Common Pitfalls in Data Governance

Most #datagovernance challenges stem from systems and processes that were designed without explicit data management principles. The larger, older, and more complex the organization is, the larger the problems tend to be. Organizations have tried to retroactively implement data governance for legacy systems and processes. I spent several years in the trenches of such programs, tracking down elusive system owners, and digging through outdated ETL scripts and technical metadata – it’s a grind. The approach involves extensive efforts to untangle #data flows, working with sometimes no longer supported technologies, and imposing governance standards on outdated or non-compliant systems. Engineers and consultants that were responsible in days past have long gone, further complicating efforts. Faced with mounting costs and frustration, many organizations simply give up. Savvy data leaders have begun to focus on “designing in” data governance from the start of new transformations, marking a move from remediation to prevention, and from reactive to proactive. The key to integrating data governance into the fibers of your data #architecture is to ensure that your organization’s transformation lifecycle methodology includes data governance commandments. Having an organizational transformation lifecycle ensures that any transformation of a certain impact or size is automatically brought into scope. This simplifies the task for data governance leaders, who would otherwise have to go out into the organization to identify the initiatives where data governance should be playing a role, each time then having to advocate for its inclusion – often a thankless task. The data governance considerations listed in the attached might sound heavy and time-consuming, but if done right, they can actually accelerate transformation, instead of delaying it. For example, using a data catalogue and adopting existing data products can save time finding and preparing data, and using an existing enterprise data model can prevent time being spent to create a new model from scratch. Trust me, the members of your data team will be much happier to enable new projects instead of to solve the mess caused by previous ones. Focusing on the #transformation lifecycle not only prevents the occurrence of new data governance issues but also yields a higher ROI. Designing systems and processes with data governance in mind from the outset reduces the costs and complexities of retrofitting governance later. Moreover, when data governance is embedded by design, the business users can leverage high-quality, well-governed data as soon as the transformation is complete. For more 👉 https://lnkd.in/e7nVGRGP 

I had a reach out from an old friend who is a CIO, but she is having problems with the Data & Analytics function. It became an interesting conversation on the top blockers for a company getting value out of D&A. 1. Though data quality and lack of governance usually tops the list, these are symptoms, NOT root causes. Usually if these are lacking, it isn't tools, or technology, but culture and process. Tools can help, certainly, but tools without changing the culture and processes are a waste of money. 2. Waiting for perfection. Though it is tempting to wait until your 3-year strategy is complete, no one is going to wait that long. Get to the "good enough" and iterate. Introduce tools which allow self-service, even if there is a manual component, knowing they will improve over time. 3. Giving into impatience. The opposite of the last point is just giving up and just letting everyone have access to the raw data and letting everyone do what they want. Sure, it feels good, and there is lots of motion, BUT if your goal is one version of the truth, there is no progress. Allow SOME people to have access, and govern what becomes your golden reports. 4. Too much IT think. Data & Analytics is, fundamentally, at best a hybrid technical function. If you try to manage in the same way people order new PC's, D&A will not produce value. If the function assumes it knows best for the business, it will fail. If you think the number of tickets resolved matter over impact and satisfaction, you are doomed. 5. Not having an agreed too strategy or roadmap. One consistent theme between all the companies I work at is I keep having to explain who we can't go faster by just hiring consultant/SI XXX. Data & Analytic functions are built on building blocks. There are certain elements that MUST be in place before you can build the next level. Robert Heinlein stated it well in The Door into Summer (1957) "When railroading time comes you can railroad—but not before." A strategy and roadmap may not eliminate having to repeat this ... but at least you can mark the needed progress and demonstrate a consistency of the message (while unlocking the value you CAN). If you notice, the top five blockers are NOT about tools or technologies, but more about leadership and how to attain business value. Though most aspects of IT have recognized this more or less as important, for the D&A function, it is essential. Any other critical blockers you have seen?

Why your data governance is failing (It's not what you think): It's not your tech stack that's broken. It's your mindset. Here's the reality check most companies need: 𝟭. 𝗧𝗵𝗲 "𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺" 𝗧𝗿𝗮𝗽 When data governance becomes everyone's job, it becomes no one's job. Result? Zero accountability and pure chaos. 𝟮. 𝗧𝗵𝗲 "𝗙𝗶𝗿𝗲 𝗙𝗶𝗴𝗵𝘁𝗲𝗿" 𝗦𝘆𝗻𝗱𝗿𝗼𝗺𝗲 You only think about data when: • Someone demands a report • Compliance issues pop up • Security breaches happen • Executives panic 𝟯. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗮𝗽 You're collecting mountains of data with no clear purpose. But random data collection isn't a strategy. 𝟰. 𝗧𝗵𝗲 𝗕𝗹𝗶𝗻𝗱 𝗦𝗽𝗼𝘁 You're focusing on 20% of your data (the structured stuff). Meanwhile, 80% of your business intel sits in unstructured chaos. Here's your rescue plan: 1/ Define Clear Ownership ↳ Name specific people responsible for specific data sets. 2/ Build Proactive Systems ↳ Stop waiting for problems. Start preventing them. 3/ Link Data to Business Goals ↳ Every piece of data should drive a business outcome. 4/ Master Unstructured Data ↳ Emails, documents, images - they all matter. Remember: Data governance isn't just an IT project. It's a business transformation. Want to assess your data governance maturity? Drop "ASSESS" below and I'll share our evaluation framework. PS: Share if your team needs this wake-up call.

Data governance is hitting a critical tipping point - and there are three big problems (and solutions) you can’t ignore: 1️⃣ Governance is Always an Afterthought: Often, governance only becomes important once it's too late. Fix: Embed governance from the start. Show quick wins so it's viewed as an enabler, not just cleanup. 2️⃣ AI Exposes - and Amplifies - Flaws: AI governance introduces exponential complexity. Fix: Proactively manage risks such as bias and black-box decisions. Automate data lineage and compliance checks. 3️⃣ Nobody Wants to ‘Do’ Governance: Mention "governance" and expect resistance. Fix: Make it invisible. Leverage AI to auto-document metadata and embed policies directly into everyday workflows, allowing teams to confidently consume data without friction. Bottom Line: → Plan governance early - late-stage fixes cost significantly more. → Use AI to do the heavy lifting - ditch manual spreadsheets. → Tie governance clearly to business outcomes like revenue growth and risk mitigation so it’s championed by leaders. Governance done right isn’t just compliance; it’s your strategic advantage in the AI era.