The Importance of Data Integrity in Consulting

If data quality is everyone's job, it's no one's priority. When a business misses its revenue targets or botches a product launch, the root cause is often easy to spot: unclear goals, flawed strategy, or poor execution. But when data goes wrong—when reports are unreliable, customer insights are murky, or machine-learning models misfire—the culprit is usually harder to pin down. Everyone was supposed to care about data quality, but no one really did. This is the hidden cost of "data quality is everyone's responsibility"—a mantra that sounds wise but often means data is no one's priority in day-to-day business. When employees are busy tackling urgent tasks—closing deals, shipping products, fixing bugs—they don't prioritize data quality. After all, data quality issues rarely explode in real time. Like technical debt, they erode progress slowly, invisibly, until major initiatives stall, and the company is left wondering why its data-driven transformation never took off. Some businesses respond by pointing to their Chief Data Officer (CDO), expecting one powerful executive to fix the company's data problems. But this approach is only part of the solution. Data is created, used, and maintained everywhere across the business. A single executive, no matter how capable, can't overhaul a company's data culture from the top down. The real work of data integrity happens on the ground, within the teams that generate and use data daily. The real solution is to treat data like other critical business assets—finances, customer relationships, or brand reputation—and make senior leaders directly accountable for the data produced in their domains. Just as the CFO ensures accurate financial reporting and the head of sales owns customer satisfaction, department heads must be responsible for the quality, accessibility, and usability of their data. Their performance evaluations should reflect it. Data health metrics—like data accuracy, completeness, and cross-functional usability—should be tracked just as rigorously as sales targets or cost controls. When senior leaders know that part of their bonuses, promotions, and reputations hinge on clean, useful data, data responsibility moves from being a side project to core business work. Real progress begins when we stop treating data quality as a collective aspiration and start treating it as what it truly is: a core business function that demands clear ownership. When leaders stake their reputations on it, clean and reliable data becomes not just a technical requirement, but a fundamental measure of business success.

𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈—𝐢𝐭'𝐬 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞𝐥𝐲 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥. AI solutions, particularly those embedded in ERP systems, are designed to deliver valuable insights and recommendations to businesses. However, the 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐥𝐢𝐧𝐤𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. In traditional ERP implementations, businesses often found themselves achieving systems that were "on time, on budget, fully functional, and disappointing." Why? Because while the system technically worked, the data feeding it wasn't accurate enough to meet real-world expectations. Incorrect customer addresses, inaccurate inventory data, or faulty financial figures significantly compromised the value of the entire system. 𝐖𝐢𝐭𝐡 𝐀𝐈, 𝐭𝐡𝐞 𝐬𝐭𝐚𝐤𝐞𝐬 𝐚𝐫𝐞 𝐞𝐯𝐞𝐧 𝐡𝐢𝐠𝐡𝐞𝐫. AI-driven recommendations depend heavily on the accuracy and quality of data. If AI bases its recommendations on inaccurate or inconsistent data, users quickly lose trust and confidence in these insights, eventually ignoring them entirely. This lack of trust diminishes the value of AI systems, no matter how sophisticated the algorithms are. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐧𝐨𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 "𝐀𝐈 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐚𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐚𝐝 𝐝𝐚𝐭𝐚" 𝐢𝐬 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐥𝐲 𝐟𝐥𝐚𝐰𝐞𝐝. While AI may process large volumes of data quickly, poor-quality input inevitably leads to poor-quality outcomes. 𝐀𝐈 𝐚𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 𝐚𝐧𝐝 𝐰𝐞𝐚𝐤𝐧𝐞𝐬𝐬𝐞𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚—meaning bad data can severely degrade your results and decision-making quality. One of the longstanding strengths of SAP systems is their reliability and trustworthiness. Businesses have confidence in SAP solutions because they know the integrity of their data is preserved and accurately managed throughout the process. This reliability is especially critical in the age of AI, where the value derived is directly proportional to the quality of data provided. 𝐒𝐢𝐦𝐩𝐥𝐲 𝐩𝐮𝐭: 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐭, 𝐞𝐯𝐞𝐧 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐰𝐨𝐧'𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐭𝐡𝐞 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐚𝐥𝐮𝐞.

As a Data Architect, I spend a lot of time talking about data quality, but it’s not just a technical checkbox, it’s something we experience every day in real life. Think about it: - If the label on your medicine is printed incorrectly, the data quality failure could be dangerous. - If your GPS misplaces a road, that inaccurate data point can cause frustration (or worse, accidents). - Even something as simple as getting the wrong price at checkout is a reminder of what happens when data integrity is not maintained. In our systems, poor data quality leads to wrong decisions, compliance risks, and loss of trust. In real life, the stakes can be just as high. That’s why, when we design architectures and processes, quality must be built in, not checked at the end. ✅ Validations at entry points ✅ Automated checks throughout pipelines ✅ Governance policies that enforce consistency ✅ A culture where teams understand the impact of quality on outcomes Data is the foundation for AI, analytics, and decision-making. But if the foundation is weak, everything built on top of it becomes unreliable. So, whether in business or daily life, let’s remember: "good data is not a luxury – it’s a necessity." #DataArchitecture #DataQuality #Governance #TrustInData

Data Integrity may be a compliance requirement but your data culture is the environment in which it thrives or dies. Deming’s System of Profound Knowledge gives a lot of insight to why your DI culture may be reactive, rather than proactive. 1. Appreciation for the system -> Data Culture is designed. What is the design in your organization? Unrealistic timelines, poorly understood processes, conflicting KPIs, weak controls? If you want a healthy data culture, fix the systems around your data. 2. Knowledge of Variation -> Data Literacy is essential. Are you reacting to the data or managing the process that produces it? Are you “fixing” numbers, fearful of failures or are people curiously investigating the process and context behind it? 3. Theory of Knowledge -> Data is for learning. Is your learning culture just a bunch of SOPs? Failure is information. Deviations are information. Variation is information. 4.  Psychology -> Fear is anti-integrity. What are the spoken and unspoken incentives and consequences? Are you protecting metrics or product? Are you investing in new tech or cognitive growth? When leaders drive out fear, and drive in empowerment, the metrics, timelines, audit findings are automatically protected. You can’t CSV your way into data integrity, you must build and nurture your data culture, you can take Deming's system to the bank.

We've all heard the saying, "Numbers don't lie." However, I've come to realize in my journey through Monitoring, Evaluation, and Data Analysis that there are instances where numbers can indeed be manipulated to craft a particular narrative. Throughout my experience in MER and data analysis, one fundamental lesson has become evident: data is a malleable tool that can be shaped to convey virtually any story. It's worth noting that all data analytics tools and statistical methodologies are reliant on human inputs to generate solutions and responses. While the temptation to manipulate data in a way that aligns with our intended narrative can be strong, we must acknowledge that such actions come with consequences that may far outweigh the benefits of steering the story in a particular direction. When we resort to tactics like biased sampling, false precision, or the art of presenting misleading percentages—essentially endeavoring to "transform the actual truth into the desired truth" within our M&E and research reports—we inadvertently inflict more harm than we might realize. In doing so, we compromise our collective efforts to make the world a better place. The manipulation and misrepresentation of data within reports have far-reaching consequences. They lead to misguided decisions, flawed policy outcomes, erosion of stakeholders' trust, waste of resources, loss of accountability, weighty legal implications, and loss of professional reputation. More importantly, they deprive us of opportunities for learning and growth. It is important to understand that we are not out to "perform," as much as we are out to "transform." Therefore, we must be promoters of data integrity understanding that it is not just about numbers; it's about the truth they represent. We must hold ourselves to the highest standards of data ethics. In summation, when designing and perusing M&E and research reports, it's paramount to bear in mind that the consequences of misleading statistics are substantial and far-reaching. Moreover, we should maintain a vigilant stance when evaluating the numbers presented to us, steadfastly advocating for transparency and unwavering accuracy. In the coming weeks, I write a book review on Darrell Huff's classic, "How to Lie with Statistics." Keep an eye for the book and an exciting infographic about the major themes in the book here Ngwoke Ifeanyi Together, we can strive for a world where data is a force for good, where numbers don't lie, and where integrity prevails. Good morning MER friends. #dataintegrity #statisticalanalysis #monitoringandevaluation #ethicaldata

$12,900,000 That's how much the average organization loses yearly due to bad data (according to Gartner). Back in 2016, IBM estimated an even wilder number: $3,100,000,000 That's 3.1 trillion - *with a T* - dollars lost annually in the United States due to bad data. I know, these numbers are so absurd that they seem made up. Well... they aren't (you can check). They are as real as the importance of data integrity throughout the sales and customer lifecycle. But let’s drill down a bit. 🛠️ 💡 It’s not just about the staggering losses. It’s about understanding the cascading impact of data integrity – from quote to revenue. Think about it: 1️⃣ Accurate Pricing: Avoid losing revenue due to underquoting or damaging trust with overquoting. 2️⃣ Streamlined Sales Cycles: Quicker decisions, fewer delays. 3️⃣ Compliance: Stay ready for audits and regulatory checks. 4️⃣ Informed Decisions: Data integrity = better forecasting and strategic planning. 5️⃣ Enhanced Customer Relationships: Transparency builds trust and loyalty. 6️⃣ Accurate Revenue Recognition: Directly affects financial health and market perception. 7️⃣ Increased Operational Efficiency: Less cleanup, more automation. 8️⃣ Competitive Edge: In a data-driven world, accuracy is king. And, as a colleague who ran revenue at an enterprise-level SaaS company once put it, "Data integrity sits at the top of the list. It's everything. It’s not just about billing and earning; it’s about fostering long-term customer commitments." Imagine being able to: - Upsell effectively by monitoring customer usage. - Identify potential churn and engage proactively. - Harness data to create meaningful customer dialogues. *That’s* the power of data integrity. 🔍 So, next time you look at your data practices, ask yourself – are you just looking at numbers or seeing the stories they tell? #DataIntegrity #RevOps #CPQ

The Importance of Data Trust in Data-Driven Decision Making. Improve decision-making in 5 key areas: 1. Data Accuracy Accurate data is the foundation of good decisions. So, ensure it: • Validate data at the point of entry, or as far left as possible. • Remove duplicate records. • Regularly audit your data sources. • Use automated tools to spot errors. 2. Data Completeness Incomplete data can lead to flawed insights. To avoid this: • Ensure all required fields are filled. • Integrate data from various sources. • Regularly update your databases. • Track data lineage to understand its origin. 3. Data Consistency Consistent data ensures reliability across systems. Here’s how to maintain it: • Standardize data formats. • Use consistent naming conventions. • Synchronize data across platforms. • Implement data governance policies. 4. Data Freshness Timely data is crucial for relevant decisions. Make sure to: • Set up real-time data feeds. • Update your data frequently. • Monitor data latency. • Use time-stamped data entries. 5. Data Relevance Relevant data drives meaningful insights. To achieve this: • Align data collection with business goals. • Focus on key performance indicators (KPIs). • Regularly review data for relevance. • Discard obsolete data. There you have it - 5 key areas to ensure data quality. I think about this quote often: "Without data, you're just another person with an opinion." —W. Edwards Deming Many organizations struggle with poor data quality, leading to misguided decisions. Ensuring high data quality is essential for making informed, data-driven decisions. Good news is you don't have to do it manually anymore. Help is available thru Data Quality and Observability solutions that build trust in data and data teams. Be confident. Trust your data. Lean on partners to help you build that Trust. #datatrust #dataobservability #datacatalog #datagovernance

In today’s fast-paced, data-driven world, ensuring data quality and integrity is essential for building trust and driving operational efficiency. To tackle these challenges, organizations are focusing on three key strategies: 1. Establish Unified Standards With data spread across multiple systems, creating a centralized governance framework is crucial. It acts as a universal language for data, ensuring consistency, reducing errors, and fostering smoother collaboration across teams. 2. Automate for Scale As data volumes increase, manual quality control becomes unsustainable. Automation tools for cleansing and validation streamline the process, enabling organizations to scale data quality efforts efficiently without adding complexity. 3. Foster a Culture of Responsibility Data integrity isn’t just an IT function—it’s a responsibility shared across the organization. Encouraging ownership and providing ongoing training ensures that data integrity becomes a core business value, enhancing decision-making across teams.

There are so many people-related challenges when you’re merging companies, aligning cultures, and integrating teams. But, the one I didn’t anticipate? Data integrity. When bringing together a mix of small, family-owned businesses -- some still working off spreadsheets, older systems, or even paper. Everyone had their own way of tracking information. Trying to integrate all of that into a single, reliable source for decision-making is harder than expected. It’s a reminder that back-to-basics work is foundational. If your HR and people data isn’t clean and consistent, leaders can’t make informed business decisions, and analytics tools can only take you so far. This is a huge focus for mSupply this year. team needs to be committed to getting our data right, and keeping it right. It’s not glamorous work, but it’s the groundwork that enables everything else. #HRLeadership #PeopleData #FutureOfWork