Imagine being a skilled CFO, just taking the first sip of your latte, when the quarterly financial report lands on your desk. Profits are down again. You\u2019ve slashed costs, streamlined operations, and haggled with suppliers so much they\u2019ve started avoiding your calls. Yet, the numbers still don\u2019t add up. What if the real culprit isn\u2019t your strategy, your team, or the market, but something far sneakier? Say hello to bad data, the silent profit killer lurking in your spreadsheets.<\/p>\n\n\n\n
Bad data is the incorrect, incomplete, or outdated information that slinks into your systems like a crafty leech, quietly draining your profits while you\u2019re busy looking the other way. It\u2019s not loud or obvious. No alarms go off when your customer database lists \u201cMickey Mouse\u201d as a VIP client or when a decimal point goes astray in your invoices. But the damage? Oh, it\u2019s very real.<\/p>\n\n\n\n
In a research article updated in 2023 by Monte Carlo Data<\/a>, over half of the respondents believe data quality issues have a significant negative impact on their company\u2019s revenue, equivalent to at least 25% of the total turnover. Put another way: for every \u00a31 million your company earns, \u00a3250,000 could slip through the cracks due to bad data. And that\u2019s just the tip of the iceberg. Missed opportunities and weakened competitiveness from poor data quality create problems that quietly erode your bottom line.<\/p>\n\n\n\n The evidence is plentiful. Take the credit bureau Equifax, as detailed in a 2025 article by Invensis.<\/a>Back in 2022, they sent inaccurate credit scores to over 300,000 people, with errors deviating by more than 25 points from the correct figures. The result? A 5% drop in share price that cost millions quicker than you can say \u201cwhoops.\u201d<\/p>\n\n\n\n A 2023 article by Datafold<\/a> dishes up more chilling yet crucial examples. Samsung\u2019s \u201cfat-finger\u201d blunder in 2018, where a typo led to an erroneous dividend payout, cost them millions. Then there\u2019s Uber, who overpaid drivers by a staggering $45 million due to data quality issues in their accounting.<\/p>\n\n\n\n Despite these cautionary tales, Gartner<\/a> reports that only four out of ten companies measure the financial cost of bad data. It\u2019s a bit like ignoring a leaking pipe while your water bill soars.<\/p>\n\n\n\n Bad data doesn\u2019t just mess up your bottom line. Bad data is a pure waste of time and resources. People spend countless hours fixing errors, reconciling mismatched figures, and making sense of confusing data. It drags down efficiency, hikes up operational costs, and leaves your company quietly crippled by mediocrity, as if it\u2019s been infected with a virus. But the fallout doesn\u2019t end there. Bad data can also land your business in hot water with laws and regulations. In the worst cases, flawed or incorrect data (often unknowingly) can lead to actions that skirt the edge of legality or plunge straight into criminal territory. Think inaccurate reporting to authorities, misuse of personal data, or breaches of trade agreements based on faulty info. The cost of bad data can therefore exceed a dip in profits; it can threaten your company\u2019s very existence and drag management into serious legal trouble.<\/p>\n\n\n\n Research backs this up, showing bad data poisons a company beyond just finances. A 2023 article by Y. Timmerman and colleagues in Decision Support Systems (find it here<\/a>) builds on Gartner\u2019s earlier findings about hefty losses from poor data quality. They break down the costs into direct hits (like Equifax\u2019s share price tumble) and indirect ones (like your team\u2019s endless battle to fix mistakes), concluding that bad data\u2019s impact is multifaceted. The expenses for prevention, detection, and correction pile up fast if you\u2019re not vigilant and proactive.<\/p>\n\n\n\n So, how do you halt this profit-guzzling plague? Here are three concrete steps to tame your data:<\/p>\n\n\n\n Bad data is more than a nuisance - it\u2019s a stealthy assassin of your earnings, working in the shadows. So, it\u2019s time to saddle up and fight back! Picture yourself as that skilled CFO again, eyeing the quarterly reports. After a deep data review, the right tools, and a well-trained team, the outlook is transformed. The leaks are plugged, inefficiency is slashed, and the bottom line finally reflects the hard work you\u2019ve put in. So, dive into your data, equip yourself with the right tools, and train your team. Do that, and you won\u2019t just stop the losses, you\u2019ll also sharpen your edge in a world where good data is worth its weight in gold. Now, that next sip of latte can be savoured with peace of mind, knowing the silent killer is under control.<\/p>\n\n\n\n Sources: Forestil dig v\u00e6rende en dygtig finansdirekt\u00f8r, som lige har drukket den f\u00f8rste slurk latte, da den kvartalsvise finansielle rapport lander p\u00e5 dit skrivebord. Profitten er nede igen.. Du har sk\u00e5ret i omkostningerne, str\u00f8mlinet driften og forhandlet s\u00e5 meget med leverand\u00f8rer, at de ikke gider dig l\u00e6ngere. Alligevel stemmer tallene stadig ikke. Hvad nu, hvis den […]<\/p>\n","protected":false},"author":1,"featured_media":218,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[34,8],"tags":[],"class_list":["post-216","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","category-it-cost-effectiveness"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/posts\/216","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/comments?post=216"}],"version-history":[{"count":1,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/posts\/216\/revisions"}],"predecessor-version":[{"id":219,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/posts\/216\/revisions\/219"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/media\/218"}],"wp:attachment":[{"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/media?parent=216"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/categories?post=216"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tiwastech.com\/en\/wp-json\/wp\/v2\/tags?post=216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}The Underlying Poison of Bad Data<\/strong><\/h2>\n\n\n\n
Three Methods to Overcome the Data Problem<\/strong><\/h2>\n\n\n\n
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<\/strong>Treat your data with the same care as your finances: schedule regular check-ups. Cross-check sources, verify entries, and weed out anything outdated. It\u2019s like checking expiry dates in your fridge\u2014you wouldn\u2019t use three-year-old yoghurt, so don\u2019t let your business run on stale customer lists. A thorough audit can spot errors before they cost you millions.
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<\/strong>Picture these tools as the healthcare team for your data, running regular check-ups to keep it in tip-top shape. Data profiling tools can spot anomalies, cleansing and enrichment tools can fix errors and enhance records, and validation and monitoring tools ensure quality holds steady over time. Yes, there\u2019s an upfront cost, but it\u2019s peanuts compared to the 25% of revenue bad data could jeopardise.
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<\/strong>Your colleagues are the frontline defence against bad data. Show them why data quality matters and arm them with the best ways to tackle it. Automate manual entry to cut risks, standardise formats, and make accuracy a team effort. Pull it off, and you and your crew can become a brilliant vaccine against bad data.<\/li>\n<\/ol>\n\n\n\nStop the Losses, Start Reaping the Rewards<\/strong><\/h2>\n\n\n\n
– Monte Carlo: The Annual State of Data Quality Survey<\/a>
– Invensis: Major Costs of Poor Data Quality: How to Avoid Them<\/a>
– Datafold: Enterprises Whose Bad Data Cost Them Millions: Lessons from Samsung and Uber<\/a>
– Gartner: How to Stop Data Quality Undermining Your Business<\/a>
– Timmerman et al.: Cost-based analysis of the impact of data completeness and representational consistency<\/a><\/p>","protected":false},"excerpt":{"rendered":"