Data Quality Statistics


Steve Goldstein
Steve Goldstein
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Steve Goldstein runs LLCBuddy, helping entrepreneurs set up their LLCs easily. He offers clear guides, articles, and FAQs to simplify the process. His team keeps everything accurate and current, focusing on state rules, registered agents, and compliance. Steve’s passion for helping businesses grow makes LLCBuddy a go-to resource for starting and managing an LLC.

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Data Quality Statistics 2023: Facts about Data Quality are important because they give you more context about what’s going on in the World in terms of Data Quality.

LLCBuddy editorial team scanned the web and collected all important Data Quality Statistics on this page. We proofread the data to make these as accurate as possible. We believe you don’t need to check any other resource on the web for Data Quality Facts; All are here only 🙂

Are you planning to form an LLC? Thus you need to know more about Data Quality? Maybe for study projects or business research or personal curiosity only, whatever it is – it’s always a good idea to know more about the most important Data Quality Statistics of 2023.

How much of an impact will Data Quality Statistics have on your day-to-day? or the day-to-day of your LLC Business? How much does it matter directly or indirectly? You should get answers to all your Data Quality related questions here.

Please read the page carefully and don’t miss any words.

On this page, you’ll learn about the following:

Top Data Quality Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 6 Data Quality Statistics on this page 🙂

Data Quality “Latest” Statistics

  • According to the CRVS systems, since 2000, there has only been a minor improvement throughout the world, with the proportion of deaths reported rising from 36% to 38% and the percentage of children under the age of 5 whose births were reported rising from 58% to 65%.[1]
  • According to Gartner, by 2022, 70% of enterprises will closely monitor data quality levels using metrics, increasing it by 60% to drastically lower operational risks and expenses.[2]
  • To solve difficulties with data quality, over half of respondents, 48%, said they utilize data analysis, machine learning, or AI solutions.[3]
  • Regarding the shortage of resources mentioned by more than 40% of respondents, there is at least some reason to believe that artificial intelligence and machine learning might give the situation a little boost, according to O’Reilly.[3]
  • According to O’Reilly, more than 60% of respondents selected “Too many data sources and inconsistent data,” followed by “Disorganized data stores and lack of metadata,” which was selected by just under 50% of respondents.[3]
  • Over half of respondents, 48%, claim to employ data analysis, machine learning, or AI solutions to solve challenges with data quality.[3]

Also Read

How Useful is Data Quality

One of the primary reasons why data quality is so crucial is because decisions are often made based on the data that is available. Poor quality data can result in inaccurate insights, leading to poor decision-making and potentially disastrous consequences for a business. For example, if a sales team is working with erroneous customer data, they may miss opportunities to upsell or cross-sell to existing clients, ultimately impacting revenue and profitability. Similarly, inaccurate business intelligence can lead executives to make misguided strategic decisions that harm the company’s bottom line.

Furthermore, having high-quality data is essential for ensuring compliance with industry regulations and standards. In sectors such as finance, healthcare, and insurance, organizations are required to maintain accurate and secure data to protect customer information and comply with regulations such as GDPR and HIPAA. Failing to maintain data quality standards can result in severe financial penalties, damage to reputation, and even legal action.

In addition to compliance and decision-making, quality data is also essential for gaining a deeper understanding of customers and market trends. By leveraging reliable data, organizations can develop targeted marketing strategies, personalize customer experiences, and gain actionable insights that drive innovation and competitive advantage. In a world where customer expectations are constantly evolving, having access to high-quality data is essential for staying ahead of the curve and meeting changing consumer demands.

Despite the importance of data quality, many organizations still struggle to achieve and maintain high standards. Challenges such as data silos, inconsistent data formats, and incomplete or outdated information can all contribute to poor data quality. Investing in data quality management tools and processes, ensuring data is clean, consistent, and up-to-date, implementing data governance practices, and fostering a culture of data literacy and accountability within the organization are all essential steps in improving data quality.

In conclusion, the usefulness of data quality cannot be overstated. In today’s highly competitive and rapidly evolving business landscape, organizations that prioritize data quality are better equipped to make informed decisions, maintain compliance, gain a competitive edge, and drive sustainable growth. By investing in data quality management strategies and tools, businesses can unlock the full potential of their data, drive operational excellence, and achieve long-term success.

Reference

  1. nih – https://pubmed.ncbi.nlm.nih.gov/25971218/
  2. gartner – https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality
  3. oreilly – https://www.oreilly.com/radar/the-state-of-data-quality-in-2020/

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