ERP LOSERS
"Measure what is measurable, and make measurable what is not so." - Galileo Galilei
The Importance of Starting With Accurate Data such as BOM, Inventory Levels Cycle Times Etc
Having 90% BOM (Bill of Materials) and 90% inventory accuracy can lead to operational inefficiencies and potential production delays. Inaccurate BOM data can lead to incorrect or incomplete product assemblies, which can result in rework, scrap, or defective products. Similarly, inaccurate inventory data can result in stockouts, overstocks, or missed opportunities to capitalize on market demand. These inefficiencies can lead to reduced productivity, increased costs, and decreased customer satisfaction. Overall, having lower levels of BOM and inventory accuracy will negatively impact the organization's ability to operate effectively and achieve its business objectives.
Data Accuracy: Reliable, Error-Free Information
Data accuracy ensures that records are free from errors and can be trusted as a reliable source of information. It is a critical component of the data quality framework in data management. Accurate and appropriate data storage is imperative in a data warehouse as it impacts crucial organizational activities such as business intelligence, forecasting, and budgeting. Irrelevant, incorrect, incomplete, or inaccurate data can disrupt processes and hamper operational efficiency.
Causes of Data Inaccuracy
Data inaccuracy stems from various causes, including:
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Poor Data Entry Practices: Inaccurate data often results from inadequate data entry practices. Without proper data governance, organizations may encounter data entered in different formats, styles, and variations. For example, one customer's name may be recorded differently by multiple representatives. Data acquired from social media is particularly prone to mistakes, typos, and copy/paste errors.
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Unregulated Data Accessibility: In situations like customer relationship management (CRM) systems accessed by multiple departments, such as sales, marketing, and customer service, duplicated and inconsistent data can arise. For instance, a marketing representative may need to verify a client's company name before publishing a case study, only to find incorrect spelling or a shortened version of the name entered by a sales representative. Rectifying such errors requires multiple verification rounds and can lead to client dissatisfaction if left unchecked.
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Neglecting Data Quality: Amidst busy selling, marketing, and promotion, teams often overlook incorrect information in datasets. Leadership's focus on investments in cloud technology, big data systems, and fancy software may overshadow data accuracy concerns. IT teams, busy assisting leadership in transformation efforts, may not prioritize addressing disparate, duplicate, or inaccurate data. Data quality and accuracy often become boardroom discussions only when significant issues arise, such as flawed reports or ineffective marketing campaigns.
The High Cost of Inaccurate Data
Statistics highlight the substantial costs associated with inaccurate data:
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Bad data costs companies an estimated 15% of their revenue. (Gartner)
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Poor data quality has an average annual financial impact of $9.7 million on organizations. (Gartner)
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US businesses lose $3.1 trillion annually due to poor data quality. (IBM)
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Research reports indicate that bad data costs businesses an average of 30% or more of their revenue.
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Nearly one-third of analysts spend over 40% of their time validating analytics data. (Forrester)
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Knowledge workers waste 50% of their time dealing with data errors, searching for trustworthy data sources, and correcting mistakes. (HBR)
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28% of those experiencing email delivery problems report that customer service suffers due to bad data. (E-Consultancy)
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Approximately 20% to 30% of operating expenses can be attributed to bad data. (Pragmatic Works)
These statistics emphasize poor data persistently impacts organizations, leading to significant losses in revenue, reputation, and customer confidence.