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.

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

Data quality is essential for organizations across all sectors, including healthcare, finance, marketing, and government, to name a few. Poor quality data can lead to serious consequences, such as incorrect analyses, flawed reports, misguided decisions, financial losses, damaged reputation, and non-compliance with regulations. On the other hand, high-quality data can help organizations gain valuable insights, improve operations, increase efficiency, identify new opportunities, enhance customer experience, and stand out from the competition.

One of the key benefits of having good data quality is the ability to trust the information being used. When data is accurate, reliable, and up-to-date, decision-makers can have confidence in the insights they derive from it. This allows organizations to make informed decisions that are based on facts rather than assumptions, intuition, or partial information. With trust in the data, organizations can move forward with strategic initiatives, investments, and innovations that drive growth and success.

Moreover, high-quality data enables organizations to build strong relationships with customers, employees, suppliers, and other stakeholders. By ensuring the accuracy and consistency of data throughout the organization, companies can deliver personalized and relevant experiences that meet the needs and expectations of their audiences. This leads to increased customer loyalty, higher satisfaction levels, and improved brand reputation.

Another significant advantage of data quality is its impact on operational efficiency and cost reduction. Clean and well-managed data can streamline processes, eliminate redundancies, optimize resources, and reduce errors and rework. This not only improves productivity and performance but also lowers operational costs, unlocks hidden value, and creates a competitive edge in the marketplace.

Furthermore, data quality is essential for organizations to comply with regulatory requirements, industry standards, and data protection laws. Poor data quality can result in legal repercussions, fines, and reputational damage. By maintaining high standards of data quality, organizations can mitigate risks, protect sensitive information, and build trust with regulatory bodies, customers, and partners.

In conclusion, data quality is indispensable for organizations seeking to enhance decision-making, drive innovation, meet customer expectations, optimize operations, and achieve sustainable growth. It is the foundation of effective analytics, artificial intelligence, machine learning, and other data-driven technologies that are revolutionizing businesses today. As such, organizations must prioritize data quality as a strategic asset and invest in the tools, processes, and expertise needed to ensure that their data remains accurate, reliable, and actionable.

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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