How to Use Master Data Management Tools to Improve Data Quality
Bad data plagues organizations of all sizes. Disorganized information scattered across multiple silos can lead to revenue loss, decreased operational efficiency and poor decision-making.
According to a study by LeadJen, each year, sales departments lose approximately 546 hours to bad prospect data. In addition, inside sales reps with validated data waste about $20,000 in productive sales time annually.
The causes of data problems are often organizational rather than technical. They arise when an organization doesn't have a centralized master data management (MDM) system to organize its data, which leads to duplication and inconsistencies.
MDM is a critical component of any data quality improvement initiative. It consolidates information from multiple master data domains into a single repository and provides a central location for managing data quality rules and processes. It also cleans up dirty data to prevent problems in business processes downstream.
What is Data Quality?
Data quality is a measure of how accurate, consistent, and complete your information is. The data source, collection method, and management processes all impact data quality.
Some of the most important aspects of data quality to consider are:
- Accuracy: Is it free of errors such as typos, duplicates, and incorrect values?
- Consistency: Is it formatted the same way across different systems and databases?
- Completeness: Does it include all the information required for a given purpose?
- Integrity: Does it accurately reflect real-world relationships between real-world entities?
- Reasonability: Does it meet the assumptions and expectations of the domain?
- Timeliness: Is it up-to-date to reflect current conditions?
- Validity: Does it conform to a defined format or set of values?
- Accessibility: Can authorized users retrieve it reliably?
Master data management solutions use these dimensions to assess the overall quality of data and identify specific areas of improvement. They provide a 360-degree view of entities like customers, products, or suppliers and compare the data against other references to ensure accuracy.
How Master Data Management Improves Data Quality
Here are some ways in which master data management improves data quality:
1. Continuous Data Validation
Data validation is a rule-based process to ensure that the data is clean, accurate and consistent. Traditional processes ensure data is accurate at a certain point in time, but don’t check again. Because they don’t account for future errors, they don’t offer any way to ensure data meets the required standards on an ongoing basis.
Master data management provides a proactive approach by consolidating all data into a single source of truth. This “golden record” lets organizations use the same business rules and processes for all data, ensuring consistency across every business unit.
Master Data Management can help improve data quality by continuously validating it and flagging any errors. It can also correct many types of errors through its built-in cleansing and transformation capabilities.
2. Data Cleansing
Data cleansing is one of the first steps in improving data quality. It fixes common inaccuracies in data like typos, misspellings, and incorrect formatting. MDM ensures automatic cleansing of source data before loading into the master dataset.
Modern master data management software provides a number of key features for data cleansing right out of the box, enabling you to improve your data quality immediately.
3. Automated Matching
Duplicate data is one of the most common data quality issues. According to Jonathan Block, Senior Director of Research at Sirius Decisions, “It takes $1 to verify a record as it's entered, $10 to cleanse and de-dupe it and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again.”
Master Data Management reduces duplicate data with its automated data matching and merging capabilities. It uses matching to identify duplicates and a merging process to combine multiple records into one.
Automated matching not only cleanses the data but also reduces the cost of storage and processing.
4. Managing Reference Data
Reference data is fundamental to data quality because it helps to provide context and improve usability. It also helps build common metrics, as well as hierarchies and accountability for efficient operations in an organization.
MDM systems have dedicated reference data management processes that help to map and standardize data between different domains. Maintaining good reference data teaches business users they can trust their information. This enables them to quickly draw insights from it and power business decisions.
5. Data Quality Dashboards
A modern master data management solution provides data quality dashboards, enabling its users to get more visibility about the overall health of the data. These dashboards offer real-time visibility so you can see a complete picture of data patterns.
This information helps to identify trends and understand where most errors occur. It also uses machine learning to identify errors and provide insights into which processes need to be improved to achieve better data quality. By helping you identify errors, these dashboards enable you to prevent any damage they might do.
Make Business Decisions Based on Timely, Trusted Data
A modern master data management tool can significantly improve data quality, improving trust and decision-making while preventing costly errors in downstream business applications.
management, enabling data stewards to be more productive while speeding identification and resolution of data quality issues. It empowers users with reliable, real-time data, supporting positive business outcomes and efficient growth.
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