Using Data To Improve Efficiency

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

The company had millions of rows of data. Nobody knew which version was correct. Different dashboards showed different numbers. Teams argued over definitions. Reports were delayed. Trust disappeared. The problem was not lack of data. It was lack of governance. More data does not automatically create better decisions. Without structure, ownership, quality checks, and controls, scale becomes chaos. That is why strong companies invest in data governance components, not just storage. Here are 8 types of data governance components every team should understand 👇 - Data Catalog Create one searchable place for datasets, owners, glossary terms, and trusted sources. - Data Lineage Show where data originated, how it changed, and where it flows across systems. - Data Quality Checks Validate duplicates, nulls, freshness, consistency, schemas, and business rules. - Data Security Protect sensitive information with encryption, masking, tokenization, and monitoring. - Access Control Ensure the right people access the right data through permissions and policies. - Metadata Management Maintain definitions, tags, schemas, relationships, and technical context. - Compliance Tracking Support regulations through consent records, retention rules, and policy enforcement. - Audit Logs Track queries, changes, user activity, incidents, and data modifications. What This Means: The biggest data problem in many companies is not volume. It is trust. When everyone uses a different version of the truth, no one can move fast. Which governance gap creates the most confusion in your experience? Follow Sumit Gupta for more such insights!!

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

As a data engineer, I was more interested in building pipelines and solving tech puzzles, not setting up policies and processes. Little did I realize that data governance was the backbone of the very systems I relied on. Fast forward to today, and my perspective has completely shifted. Working on an entire data platform taught me that data governance is more than rules and restrictions; it’s the glue that holds everything together. Think of it as being the GPS for your organization’s data—it helps you navigate, keeps your data secure, and ensures everyone reaches their destination smoothly. I started seeing data governance as essential when I faced real-world problems: ▪️ Reports built on inaccurate data. ▪️ Duplicate or missing records causing business losses. ▪️ Sensitive information being exposed due to improper controls. It became clear that governance wasn’t an optional add-on; it was the foundation for ensuring trust in the data. So, What is Data Governance? It’s like onboarding a new employee. Just as every new hire is introduced to the company’s policies and trained for their role, every piece of data needs rules and a structure to follow. This ensures: ▪️ The data is high-quality and trustworthy. ▪️ It’s accessible only to the right people. ▪️ It’s traceable, so you know where it came from and how it’s been used. Here’s how I like to explain the main aspects of data governance: 1. Metadata Management Imagine a treasure map where the “X” marks the data you need. Metadata is that map. It tells you what the data represents, its origin, and how to use it effectively. Without it, you’re just guessing in the dark. 2. Data Access Control Think of a vault in a bank. Not everyone gets the same key. Permissions are granted based on roles, ensuring sensitive data stays protected while authorized users get what they need. 3. Data Lineage Ever traced a package you ordered online? Data lineage works the same way. It tracks where the data came from, where it’s going, and what’s been done to it. This visibility ensures accuracy and helps fix issues faster. 4. Data Access Audit This is your security camera. It logs who accessed what and when, providing a trail that keeps the system secure and compliant. 5. Data Discovery Finally, imagine a search engine for your organization’s data. It helps you find the exact dataset you need, fostering innovation and smarter decisions. So, next time you think of governance as just red tape, remember: It’s the invisible infrastructure making everything else work smoothly. The cleaner and safer your data, the more power it holds. What’s your take on data governance? Have you faced any challenges or successes with it? ❣️Love it...spread it ♻️

Roles & Responsibilities are probably the most crucial aspect to consider when implementing Data Governance properly So what do you need to know? Well, there are five groups of roles I’m quickly defining here: 🏛️ 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗢𝗳𝗳𝗶𝗰𝗲 – Translates the Governance strategy into operational success. The DGO is accountable for the implementation of DG standards, policies and practices, while supporting embedded roles 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀: Act as executional, internal consultants for data and business teams to solve issues related to ownership, standards and accountability 🧠 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗖𝗼𝘂𝗻𝗰𝗶𝗹 – The Council would meet once a month/ quarter to break deadlocks, allocate resources, and ensure governance priorities align with business & data needs. This is about decisions, not status updates 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀: Clear escalation criteria must be established for the DG Council so that matters aren’t deferred to Council involvement 🧑💻 𝗗𝗮𝘁𝗮 𝗢𝘄𝗻𝗲𝗿𝘀 – Align on the data within their domain, determine the highest priority use cases and define what "good enough" means. Owners set quality standards, approve access policies, and ensure data serves business objectives within their teams 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀: Owners must have genuine authority over their domain and clear consequences for poor data quality. Set up touchpoints with the DG team and other roles to ensure this 🔗 𝗗𝗮𝘁𝗮 𝗦𝘁𝗲𝘄𝗮𝗿𝗱𝘀 – Stewards are the SME for particular data assets and will manage their day-to-day data quality, resolve issues, and serve as the primary interface for business stakeholders and technical teams. They are responsible for facilitating and coordinating to ensure that any issues are identified and fixed 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀: Stewards should be provided clear guidelines on escalation points, priorities and standards. They should spend more time solving problems than documenting them 🧹 𝗗𝗮𝘁𝗮 𝗖𝘂𝘀𝘁𝗼𝗱𝗶𝗮𝗻𝘀 – Embed governance controls into systems and processes. Custodians implement the technical aspects of governance based on standards defined by Owners and Stewards 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀: Implementing governance in a way that is completely automated and largely invisible In the end, success depends on clear handoffs between roles Owners define requirements, Stewards translate them into operational processes, Custodians implement technical controls, and the DGO coordinates across all levels. The Council intervenes only when the normal flow breaks down The key is ensuring that these roles are actually delivered on, rather than ‘in name only’. And every role should have a measurable impact and be 𝘁𝗶𝗲𝗱 𝘁𝗼 𝗿𝗲𝘄𝗮𝗿𝗱𝘀, 𝗶𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲𝘀 𝗮𝗻𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀 Read this week’s article on implementing DG and let me know what you think! Any builds?

Data without intelligence is potential; intelligence without action is waste. Databricks' 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 showcases a decisive shift as industries transition from AI experimentation to widespread production, with manufacturing emerging as a standout sector. Companies are leveraging AI to optimize production, enhance quality control, and integrate operational data into decision-making processes. Key takeaways from the report include: • 𝟏𝟏𝐱 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in machine learning models reaching production, indicating industries are prioritizing real-world AI applications. • 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐠𝐫𝐨𝐰𝐭𝐡 in natural language processing (NLP) use in manufacturing, driving improvements in quality control and customer feedback analysis. • 𝟑𝟕𝟕% 𝐠𝐫𝐨𝐰𝐭𝐡 in vector database adoption, supporting retrieval augmented generation (RAG) to integrate proprietary data for tailored AI applications. • Manufacturing and Automotive lead the charge with a staggering 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in adopting Natural Language Processing (NLP).  Would anyone have picked Manufacturing growing the fastest in NLP?!?! 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐈𝐧𝐟𝐨? If you’re still debating AI’s value, you’re already late to the game. Manufacturers are moving from “what if” to “what’s next” by putting more AI models into production than ever before — 𝟏𝟏 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫!  The most successful organizations are cutting inefficiencies, standardizing processes with tools like data intelligence platforms, and deploying solutions faster. This isn’t just about keeping up with the Joneses; it’s about outpacing them entirely. 𝟏) 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Retrieval Augmented Generation (RAG) and vector databases to turn AI into a competitive advantage by integrating your proprietary data. Don’t rely on off-the-shelf solutions that lack your industry’s nuance. 𝟐) 𝐀𝐝𝐨𝐩𝐭 𝐚 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐩𝐞𝐞𝐝: The report highlights a 3x efficiency boost in getting models to production. Speed matters — not just for innovation, but for staying ahead of market demands. 𝟑) 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:  The rise of open-source tools means you can innovate faster without vendor lock-in. Build smarter, more cost-effective systems that fit your needs. 𝟒) 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐚𝐢𝐧𝐬: AI isn’t just for customer-facing solutions. Use it to supercharge processes like real-time equipment monitoring, predictive maintenance, and supply chain resilience. 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/eZCrq_nF ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

How To Kickstart Your Data Governance Plan Last week, I had the opportunity to discuss AI and data governance with a select group of leaders and entrepreneurs. Here is an excerpt from that discussion. Data governance is key to a successful data-powered organization. Here are three steps to get started: 1. Address the IT-Business Disconnect - IT as Custodians, Business as Experts: IT manages data, but business teams know its impact on operations. - Empower Business Users: Provide self-service data tools to reduce reliance on IT. - Define Data Flows: Let departments/functions define their own data needs for better efficiency. 2. Show the Value of Data Governance - Not Just an IT Concern: Data governance benefits the entire organization. - Show ROI: Demonstrate value for different teams: - Sales & Marketing: Better data quality boosts campaigns and sales. - Procurement: Governed data optimizes purchasing, reducing costs. - Legal & Compliance: Clear policies prevent non-compliance. - Finance: Well-governed data improves reporting. 3. Implement Technology Wisely - Use Modern Tools: Enhance data discovery with tech, but ensure human oversight. - Human-Driven Processes: Some processes need human input—automation isn’t enough. - Support System: Use tech to support, not replace, human decision-making. Key Takeaway Data governance creates value by bridging IT and business, communicating benefits, and using tech with human oversight to drive efficiency and reduce risks. How are you bringing IT and business closer in your data governance journey?

Here are some realistic KPIs that project managers can actually track : 1. Schedule Management 🔹 Average Delay Per Milestone – Instead of just tracking whether a project is on time or not, measure how many days/weeks each milestone is getting delayed. 🔹 Number of Change Requests Affecting the Schedule – Count how many changes impacted the original timeline. If the number is high, the planning phase needs improvement. 🔹 Planned vs. Actual Work Hours – Compare how many hours were planned per task vs. actual hours logged. 2. Cost Management 🔹 Budget Creep Per Phase – Instead of just tracking overall budget variance, break it down per phase to catch overruns early. 🔹 Cost to Complete Remaining Work – Forecast how much more is needed to finish the project, based on real-time spending trends. 🔹 % of Work Completed vs. % of Budget Spent – If 50% of the budget is spent but only 30% of work is completed, there's a financial risk. 3. Quality & Delivery 🔹 Number of Rework Cycles – How many times did a deliverable go back for corrections? High numbers indicate poor initial quality. 🔹 Number of Late Defect Reports – If defects are found late in the project (e.g., during UAT instead of development), it increases risk. 🔹 First Pass Acceptance Rate – Measures how often stakeholders approve deliverables on the first submission. 4. Resource & Team Management 🔹 Average Workload per Team Member – Tracks who is overloaded vs. underloaded to ensure fair distribution. 🔹 Unplanned Leaves Per Month – A rise in unplanned leaves might indicate burnout or dissatisfaction. 🔹 Number of Internal Conflicts Logged – Measures how often team members escalate conflicts affecting productivity. 5. Risk & Issue Management 🔹 % of Risks That Turned into Actual Issues – Helps evaluate how well risks are being identified and mitigated. 🔹 Resolution Time for High-Priority Issues – Tracks how quickly critical issues get fixed. 🔹 Escalation Rate to Senior Management – If too many issues are getting escalated, it means the PM or team lacks decision-making authority. 6. Stakeholder & Client Satisfaction 🔹 Number of Unanswered Client Queries – If clients are waiting too long for responses, it could lead to dissatisfaction. 🔹 Client Revisions Per Deliverable – High revision cycles mean expectations were not aligned from the start. 🔹 Frequency of Executive Status Updates – If stakeholders are always asking for updates, the communication process might be weak. 7. Agile Scrum-Specific KPIs 🔹 Story Points Completed vs. Committed – If a team commits to 50 points per sprint but completes only 30, they are overestimating capacity. 🔹 Sprint Goal Success Rate – Tracks how many sprints successfully met their goal without major spillovers. 🔹 Number of Bugs Found in Production – Helps measure the effectiveness of testing. PS: Forget CPI and SPI - I just check time, budget, and happiness. Simple and effective! 😊

𝗡𝗼 𝗼𝗻𝗲 𝘁𝗼𝗹𝗱 𝘆𝗼𝘂 𝗮𝗯𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝗲𝗳𝗼𝗿𝗲. Most people think Data Governance means: 📑 Policies 📘 Frameworks 📊 Maturity models 👥 Governance councils 📅 Endless meetings And organizations proudly say: "𝗪𝗲 𝗮𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸." Months pass. Documents grow. Committees expand. But something strange happens… 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗿𝗲𝗺𝗮𝗶𝗻 𝘂𝗻𝘀𝗼𝗹𝘃𝗲𝗱 😥 The truth about Data Governance There are two very different ways organizations approach governance. ❌ Framework as the destination Many companies focus on: • Governance handbooks • Data policies • Framework mapping • Industry standards • Governance committees It looks very impressive. Everyone is busy. But when you ask one question: “𝗪𝗵𝗶𝗰𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗱𝗶𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗼𝗹𝘃𝗲?” The room goes silent. ✅ Value as the destination The organizations that succeed take a completely different approach. They start with real business problems. Example: 🔴 Sales team does not trust pipeline data. Instead of writing another governance document, they do something simple: 👉 Assign clear ownership 👉 Fix data quality issues 👉 Standardize definitions Suddenly something magical happens. ✔ Data becomes trusted ✔ Decisions become faster ✔ Teams become aligned And governance starts delivering real value. The biggest misconception about Data Governance 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗲𝘅𝗲𝗿𝗰𝗶𝘀𝗲. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺. Its job is to ensure: • the right data exists • the data is trusted • teams can make faster decisions The real test of Data Governance Not: ❌ Number of policies created ❌ Number of governance meetings ❌ Number of frameworks adopted But: ✅ Number of business problems solved. 💡 Final Thought The best Data Governance programs don’t start with frameworks. 𝗧𝗵𝗲𝘆 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. Because when governance solves one problem… 𝗜𝘁 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗿𝘂𝘀𝘁. Then another. Then another. And slowly governance becomes a real business advantage. 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝘁𝗼 𝗸𝗻𝗼𝘄: In your organization, is Data Governance currently focused on: 📑 Frameworks or 📈 Business Value #DataGovernance #DataStrategy #DataLeadership #BusinessIntelligence #AnalyticsLeadership

Starting in data governance can feel overwhelming. There’s pressure to “get it right,” but very little practical guidance on where to begin. That’s why I created this: The 7-step roadmap to implement (or improve) your data governance program. 👇 Here’s the breakdown: 1. Perform the initial assessment Understand where your organization stands, what's driving your DG program, and where to focus limited resources. Use a maturity model or even a simple self-assessment. 2. Get buy-in Programs fail without support. Build your business case, highlight tangible/intangible benefits (there are plenty of each), define risks, and secure at least one sponsor. 3. Set up the data governance program Time to get tactical: ✔ Define the scope ✔ Establish guiding principles ✔ Map out your data domains (start with 1-3) ✔ Build the governance organizational framework ✔ Set up a DG body and assign stewards 4. Develop metrics & KPIs If you can’t measure it, you can’t manage it. Choose a few impact and progress metrics to start. 5. Create policies, standards & processes This is where the rubber meets the road. Align these with your governance scope and business needs. 6. Set up and deploy tools Sure.. tools matter, but later. Start with essentials: business glossary, data dictionary, and a data catalog. 7. Manage change This is ongoing and it needs to start on day one. Even positive changes bring resistance. You’ll need communication, training, and champions. Do you want to learn all the above and MORE? 👉 That’s why I built an online course that walks you through every one of these steps — in detail, with templates, examples, and a clear action plan. Check it out here: https://lnkd.in/gwBcYQg5 If: ✅ You need a clear plan to implement or improve DG ✅ You’re tired of scattered or overly theoretical advice ✅ You want practical, real-world guidance Then this course is for you. It even comes with TEMPLATES. It doesn’t get easier than this. — Follow me here for more practical tips, cheat sheets and frameworks for data professionals. - George Firican