Hidden Costs of Poor Data Quality

She started invoicing her company for data requests. $200 per PowerPoint. $500 per dashboard. What happened next: It began as a joke during her performance review. "You say I'm not strategic enough," she told her manager. "But I spend 60% of my time on executive data requests." "That's part of the job," he replied. That night, she built a simple system. Every data request generated an internal invoice: - Time required - Hourly rate - Opportunity cost - Total "charge" She didn't send them. Just tracked them. Month 1 total: $18,400 Month 2 total: $22,100 Month 3 total: $19,750 During her next one-on-one, she presented the receipts. "I've generated $60,250 in data services this quarter. My actual job contributed $0. Which one should I prioritize?" Her manager went pale. She continued: "If we outsourced this to a data analyst at $50/hour, it would cost the company 75% less. And I could do my actual job." Word spread. Other employees started tracking their "invoices." The numbers were staggering: Engineering: $147,000/month in data services Product: $89,000/month in reporting Design: $34,000/month in presentations Someone built a company-wide dashboard: "Internal Data Services Inc." Running total: $4.2M annually The CFO called an emergency meeting. "This is ridiculous. You don't actually invoice internally." Someone responded: "Why not? Every external agency does. We're just the agency that also tries to do our real jobs." That's when it clicked. They were running two companies: 1. The actual business 2. An internal data agency with no billing department The CFO did what CFOs do. Ran an ROI analysis. Option A: Keep status quo ($4.2M hidden cost) Option B: Hire 3 dedicated analysts ($350K) Option C: Buy proper tools and train execs ($100K) The decision took five minutes. Within 30 days: - Executives learned self-service dashboards - Three analysts hired for complex requests - "Invoice system" retired The woman who started it all? Got promoted to Chief of Staff. First initiative: "Time is Money" visibility program. Now every team tracks the true cost of interruptions. Not to invoice. To inform. Because when you make invisible costs visible, behavior changes instantly. The company motto became: "Would you pay $500 for that PowerPoint? Then don't ask someone else to." Revenue grew 40% the next year. Not from new features. From people actually building them. Try it at your company. Track the invoice you'll never send. Watch how fast things change. Because nothing shifts behavior like a price tag.

Managing data quality is critical in the pharma industry because poor data quality leads to inaccurate insights, missed revenue opportunities, and compliance risks. The industry is estimated to lose between $15 million to $25 million annually per company due to poor data quality, according to various studies. To mitigate these challenges, the industry can adopt AI-driven data cleansing, enforce master data management (MDM) practices, and implement real-time monitoring systems to proactively detect and address data issues. There are several options that I have listed below: Automated Data Reconciliation: Set up an automated and AI enabled reconciliation process that compares expected vs. actual data received from syndicated data providers. By cross-referencing historical data or other data sources (such as direct sales reports or CRM systems), discrepancies, like missing accounts, can be quickly identified. Data Quality Dashboards: Create real-time dashboards that display prescription data from key accounts, highlighting any gaps or missing data as soon as it occurs. These dashboards can be designed with alerts that notify the relevant teams when an expected data point is missing. Proactive Exception Reporting: Implement exception reports that flag missing or incomplete data. By establishing business rules for prescription data based on historical trends and account importance, any deviation from the norm (like missing data from key accounts) can trigger alerts for further investigation. Data Quality Checks at the Source: Develop specific data quality checks within the data ingestion pipeline that assess the completeness of account-level prescription data from syndicated data providers. If key account data is missing, this would trigger a notification to your data management team for immediate follow-up with the data providers. Redundant Data Sources: To cross-check, leverage additional data providers or internal data sources (such as sales team reports or pharmacy-level data). By comparing datasets, missing data from syndicated data providers can be quickly identified and verified. Data Stewardship and Monitoring: Assign data stewards or a dedicated team to monitor data feeds from syndicated data providers. These stewards can track patterns in missing data and work closely with data providers to resolve any systemic issues. Regular Audits and SLA Agreements: Establish a service level agreement (SLA) with data providers that includes specific penalties or remedies for missing or delayed data from key accounts. Regularly auditing the data against these SLAs ensures timely identification and correction of missing prescription data. By addressing data quality challenges with advanced technologies and robust management practices, the industry can reduce financial losses, improve operational efficiency, and ultimately enhance patient outcomes.

Every D2C brand aiming at the next $10 million milestone should hear Mike Beckham's experience about the "true cost of underinvesting in data infrastructure". Don't repeat these mistakes in your #growthjourney For Simple Modern, he confirmed that "One of the things we've seen as we start to build our data infrastructure, it can get pretty expensive pretty quickly, especially if you're piecing together several different software solutions." This resonates deeply with what I'm seeing across the industry. When companies hit that $10M revenue mark, executives often balk at the price tag of robust data systems without calculating the hidden costs of poor data infrastructure: • Decision latency costing up to 5-7% in missed revenue opportunities • Data silos creating redundant work across departments (we measured 12-15 hours/week of duplicate analysis) • Inability to perform proper attribution, leaving marketing dollars on ineffective channels • Security and compliance vulnerabilities from patchwork solutions He mentions "When people look at investment in technology, they're thinking about what it's going to cost, but they don't think about the return on that investment." The ROI calculation isn't just about software costs, it's about business velocity. In our experience, companies that invest in proper #datainfrastructure at the $10M stage outperform peers by 22% in growth rate over the following 24 months. Imagine what can happen if you make those fixes ahead. This video provides excellent framing for your next executive conversation. Rather than presenting data infrastructure as a cost center, position it as the nervous system that will power your company's next growth phase. #DataStrategy #D2C

The iceberg that's sinking your profits... Everyone sees the $175K in rework and scrap. Nobody talks about the $1.2M hiding below the surface. Here's what Cost of Poor Quality (COPQ) really looks like: Visible costs (20%): ↳ Inspection and testing ↳ Rework and repairs   ↳ Scrap and waste material ↳ Quality audits Hidden costs (80%): ↳ Production delays and bottlenecks ↳ Customer complaints and service calls ↳ Lost sales and damaged reputation ↳ Equipment downtime from quality issues ↳ Excessive documentation and admin ↳ Expedited shipping to fix problems ↳ Management time spent firefighting The shocking truth? Poor quality costs most manufacturers 15-20% of total revenue. Best-in-class companies keep it under 5%. What's your COPQ costing you? Start measuring what you can't see.

$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

Bad data costs more than acquiring good data In my experience over the last few years, I've noticed an interesting pattern while helping clients scale their data initiative. Most initial conversations start with the client stating their data quality is on par with what they want to achieve. However, this statement seems to be misleading when digging a bit deeper into the matter. Usually, we associate data with a company's IT department. While this is somewhat true, they primarily work on implementing a technical system defined by higher-ups. To them, it's another task they want to finish and move on to the next one (early on in my career, I was guilty of this as well). Those who implement these methods are different from the ones using its data byproduct and, in turn, can be dismissive about the data quality. I researched this topic and found that, even in 2016, IBM reported that the cost of handling bad data in the US alone was nearly $3.1 trillion! This cost is, of course, distributed between tasks and issues such as: ⚙️ Scrap and rework 🛠️ Workarounds and hidden correction processes ⚖️ Organizational inefficiencies or low productivity ⚖️ Organizational conflict 🚫 Low job satisfaction 🚫 Customer dissatisfaction 💰 Opportunity costs, including an inability to innovate 💰 Compliance costs or fines 💰 Reputational costs With how data-centric the world has become in the past few years, I expect these costs to be even higher. Teams usually assume their data is reliable until they have a reason to suspect it's not. Once they do, making the data quality operational can be costly. That's why it is essential to make sure everyone who has contact with the data, whether they are extracting or using it, is on the same page about the purpose of the data. #datastrategy #datamanagement #dataanalytics

8 silent ways your business bleeds money through poor data management Your business is leaking cash right now. Not through obvious holes. Through invisible cracks in your data management. Here's what's draining your profits: 𝟭/ 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝘄𝗮𝘀𝘁𝗲 ↳ 80% of your data sits unused, yet you're paying top dollar to keep it 𝟮/ 𝗗𝗮𝗿𝗸 𝗱𝗮𝘁𝗮 𝗯𝘂𝗶𝗹𝗱𝘂𝗽 ↳ Forgotten information eating server space and budget 𝟯/ 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗰𝗵𝗮𝗼𝘀 ↳ Your teams store the same files across 5+ locations 𝟰/ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗴𝗮𝗽𝘀 ↳ Poor data management = expensive breaches waiting to happen 𝟱/ 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 ↳ Money-making patterns vanish in messy data 𝟲/ 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗻𝗶𝗴𝗵𝘁𝗺𝗮𝗿𝗲𝘀 ↳ Disorganized data creates $100K+ regulatory fines 𝟳/𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 ↳ Bad data structure kills team productivity 𝟴/ 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗽𝗮𝗿𝗮𝗹𝘆𝘀𝗶𝘀 ↳ Messy data blocks growth opportunities The brutal truth? The average business loses $12.3M yearly to these problems. But here's the good news: You can fix every leak with: • Smart AI tools • Clean data architecture • Clear governance • Quality controls Want to stop the bleeding? Drop "SAVE" below for our free data management checklist. Let's plug those expensive leaks together.

Be Great. Two syllables that sound like a pep talk. Until life decides to throw them 'bows {in my best Ludacris voice}. Just last week: Your plan was Amy Sherald-levels of ready by 8 am. Clear, realistic, and accessible. But by 10, it looked more like a Basquiat. Chaotic, open to interpretation, and just a little bit confounding. You spent twenty minutes hunting for the "true" version of a file that should have taken two. You juggled priorities like a circus act, just to have an urgent request leg sweep the evening you set aside for deep thought - or your family. If any of that feels familiar, congratulations: you're human. This isn't a talent problem. Most often, it's an organizational friction problem. And it's costly. The Hidden Tax on Ambition: 🔹 Eroded Decision Confidence: Leaders hesitate when data's unreliable, delaying decisions and giving up advantage - not from poor strategy, but untrustworthy tools. 🔹 Innovation Stagnation: Teams dragged down by manual work lose creative energy, letting great ideas slip away. 🔹Talent Attrition and Onboarding Drag: New hires waste time solving old puzzles, fueling frustration and turnover as know-how walks out. 🔹 Brand Risk from Inconsistent Narratives: Mixed messages in reports or calls confuse investors and negatively impact your brand. 🔹 Strategic Agility Lost: Siloed data and slow choices mean missed opportunities and timid moves. 🔹 Mounting Invisible Risk: Shadow IT eats up to half your tech budget, risking multimillion-dollar fines and breaches. These are some of the hidden costs of friction. The ones that don't always show up on a dashboard but slowly drain the lifeblood of your business. The quiet "W" on my cap? It's Workiva - the platform I serve Financial Services companies with every day. It doesn't shout for attention, but the difference it makes is anything but subtle. Most platforms promise speed -fewer meetings, faster files, more automation. But Workiva doesn't just streamline. It liberates trapped potential. Customers tell us about outcomes you won't find in the headlines. Here's what they experience: 🔸 A single, live data chain that feeds slides, spreadsheets, and narratives. Strategy begins at minute one, not minute 19. 🔸 Comments, evidence and audit trails stay married to the numbers, so new hires inherit wisdom, not puzzles. 🔸 Real-time validation flags issues mid-week, so weekends stay sacred [and heart rates stay human]. 🔸 Connected data kills cloning. Hours migrate from reconciling cells to exploring scenarios. 🔸 One workspace facilitates a common vocabulary. Silos get noisy, then disappear. And these "soft" wins translate into hard results: ▫️ 204% total total economic impact. ▫️ Up to 1,600 hours a year reclaimed at one of our global customers, once spreadsheets stopped mulitplying. When truth becomes the easiest thing to find, teams reroute that reclaimed capacity into innovation, customer insight, and - yes - Greatness.

The Hidden Cost of Bad Procurement Data I’ve heard the same comment repeated lately from Directors of Procurement, Heads of Purchasing, and Supply Chain leaders in manufacturing: “We know our data isn’t great… but it is not a priority right now.” Here’s the problem: that mindset doesn’t hold up anymore. As we enter the AI era, poor data hygiene isn’t just a nuisance. It's becoming a blocker. If your procurement data is outdated, scattered across systems, or simply incomplete, AI won’t fix it. You will either be unable to deploy AI solutions, or their output will be terrible. What does bad procurement data look like in real terms? ❌Reports that don’t reflect reality ❌Sourcing cycles that take too long ❌Missed cost-saving opportunities ❌No clear view on supplier performance But I have also seen great teams investing in clean, connected procurement data. Here are some of the benefits they are gaining: ✅Faster, more confident decisions ✅Real visibility across their supply chain and early problem detection ✅Operational leverage through automation The takeaway: AI won't fix bad data. It will scale it. I believe that if data quality isn’t on your roadmap this year, you’re setting yourself up for stalled pilots and wasted budgets. But if you get it right now, you’ll be in a strong position while others are still cleaning up. I would love to hear how others are approaching this. What’s working, and what still needs fixing?

🔫 Data Debt: The Silent Killer of CRM Efficiency 🔫 Companies often underestimate the hidden cost of poor data quality in Salesforce. What I call "Data Debt." Like technical debt, Data Debt accumulates silently, slowing down your teams, sabotaging user adoption, and reducing your Salesforce ROI. Why does Data Debt happen? ➡️ With data decay rates exceeding 20% per year, ➡️ frequent human errors during data entry—often accidental, sometimes intentional shortcut, ➡️ and messy integrations from various sources, Businesses inevitably make critical decisions based on flawed information. Poor CRM data leads to: ➡️ Inefficient sales processes ➡️ Missed revenue opportunities ➡️ Frustrated teams drowning in manual data cleanup 🤖 Enter AI Agents: Autonomous tools specifically built to eliminate Data Debt by continuously monitoring, cleansing, enriching, and maintaining CRM data accuracy. 💪 Let AI Agents do the heavy lifting—so your team can focus on selling, not scrubbing spreadsheets. 🚀 The result? Immediate gains in efficiency, increased Salesforce adoption, and significantly higher ROI.