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Data Quality Assurance: Importance & Best Practices in 2024

Updated on Jun 23
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Optimal decisions require high-quality data. High-quality data means data that represents its underlying real-world phenomena correctly. To achieve high data quality and sustain it, companies must implement data quality assurance procedures.

This article explains what data quality assurance is, why it is essential, and the best practices for ensuring data quality.

What is data quality assurance?

Data quality assurance is the process of determining and screening anomalies by means of data profiling, removing obsolete information, and data cleaning. Throughout the lifecycle of data, it is at risk of being distorted by the influence of people and other external factors. To protect its value, it is important to have an enterprise-wide data quality assurance strategy. Such a strategy includes corporate governance measures as well as technical interventions.

Why is data quality assurance important now?

According to the image, employees spend an additional 75% of their time on "non-value-adding activities" when data is either unavailable or of poor quality.
Source: McKinsey

We rely on AI/ML models to gain insights and make predictions about the future. Data quality is directly related to the effectiveness of AI/ML models, as high-quality data means that knowledge about the past is less biased. Consequently, this leads to better forecasts.

As the image above suggests, low-quality data or data scarcity leads workers to spend more effort on tasks that do not add value. This is because without AI/ML models, every task must be done manually regardless of its yield. So, ensuring data quality is of great importance in guaranteeing the efficiency of business operations.

What are the best practices for ensuring data quality?

Ensuring data quality requires the efforts of both management and IT technicians. The following list includes some important practices:

  • Corporate measure: By establishing a data quality management department within the company that tracks and monitors the company’s data strategy, company can achieve data quality. Data quality management generates data quality rules compatible with business data governance to ensure the suitability of data utilized for analysis and decision-making.
  • Relevance: The data should be interpretable. This means that the company has appropriate data processing methods, that the data format is interpretable by the company software and that the legal conditions allow the company to use such data.
  • Accuracy: Ensuring the accuracy of the data by techniques like data filtering and outlier detection.
  • Consistency of data: By checking internal and external validity of the data you can ensure consistency.
  • Timeliness: The more up to date data suggests more precise calculations.
  • Compliance: it is important to check whether the data used complies with legal obligations or not.

Corporate measure

Data is an asset and strategic tool for companies. Therefore, it is reasonable for companies to establish a department that focuses on the quality and security of the data in a sustainable way. Data quality and security department might rise above three pillars as follow:

  • Central Data Management Office: This office determines the overall strategy for data monitoring and management. The office also supports the underlying teams with the necessary budget and tools to interpret the data. 
  • Domain leadership: responsible for executing tasks determined by the Central Data Management Office. 
  • Data Council: A platform that enables the necessary communication between the divisional leaders and the central data management office to take the enterprise data strategy to the implementation level.

Relevance

When importing data, it is important to consider whether or not the data is relevant to the business problem the company is trying to solve. If the data originates from third parties, it must be ensured that the data is interpretable. This is because the format of the imported data is not always interpretable by the company’s software.

Accuracy

To assess data completeness and healthy data distribution, companies can use some statistical tools. For example, a non-response rate of less than 2% indicates fairly complete data. Data filtering is another important task to ensure data completeness. Due to recording mistakes, values may be included in the dataset that are impossible to observe in reality. For example, the age of a customer could be given as 572. Such variables must be cleaned. 

Second step to ensure data accuracy is to identify outliers using distribution models. Then the analysis can be performed taking the outliers into account. In some cases, eliminating outliers can be beneficial for ensuring data quality. However, it is important to note that such outliers may be valuable depending on the task.

Consistency of data

It is important to check both the internal and external consistency of the data to assess whether the data is insightful or not.

If data is stored in multiple databases, data lakes, or warehouses, you must ensure consistency to keep the information uniform. To check internal consistency, companies can use statistical values such as the discrepancy rate and the kappa statistic, which assess the internal consistency of the data. For example, a kappa value between 0.8 and 1 refers to significantly consistent data, while values between 0.4 and -1 indicate untrustworthy data.

Checking external consistency requires literature searches. If other researchers report similar results with your data interpretation, it can be said that the data are externally consistent.

Timeliness

Business decisions concern the future. To better predict the future, data engineers prefer data that contains current trends of the research topic. In this context, it is important that the data is up to date. When data are imported from third parties, it can be difficult to ensure that the data is current. In this regard, an agreement that provides for live data flow would be beneficial. Versioning data can also be useful for companies to compare trend changes in the past with the present.

Compliance

Legal hurdles can be problematic. Therefore, the company must ensure that the interpretation of the imported data will not result in legal investigations that could harm the company. Data center automation can also help companies to comply with data regulations. By integrating certain government APIs, these tools can follow regulatory changes.

You can read our article on training data platforms that includes a list of the top vendors.

You can also check our list of data quality software to find a suitable software for your business.

If you need assistance in selecting data quality assurance vendors, we can help:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Görkem Gençer.

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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