{"id":2318,"date":"2025-09-24T16:36:17","date_gmt":"2025-09-24T15:36:17","guid":{"rendered":"https:\/\/clouddirect.net\/learning-hub\/?p=2318"},"modified":"2025-09-24T16:47:42","modified_gmt":"2025-09-24T15:47:42","slug":"bad-data-costs-and-how-to-fix-them","status":"publish","type":"post","link":"https:\/\/clouddirect.net\/learning-hub\/bad-data-costs-and-how-to-fix-them\/","title":{"rendered":"The Hidden Cost of Bad Data (and How to Fix It)\u00a0"},"content":{"rendered":"\n
Data quality is fundamental, with the cost of bad data running into millions for UK businesses. Here, we look at how and why poor data is costing your organisation dearly, and the practical steps you can take to improve data quality.<\/strong>\u00a0<\/h5>\n\n

Bad data is costly. Research by analysts Gartner<\/a> puts the average cost to organisations at a staggering $12.9 million a year \u2013 around \u00a310 million. Whether that figure seems high or low to you will depend on a variety of organisation-specific factors, but the key point is that poor data is costly.\u00a0\u00a0<\/p>\n\n

As we become more AI-reliant, that cost is likely to increase. And for most, it\u2019s completely hidden.\u00a0\u00a0\u00a0<\/p>\n\n

Six ways data deficiencies could be costing your organisation<\/strong><\/h2>\n\n

Poor data typically impacts organisations through operational waste, revenue leakage, and strategic shortcomings.   <\/p>\n\n

    \n
  1. Lost revenue <\/strong>\u00a0
    Data inaccuracy can result in poor decisions, causing lost sales, poor targeting, and underachieving campaigns. Old or incomplete customer data can mean missed upsell or cross-sell opportunities.\u00a0\u00a0<\/li>\n\n\n\n
  2. Wasted time<\/strong>  
    Employees waste hours finding, verifying, or correcting information.  <\/li>\n\n\n\n
  3. Increased risk<\/strong> \u00a0
    Data errors cause GDPR and regulatory breaches, possibly incurring fines and certainly requiring time-consuming corrective action. Risk models that use poor data will fail to flag preventable issues.\u00a0<\/li>\n\n\n\n
  4. Negative customer experience<\/strong> 
    Incorrect contact details, preferences, or histories lead to irrelevant messaging or service errors, which undermine customer confidence, damage brand reputation, and add to customer churn.  <\/li>\n\n\n\n
  5. Operational inefficiencies<\/strong> 
    Errors in billing, shipping, or inventory management cause rework, returns, and delays \u2013 all of which carry a cost.  <\/li>\n\n\n\n
  6. Strategic failings<\/strong> 
    Decisions based on <\/strong>flawed insights can misallocate investment, degrade valid opportunities, ignore key risks, and cause growth strategies to fail. <\/li>\n<\/ol>\n\n

    Why data matters more than ever<\/strong> <\/h2>\n\n

    As John Doublard, CTO at Oak Group and one of the industry contributors to \u2018The Data & AI Readiness Playbook\u2019, notes: \u201cThe future is data-driven.\u201d<\/em><\/strong> <\/p>\n\n

    \u201cAI can only drive genuine business value when it addresses real business issues AND is fuelled by valid data,\u201d <\/em><\/strong>explains Cloud Direct\u2019s Data & AI Practice Lead Dan Knott. <\/p>\n\n

    Understanding the limitations of your data, which parts need improving, and how to make those improvements is key to this.  <\/p>\n\n

    Eight practical steps for data quality improvement<\/strong><\/h2>\n\n

    Improving data quality isn\u2019t just the responsibility of IT \u2013 although there are technical actions that will help \u2013 but something for the whole organisation.   <\/p>\n\n

    \u2018When you\u2019re about to invest heavily in becoming a data-driven<\/em> business<\/em><\/strong>, you need to make sure that the data you\u2019re working with is going to provide for you and not cost you more\u2019.<\/p>\n\n

      \n
    1. Define what \u2018good\u2019 means<\/strong>\u00a0
      If you\u2019re working with bad data, you\u2019ll get bad outcomes, so establish clear data quality dimensions. The Data & AI Readiness Playbook outlines the importance of accuracy, completeness, consistency, validity, uniqueness and timeliness. Add to this any business-specific requirements, such as capturing an accurate address or company registration number for credit checking.\u00a0\u00a0<\/li>\n\n\n\n
    2. Perform a data audit<\/strong> 
      Identify your key data assets, such as CRM\/ERP and finance data, and assess current data quality. Prioritise high-impact areas and make use of tools to help you highlight errors, duplicates, gaps, or inconsistencies.<\/li>\n\n\n\n
    3. Fix existing issues<\/strong> \u00a0
      Involve data owners (see below) with data cleansing. The objective is to de-duplicate records, fill in missing fields, standardise formats, and identify old and obsolete data for archiving and removal. Tools like Microsoft Purview and Power Query can helpfully automate some of the work.\u00a0<\/li>\n\n\n\n
    4. Embed good data governance practices<\/strong> 
      Appoint data owners and data stewards who will be responsible for data quality in key areas. Involve them in creating policies for data entry, usage, retention, and updates, and make use of Microsoft Purview, or similar, to enforce policies and manage access, lineage, and classification.<\/li>\n\n\n\n
    5. Educate and engage users<\/strong>\u00a0
      Data quality needs to be seen as a shared responsibility, and not IT\u2019s problem alone. Run awareness campaigns or training to help users understand their role in maintaining good data. Within this, celebrate success by showing how clean, reliable data is enabling better decisions and results, and continue running campaigns until data quality is embedded in the organisation\u2019s culture.\u00a0<\/li>\n\n\n\n
    6. Improve data entry at the source<\/strong>\u00a0
      You don\u2019t want to be continually drawn into corrective action, so employ measures to improve data entry at the source. Add validation rules, drop-downs, and formatting controls where data is input, and ensure that departmental training covers the importance of entering accurate data. Wherever possible, automate data capture utilising integrations, forms with logic, and barcode scanners.\u00a0Improving data quality requires both technical and cultural change. Start with what matters most, fix the root causes, and embed quality into everyday workflows. Over time, better data leads to faster decisions, happier customers, and lower costs.<\/li>\n<\/ol>\n\n

      Next Steps<\/h3>\n\n

      Download a copy of<\/strong> The Data & AI Readiness Playbook<\/a> to learn more<\/strong>. You\u2019ll discover how others are preparing for and using AI, and a seven-step process to unlock the value of your business data.<\/p>\n","protected":false},"excerpt":{"rendered":"

      Data quality is fundamental, with the cost of bad data running into millions for UK businesses. Here, we look at how and why poor data is costing your organisation dearly, and the practical steps you can take to improve data quality.\u00a0 Bad data is costly. Research by analysts Gartner puts the average cost to organisations […]<\/p>\n","protected":false},"author":44,"featured_media":2339,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2,52],"tags":[62],"post_media_type":[24],"class_list":["post-2318","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-azure","category-data-ai","tag-data-and-ai","post_media_type-blog"],"acf":[],"yoast_head":"\nThe Hidden Costs of Bad Data \u2013 And How to Fix Them<\/title>\n<meta name=\"description\" content=\"Discover the hidden costs of bad data and learn practical steps to improve data quality with insights from The Data & AI Readiness Playbook.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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