![]() Nearly one-third of analysts spend more than 40 percent of their time vetting and validating their analytics data.Multiple research reports have shown that bad data is on average costing businesses 30% or more of their revenue.In the US alone, businesses lose $3.1 trillion annually due to poor data quality.The average financial impact of poor data quality on organizations is $9.7 million per year.Bad data costs companies an estimated 15% of their revenue.It only comes into the spotlight when something goes drastically wrong like a flawed report or an ineffective marketing campaign.Īll these causes lead to losses in millions. Data quality or accuracy is not a boardroom discussion matter. IT teams are too busy helping leadership in ‘transforming’ to fret about disparate, duplicate, inaccurate data. Leadership is too busy thinking of investments in cloud, big data systems, fancy software and technologies to worry about data. Worse, the client may end up pointing the error if the case study was published without verification.ĭata Quality is Not Usually Addressed: Teams are too busy with selling, marketing and promotion to think about incorrect information in the data set. The marketing rep will have to go through multiple verification rounds to fix this error. ![]() For instance, a marketing rep may want to verify the client’s company name before publishing a case study, only to see an incorrect spelling or a short form of the name in the Company Name field, that was perhaps typed or modified by a sales rep. Not Regulating Data Accessibility: The CRM is a good example of this point. Accessed simultaneously by sales, marketing, customer service, and account managers, CRMs can become a hotbed of duplicated, inconsistent, inaccurate data. Worse, data acquired from social media is highly prone to mistakes, typos and copy/paste errors. For instance, one customer’s name may be written in three different ways by three different reps. An organization that does not have data governance in place will see data entered in multiple formats, styles and varieties. Poor Data Entry Practices: Data inaccuracy is the outcome of poor data entry practices. If the data is irrelevant, incorrect, incomplete, and inaccurate it can disrupt processes & hamper operational efficiency. It impacts an organization’s business intelligence, forecasting, budgeting, and other critical activities. It’s imperative that any data stored in a data warehouse is accurate and appropriate for use. And this generally happens due to the lack of data standardization and rules. Is it September 10 th or is it 9 th October? This is the classic meaning vs form problem that threatens data accuracy. If was converted to content, which of the two do you think would be accurate? In the US database, dates follow the MM/DD/YYYY format, whereas, in the EU database and other countries of the world, it’s DD/MM/YYYY. A popularly cited work, Data Quality: The Accuracy Dimension by Jack Olson explains form and content as two of the most important characteristics of data accuracy. ![]() In data management, data accuracy is the first and critical component/standard of the data quality framework.
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