform (CDP) can be a game-changer by integrating information and providing a unified view of the customer journey. 🎯 ✅Leveraging AI for Deeper Insights: Once you have a clear objective and centralized data, artificial intelligence (AI) and machine learning can identify patterns and uncover hidden insights. Tools like IBM Watson and Salesforce Einstein can help you go beyond basic analysis and start making data-driven decisions at scale. 🤖✨ ✅Turning Insights into Action: Analysis without action is a wasted effort. Make sure you have a plan to act on your findings. Whether through sentiment analysis, predictive analytics, or feedback loops, the ultimate goal is to improve customer outcomes and drive business success. As CX leaders, the work we do matters. 🌍 We are shaping experiences not only to build loyalty but also to change lives. Feeling overwhelmed by big data or AI? We're here to support you. Learn more about our strategic approach and check out our resources at Experience Investigators. You've got this! https://lnkd.in/gGmC9cqZ #CustomerExperience #CXStrategy #DataInsights #AIinCX #ExperienceAction #CXLeadership #BusinessTransformation #CustomerSuccess #ExperienceInvestigators

If I were to write a book on data, here are 10 best practices I would include. 1) Meet with every single person on the C-suite on a regular basis and ask them what's top of mind on their side. Then find ways to help. It's the best way to identify high value stuff to work on. Once a week. Once every month as a worst case scenario. 2) Use video recording software for your internal calls. Sales and product teams record external calls. Why not record your calls with them? It allows you to go back and hear the actual things people asked for (instead of working off a backlog of made-up stuff). If someone complements work you've done, amazing, it's a great testimonial of how you're adding value. 3) Data quality doesn't have to be complicated. It's about trust. Add alerts at the top of dashboards that turn red if any checks fail on your data (i.e. Is the data stale? Do important numbers not match? Do the books not balance?). If a new thing goes wrong and you're not already checking for it, apologize, and add a new check quickly. You'll build trust because people will know that you don't make the same mistake twice. 4) Limit the number of dashboards your team is willing to support. Pick a number (I'd recommend less then 10), and stick to it. If anyone ever asks for more dashboards, force them to successfully deprecate another dashboard first. 5) Never create 1000 SQL queries. It's just stupid and leads to absolute chaos. (See bullet 4). 6) On dashboards, add a text box above every chart or table that says 'Action Item' and explains exactly what someone is supposed to do with the information. If the answer is nothing, find a different chart. 7) If you get asked for a new dashboard (on a topic you're not an expert in), start with a Google search. Specifically, a Google image search that shows dashboards other people have created. For instance, if someone asked you to build a dashboard to help reduce churn, do a Google image search for 'churn reduction dashboard', and see what other people have already done. You'll learn fast. 8) Once you know SQL (even if your queries are garbage), focus your efforts on learning business concepts. Do research into things that matter to YOUR business. If your company drives traffic via SEO, research the levers that matter in SEO. If your company is focused on growing headcount by 5x in 5 years, start learning about best practices for growing a team. The business context will inform the deliverables you build. 9) Don't get caught up in the tools. If you find yourself talking about your favorite architectures, or data stack tools to people in the business, you've gone too far into 'infrastructureland' and you need to refocus on what matters. 10) Create a production path and an ad-hoc development path. Don't end up in a world where every new data asset takes 4 weeks to roll out (so people start doing shadow analytics outside of the data team). Also stay away from a world where everything is ad-hoc. Find balance.