Data Quality Management Services & Best Practices To Follow

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By: Manish Shewaramani

A Brief On Data Quality Management Services & Best Practices To Follow

Any type of enterprise big or small needs to understand that their data has a tremendous impact on their business. The growth and development of the business depend on the data and its quality as various factors depend on this data.

This includes the prediction of the customer expectations, effective management of the product, making crucial decisions, providing tailor-made services, etc. As many factors are dependent on the data available, it is important to maintain the quality of this data.

What is Data Quality Management Services?

It is essential for the organization to follow the principles of Data Quality Management (DQM) that has the combination of the right set of people using the best process and technologies for achieving the goal of improving the quality of data for the betterment of the enterprise.

The main goal of the data quality management services is to improve the quality of the data along with achieving the required business outcomes like achieving customer relationship management.

The data quality management services allow the organization in combining organizational technology, data, and culture in delivering the best and most accurate results that are useful for business growth and development.

Having the best quality data allows the organization to meet the regulatory requirements and thus reduces the risk of fines. It helps in taking the major decisions and setting up the foundation for business initiatives.

Importance of Having Data Quality Management Services for Modern Businesses

In this digital age, a lot of data is generated and the most important task of the data management services is to have the data that does not depend on the volumes but on it’s quality.

These services help the organization to point out the errors in their data and resolve them to improve their data quality. The different reasons for understanding the importance of the data quality management services for the business include:

Improved business functioning

The management of the quality data defines the proper and efficient functioning of the basic operations. High-quality data enables the organization to make enhanced decisions at different levels of operations and management.

Efficient use of resources

Having the low quality of data in the organization can lead to inefficient use of their resources including the finances. This can adversely affect the business. Thus implementing the DQM practices allows the organization in preventing the wastage of resources and gain better and bigger results.

Competitive advantage

There is tremendous competition increasing and each enterprise takes all the efforts to stay ahead in this competitive business. They also need to maintain their reputation in the market to gain a higher competitive advantage over the other business players.

Implementing the data quality management services ensures that high-quality data is maintained for retaining the reputation. Having low or poor quality data has proved to have customer distress that leads to customer dissatisfaction with the products and services provided by the business.

This negatively impacts business growth. Thus to stay ahead in the competition, it is essential to maintain high data quality.

Good business deals

The marketing campaigns help in targeting the right types of customers for business growth. But having inaccurate or poor quality data will create errors in targeting the right type of customer population leading to the failure of the campaign.

Maintaining a good quality and accurate data helps the enterprise in gaining proper customer details that help in accurately targeting the crowd and reaching the business goals.

Visit Importance of Data Quality Management Process For Businesses to explore different data quality management practices.

How to Implement Data Quality Management Services?

For maintaining the quality of the data, the organization needs to implement data quality management services. These services will enable the organization in analyzing the available data and provide methods for improving it.

The data quality management implementation is beneficial to the organization in many ways. The different steps involved in the implementation of the data quality management services include:

1. Set up an organizational structure

For any organization, it is important to first understand the importance of the data. Poor data harms the business and can lead to delays in operations, decreased revenues, loss of customer trust, etc.

Thus, it is important to have quality data and for this, the organization should have a dedicated team to look after the data quality. The data quality management services are specialists that help in outlining the requirements for data quality.

The key members included in the DQM team includes the data consumer, data analyst, data officer, DQM program manager, data steward, data custodian, etc. These members help in identifying the crucial data and accessing the degree to which the poor data quality impedes the business objectives.

2. Define data quality

On understanding the available data, it is important to analyze this data to check if the data needs the data quality standards and what measures to take to improve the data quality. It is necessary to check if the data is accurate and confirm the values of the given dataset.

These should be a consistent method for data collection and data storage. The data should be updated timely and should be relevant to the current circumstances.

The importance of the different types of data depends on the different types of business. The product-based business will focus more on the quality of the product data.

Thus, it is important to define the quality of the data based on specific business needs. It is necessary to define the data quality rules, perform measurements, and set up performance targets.

3. Initiate data profiling

Once the organization knows all the data they have, they need to prioritize the type of data based on their business needs. After this the actual process of data quality management initiates.

This involves an audit of the available data that investigates the data thoroughly to find the flaws or poor quality data and then proceed with the rectification of data quality issues. These quality issues include data duplication, lack of accuracy, lack of consistency, incompleteness, etc.

The process of data profiling has four steps that include:

  • Testing the data quality at the project starts to understand if it is appropriate for data analysis.
  • Diagnosis and fixing of the data quality issues in the initial stage before moving the data to the target database.
  • Detecting the data quality issues while moving the data from the source to its final target database using the Extract-Transform-Load (ETL) method.
  • Identification of the interrelationship that can have an impact on the data profiling process and refining the data profiling as required based on the data availability.

There are numerous tools available for the data quality management implementation that are available free of cost as well as on subscription. This stage includes the data quality improvement processes that will help in remediating the process flaws.

4. Reporting and monitoring of the data

This stage includes the process of monitoring the data, reporting and recording any exceptions recorded in the data, and processing the data. These exceptions can be recorded using the business intelligence (BI) software. This helps in identifying the exact location of the data exception that can be addressed and rectified.

Once the DQM team analyzes the data, they can aggregate the data exceptions to generate the patterns that will determine the reason for deviation during data collection and procession from the set standards.

On identification of the problem, the DQM team can strategize their plans for the data remediation process to improve the data quality.

5. Data repairing

The data remediation step involves the identification of the best possible way for repairing the data and implementing the changes in the data collection, data processing, and data analysis for improving the data quality. In today’s market, you can find numerous tools cleansing your data, but none will deliver the results as DQM provides.

The different activities involved in the data remediation include:

Root cause analysis

This helps the team in the identification of the source of erroneous data and the reasons for the errors, factors contributing to the issues, and finding the solution for these errors.

Parsing and standardization

It involves analyzing the reports to define the pattern for identification of the errors and formatting the issues.

Matching

This includes identification of the same or similar entities and removal of the duplicates to prevent their merging.

Enhancement

This step includes the addition of the extra data from the internal or the external source that will add value to the available data.

Monitoring

The final step of this data remediation includes evaluating the data at certain time intervals to ensure that the remediation steps followed serves the intended purpose of maintaining the data quality.

This stage also includes reviewing and improving the data quality rules. Once the data is refined, the quality of the data enhances and this high-quality data is very essential for making critical business decisions.

For a product-based industry, it is very essential to provide updated and accurate product details to their end-users to gain the customer confidence that plays a key role in making their purchases.

Best Practices for Data Quality Management

The best practices that an organization needs to follow for starting the quality management process include:

Review current data

For maintaining the data quality, it is important to review the data that is already available. For building quality data, the organization does not need to start building the data from scratch while discarding all the available data.

The DQM looks into the current data and builds the data to improve the quality. The organization needs to start data quality management with the audit of the current data. This will involve taking a detailed inventory of all the errors, inconsistencies, duplicates, and poor-quality data.

It records the details and allows for correcting the problems and making sure that the final data is available is of high quality.

Data quality firewalls

The data quality firewall includes incorporating the automated process that allows for blocking and preventing the figurative bad data. This firewall protects the organization from bad data and keeps the system free from errors.

This firewall prevents the entry of bad data at the entry point. Because the people who feed product or business data into the infrastructure affect the quality of the data. Having multiple entry points leads to increased errors.

The firewall helps in error-free data entry even if there are several members having access to making the data entry.

Integration of DQM with BI

There are various systems in the organization that are essential for the proper functioning of the different processes and operations. Integrating the DQM process with the BI software allows for automation and enables accessing the new datasets while auditing during the DQM cycles. This ensures that the data generated by the different operations and processes within the organization is of better quality.

Implementing the right people in the right place

Several positions are responsible for the quality of the data. Implementing the DQM ensures that the different positions are seated and provides dedicated jobs for maintaining the governance standards.

Ensuring data governance

It is essential for creating a data governance board for protecting the business from the risk evolving from decision-making based on the available data. The board panel includes the members of executives, business, and IT users.

These members help in setting up the policies and standards essential for data governance. It should help in monitoring the success of data quality management and setting up the new data quality goals.

To explore more practices you must read All You Need to Know about Data Quality Management & Best practices to Follow.

Bottom Line

The business needs to implement data quality management services as it helps in running efficient business processes based on quality data.

It also allows for setting up the right business goal and working efficiently towards reaching better outcomes while considering a better view with their customers, marketers, vendors, etc.

The improved data quality also allows the business to be more confident while taking important decisions for the growth and expansion of their business.

Say goodbye to data chaos and embrace the power of PIM/MDM to supercharge your business growth!