Posted by  Lance Mercereau  Published on  11 Feb 2014
  • Data Cleansing
  • Analytics Best Practice

75% of executives questioned in a recent Harris Interactive study acknowledged having made at least one bad decision because of bad or inadequate data quality, and according to Gartner, for some organizations, the cost of poor data can exceed $100 million per year.

Bad data has a wide impact across all departments:

  • Finance - inaccurate, incomplete, inconsistent, untimely or unconnected data has traditionally resulted in process inefficiencies and sub-optimal decisions across a variety of business functions, including cash flow management, duplicate payments and fraud.
  • Procurement - the inability to accurately and consistently categorize and classify spend data has made it hard to control costs, to manage suppliers effectively, to control maverick spend, or to identify and act upon opportunities for improved efficiencies.
  • Sales - Keeping inaccurate records of sales activities will produce inaccurate reporting on metrics. For example, if wins aren’t correctly reported, your rep or sales team may experience a lower sales conversion rate than it truly achieved. 
  • HR - when used to determine how to engage talent, cultivate strong leaders, and drive change, it needs to be correct. With so much data to capture in organizational structure, payroll and benefits, performance management, training, and workforce planning/talent analytics, accuracy is a must.

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