How to Make Data Actionable

📌 The True ROI of Business Intelligence Every company wants to be data-driven. They invest in modern data stacks, hire analysts, launch dashboards. And then… nothing really changes. Decisions are still made based on gut. Insights are acknowledged but not acted on. Dashboards are checked but not really used. Here’s the truth no one wants to admit: Being “data-driven” doesn’t mean collecting data. It means consistently taking better actions because of it. And that’s where most companies fall short. The real ROI of data analytics doesn’t happen when the report is delivered. It happens when a business process improves because of it. Let’s break it down with some examples to better understand my point: 1) A churn report doesn’t create value. → But an ops team that launches a new retention workflow based on that report? That’s ROI. 2) A marketing dashboard doesn’t grow revenue. → But reallocating ad spend based on performance patterns? That’s ROI. 3) A sales funnel visualization doesn’t close deals. → But identifying and removing a drop-off bottleneck? That’s definitely ROI. Do you see my point? So the question now becomes: How do you ensure your analytics actually lead to action? Here’s a playbook I would recommend: 1️⃣ 𝐓𝐢𝐞 𝐄𝐯𝐞𝐫𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐭𝐨 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 Before you publish a report, ask yourself: “What is someone supposed to do with this?” If the answer isn’t obvious, the insight isn’t useful yet. Make it actionable and not just interesting. 2️⃣ 𝐀𝐬𝐬𝐢𝐠𝐧 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 If a KPI has no owner, it has no future. → Every critical metric should have a name next to it. Not to blame, but to empower. Because action requires accountability. This is the easiest way to make people adopt your dashboards. 3️⃣ 𝐌𝐚𝐤𝐞 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐒𝐭𝐞𝐩 𝐂𝐥𝐞𝐚𝐫 Your dashboard isn’t an outcome. It’s a means to make better decisions. And you should definitely make it easy for your end user. → Schedule recurring check-ins for feedback → Create a simple action log linked to KPIs → Use alerts to notify the right person when a critical KPI changes Most organizations don’t fail because they don’t have insights. They fail because they don’t have systems for what happens next. The bottom line is: A lot of companies say they want to be data-driven. But in practice? If your BI initiative doesn’t lead to action, it’s not complete. The ROI of analytics lives in the next step. Design everything you build to make that step easier, clearer, and faster. #BusinessIntelligence #DataAnalytics

Your sales data is a goldmine. Here's how to extract the gold without hiring a data scientist. Your CRM knows which deals are slowing down. Your email platform tracks engagement patterns. Your calendar shows meeting velocity changes. But these insights stay buried because we're still playing data archaeologist. 𝗧𝗵𝗲 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗕𝘂𝗶𝗹𝗱 𝗶𝗻 𝟰𝟴 𝗛𝗼𝘂𝗿𝘀: 𝗗𝗮𝘆 𝟭: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 Start with the big three: • CRM (deal stages, velocity, win rates) • Email/Calendar (engagement patterns, meeting frequency) • Product usage (if applicable - login frequency, feature adoption) Use native integrations or simple tools like Zapier. Don't overthink it. 𝗗𝗮𝘆 𝟭: 𝗗𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗙𝗶𝘃𝗲 𝗚𝗼𝗹𝗱𝗲𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 Stop tracking everything. Focus on what moves revenue: • Deal velocity by stage (where deals get stuck) • Engagement score trends (are champions going cold?) • Pipeline coverage by rep and segment • At-risk indicators (no activity in 14+ days) • Expansion signals (usage spikes, new users added) 𝗗𝗮𝘆 𝟮: 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗩𝗶𝗲𝘄𝘀 This is where AI becomes your analyst: • Use Excel's new AI features or Google Sheets' Explore • Create anomaly detection for deal behavior • Build predictive models for close probability • Set up automated alerts for critical changes 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝗮𝘂𝗰𝗲: 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗡𝗼𝘁 𝗩𝗮𝗻𝗶𝘁𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 Your dashboard shouldn't just show numbers. It should tell you what to do: • "Deal X has slowed 40% - schedule executive check-in" • "Account Y showing expansion signals - book upsell call" • "Rep Z's pipeline velocity dropped - review deal strategy" 𝗠𝘆 𝘁𝗮𝗸𝗲: Stop waiting for perfect data infrastructure. Start with what you have. The best revenue intelligence system isn't the most sophisticated. It's the one that gets used every day because it answers real questions with real insights. Your sales data is already telling you where the gold is. You just need to start listening. What's the one metric you wish you could track in real-time but can't today? If you found value from this post, please ♻️ Repost. We are all learning together.

Reporting is NOT delivering insights. Unfortunately, many data & analytics professionals think it is. Reporting dashboards show WHAT's happening and enable basic slicing and dicing, but fail to deliver WHY. Example - "Performance is down 15% WoW" This is just stating the obvious. It's not a real insight. It's not actionable. This leaves many business leaders frustrated. When business stakeholders ask for more dashboards, what they are ultimately trying to achieve is "I need to know what's impacting my key business metrics and what I should do to improve it". Adding 15 more charts/views/slices won't help much to understand what's impacting the key business metrics and which actions should be taken. The key to REAL INSIGHTS that can move the needle? ROOT-CAUSE ANALYSIS to find the WHY (i.e., DIAGNOSTIC analytics) This is the most effective way to drive change with data & analytics. This can make the data & analytics team a TRUSTED ADVISOR and get a seat at the leadership and decision-making table. Insights need to be: 🟢SPEEDY: business stakeholders need quick insights into performance changes to make decisions before it's too late 🟢PROACTIVE: don't wait for business stakeholders to ask. Monitor key metrics and proactively share insights to become that trusted advisor 🟢IMPACT-ORIENTED: focus on the key drivers that drove most of the change and communicate accordingly 🟢EFFECTIVELY COMMUNICATED to drive the right action #data #analytics #impact #diagnosticanalytics

My nonprofits in the community - are you planning a donor survey in the next two months? Here are some examples of how you can ensure that the data does not sit silently in your work folders but actually lets it help you take meaningful actions. Example 1: Say your survey question is: "How likely are you to continue donating to our organization in the next year?" ● Data says: If 60% of donors say they are "very likely" to continue donating, but 30% are "somewhat likely" and 10% are "unlikely," this indicates a potential drop-off in donor retention. ● Turning that data into action: Focus retention efforts on the "somewhat likely" group. Create a targeted campaign that re-engages these donors by highlighting recent successes, impact stories, or new initiatives they might care about. Additionally, reach out to the "unlikely" group to understand their concerns and see if any issues can be addressed. Example 2: Say your survey question is: "Which of the following areas do you believe your donation has the most impact?" ● Data says: 50% of respondents say their donation has the most impact on "Education Programs," while only 10% say "Healthcare Initiatives." ● Turning that data into action: Understand the why and promote the success and need for your "Healthcare Initiatives" more prominently, aiming to increase donor awareness and support in this underfunded area. Example 3: Say your survey question is: "What is your primary reason for donating to our organization?" ● Data says: If the top reason to engage is "Alignment with my values" (40%) followed by "Transparency in how funds are used" (35%). ● Turning that data into action: Emphasize your organization's values and transparency in all communications. Regularly update donors on how their funds are being used with clear, detailed reports, and align your messaging with the core values that resonate with your donor base. Example 4: Say your survey question is: "How satisfied are you with the level of communication you receive from our organization?" ● Data says: If 70% of donors are "satisfied", 20% are "neutral," and 10% are "dissatisfied," there's room for improvement in communication. ● Turning that data into action: Understand the "neutral" and "dissatisfied" groups to pinpoint where communication may be lacking. This could involve increasing the frequency of updates, personalizing communications, or providing more opportunities for donor feedback and engagement. Sit with the data you collect. Read the numbers. Read the stories. Read the hopes, barriers, and interests of those humans in your data. The best possibility of a survey is to make the humans in that data feel included and belong by listening and acting on their perspectives. Co-create change with your community in those surveys. #nonprofits #nonprofitleadership #community #inclusion

What makes network data truly fit for purpose? Most operators assume their data is “good enough” — until they try to plan a new rollout, migrate to a new platform, or respond to an auditor. Then the gaps start to show. Here’s what best-practice network data should look like — and what’s usually missing: 1. Accessibility Your data must be easily available to the right teams — planning, engineering, finance, and ops — not buried in PDFs, offline spreadsheets, or locked inside vendor portals. API-ready, searchable, and integrated. 2. Accuracy Your logical and physical records must reflect reality — not theory. Sites, devices, ports, circuits, IPs — clean, verified, and matched to the field. 3. Consistency No conflicting statuses or naming chaos. A “retired node” in one system shouldn’t be “live” in another. 4. Completeness All network elements and relationships should be documented: From sites and fibres to VLANs, services, and customers — active, passive, and metadata included. 5. Timeliness Data should reflect the current network — not how it looked six months ago. Updates should be near real-time or run on a defined change workflow. 6. Interoperability Data must flow across OSS, GIS, inventory, billing, and NMS. Standard formats (JSON, CSV, XML), clean exports/imports. 7. Security & Permissions Data must be accessible but controlled. Role-based views, auditable changes, and protection of sensitive info. 8. Contextuality Data is more useful when enriched. Status, vendor, customer links, dependencies — not just raw asset records. 9. Cleanliness No duplicates, no “TBD” fields, no ghost circuits. 10. Actionability Your data should help people do things — not just sit in a database. Plan, forecast, troubleshoot, report — with confidence. If your teams struggle with any of the above, we’ve developed a practical, insight-led course that helps telcos clean, align, and take control of their network data. “The Hidden Cost of Bad Network Data” Enrolment open now. Send me a message on LinkedIn, WhatsApp, Email or Call me to learn more.

Working Smarter with Marketing Data (Even If You’re Not a Numbers Person) I want to talk about working smarter—not harder—with your marketing data. Especially if you’re like so many people I meet: creative, strategic, insightful... but somewhere between “mildly allergic” and “outright panicked” when faced with a spreadsheet. So here’s a beginner-friendly roadmap to make data work for you—not become your full-time job. Step 1: Start With the Question, Not the Chart Before opening any tool or pulling any numbers, pause and ask: “What am I trying to learn or prove?” Seriously. That’s 80% of smart data work. 📌 Are you trying to figure out what content drives the most leads? 📌 Are you wondering if your email subject lines are resonating? 📌 Are you testing whether a certain audience segment is underperforming? Data becomes much easier to navigate when it’s anchored in a curiosity. Step 2: Track Only What You’ll Use📊 There are a lot of metrics out there: impressions, CTR, bounce rate, MQLs, ROAS, CAC… Do not try to track them all. 💡 Ask yourself: Which 2–3 metrics actually inform my decisions? If you’re running social campaigns, maybe that’s engagement rate + link clicks. If you’re optimizing email, maybe it’s open rate + conversion. Let the decision drive the data—not the other way around. Step 3: Use Simple Tools First You do not need a 20-tab dashboard or enterprise software to start. Some of the best marketers I know work out of: A Google Sheet with weekly performance highlights UTM links in a free link tracker A quick weekly notes doc: “What worked / What flopped / What we’ll try next” ✨ Pro tip: Set a recurring 15-minute block each week to review and reflect. No deep dive, no pressure. Just a check-in with your numbers. Step 4: Turn Data Into Stories This is where the magic happens. Don’t just say “Our bounce rate dropped.” Say: “After redesigning our landing page, visitors stayed 45 seconds longer on average. That tells us the new content is keeping people engaged.” Your stakeholders—and your future self—will thank you. Even a single sentence summary can be powerful: “Our IG Reel on behind-the-scenes bouquet prep had 3x the usual engagement. It suggests our audience wants more human, real-time content.” Step 5: Build a Repeatable Habit, Not a Perfect System Working smarter with marketing data doesn’t mean perfection. It means building a repeatable, low-stress system that supports your decisions. ✔️ Ask a question ✔️ Pull 1–2 metrics that help answer it ✔️ Reflect and adjust ✔️ Repeat next week Don’t wait until you feel “ready” to be a data-driven marketer. You’re ready right now—with the curiosity you already have and the tools you already use. You don't need to be technical, just intentional, and you'll have great data before you know it. What’s one small data habit that’s helped you work smarter, not harder? #MarketingAnalytics #MarketingTips #DigitalStrategy #WomenInMarketing #LIPostingDayApril

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.

🚨 The data community has been chasing the wrong metric. We’ve spent years perfecting analytics: building data warehouses, lakehouses, scalable data pipelines, beautiful dashboards. We have convinced ourselves that faster insights = bigger impact. But insight alone doesn’t change outcomes.  Action does. In the past few months, I’ve had the opportunity to step out of my own data bubble and work closer with operations. I’ve realized we need to rethink how data teams create impact.  I’ve come to see three stop-and-starts that our community needs to embrace: 1️⃣ Stop doing just analytics. Start building feedback loops from analytics to operations. Insights that don’t drive action are wasted potential. It’s time to connect the dots. Connect analytics to workflows. Automate decisions. Partner with business lines to close the loop between data and impact. 2️⃣ Stop focusing only on BI semantic layers. Start building your enterprise ontology. BI Semantic layers are great, but they mostly serve analytic tools. Go deeper: model how your enterprise truly works. We need to create a map that connects customers, products, suppliers, vendors, risk, and outcomes. That’s where intelligence becomes actionable. 3️⃣ Stop staying in the data bubble. Start co-designing goals and OKRs with operations. Data can’t shape outcomes if it’s detached from them. Sit with the people who own the results. Define shared goals and OKRs. Make data a co-pilot in decisions, not a post-mortem observer. If you don’t know what the top level objectives of your company are... then that is where you should start! I truly believe that the data community must move from “faster time to insight” to “insights that drive action and outcomes.” 💭 How are you closing the loop between data & analytics and operations in your org?

This one shift in my data strategy transformed my business decisions: Actually using the insights we gathered. Sounds obvious, right? I used to obsess over collecting data. More numbers, more charts, more reports. A new trend emerged? I'd add another dashboard. Team struggled with analysis? I'd buy fancier tools. Sound familiar? For months, we were drowning in data but parched for actionable insights. It was overwhelming. And pointless. Then it hit me: Data isn't about collecting. It's about applying. Here's the truth: Unused insights are just expensive decorations. They make us feel smart instead of actually being smart. What changed? I started treating data like a compass, not a trophy case. 3 tips to ensure you use data analytics insights effectively: ▶️ Start with questions, not tools → What decision are you trying to make? Let that guide your analysis. ▶️ insights accessible → Fancy reports gather dust. Simple, shareable insights drive action. ▶️ Set insight expiration dates → Old data can mislead. Regular review keeps your strategy fresh. The result? Our decision-making speed doubled. Why? Because we were acting on real insights, not drowning in numbers. Don't get me wrong. I still believe in thorough analysis. But now, I let business needs drive the data conversation. Insights inspire. Data alone paralyzes. It wasn't easy at first. Changing habits is tough. But the payoff was worth every growing pain. Now, I ask myself: "What action will we take based on this insight?" If there's no clear answer, it's not an insight. It's just noise. #data #business #sales

Dashboards should be designed for action, not data. Most dashboards contain plenty of data. Dozens of metrics and pretty charts. We've been taught that data drives action, but in practice, it rarely does. As you build your dashboards & and reports, consider the question: What is the user's "next best action"? Then, build solutions to prompt (or enable) that action. Some examples of "next best action": 1.) More Data Sometimes, the user will have more questions. That's ok! We build in self-service filters, segments, and drill-downs to dive in deeper. Self-service > fewer questions for the data team > faster time to action. 2.) Related Data Most businesses will have dozens of reports, often fragmented and disjointed. We can build links to bridge between the reports. Additionally, those links can be dynamic to carry through important filters (date ranges, segments applied) and help users keep their contextual flow. Less time hunting for reports > faster action. 3.) Sharing the data Once users find interesting data, they want to save it or send it to a coworker or client. Enable sharing via email, slack, raw export, etc. Sharing > More distribution > more action. 4.) Actions in another platform (Shopify, Meta, Salesforce, etc) Based on the data, users will need to make a change in another tool. Take someone in merchandising. They see product reports showing that certain products have low conversion rates, likely due to dwindling inventory levels. We can build a link in the dashboard that takes them DIRECTLY to the Shopify admin portal to the product setup and re-merchandise their collection. With one click, they've gone from data > to action. Fewer clicks > faster action. 5.) Alerts Users may see a number and wish they knew about it sooner. For this we setup alerts (email, slack, sms, webhook, etc.) Faster alerts > faster action. Our goal is to transform data-heavy dashboards into tools for action. Consider: - Can we make them more self-service? - Can users set up alerts? - Can they export and share the data easily? - Can we link tools and reports together to avoid context switching? - Can we automate the data to drive action? Are there any tricks you're using to make your dashboards more actionable? #businessintelligence #looker #ecommerceanalytics #measure