Best Practices for Data Management

I can always tell if an analyst is illogical by this one sign: Their presentations. When it comes to sharing insights, *how* you structure your message matters just as much as *what* you’re saying. Two simple concepts can make your presentations more effective: 1) Vertical logic 2) Horizontal logic ——— 1) Vertical logic is about focus Every slide should have one clear takeaway, which is stated right in the title (not descriptive like “Overview” or “Results” but something like “Sales dropped due to supply delays”). Everything else on the slide - charts, text, visuals - should support that message. ——— 2) Horizontal logic is about flow Each slide should build naturally from the one before it. Click through your deck and read only the slide titles. Does it read like a story from start to finish? ——— When you have both vertical and horizontal logic, your message is clearer, your deck flows, and your audience remains engaged. Here’s my quick checklist before presenting: ✅ Can I follow the storyline by reading just the slide titles?  ✅ Does each slide build on the one before it? ✅ Is the takeaway of each slide instantly clear? If not - back to editing! P.S. New here? I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Data Governance: Understand Key Focus Areas 🎯 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 & 𝐊𝐏𝐈𝐬: 🔘 Data Quality: Measure accuracy, completeness, and consistency. 🔘 Stakeholder Satisfaction: Ensure data governance efforts meet stakeholder expectations. 🔘 Security: Track how well your data is protected against breaches. 🔘 Operational Efficiency: Assess the effectiveness of your data processes. 🔘 User Adoption: Gauge the extent to which data tools and processes are utilised. 🔘 Data Value: Quantify the business value derived from data. 🔘 Data Risk: Identify and mitigate potential data-related risks. 🔘 Compliance: Ensure adherence to relevant laws and regulations. 🔍 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 & 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 🔘 Data Quality Management: Maintain high standards for data accuracy and reliability. 🔘 Regulatory Compliance: Stay compliant with laws and regulations to avoid penalties. 🔘 Cost Efficiency: Optimize data-related costs for better financial management. 🔘 Data Stewardship: Assign responsibility for data management and policies. 🔘 Data Usability: Ensure that data is accessible and usable for stakeholders. 🔘 Data Transparency: Promote openness in data practices and policies. 🔘 Data Ethics: Uphold ethical standards in data collection and usage. 🔘 Decision-Making: Use data to inform strategic decisions. 🔘 Data Security: Protect data from unauthorised access and breaches. 🔘 Data Ownership: Clearly define who owns and is responsible for data. 🔘 Data Integrity: Maintain the accuracy and consistency of data over its lifecycle. 🔘 Data Auditing: Regularly review data and governance practices to ensure compliance and performance. 👥 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬: 🔘 Executive Leadership: Drive data governance strategy and ensure alignment with business goals. 🔘 Data Owners: Responsible for specific data assets and their quality. 🔘 Data Stewards: Manage data policies and quality. 🔘 Data Users: Utilise data for various business functions. 🔘 IT Departments: Support data infrastructure and security. 🔘 Legal and Compliance Teams: Ensure data governance practices comply with legal requirements. 🔘 Business Analysts: Analyse data to derive business insights. 🔘 External Partners: Collaborate on data sharing and governance. 🛠 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 & 𝐓𝐨𝐨𝐥𝐬: 🔘 Data Dictionary: Defines data elements and their meanings. 🔘 Data Catalogue: Organises data assets for easy discovery and access. 🔘 Metadata Management: Manages data about data for better understanding and use. 🔘 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 🔘 𝐏𝐨𝐥𝐢𝐜𝐲 𝐚𝐧𝐝 𝐑𝐮𝐥𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 🔘 𝐃𝐚𝐭𝐚 𝐋𝐢𝐧𝐞𝐚𝐠𝐞 🔘 Reporting Tools: Generate reports to monitor and manage data. 🔘 Governance Dashboards: Visualise key metrics and governance performance. 🔘 Audit and Compliance Tools: Ensure data governance policies and regulations are adhered to. #DataGovernance #DataQuality #Compliance #DataSecurity #DataEthics #DataIntegrity #DataManagement #AI #DataScience #DataStrategy

Data quality is a holistic process. If you ignore one practice, the rest of your effort may be worthless. Some practices are essential. You can't ignore them: ⚡Profiling the data to understand its structure ⚡Data stewardship to establish communication between data engineers and business users to decide what is good data ⚡Data quality issue tracking to react to and track problems Other practices can be ignored, but you will have to spend twice as much effort on other practices to fix them. ⚡Missing data contracts will not make the data publishers accountable ⚡Missing data observability will delay the detection of issues until users see them ⚡Missing data quality testing will make users test the data ⚡Without reporting, you cannot prove your effort in data quality and show which datasets are reliable over time ⚡Without standards and practices, you will apply different metrics and data quality testing methods across data domains ⚡No automation means that setting up data quality is time consuming There are other practices that you can also implement, such as: 🔸Validating data at the source 🔸Automated data cleansing 🔸Data lineage tracking #dataquality #datagovernance #dataengineering

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

The hardest part of #datastorytelling isn't the analysis or crafting the narrative—it's delivering unwelcome truths to the people who sign your paycheck. Frequently, the insights we reveal expose inconvenient truths: failed strategies, risky trends, and performance gaps that leadership doesn't want to hear. As data storytellers, we are tasked with representing reality, not fantasy or flattery. Unfortunately, many of our truths will be unpopular or uncomfortable. Despite the saying "don't shoot the messenger," there's always risk when sharing unpleasant information where power and egos are involved. How do we 𝐬𝐩𝐞𝐚𝐤 𝐭𝐫𝐮𝐭𝐡 𝐭𝐨 𝐩𝐨𝐰𝐞𝐫 as data storytellers in these intimidating situations? Here are five survival strategies: 1️⃣ 𝐀𝐧𝐜𝐡𝐨𝐫 𝐢𝐧 𝐬𝐡𝐚𝐫𝐞𝐝 𝐠𝐨𝐚𝐥𝐬. Frame your message around what's best for the organization. Leaders are less defensive when you position your story around what they value. For example, when showing declining customer retention, focus on how addressing it supports growth targets. 2️⃣ 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐭𝐡𝐨𝐫𝐨𝐮𝐠𝐡𝐥𝐲 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐝 𝐜𝐫𝐞𝐝𝐢𝐛𝐢𝐥𝐢𝐭𝐲. If you anticipate objections, ensure your data story is bulletproof (data, narrative, visuals). If you’ve already established a reputation for being objective, detail-oriented, and trustworthy, your messages will carry more weight. 3️⃣ 𝐀𝐝𝐣𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐩𝐨𝐰𝐞𝐫 𝐝𝐲𝐧𝐚𝐦𝐢𝐜𝐬. Preserve decision makers' dignity. It shouldn't be about calling someone out but collaborative problem-solving. Consider using a private setting initially. For example, share concerning metrics with a team lead privately before the department meeting. 4️⃣ 𝐁𝐫𝐢𝐧𝐠 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬. You're not just a critic—you're a collaborator. When highlighting bad news, also highlight opportunities. When presenting declining sales, include an analysis of which segments remain strong for future targeting. 5️⃣ 𝐒𝐭𝐚𝐲 𝐜𝐚𝐥𝐦, 𝐥𝐢𝐬𝐭𝐞𝐧, 𝐚𝐧𝐝 𝐛𝐞 𝐫𝐞𝐬𝐩𝐞𝐜𝐭𝐟𝐮𝐥. If leadership pushes back, resist the urge to argue. Acknowledge their concerns, listen to their unique perspectives, and maintain a respectful tone to lower barriers. Not all of your data stories will be well received. Some leaders only want affirmation or appeasement, not information. Be clear on your values and boundaries because sharing unwelcome insights may have consequences. Know when to fight for truth and when to live to tell a story another day. Have you successfully delivered difficult data insights to leadership? What approach worked for you? 🔽 🔽 🔽 🔽 🔽 📬 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 📚Check out my new data storytelling masterclass: https://lnkd.in/gy5Mr5ky 🛠️ Need a virtual or onsite data storytelling workshop or speaker? Let's talk. https://lnkd.in/gNpR9g_K

This might be a controversial take but… As organizations rethink their data strategies, they must focus on “minimizing” the amount of data they collect and store. By eliminating unnecessary data, companies can significantly reduce their data footprint, thereby saving time and resources in processing. This approach not only mitigates the risk of data breaches but also lowers the potential impact if a breach does occur. Furthermore, with less data to manage, organizations can allocate their efforts more effectively, ensuring that the data they retain is used to its fullest potential. Actionable insights for internal auditors: 1. Encourage a mindset within the organization that values responsible data collection and usage. 2. Identify areas where data privacy and protection are most vulnerable. Focus audit efforts on these areas to mitigate potential risks. 3. Regularly audit the organization’s data retention policies to ensure that only necessary data is being stored. Recommend the elimination of redundant or outdated data. 4. Facilitate discussions and workshops that bring together stakeholders from different departments to align on data governance practices and privacy standards. What do you think about data minimalization? #internalaudit #ITaudit #digitaltransformation

A respected, passionate R&D leader recently shared how he had tried to influence the investment committee to back a key project. Whilst presenting, he described how his team had 'slashed' multiple projects to arrive at this promising innovation opportunity. He was surprised when committee members were visibly taken aback by his vivid choice of word. On reflection, he wondered whether his highly animated, direct style, coupled with the potent language may have created an unintended impression. And so to Aristotle's Golden Triangle of Communication. Aristotle long ago recognized the importance of blending these three capabilities effectively in order to influence positively: Ethos - Logos - Pathos Ethos, or credibility. What is the level of trust you evoke in others by being you - through your track record, your knowledge and your (non-designation related) authority? How do you communicate your expertise and experience to your audience, in a way that they appreciate and acknowledge? Logos, or reason. What logic, reason or evidence do you use to support an argument? What facts, figures, and structured thinking do you display to lead your audience to the conclusion you have in mind? Pathos, or emotion. How do you create an emotional connection with your audience, so that they believe in your point of view? Do you share a relatable, emotive story? Use vivid imagery, or evoke a strong memory or emotion that propels them to action? Different audiences require different blends and different ways of communicating the three ingredients. Many organizations pride themselves on being data-driven. And data is important. But always remember that emotions drive people and people drive performance. Facts and figures provide an opportunity to tell a story that evokes emotion. And facts and figures that are presented without telling the story - or without the certainty that the audience can deduce that story - can fail to evoke the necessary emotion that propels people to action. #communication #influence #eq #sixseconds #sixecondsmeai

𝐓𝐡𝐞 𝐚𝐫𝐭 𝐨𝐟 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐨 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐬𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 One of the most underappreciated challenges in leading data initiatives isn't the technology, it's effectively engaging with multiple stakeholder groups who each need different information, presented differently. Success can be best supported by tailoring your approach across three distinct audiences: 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞/𝐁𝐨𝐚𝐫𝐝 𝐋𝐞𝐯𝐞𝐥 These stakeholders need the 30,000-foot view focused on: 🔹 Business impact and ROI 🔹 Risk mitigation strategies 🔹 Resource allocation justification 🔹 Clear timelines with defined milestones When presenting here, focus on outcomes rather than methods, using business metrics they already value and understand. 𝐂𝐫𝐨𝐬𝐬-𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 Department leaders and business partners require: 🔹 How the project will affect their operations 🔹 Specific benefits to their teams 🔹 Required involvement and resource commitments 🔹 Timeline of when they'll see tangible results Ensure you translate technical concepts into functional benefits, always answering their implicit question: "What's in it for my team?" 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐌𝐄𝐬 / 𝐃𝐨𝐞𝐫𝐬 These specialists need: 🔹 Architectural decisions and their rationale 🔹 Technical dependencies and integration points 🔹 Clear technical requirements and acceptance criteria 🔹 Roadmaps for implementation and technical debt management With this group, go deeper into the "how" while still connecting it to the "why." The true art lies in maintaining consistency across these different views. The timeline shown to executives must align with what the technical team is building and what business stakeholders are expecting. The promised business outcomes must be technically feasible. Successful data leaders don't just understand data, they understand people and can adapt their communication to bring everyone along on the journey. What challenges have you faced when communicating complex data initiatives across different organisational levels? #DataLeadership #StakeholderManagement #DataStrategy #TechnicalLeadership

All data ultimately has a human source—it is not collected, but created. Data-savvy leaders understand this nuance. Decision infrastructures are often built on the premise that data is objective, definitive, and value-neutral. This leads organizations to treat data as an infallible compass. However, every byte of information springs from human actions, decisions, interactions, goals, and biases. Customer data, for example, doesn't just show behavior but reflects how people navigate interfaces we've designed, within constraints we've established. Even pristine financial data carries the imprint of human judgment—from revenue recognition timing to expense categorization—codified in vast accounting guidelines, but human-made nonetheless. Does this mean data is just subjective figures open to any conclusion? Of course not! It means that for proper understanding and interpretation, data's context is vital. All that metadata and methodology documentation isn't a footnote, but a crucial user's manual. Even the most carefully constructed dataset can be misinterpreted without proper context. This demands a targeted response. Implementing the following five specific structural changes can help address this reality: 1️⃣ Make the documentation of collection methods, decision points, known biases, and limitations a part of your data quality metrics. 2️⃣ For major decisions, require stakeholders to articulate which assumptions the data implicitly reflects and how changes would affect conclusions. 3️⃣ Pair data specialists with subject matter experts who understand the contexts generating the data. Formalize this collaboration for critical insights. 4️⃣ Integrate behavioral variables into risk assessment by testing how human motivations could invalidate data patterns. Create alternate scenarios for more robust strategies. 5️⃣ Establish mechanisms to test data-derived insights against lived experiences, where frontline observations can challenge or validate data-based conclusions. When businesses acknowledge that humans shape every piece of data, they gain insights that others miss and avoid misinterpretations, strategic missteps and compliance failures (like algorithmic bias). Success comes not from making data more human-friendly, but from recognizing data as fundamentally human in the first place.