Data is the lifeblood of an organization that forms the basis for many critical business decisions. However, organizations should have an extensive data quality process flow to ensure the viability of data, as accurate data can help deliver valuable results. Therefore, to capitalize on the explosive growth of big data, businesses need to employ a data quality management framework before they can start extracting actionable insights from information. A data quality management framework ensures consistency, accuracy, and validity.
This article explains data quality management, identifies the steps required to ensure data quality, describes the data quality management assessment characteristics, explains why data quality management is essential, and introduces data quality management tools.
What is Data Quality Management?
Data quality management (DQM) refers to the set of business practices that involve employing the right people, processes, and technologies to derive actionable insights from the available information. A well-established data quality and integration framework ensure that the data quality process flow is maintained throughout the data lifecycle.
For example, as part of a corporate data quality management plan, users specify certain data quality checks throughout the journey to eliminate inconsistencies or errors and ensure reliable data for analytics and business intelligence processes.
Common Reasons for Poor Data Quality Management
Research shows that 40 percent of business initiatives fail to achieve their targets due to data quality problems. Hence, data stewards must identify the root causes of bad data quality and build a robust data profiling and validation plan to improve the accuracy of information used for decision making.
According to 451 Research, the top three reasons for poor data quality include:
1. Manual Data Entry
Many organizations rely on their employees to manually feed data into business systems, leading to errors due to lack of expertise, human error, or the monotonous nature of work. Other common consequences of data quality issues, such as manual data keying, include duplicate records and missing information.
2. Data Migration and Conversion Projects
Data migration projects involve transferring data between different file formats, databases, and storage systems, often duplicating or missing records. Moreover, migrating from a legacy information system to a new one usually involves converting data into a compatible format that can result in poor data quality if not done correctly.
3. Entries by Multiple Users
In many departments, multiple employees are involved in handling and modifying data. This can cause discrepancies, such as different names for the same supplier. For instance, some employees might enter the supplier’s name as ‘Dell,’ while others might use ‘Dell Inc.’ for the same vendor.
This problem can easily be solved with the help of data quality checks. DQM tools can help automatically add multiple data quality checks for each data set.
Benefits of Data Quality Management Framework: Why is Data Quality Management Important?
High-quality data can improve business operations and make them more efficient and profitable. A few benefits of undertaking a data quality improvement process at every step of the business process are:
Data Helps Identify New Opportunities and Improve Business Outcomes
Business decisions based on quality data are more likely to result in positive outcomes, as managers have an accurate, up-to-date, and complete picture of critical data assets. Moreover, high-quality data helps managers identify and leverage new opportunities, enabling the business to grow and stay competitive.
For example, incorrect financial information, such as overstated profits, may result in misleading financial ratios, often used to evaluate a company’s past performance. This analysis should be based on accurate and trusted data, as it lays the foundation for many important decisions, such as potential target markets and price changes. Similarly, updated financials can help the company decide which market segments are more profitable so that managers can explore new growth opportunities in those areas.
Data Quality Aids Successful Data Migrations
Poor data quality is one reason why data migration projects fail, as these projects involve the movement of high volumes of data in disparate formats. Efficient data quality management is necessary to ensure a high migration success rate. Data quality rules should be used to identify and correct any errors before the migration occurs. This helps in carrying out data migration projects faster and with greater accuracy.
For instance, to create a unified repository for customer data, a company plans to move from a decentralized information storage system to a centralized one such as a data warehouse. Previously, employees manually entered data and had errors, including duplicate records and missing information. An effective data quality management software can help the business identify those errors and correct them before migrating existing data records into a data warehouse.
Ensuring Data Quality Reduces Data Processing Time and Costs
According to Gartner, poor data quality can have an average financial impact of $9.7 million per year. In addition, inaccurate data means incorrect information is being processed, which might involve rework. However, if companies make a data quality management framework a part of their overall business process, the time and cost spent on rework can be minimized.
What Are The Characteristics of Data Quality?
Having a well-defined set of data quality management assessment metrics in place is vital for assessing the performance of an enterprise’s data quality management initiatives. It helps determine whether the data quality management strategy bears fruit to meet organizational goals.
Some key data quality dimensions include:
- Completeness indicates whether the data gathered is sufficient to draw insights. This can be assessed by ensuring no missing information in any data set.
- Consistency ensures data standardization across all the systems in an organization is synchronized and reflects the same information. An example of consistent data includes recording the shipment date in the same format as a customer’s information spreadsheet.
- Accuracy implies whether the data that has been collected accurately represents what it should. This can be measured against source data and validated against user-defined business rules.
- Timeliness means that the data is available as and when expected to facilitate data-driven decision-making. Many businesses leverage tools that support real-time data integration to gain up-to-date business knowledge. However, it is essential to note that data quality and integration go hand in hand. Before embarking on data integration, the data needs to be profiled and cleansed, which will, in turn, fasten the development of data mapping and workflows.
- Uniqueness involves making sure that there are no duplicates present in the data. For example, the lack of unique data can send multiple emails to a single customer due to duplicate records.
- Validity measures whether the data meets the business user’s standards or criteria. For instance, a business can place an enterprise data quality check on the order quantity field, i.e., ‘Order Quantity >= 0’ as negative order quantity implies invalid information.
What Are Data Quality Management (DQM) Tools?
Data quality management (DQM) tools are technologies used to identify, comprehend, and fix any flaws in data. DQM tools support business decision-making and business processes for efficient data governance.
Choosing DQM Tools
Data drives decision-making, which is why data quality management has become a top priority for businesses. However, due to increased data volumes and disparity, manually undergoing the process can lead to data quality errors and time. This is where DQM tools come into play.
Here are some important factors that businesses should consider when selecting the right DQM tool:
- Data Profiling and Cleansing Functionality
An effective data quality tool should include data profiling features. A DQM tool helps automate the identification of metadata and provides clear visibility into the source data to identify any discrepancies.
Moreover, data cleansing capabilities in a data management tool can help prevent errors and resolve them before being loaded onto a destination.
- Data Quality Checks
Advanced DQM software contains objects and rules integrated into the information flow for monitoring and reporting any errors that may occur while processing data. They ensure that the processed data is validated based on defined business rules to ensure data integrity.
- Data Lineage Management
A DQM tool aids data lineage management, which helps control and analyze the flow of information by describing the data origin and its journey, such as the steps at which the data was transformed or written to the destination.
- Connectivity to Multiple Data Sources
With the increasing variety and number of data sources, it has become crucial to assess and validate internal and external data sets. Therefore, businesses should select DQM tools that support data in any format and complexity, whether structured or unstructured, flat or hierarchical, legacy, or modern.
Creating a Centralized Data Quality Management Strategy
Ensuring data quality is an ongoing process that evolves with the organization’s changing needs. This means organizations must have a centralized DQM strategy with a robust framework to address the data quality challenges and reap the benefits of high-quality data.
The question that business users often ask is, how do you check data quality? We have listed five steps for creating a centralized data quality management strategy include:
- Define the key success objectives for the data quality program
This involves defining the data completeness metrics, such as data-to-errors ratio and percentage of blank records. This provides users a clear understanding of the data being analyzed and the dimensions, including completeness, uniqueness, accuracy, etc., that will be used to assess data integrity.
- Communicate the DQM plan organization-wide
Ensuring data quality is the responsibility of all information stakeholders, including data architects, business analysts, and IT. Hence, employees should know the expected data management levels, business benefits of the set data management standards, and assessment metrics for the smooth implementation of the DQM strategy.
- Assess incoming business data against the set data quality parameters
Ensuring enterprise data quality is easier with an advanced DQM tool as it enables users to define data management rules and assess incoming data based on predefined criteria.
- Analyze data quality results and identify the root causes of insufficient data
Once the data has been processed in the DQM software, users can evaluate the data quality and identify the reasons for flagged records. For instance, the screenshot below shows that one of the records was erroneous because of the incorrect email address.
- Monitor and adjust the data quality workflows based on changing data needs
Users must verify the data validation workflows at periodic intervals to ensure that the data quality rules sync with the overall business goals. This includes taking necessary actions to improve data quality standards based on prior results.
Ensure Data Quality Management with Astera Centerprise
Astera Centerprise is an end-to-end data management solution that enables businesses to accomplish complex data quality and integration tasks while ensuring robust data quality management. The advanced data profiling and data quality capabilities allow users to measure the integrity of critical business data, speeding up data integration projects in an agile, code-free environment.
Want to find out how Centerprise can aid successful enterprise data quality management? Download the free trial version and experience for yourself!