What Is Data Quality And Why Is It So Significant?

Data Quality

Managing data effectively is an essential process for businesses. In today’s digital world, companies amass large amounts of data and accessing, storing, and managing it properly is essential to make use of the information. Business process outsourcing services such as data entry, data cleansing and document scanning play a key role in helping businesses manage their data processing tasks. With evolving technologies like automation, machine learning, artificial intelligence and the Internet of Things, companies need to know how to extract value from their data to make the most of it. The value of data depends on its quality. During the COVID-19 pandemic, companies realized the value of high-quality data to take quick and smarter decisions to stay competitive. So what does ‘data quality’ imply and what is its significance?

data quality dimensions

According to TechTarget, “Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it’s up to date”. Companies need to identify data errors that need to be resolved and see whether their digital data can meet its intended purpose. But data quality is not as good as it should be. A Harvard Business Review study reported that data quality is far worse than most companies realize, and that a mere 3% of the data quality scores in the study were rated as “acceptable” (www.forbes.com).

Quality data is data which is accurate, complete, reliable, relevant, and timely.

  • Accuracy – This means that all the details are correct. For instance, in a database of physicians in New York, all information such as the names, addresses (physical and email), phone numbers and other details must be accurate. In addition to being error-free, there should be no missing or obsolete records.
  • Completeness – This refers comprehensiveness of the data. There should be no missing information or gaps. For instance, consider a school database with information on data about pupils, teachers and so on. If there are missing records and missing attribute values, the data would be incomplete.
  • Reliability – This means that there are no contradictions between information in one system and information in a different source or system. For example, if a person’s date of birth is entered as June 7, 1980 in one system and as June 7, 1982 in another system, the information is unreliable and you can’t trust it. Data reliability also means that the data should come from reliable sources.
  • Relevancy: Data relevancy means the data is relevant for the purposes for which it was collected. If you are collecting or have collected information which is not pertinent to your task, it would be of no value. Irrelevant data is that which is not needed. For example, if you are researching teenagers’ online shopping habits and your dataset includes adults, you would need to remove these irrelevant observations.
  • Timeliness:Timeliness as a quality of data refers to up-to-date information being available and accessible for decision making. Timeliness of data is one the most crucial elements of database management. In his book The Practitioner’s Guide to Data Quality Improvement, David Loshin stated, “timeliness refers to the time expectation for the accessibility of data. Timeliness can be measured as the time between when data is expected and when it is readily available for use.” For instance, in healthcare, timely and accurate EHR data entry for the system to provide care quickly after a need is recognized.

Importance of Good-quality Data for Businesses

As good-quality data has a positive impact on many areas of business performance, companies should focus on improving data quality.

  • Reduces risk in decision making: The maxim “garbage in, garbage out” and its opposite applies when it comes data. Low-quality datasets can lead businesses to make faulty decisions. KPMG’s “2016 Global CEO Outlook” noted that 84% of CEOs are concerned about the quality of the data used for making decisions. With superior quality data, you can make decisions confidently and avoid guesswork.
  • Enhances competitive advantage: Having reliable data helps companies gain insights to support new product development or customer needs. With quality data, they can take advantage critical opportunities and can stay competitive.
  • Boosts productivity and revenue: With quality data, employees can focus on their core tasks instead of wasting valuable time fixing data errors and validating information. Accurate data can also boost improve relationships with both customers and business partners, and enabling more accurate targeting and communications. Quality customer data leads to understanding them better and smarter decision making to meet their needs, which can increase conversions, sales and revenue. Likewise, reliable data on industry trends can help businesses provide better services and products.
  • Drives compliance: As new regulations come into force, many businesses are under increasing pressure to maintain compliance or face heavy fines. According to a Forbes article, graph databases are helping finance firms better understand the customers they are trading with and conform to anti-money laundering regulations.
  • Reduces threat of reputational damage: Threats to a business’s reputation from dirty data can range from small mistakes to major blunders. In one survey, 21% of companies reported facing reputation damage due to bad data. Data quality’s reputational effects can be caused by invalid email addresses, incorrect spelling of a customer’s name, irrelevant marketing messages, missing data, issues with email delivery and high bounce rates. All of this can impact customer satisfaction and the company’s reputation would be damaged if customers take to social media to express their concerns.

In his article published in Harvard Business Review, data quality consultant Thomas Redman referenced IBM’s estimate of the yearly cost of inferior quality data in the US alone – a staggering $3.1 trillion in 2016. Implementing the best measures to ensure good data is the responsibility of not only managers and data scientists but also every employee in the organization. Companies can leverage advanced technologies to store, process and analyze their data and take meaningful decisions to boost growth and revenue. Relying on an experienced business process outsourcing company for data cleansing services, data entry, document scanning and conversion is a practical option to prepare large volumes of data for such analysis.

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