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How to manage and improve data quality

07/07/2023 minute read Nadine Sutton

Data quality pertains to the degree of completeness, consistency, and accuracy inherent in data. It also signifies the extent to which the data aligns with the specific purpose intended by its creator or recipient. The quality of data can vary, ranging from high to low, based on how it is extracted and processed.

Given the exponential growth of data volume, doubling every two years, and the limited availability of human resources to thoroughly analyse it, it is understandable that the implementation of a data quality management system is becoming increasingly prevalent.

Throughout the data's lifecycle, it is crucial to regularly assess its quality to ensure that we only obtain, store, and analyse the data that is essential for our needs.

In the realm of corporate strategy, data holds significant importance as a valuable asset. For organisations to make data-driven decisions, it is crucial to prioritise both data quality and data integrity. While data quality serves as an initial foundation, data integrity enables businesses to make informed and effective decisions.

What is data quality?

Data dependability, accuracy, consistency, comprehensiveness, and pertinence are all aspects of data quality. It measures how well data achieves the desired standards and serves its purpose. Excellent data quality denotes that the data is trustworthy, free of mistakes or inconsistencies, and follows set standards and procedures.

How is data quality different from data integrity?

Although they have different focuses, data quality and data integrity are closely related concepts in data management.

Data reliability, accuracy, consistency, completeness, and relevance are all terms used to describe data quality. Data quality focuses on ensuring data complies with specifications and is appropriate for its intended use. It includes procedures and policies designed to assess, improve, and preserve data during its existence.

Correctness, consistency, and reliability can be related to data integrity when it comes to information within a database or system. This also involves maintaining an absence of data corruption throughout storage and processing. To avoid unauthorised changes, data corruption, or violations of data integrity limitations, safeguards must be put in place.

While data integrity explicitly emphasises the preservation of data accuracy inside a system, data quality incorporates several characteristics of data fitness, including completeness and relevance. Data integrity and data quality are both interrelated to an extent, as they both seek to guarantee information is trustworthy and current.

What are the principles of data quality management?

Data quality principles provide actionable instructions to ensure the reliability of data. Here are some key principles to consider:

1. Accuracy

   - Compare facts with reliable sources.

   - Use data validation guidelines to find and fix mistakes.

   - Update data frequently to ensure accuracy.

2. Consistency

   - Implement standardised data definitions and formats.

   - Address the underlying reasons for data discrepancies.

   - Align data from various sources and systems.

3. Completeness

   - Specify the data needs and record all essential components.

   - Track and evaluate data completeness in relation to predetermined standards.

   - Use validation to reduce the amount of missing data.

4. Relevance

   - Identify the goal and domain of the data for certain use cases.

   - Continually examine and update data in light of business requirements.

   - Eliminate useless or outdated information.

5. Timeliness

   - Set up times for data collection and updating.

   - Automate data extraction and loading operations for efficiency.

   - Fix data latency problems for up-to-date information.

6. Integrity

   - Use data governance procedures to preserve integrity.

   - Be sure to take data security precautions to thwart unauthorised access.

   - Find/fix inconsistencies and conduct routine data audits.

How to manage and improve data quality effectively

Here are some practical ways CFOs can implement effective data quality management and improve data quality across the business:

Establish a data quality improvement plan

Inaccurate decisions and missed opportunities often result from poor data. To address this issue, organisations must establish data quality improvement plans, especially when faced with ever-growing datasets.

A data quality improvement plan encompasses a structured series of activities to improve the quality of customer data. It begins with the collection of data, followed by data cleansing and analysis to identify errors. This plan includes error identification and resolution, enhancement of data quality procedures, and staff training.

By implementing a well-defined plan, organisations can systematically address and prevent data errors, and equip staff with the necessary skills and knowledge to contribute. This proactive approach helps mitigate the risks associated with poor data and promotes more accurate decision-making and seizing of opportunities.

Implement continuous training and education

Fostering a data-driven culture within an organisation is crucial for ensuring widespread participation in data quality efforts. However, sustaining interest and engagement requires innovative approaches.

Regular training sessions on concepts, metrics, and tool usage can help reinforce the importance and benefits of data quality. Sharing quality issues and success stories throughout the organisation can also serve as friendly reminders.

Good data quality management extends beyond merely correcting existing errors; it involves preventing future errors as well. To address these issues at their core, it is essential to assess and tackle their root causes within your organisation.

Are the processes predominantly manual or automated? Are the measurement metrics accurately defined? Can stakeholders directly rectify errors? Are the data quality techniques effectively implemented? Is the data quality culture firmly established?

Your strategy should focus on integrating data quality techniques into applications and business processes to unlock greater value from your data assets. The chosen solution should prioritise delivering continuous data quality throughout the company.

Leverage data quality tools and technologies

A data quality management solution offers several benefits to businesses seeking to address poor data and mitigate potential damage:

  • Improved data accuracy: Identify and correct errors, inconsistencies, and other issues that impact data accuracy.
  • Increased productivity: Reduce manual effort required for data quality maintenance, freeing up resources for other activities.
  • Enhanced data governance: Provide a centralised platform for managing data governance activities, including data policies, rules, and procedures.
  • Better decision-making: Improve decision-making quality by leveraging reliable and accurate data.
  • Increased regulatory compliance: Ensure data compliance with regulations such as GDPR and HIPAA, while also maintaining financial transparency.
  • Improved customer satisfaction: Ensure customer data is accurate and current, leading to higher customer satisfaction.
  • Cost savings: Reduce the energy involved with maintaining the quality of data, thus minimising costs.

Decide which data quality metrics to measure

As mentioned previously, there are multiple dimensions available to gauge data quality levels. Collaboration between the finance function, IT personnel, business managers, and users is essential for identifying and documenting the metrics that should be utilised. Especially when it comes to the analysis of data sets and the evaluation of data quality improvement initiatives.

It is crucial to maintain a proactive approach in this regard. Both the analysis procedures and data quality metrics may require adjustments over time. The finance function must validate the continued relevance of specific KPIs before employing them in future evaluations.

Furthermore, processes and metrics that have proven effective in certain evaluations may only be applicable to some data sets within an organisation. Flexibility and adaptability are necessary to ensure ongoing data quality enhancement.

Improve poor data quality with OneAdvanced Financials

Prioritising data quality is crucial for finance teams. Even for those that are yet to embrace the power of Cloud technology, it is essential to prepare and clean up data before migration to such an innovation, so the full benefits of this digital transformation can be harnessed.  

By consolidating all financial data in a centralised Cloud-based accounting system, quality management becomes much easier, reducing data errors and inconsistencies that can arise when data is scattered across multiple spreadsheets. Additionally, collaboration among employees is seamless, enabling quick identification and resolution of discrepancies.

Financials from OneAdvanced is a Cloud financial management software that provides finance professionals with real-time access to information, so they can view and update data remotely via the internet.

This system ultimately makes sensitive data more secure, as it is stored at an offsite server (rather than on-premises). With integration capabilities too, automation becomes a real possibility, and operations can become far more streamlined.