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Data Quality Management: the What, Why and How
That’s where ; a crucial discipline that focuses on maintaining the accuracy, completeness, consistency, and reliability of data assets.
In this blog, we will explore the what, why, and how of data quality management, uncovering its significance in today's data-driven landscape and providing practical insights on implementing effective data quality management processes.
So, let's dive in and unravel the world of data quality management.
What we’ll cover
- Defining data quality
- What is data quality management (DQM)?
- Why is data quality management important?
- How to implement data quality management processes
- 1. Data profiling
- 2. Data governance
- 3. Data cleansing
- 4. Data standardisation
- 5. Data integration
- 6. Data validation
- 7. Data monitoring
- 8. Data enrichment
- Data quality management tools
- Final thoughts
Defining data quality
When discussing data quality management, it’s important to first define what we mean by data quality. High quality data can be defined as data that is fit for purpose. That is, it reaches the standard required to be able to support the outcome it’s being used for.
There is no single factor that determines the quality of data. Instead, it’s often measured against a variety of factors. The metrics used to assess data quality typically include:
- Accuracy
- Completeness
- Consistency
- Conformity
- Timeliness
- Relevance
- Uniqueness
Low quality data can affect business decisions, that’s why it’s important that data is assessed against these metrics, and only used if it meets a certain level.
What is data quality management (DQM)?
Data quality management refers to the set of processes, techniques, and practices implemented to ensure that data within an organisation meets a certain level of quality. It involves various activities aimed at improving the accuracy, completeness, consistency, timeliness, and relevance of data.
The goal of data quality management is to ensure that data is fit for its intended purpose and can be relied upon to support effective decision-making, reporting, analysis, and other business functions.
The primary objectives of data quality management are to enhance data accuracy, eliminate inconsistencies, improve data completeness, validate data integrity, and ensure that data is fit for its intended purpose. It involves identifying and resolving data issues, implementing data governance policies, establishing data quality metrics, and deploying tools and techniques to assess and improve data quality.
Why is data quality management important?
Data quality management is of paramount importance for any organisation in the modern world. Reliable and accurate data is the foundation for effective decision-making, business operations, and achieving organisational goals.
Here are key reasons why data quality management is essential:
- when data is trustworthy, decision-makers can make informed and confident choices, leading to improved business outcomes, competitive advantage, and increased customer satisfaction.
- poor data quality can lead to inefficiencies and errors in business processes. Maintaining data quality streamlines operations, minimises errors, and optimises resource allocation. For instance, reducing costs associated with data rework, customer complaints, and process delays.
- inaccurate or incomplete data can lead to incorrect billing, shipping errors, or poor customer service experiences. With accurate and complete data, businesses can build trust with customers, deliver personalised experiences, and form long-term relationships.
- high data quality can ensure compliance, protect sensitive information, and mitigate the risk of penalties, reputational damage, and data breaches. For instance, keeping customer data up-to-date and accurate is a requirement for GDPR compliance.
- data quality management plays a vital role in ensuring data compatibility, consistency, and interoperability across different platforms, applications, and databases. This enables seamless data integration, data sharing, and efficient collaboration.
How to implement data quality management processes
Implementing data quality management processes is crucial for businesses to ensure the reliability and accuracy of their data. The management process encompasses a range of activities, including:
- Data profiling
- Data governance
- Data cleansing
- Data validation
- Data standardisation
- Data integration
- Data monitoring
These activities help identify and address data anomalies, errors, duplicates, missing values, and discrepancies. By implementing effective data quality management practices, organisations can make better-informed decisions, enhance operational efficiency, improve customer satisfaction, and achieve business objectives more effectively.
Here's a step-by-step guide on how to implement data quality management processes.
1. Data profiling
Data profiling is an essential method in data quality management that involves analysing data to gain insights into its structure, content, and overall quality. This process allows businesses to uncover key data quality issues, such as missing values, duplicates, and inconsistencies across datasets.
For example, uncovering inconsistencies in customer records that are holding back personalised marketing efforts.
The purpose of data profiling is to gather a deep understanding of the quality of data, helping make informed decisions about how the data needs improving and the prioritisation of improvements. From this, businesses can develop a clear data governance strategy.
2. Data governance
Data governance is the overall framework and set of procedures that ensure the availability, usability, integrity, and security of an organisation's data assets. This involves establishing guidelines, policies and standards to develop a structured approach to managing and maintaining high-quality data.
A well-defined data governance framework assigns clear roles, responsibilities, and accountability for data quality across the organisation. It fosters a common understanding of data definitions, rules, and quality requirements, promoting consistency and standardisation in data practices throughout different departments and systems.
3. Data cleansing
Data cleansing involves correcting, removing, or updating data to improve its quality. This data quality management exercise is one of the most critical, considering that around 30% of data becomes invalid over the course of a year.
It addresses data quality issues like duplicates, inconsistencies, and missing values, ensuring that the data is accurate, reliable, and fit for use. For a detailed understanding of how to address duplicate data, explore our guide to data deduplication.
This has significant impacts across different business areas. In customer relationship management, clean data can help foster personalised marketing campaigns, encouraging customer satisfaction and higher conversion rates. In finance, data cleansing ensures the accuracy of customer billing, reducing the risk of errors and compliance issues.
To ensure the accuracy and reliability of your data, it's important to consider professional data cleansing services. These services employ advanced technology and techniques to effectively identify and resolve data quality issues – without having to lift a finger.
4. Data standardisation
Data standardisation involves formatting and structuring data consistently across systems, tools and applications. This is key for ensuring uniformity, comparability, and compatibility of data across the business, enabling seamless data integration and analysis.
When data is standardised, businesses will have a much easier time with integrating data from different sources and systems, ensuring data coherence and enabling accurate cross-system analysis. Without it, it’s much more difficult for departments to collaborate from an operational perspective.
5. Data integration
Data integration is concerned with combining data from multiple sources into a unified view or data storage such as a data warehouse. It aims to achieve a holistic and comprehensive understanding of data by integrating diverse data sets.
This alleviates the issue of data silos; a problem that occurs when data is stored across separate, disconnected systems.
By integrating data, businesses can derive valuable insights by analysing data from multiple perspectives - all in one holistic view. For instance, combining data from different departments in order to make strategic decisions across the business.
6. Data validation
Data validation ensures that the data entries themselves meet specified rules, standards, and requirements. This process is concerned with verifying the accuracy, completeness, and adherence of data to predefined rules, preventing the use of unreliable or erroneous data.
As an example, data validation can ensure the accuracy and integrity of transactions, reducing the chances of financial discrepancies or errors. In fraud detection, data validation plays a crucial role in identifying abnormal patterns or inconsistencies that may indicate fraudulent activities.
For instance, a banking institution implementing data validation can prevent financial losses by flagging transactions that deviate from predefined rules or patterns.
7. Data monitoring
Data monitoring plays one of the most crucial roles in data quality management. As a process, it involves continuously observing and tracking data in real-time. This allows businesses to keep track of data governance practices and data quality, and proactively identify and address issues before they escalate into larger problems.
By continuously monitoring data, it’s easier for businesses to identify inaccuracies, incompleteness, duplication, or inconsistencies that can undermine its reliability and usability.
8. Data enrichment
Another way of managing the standard of your data is through data enrichment, which involves enhancing existing data with additional information from various sources. This ensures that your data is meaningful, and holds valuable information about your customers.
Here are some examples of how our marketing data enrichment solutions help enhance data:
- This comprises demographic and lifestyle behaviours in order to build a more comprehensive, 360-view of your customers. This means you gain a deeper understanding of your customer that goes beyond the basics of age, gender, and location. It provides you with a set of predictive and descriptive attributes, designed to increase the success of your marketing efforts.
- This takes a range of data points and compares them against locations, allowing you launch targeted ads and marketing campaigns.
- This assesses data points such as; property type, number of bedrooms, and the energy efficiency of a home, in order to inform promotions and advertising.
- A postcode level product containing all PAF valid residential postcodes, with indicators across over 300 variables. This data supports location analysis, marketing strategies, targeting and product development.
Data enrichment strives to combine data sets in order to improve the completeness and quality. It’s a valuable data quality management tool that shouldn’t be overlooked.
Data quality management tools
One way that businesses are managing their data quality is through online data management platforms (DMPs), such as Online.
Online data management systems are designed to make data quality management easy. We offer a self-serving platform that lets you audit the quality of your marketing data – the easy way. Simply upload your data and identify duplicates, goneaways, deceased individuals and other invalid data entries. This helps you keep your data clean, as well as manage data from a range of different sources.
Online data management systems can also clean your data through the following methods:
- Address screening
- Mail suppression
- Telephone numbers
- Data collating
Final thoughts
Data quality management is an ongoing process. Data comes from all directions, and it’s important for businesses to manage new data as more of it is acquired. As technology and data platforms become more advanced, there is a need to ensure data is high quality, consistent, correctly formatted and above all else – purposeful to your business goals.
Get in touch to find out more about data quality management.
