Strategies to Overcome Resistance to Data Governance Implementation

We discovered an extremely fundamental yet key trick to enforce practical governance in large, seemingly uncontrollable environments during a recent conversation with a designer working on development projects. 𝐓𝐡𝐞 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 A project required data collection by on-ground personnel: Forest guards tasked with scanning trees through a mobile app. ⚠️ But why would any forest guard willingly do it at all and do it the right way? 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: 𝐓𝐡𝐞 𝐈𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐞 For every thousand photographs properly taken and uploaded, the forest guards were rewarded with a financial incentive 💰 It may be an ugly yet undeniable truth that everything boils down to monetary assets, be it revenue or income. Be it a giant business with thousands of stakeholders or one individual contributor with a salary income. 🪡 We need to imbibe this insight (fact) into our and our workforce's lives in a way that 𝐛𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐭𝐚𝐤𝐢𝐧𝐠 𝐚𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐞𝐢𝐭𝐡𝐞𝐫 𝐭𝐡𝐞 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐨𝐫 𝐨𝐫 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 This is why Governance is a subset of the Data Product Strategy. When applied, the Data Product Strategy enforces Governance as a subsidiary by 𝐛𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐭𝐨 𝐄𝐟𝐟𝐨𝐫𝐭𝐬/𝐈𝐧𝐢𝐭𝐢𝐚𝐭𝐢𝐯𝐞𝐬, 𝐦𝐚𝐤𝐢𝐧𝐠 𝐰𝐚𝐲 𝐟𝐨𝐫 𝐭𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐭 𝐢𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐢𝐬𝐚𝐭𝐢𝐨𝐧. Incentivisation could come in many forms, such as direct financial incentives (bonuses, target-driven, profit-sharing, commission) or as recognitions driven by high transparency in impact on business. How Data Products Enforce Governance with Systemic Incentivisation? Imbibing incentives, monetary or not, requires the ability to tie end outcomes to efforts. The requirement of the governing party is to enforce best practices, which are often not enforceable on a point-to-point basis: The point of control or action lies with the individual contributor (as we saw with the forest guard). To incentivise individuals against any vertical, say governance or maintenance, we need the ability to imbibe outcomes or key business KPIs into the granular metrics that these individuals are working with on a day-to-day basis. For a long time, this was seemingly impossible. In traditional stacks, there’s hardly any way to map how singular efforts of data engineers or analysts are adding to the KPIs (north star metrics) such as Revenue or ARR. This also makes incentivisation difficult (incentives driven on granular metrics end up costing the business). Data Products enable granular efforts to be bridged to the final outcome or the North Stars. Each data product is bound to serve a dedicated purpose or business goal(s). A purpose-driven nature brings direct KPI/business alignment, which naturally drives governance through direct value mapping and incentivisation. #datagovernance #dataproducts

The last two weeks, I posted about the common challenges in data governance projects, particularly when business stakeholders aren’t engaged early enough, and shared a rare success story of a #CDO who avoided this pitfall through proactive engagement from day one. This week, we’ll turn to remedies: strategies for early stakeholder engagement. To lead with the conclusion, for the most part, there is no secret methodology. It’s as simple as identifying the #business unit or functional leaders and asking them a few questions. In fact, especially in the beginning, the simpler, the better. No slides, no demos – focus on the business context. One recommendation is to separate the understanding of the use cases from the data-related analysis. For example, if it has been determined that a company cannot create effective customer segmentation because data from different sources cannot be linked together, the first step is to thoroughly understand the use case. Articulate this understanding clearly—don’t settle for a mental check. Ensure the use case is explicitly defined and described. Describe at a high level what is being done and how it drives value. Also ask the business stakeholders about the impact. In the case of the segmentation, how much could customer experience increase, how much could churn be reduced, how much could cross-sell be increased? This step is extraordinarily critical now. It is very tempting to dive into practical solutions for the data integration problem, but if you cannot articulate a defensible value rationale at this point, you will not be able to build your business case later on either. The business case for the data capabilities will have an upper bound that is equal to the value of the entire use case, as only a part of that value can later be attributed to the data solutions. My advice is to make this a hard go/no-go tollgate: if you do not have a strong value rationale that is supported by the business, do not proceed. This first hurdle is the most substantial one. Once you have a clear value statement, then business buy-in will be guaranteed. Moreover, because the expected outcome is clearly quantified, throughout the program, solution requirements can be much more transparently reviewed. To effectively engage stakeholders early in the project, consider the techniques shown on the attached image to: 🗣️ Initiate Open Dialogues 🧑🤝🧑 Create Collaborative Workshops 🌟 Bring in External Success Stories For more details, see the full article ➡️ https://lnkd.in/exkiQYXE One Data #datastrategy #datagovernance

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.

One of the craziest things about data governance is that nobody is willing to admit that humans respond to incentives. In 99% of organizations where data is a mess it is that way because the incentives aren’t in place to take care of data. Virtually every person in every organization is incentivized to take shortcuts, to skip documentation, and deliver their work regardless of what it means for the data habitat. Want people to change their behavior? Create an incentive. Here’s how: 1. Run a random acts of data governance competition: Winner gets $1000, 2nd place $500, 3rd $250, and anybody who does at least 20 traceable governance acts is put in a drawing for $1000 2. Repeat for 3 months (refine rules if necessary$ 3. Have a retro, and figure out what can be automated or built into existing processes 4. Build the automations, document the processes, and hold teams accountable to them. 5. Continue to have competitions and quarterly retros to improve continually. Alternatively, build a CI/CD pipeline that blocks code from deploying unless there is a metadata tag