What is Data Quality Management? Why does Data Quality Management matter?
Every business has vast data generated within its organization as a result of several operations and processes ongoing. It is important to handle this data efficiently and keep it updated with real-time details. The data has a tremendous impact on the success of the business. This vast amount of data is related to products, services, customers, processes, and operations.
Manual handling of the vast data is tiresome and time-consuming; thus, the organization prefers implementing systems for handling this vast data. The data quality management tools help in handling the variety of data and giving priority to data quality.
What is Data Quality Management (DQM)?
Data plays an important role in every organization and Data Quality Management (DQM) includes the set of practices adopted by the data manager or the organization for maintaining the high quality of the data.
The data quality management service includes the right combination of people, technology, and process for achieving the common goal of improving the quality of the data. This high quality of data enables the organization to achieve business outcomes that depend majorly on the quality of the data.
The impact of this data relates to the predictions of the customer expectation, assistance ineffective product management, influence on top-down decision making, providing tailored customer service innovations by focusing on the shopping habits of the customers, providing competitor information, etc.
Implementing data quality management services helps in analyzing the data, identifying the errors in the data, and timely rectification those errors for improving the data quality.
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Why Data Quality Management Is Important for Your Business?
The proliferation of data is the major challenge present in this digital age causing a data crisis. As the business grows, the data expansion is exponential and it becomes difficult to handle this data manually. Manual data handling can lead to the incorporation of errors and can adversely affect the quality of the data and business as well.
DQM is an important process that enables the organization to make sense of the available data. The data quality management service aims at accessing the data in the system for its accuracy and its use for its intended purpose. The main reasons for implementing the data quality management services include:
Better functioning of the business
The proper management of the data enables the business to efficiently manage the different processes and operations taking place in the organization. The high-quality data allows for decision-making at the different levels of operations and management.
Efficient use of resources
Poor quality data leads to loss of resources including finance inefficiency. Following the practice of DQM helps in the wastage of valuable resources leading to better results.
Providing high-quality data enables the organization to gain a higher competitive advantage over other businesses. Low-quality data can lead to distrust from the customers leading to customer dissatisfaction and affecting the business.
Good business leads
Including high-quality, accurate, and error-free data for the marketing campaign allows for accurate customer targeting. This leads to better conversion rates resulting in enhanced business.
The different key features included in the data quality management services for improving the data include data cleansing, data profiling, validation of data, creating data relationships, and verification of data.
There are different metrics used for measuring the data quality and the different categories for measurement include metrics for enhancing data accuracy, data completeness, data integrity and consistency, identification and reduction of errors, etc.
To get better insights into the role of data quality management, read the Importance of Data Quality Management Process for Businesses. It unveils the different aspects of DQM that help your business to grow and succeed.
How Does Low Data Quality Affect Your Business?
In any business, data plays a key role in its growth. Achieving optimal internal growth and adhering to the latest technologies without improving the data quality wouldn’t produce a better result. Data helps businesses to monitor performance, solve problems, optimize their business operation, and provide better insight to businesses to tackle the evolving market.
Having poor quality data will create a lot of problems including both short and long-term issues which will harm your ROI. In addition, here are a few additional impacts of low-quality data that increase the risk & failure of compliance management apart from wasting time & money.
1. Financial Costs
Poor data will cause business leaders to make inappropriate or inaccurate business decisions. This will lead to significant financial loss and inconvenience in running the business smoothly. The research conducted by IBM officials reveals that poor data quality in the US cost around USD 3.1 trillion in the year 2016.
Hiring a dedicated staff to update the data constantly will not ensure you the data quality which you are seeking to achieve. So, it’s better to implement a complete data quality management system that cleans and optimizes all data types handled by the organization.
2. Reputational Damage
Product return is the major cause that every business involved in eCommerce activity suffers these days. This happens mainly due to poor data quality. Also, in some cases, poor data quality would cost you more than just product return.
When a customer faced a bad experience with your brand surely, he/she would not visit again and also not suggest anyone. This will create a bad reputation for your business among the target audience.
3. Incorrect Business Strategies
Data plays a key role in predicting the future of the business. They are vital for identifying patterns and forecasting business problems. However, on having poor data quality business leaders and stakeholders may end up with faulty conclusions.
This may drive business exactly opposite to the predefined business goals and plans. Implementing a proper data quality management system will enable the business to uncover the full potential of data for business growth.
4. Reduced Operational Efficiency & Productivity
If an organization prefers to fix the poor data quality manually it will take a lot of time and it will cause strain on workers’ efficiency. Many data analysts and data scientists have pointed as spending more time on data preparation and cleansing will reduce productivity rates.
To avoid such scenarios, organizations/businesses can prefer using an advanced data management system that wills all data governance and data cleansing operation automatically.
5. Missed Opportunities
Due to incorrect business decisions because of having poor data quality, businesses are prone to miss an opportunity. Apart from that, poor data quality will cause you strain and the inability to create/build an effective business strategy. Without having a proper strategy, developing a product according to the market needs is nearly impossible.
This can be avoided by implementing a good data quality management system; this will ensure your products get the needed recognition among the target audience.
Don’t miss reading Impact of Having Poor Data Quality & Importance of Data Quality Management System (DQM) to explore the advantages of DQM in detail and understand the essence of Data Quality Management in your business.
Data Quality Management Implementation & How It Works
Data quality management includes a continuous process for improving and maintaining the quality of the data across the business unit. This continuous cycle includes the process of ongoing observation, analysis, and improvisation.
The main aim of this cycle is to control the health of the data by identifying the flaws and fixing these issues. The five different stages involved in the data quality management process include:
1. Use data quality assessment for defining the impact of poor data on performance
The analyst reviews the data for identifying the potential issues causing the delays in specific operations that consequently decrease the revenues by influencing margins.
The review of the qualitative data provides a basic understanding of the flaws and their bigger impact on the business. The next step involves the data quality assessment using the top-down and bottom-up approaches.
The top-down approach allows for an understanding of how employees create and use the data and the problems faced during the process. It helps in defining the operations responsible for poor-quality data.
This assessment is done through individual interviews, surveys, etc. The bottom-up approach uses statistical and analytical tools and techniques for exploring the contents of the dataset. The data profiling includes structure discovery, content discovery, and relationship discovery for identifying the issues at various levels.
2. Defining data quality rules and metrics
On completion of the data quality assessment by the analyst, the team defines the critical elements for improving the data quality. The DQ analyst correlated the flaws available and the impact they can have on the business via defined business rules.
Based on the results they will define the metrics required for ensuring the data accuracy for satisfying the analytical needs. The acceptability thresholds are defined for the metric score. The metrics with lower scores are improved.
3. Defining data standards
Standards for metadata management, and data validation rules: On completion of the analysis for the impact of poor data, data quality rules, and metrics, different techniques are introduced for quality improvement. The main aim at this stage is the unification of rules for data and metadata across the data lifecycle.
The data standards define the agreements on data entry, formatting, representation, and data exchange across the organization.
Metadata management standards define the policies and rules for the creation and maintenance of the metadata that provides the baseline for successful data analytics and data governance. These rules are categorized into business, technical, and operational.
The data validity rules enable data evaluation against inconsistencies. The developers define the data validation rules and integrate them into the applications for the identification of mistakes. It acts as proactive data quality management and helps in deciding how to track the data problem.
The data quality issue tracking log helps in keeping information about the flaws, their status, criticality, the employee responsible, report notes, etc.
4. Implementation of data quality and data management standards
In this step, the data quality management team implements the quality standards and process by documenting it. They provide training to the employees in using the quality tool for remediation performance.
5. Data monitoring and remediation
Data cleaning includes the remediation and preparation of data. This includes the detection of errors and incomplete data and modifying or rectifying those errors.
The different ways of performing data remediation include:
- Root cause analysis: It includes identification of the source of erroneous data, its reason, factors responsible, and solution for the issue,
- Parsing and standardization: It includes reviewing the records to the identification of error values, and wrong fields and formatting them,
- Data matching: Identification of similar entries and merging them into one and maintaining the record linkage details for the same,
- Data enhancement: It includes the addition of extra data from internal and external sources,
- Data monitoring: Evaluation of the data at given intervals for ensuring the serving of a defined purpose.
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Best Practices for Better Data Quality Management
Use buy-in and make data quality an enterprise-wide priority
It is important to make the stakeholders understand the importance of data quality and implement the techniques for enhancing the data. The organization can get the enterprise buy-in application for data quality. This needs support from the different levels of management. Every level needs to prioritize the data and ensure data quality.
The organization needs to implement different metrics for the measurement of data quality. These metrics ensure achieving the business goals and targets by enhancing the data.
The measurement of data quality is important for the following reasons: advising the management on the effectiveness of quality data, understanding the accuracy of the available data, quantification of the missing, inconsistent, and incomplete data, and taking corrective measures for improving the quality of data.
Investigate data quality failures
Investigation of data failures is very important as it helps in identifying the root cause for the errors. If this cause is not identified, the errors get repeated thus affecting the quality of the data. Correction of these errors is a difficult and time-consuming process.
Data errors can arise from various causes like human error, different data sources, lack of communication between different departments, etc. If the cause of the error is known, it becomes easier for taking action and preventing the errors.
Invest in internal training
It is difficult to obtain high-quality data and requires a deep understanding of the quality principles, processes, and technology involved.
The organization can provide training to their employees through several formal data management certifications like Certified Information Management Professional (CIMP), Certified Data Management Professional (CDMP), or Certified Data Steward (CDS). The organization should encourage the staff to earn certifications.
Establish and implement data governance guidelines
It is essential to follow the rules set for data protection. Data governance includes the collection of processes, policies, rules, metrics, and standards for ensuring effective and efficient use of the information for achieving organizational goals.
Every organization should have a defined set of data governance guidelines for its process, structure, and use cases. The organization should implement the following best practices and use the collaborative approach by running reports and using data-driven information.
Establish a data auditing process
The organization should implement audits for checking the data within the repositories. It helps in keeping a check on the cases of poor-quality data and other issues like poorly populated fields, inaccuracies, incomplete data, duplicate entries, outdated details, etc.
The organization can define the frequency of these audits which can be yearly or quarterly depending on the history of the errors. These audits should be automated and continuous processes with periodic incremental audits.
Assign a data steward in every department
A data steward is a person responsible for maintaining the integrity and quality of the data. He plays a crucial role in ensuring good quality data. These stewards need to make sure that the data quality is maintained as defined by the governance team.
As data management is the responsibility of the IT department, the stewards can belong to the IT team. The steward can be selected based on the data, if the data is based on the customer database, then the best person selected for stewards can be from the CRM team or sales administration.
Implement a single source of truth
The single source of truth (SSOT) concept ensures that the data is collected from a single consistent source. All the teams and stakeholders must agree and accept the source of accurate data across the organization.
The source selected for the data must provide accurate and high-quality data. The data received can be maintained using the organization’s data quality standards and used efficiently for gaining trusted business insights.
Integrate and automate data streams
Cloud computing helps in centralized storage of the data and thus allows for integrating the data from different sources and formats. The data at a single repository can be easily cleaned, de-duplicated, and removed from irrelevant and inaccurate details. Automation of the process enables us to provide quality data.
Leverage the use of the cloud
Implementing the cloud-native solution for data management will enable providing the features like high availability, pay-as-you-go pricing, elasticity based on fluctuating demands, etc. It also allows for offloading the burden of building, configuration, maintenance of the servers, storage, managing of the hardware updates, upgrades, etc.
Implementing the data quality management strategy has become mandatory for staying ahead in the competitive business world. It helps in combining the people, tools, and processes for enhancing the quality of the data. The different tools are used to enable automation of various activities and help the organization in gaining the desired outcomes.
The robust data quality management service will provide the continuous process of monitoring, interpretation, and enhancement of data for providing high-quality valuable data.