Data Completeness and Stakeholder Trust

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

Want a simple way to earn trust from your stakeholders, analysts? Send them data quality alerts when things go wrong. This is data 101 for engineers, but my team and I are citizen developers. We don't have the same kind of training - things like this simply aren't immediately obvious to us. Here's an example of why you should do this, from just this week: An analysis that we run depends on A LOT of inputs, including some manually uploaded files. Lots of opportunity for things to go wrong. On Monday, I heard from one of the file providers that her upload had been failing for almost 2 weeks. One of my end users spotted the problem at about the same time that I heard from my file provider. Not great being the last one to find out about a data quality problem in an analysis that you're responsible for. I had been working on some data quality alerts, and sure enough, they would have spotted the problem right away. So I'm eager to finalize them and get them into production. Here are some easy things I'm implementing: 1. Record count checks: do today's inputs have roughly the same number of records as yesterday's? This doesn't catch all problems, but it's very easy to implement - it's all I needed to spot the problem I just described. 2. Consistency check: Make sure your inputs "look" the way you expect them to. In this case, the reason the file upload was failing was that one of the columns in the file changed from being numerical to text, and our SQL database didn't like that. 3. Check for null values: You might get the right number of records and the right data types, but the data could all be null. 4. Automated alerts: You don't want to hear from your stakeholders about data quality issues the way that I did. Put in some basic alerts like these with automatic emails when they're triggered. Copy all your stakeholders. This will sound remedial to data engineers, but these are habits that we citizen developers don't always have. There's a lot that we citizen developers can learn from our friends in IT, and simple things like this can go away toward earning our stakeholders' trust. #citizendevelopment #lowcode #nocode #analytics #supplychainanalytics

Data won’t stay clean. The job is to keep trust intact when it doesn’t. Too many data quality efforts focus on tests and tools, but miss the bigger picture: trust, ownership, and how we respond when things break. Data teams spend endless hours writing tests and setting up monitoring for the issues they know about today. But tomorrow, priorities shift. Data structures change. New sources get added. Old assumptions break. You build the perfect system for catching yesterday’s mistakes and still trip over what comes next. So what’s the fix? 👇 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝘄𝗮𝘆𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸𝗶𝗻𝗴. These are the habits and processes that protect trust when things go wrong and get stronger over time. That looks like: 1.) Proactive, transparent communication. When something breaks, don’t quietly patch and move on. Tell impacted teams what happened, what you’re doing about it, and when it’ll be fixed.   2.) Involving accountable owners and enabling self-monitoring. If data quality issues start at the source, the source owner should have the tools and visibility to catch it, not rely on a downstream fire drill.   3.) Reinforcing trust through consistency. People notice patterns. When issues arise, show them the same thing every time: a clear plan, fast action, and lessons learned. Striving for perfect data doesn’t build trust. Resilient teams do.

Bad data doesn’t just slow you down... it silently drains revenue and erodes trust across your organization. Every CRM, ERP, and marketing system holds the potential to accelerate growth… or to mislead. The difference comes down to whether the data inside is complete, current, and connected. Too often, it isn’t. Inaccurate fields, duplicates, and siloed records create false signals that drive poor decisions. The reality is this: a business is only as strong as the data that powers its decisions. Without unified and trustworthy information, analytics become noise, segmentation falters, and customer engagement misses the mark. The solution is not simply buying more tools—it’s building a disciplined foundation around data. That means: Standards: Defining required fields, formats, and naming conventions. Stewardship: Assigning clear ownership and accountability for quality. Integration: Connecting data across systems to remove silos. Unification: Creating a single version of truth that everyone can trust. Leaders who treat data as a strategic asset, rather than an afterthought, unlock sharper decisions, stronger customer experiences, and measurable ROI. Those who don’t are making choices on borrowed time. The question isn’t if you’ll prioritize data quality and unification. The question is when. #DATA #CRM #ERP #UNIFICATION #GTM #SALES #MARKETING

In order to prove their social impact, mission driven organizations need to generate measurable outcomes. The CRM system (customer relationship management) is the centerpiece of this effort. But while 9 out of 10 companies with 10+ employees are using a #CRM , 91% of data in CRM systems is incomplete, stale, or duplicated – leading to lack of trust in the data. Consider the example of a nonprofit organization helping inner-city youth to acquire job-ready skills in tech. 📊 Data going in: student profiles (demographics, educational background, technical skills, interests, and goals), attendance, progress indicators, achievements. If the data is not aligned to the organization’s #GoToMarket strategy, trust in the data erodes. With that alignment, however, they can deliver individualized support and interventions, helping each student address their unique barriers to success. The outcomes? Higher graduation and job placement rates. Better starting salaries leading to increased lifetime earnings. Higher probability of program participants having stable salaries to contribute back to their communities. 🏆 All of this can be measured, which then drives action and performance among the other stakeholders (donors, volunteers, community groups). But all of it depends on reliable data, maintained in alignment with the organization’s mission, vision and values. Memo to #missiondriven leaders: is your CRM a strategic asset proving your #socialimpact ? Is it a crystal ball proving your success in your mission? Or is it just a fancy Rolodex?

Can you truly trust your data if you don’t have robust data quality controls, systematic audits, and regular cleanup practices in place? 🤔 The answer is a resounding no! Without these critical processes, even the most sophisticated systems can misguide you, making your insights unreliable and potentially harmful to decision-making. Data quality controls are your first line of defense, ensuring that the information entering your system meets predefined standards and criteria. These controls prevent the corruption of your database from the first step, filtering out inaccuracies and inconsistencies. 🛡️ Systematic audits take this a step further by periodically scrutinizing your data for anomalies that might have slipped through initial checks. This is crucial because errors can sometimes be introduced through system updates or integration points with other data systems. Regular audits help you catch these issues before they become entrenched problems. Cleanup practices are the routine maintenance tasks that keep your data environment tidy and functional. They involve removing outdated, redundant, or incorrect information that can skew analytics and lead to poor business decisions. 🧹 Finally, implementing audit dashboards can provide a real-time snapshot of data health across platforms, offering visibility into ongoing data quality and highlighting areas needing attention. This proactive approach not only maintains the integrity of your data but also builds trust among users who rely on this information to make critical business decisions. Without these measures, trusting your data is like driving a car without ever servicing it—you’re heading for a breakdown. So, if you want to ensure your data is a reliable asset, invest in these essential data hygiene practices. 🚀 #DataQuality #RevOps #DataGovernance