Data Insights Utilization

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

Harsh truth for breaking into Data Analytics: ↳ You can complete certifications in Power BI or Tableau. ↳ You can read every blog or book on data analytics. ↳ You can watch multiple tutorials on data analysis. 𝐁𝐮𝐭 𝐢𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞𝐧’𝐭/𝐝𝐨𝐧’𝐭: ↳ Solving real business problems with data. ↳ Analyzing real-world datasets consistently. ↳ Understanding the 𝐖𝐇𝐘 behind every analysis. ↳ Building dashboards to share insights effectively. ↳ Improving by seeking feedback and fixing edge cases. You won’t get the results you expected. We get so caught up in learning the 𝐖𝐇𝐀𝐓 and 𝐇𝐎𝐖, that we forget that we actually need to 𝐃𝐎. Start working on open datasets, answering business problems, and building projects that showcase your skills. Master the fundamentals, challenge yourself with real-world scenarios, and repeat every day. The key to breaking into data analytics is consistent practice, solving real problems, and learning from every experience. #dataanalytics

This is the full data journey. And most teams stop too early. Do you? Data → Sorted → Arranged → Visualized → Explained with a story All useful steps. None of them are the finish line. Insight without action remains unfinished work. The real value of data appears only when it becomes actionable: • A priority shifts • A risk is avoided • A decision changes • A behavior improves Dashboards don’t move businesses. Stories alone don’t move businesses. Actions do. The best data professionals don’t just explain what happened. They make it obvious what to do next. If your analysis ends with “𝘏𝘦𝘳𝘦 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘯𝘶𝘮𝘣𝘦𝘳𝘴,” you’re not done yet. What’s one insight you’ve seen that actually led to a concrete action? P.S. Data earns its seat at the table only when it earns the right to change something.

Collecting Your Job Search Data Could Be the Game-Changer You Need—Here's Why As a data career coach for over three years, I've helped clients consistently land jobs—averaging more than one placement each month. Recently, I analyzed a client's job search data over a 3-month period, and the insights were eye-opening. 📊 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮: • 200 applications sent • 6 interviews received (4 were from referrals) • 350 connection requests sent • 175 new connections made • 27 conversations started (0 with hiring managers) • 10 informational interviews conducted • 20 referrals received • 2 interviews from new connections • 2 interviews from informational interviews 🔎 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱: • Applications to Interviews: Approximately 1 interview for every 90 applications—slightly above the average 1% conversion rate. • Referrals to Interviews: 1 interview for every 9 referrals—below the desired 33% success rate. • Warm Referrals: Every warm referral (directly passed to the hiring team) led to 1 interview—exceeding the 33% average. • Connection Acceptance Rate: 50% of connection requests were accepted—above the typical 33% average. • Conversations Started: Only 15% of connections led to conversations—below the 33% average. • Informational Interviews to Referrals: 20% of informational interviews resulted in referrals—below the 33% benchmark. 🚀 𝗔𝗰𝘁𝗶𝗼𝗻 𝗦𝘁𝗲𝗽𝘀 𝗪𝗲 𝗧𝗼𝗼𝗸: • Optimize Outreach Messages • Began A/B testing messages to hiring managers to improve response rates. • Focus on Genuine Networking • Shifted efforts toward building meaningful relationships rather than directly asking for help, aiming to increase conversation rates. • Enhance Informational Interviews • Invested more time researching individuals and companies to make informational interviews more impactful. • Refine Networking Strategy • Reduced direct requests for assistance from new connections due to low conversion, focusing instead on providing value first. 💡 The Result? By collecting and analyzing job search data, we pinpointed areas for improvement and implemented targeted strategies to enhance success rates. Your Turn: Do you track your job search data? What insights have you gained from analyzing your efforts? Let's discuss! Share your experiences or ask questions in the comments below.

The real value in analytics isn’t just knowing the tools it’s knowing why you’re using them. Early on in my analytics journey, I did what most people are told to do. Learn SQL. Learn Python. Learn Excel. Learn Power BI. I followed tutorials, built mini-projects, practiced queries… all of it. And don’t get me wrong these tools are important. They’re powerful, and you do need them to execute your work. But here’s what I wasn’t prepared for: You can know every function, every syntax, every visualization… and still feel stuck when someone asks: ▪️“So what does this actually mean for the business?” ▪️“What should we do with this insight?” ▪️ “What’s the story behind these numbers?” That’s when it hit me , Analytics isn’t just technical. It’s contextual. It’s strategic. What I gave less importance to at first (and I now realize how crucial it is) was learning the business story behind the data. I spent more time figuring out how to calculate a metric than asking what that metric really meant. I was focused on how to do things, not why we were doing them. Now I "try" to approach problems differently. Before jumping into code or visuals, I ask: 📌What’s the business goal here? 📌Who’s the audience for this insight? 📌What decision are they trying to make? 📌What would be genuinely useful to them? Because once you know the why, the how becomes clearer. You choose the right tools, you focus on the right metrics, and most importantly your analysis actually makes an impact. It’s a mindset shift I’m still working on. Still figuring things out. Still learning how to connect data with real-world business needs. But this shift has made a huge difference in how I see my work. If you’re starting out in analytics - Please, do learn the tools. But don’t stop there. Learn to ask better questions. Think like the person who needs the insight not just the person who can build the chart. That’s where the real value in analytics lives. Not in the code. Not in the dashboards. But in the thinking behind them. ♻️ Repost : If you found this helpful, to reach others who might need it. ✳️ Follow Mariya Joseph for more daily content!

Cold email outreach is often misunderstood as a numbers game - send more, get more. But the truth is, quality always beats quantity. Your success largely depends on the quality of your email list. A well-curated list means you’re reaching the right people with the right message, while a poorly managed list can lead to low engagement, high bounce rates, and damage to your sender reputation. Here’s a few key steps to refine your cold email lists for better results: 1️⃣ Segment your audience: start by dividing your list into segments based on criteria such as industry, job title, company size, or previous interactions with your brand. 2️⃣ Regularly clean your list: over time, email lists can become cluttered with inactive or invalid addresses. Regularly clean your list to remove bounced emails, unsubscribes, and unengaged contacts. This not only improves your deliverability rates but also ensures you’re focusing on prospects who are more likely to engage. 3️⃣ Use data enrichment tools: tools like Clearbit, ZoomInfo, or Hunter.io can help you enrich your email list by adding valuable data points such as the prospect’s role, company details, and social profiles. This extra information enables you to personalize your outreach even further. 4️⃣ Monitor engagement metrics: pay close attention to open rates, click-through rates, and responses. If certain segments of your list are underperforming, it may be time to re-evaluate those contacts. Use these insights to continuously refine your list and improve your targeting. 5️⃣ Leverage intent data: incorporate intent data into your list-building process. This data shows which companies are actively researching solutions like yours, allowing you to target prospects when they’re most interested. Tools like Bombora or 6sense can provide these insights. If you do something extra special to your email lists, share below 🤓 #Sales #ColdEmail #EmailMarketing #Outreach #SalesStrategy

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

Most teams are just wasting their time watching session replays. Why? Because not all session replays are equally valuable, and many don’t uncover the real insights you need. After 15 years of experience, here’s how to find insights that can transform your product: — 𝗛𝗼𝘄 𝘁𝗼 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗥𝗲𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗥𝗲𝗽𝗹𝗮𝘆𝘀 𝗧𝗵𝗲 𝗗𝗶𝗹𝗲𝗺𝗺𝗮: Too many teams pick random sessions, watch them from start to finish, and hope for meaningful insights. It’s like searching for a needle in a haystack. The fix? Start with trigger moments — specific user behaviors that reveal critical insights. ➔ The last session before a user churns. ➔ The journey that ended in a support ticket. ➔ The user who refreshed the page multiple times in frustration. Select five sessions with these triggers using powerful tools like @LogRocket. Focusing on a few key sessions will reveal patterns without overwhelming you with data. — 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲-𝗣𝗮𝘀𝘀 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 Think of it like peeling back layers: each pass reveals more details. 𝗣𝗮𝘀𝘀 𝟭: Watch at double speed to capture the overall flow of the session. ➔ Identify key moments based on time spent and notable actions. ➔ Bookmark moments to explore in the next passes. 𝗣𝗮𝘀𝘀 𝟮: Slow down to normal speed, focusing on cursor movement and pauses. ➔ Observe cursor behavior for signs of hesitation or confusion. ➔ Watch for pauses or retracing steps as indicators of friction. 𝗣𝗮𝘀𝘀 𝟯: Zoom in on the bookmarked moments at half speed. ➔ Catch subtle signals of frustration, like extended hovering or near-miss clicks. ➔ These small moments often hold the key to understanding user pain points. — 𝗧𝗵𝗲 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 + 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Metrics show the “what,” session replays help explain the “why.” 𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 Gather essential metrics before diving into sessions. ➔ Focus on conversion rates, time on page, bounce rates, and support ticket volume. ➔ Look for spikes, unusual trends, or issues tied to specific devices. 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗪𝗮𝘁𝗰𝗵 𝗟𝗶𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 Organize sessions based on success and failure metrics: ➔ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗖𝗮𝘀𝗲𝘀: Top 10% of conversions, fastest completions, smoothest navigation. ➔ 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗖𝗮𝘀𝗲𝘀: Bottom 10% of conversions, abandonment points, error encounters. — 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗥𝗲𝗽𝗹𝗮𝘆 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 Make session replays a regular part of your team’s workflow and follow these principles: ➔ Focus on one critical flow at first, then expand. ➔ Keep it routine. Fifteen minutes of focused sessions beats hours of unfocused watching. ➔ Keep rotating the responsibiliy and document everything. — Want to go deeper and get more out of your session replays without wasting time? Check the link in the comments!

Surveys can serve an important purpose. We should use them to fill holes in our understanding of the customer experience or build better models with the customer data we have. As surveys tell you what customers explicitly choose to share, you should not be using them to measure the experience. Surveys are also inherently reactive, surface level, and increasingly ignored by customers who are overwhelmed by feedback requests. This is fact. There’s a different way. Some CX leaders understand that the most critical insights come from sources customers don’t even realize they’re providing from the “exhaust” of every day life with your brand. Real-time digital behavior, social listening, conversational analytics, and predictive modeling deliver insights that surveys alone never will. Voice and sentiment analytics, for example, go beyond simply reading customer comments. They reveal how customers genuinely feel by analyzing tone, frustration, or intent embedded within interactions. Behavioral analytics, meanwhile, uncover friction points by tracking real customer actions across websites or apps, highlighting issues users might never explicitly complain about. Predictive analytics are also becoming essential for modern CX strategies. They anticipate customer needs, allowing businesses to proactively address potential churn, rather than merely reacting after the fact. The capability can also help you maximize revenue in the experiences you are delivering (a use case not discussed often enough). The most forward-looking CX teams today are blending traditional feedback with these deeper, proactive techniques, creating a comprehensive view of their customers. If you’re just beginning to move beyond a survey-only approach, prioritizing these more advanced methods will help ensure your insights are not only deeper but actionable in real time. Surveys aren’t dead (much to my chagrin), but relying solely on them means leaving crucial insights behind. While many enterprises have moved beyond surveys, the majority are still overly reliant on them. And when you get to mid-market or small businesses? The survey slapping gets exponentially worse. Now is the time to start looking beyond the questionnaire and your Likert scales. The email survey is slowly becoming digital dust. And the capabilities to get you there are readily available. How are you evolving your customer listening strategy beyond traditional surveys? #customerexperience #cxstrategy #customerinsights #surveys

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