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 major benefit of good data quality is that it ensures accuracy and reliability in decision-making. When executives are presented with clean, accurate data, they can make informed decisions that are based on facts rather than speculation. This leads to better outcomes and a more strategic approach to business operations. On the other hand, poor data quality can result in misleading information and incorrect decisions, leading to potential financial losses or missed opportunities.

Another key aspect of data quality is its influence on customer satisfaction. Today’s consumers expect personalized experiences and swift resolutions to their queries. However, this level of service can only be achieved with accurate data. By maintaining high-quality customer information, organizations can provide tailored services and respond promptly to customer needs. In contrast, inaccurate data can result in customer frustration, delays in resolution, and an overall decline in customer satisfaction.

Furthermore, data quality plays a crucial role in compliance and risk mitigation. With data regulations becoming stricter and data breaches on the rise, organizations must prioritize data quality to protect sensitive information and maintain compliance with regulations. High-quality data ensures that legal requirements are met, sensitive data is protected, and overall risk is minimized. On the other hand, poor data quality can lead to regulatory fines, reputational damage, and security breaches, all of which can have long-lasting consequences for an organization.

In addition to these benefits, good data quality also enhances operational efficiency. When data is clean and consistent, processes flow smoothly, and resources are utilized effectively. For example, accurate product information ensures timely inventory management, while reliable customer data facilitates targeted marketing campaigns. In contrast, data errors can result in operational bottlenecks, wasted resources, and inefficiencies that hinder organizational performance.

Overall, the importance of data quality cannot be overstated in today’s business environment. It is a critical foundation for effective decision-making, customer satisfaction, compliance, and operational efficiency. Organizations must prioritize maintaining high-quality data to drive success, improve outcomes, and stay ahead of the competition. By investing in data quality management tools, processes, and training, companies can harness the power of clean, accurate data to boost performance and achieve strategic objectives.

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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