ARTICLES

January 11th, 2024  |  10 min read

How to improve data quality without a Master Data Management platform

In the digital age, the quality of your data can make or break your business. While Master Data Management (MDM) solutions are often touted as the go-to for ensuring high-quality data, it is possible to make improvements without them.

Here are six ways to increase the quality of your data without using an MDM solution:

Establish Clear Data Standards

Data standards refer to the rules and guidelines for how data should be recorded and formatted. They are essential for maintaining consistency, accuracy, and clarity in data management. For example, one common standard is to establish protocols for how data should be entered into systems. This includes specifics on data formats (e.g., date formats like MM/DD/YYYY vs. DD/MM/YYYY), naming conventions, and acceptable values for different data fields. You may also want to determine the structure of data – such as how data fields are organized and related to each other within your databases and systems. Another recommendation is to create validation rules to ensure that the data entered into your systems meets your standards. These rules can automatically check for errors or inconsistencies, such as incorrect data types (e.g., text in a numeric field), out-of-range values, or missing information.

Provide Documentation and Accessibility

Develop detailed documentation that clearly outlines all data standards, including formats, structures, naming conventions, and validation rules. This should cover every aspect of data handling, from entry to storage and retrieval. Make sure to include examples and scenarios in the documentation to illustrate how the standards should be applied in practice, and avoid technical jargon or, where necessary, provide explanations to ensure that all staff, regardless of their technical expertise, can understand the content. Your documentation will need to be kept up-to-date with any changes in data standards, and updates should be regularly communicated to all relevant staff promptly.

Deliver Training and Communicate Regularly

It’s a good idea to create a comprehensive training program that educates staff about the importance of data standards in the organization. This should include understanding how data standards contribute to data quality, consistency, and reliability. Training should be tailored to different roles and departments, ensuring that each team understands how data standards specifically apply to their work. Some ideas you might want to use include organizing regular, interactive training sessions where staff can engage with the material, and including practical exercises, case studies, and real-life examples to demonstrate the application of data standards. It’s also important to highlight how adhering to data standards positively impacts day-to-day operations, decision-making processes, and overall business strategy.

Conduct Regular Data Audits and Cleansing

To maintain data quality over time, you’ll need to establish a regular schedule for conducting data audits. The frequency of these audits can depend on factors like the volume of data, rate of data change, and criticality of the data to business operations. Plan audits to cover different aspects of data, such as accuracy, completeness, consistency, and relevance. For each audit, you should clearly define the objectives and focus – for example, whether you’re focusing on a particular type of data such as customer information, or assessing a specific aspect, like data accuracy or compliance. The scope of the audit is also important, it may be organization-wide, or you could focus on specific departments or datasets. Utilize a combination of manual checks and automated tools to review and analyze data. This might include reviewing sample records for errors, using software to identify duplicates, and running queries to find anomalies. The findings of the data audit should include issues identified and actions taken to address them. This documentation is crucial for tracking progress and for accountability, and the results should be shared with the relevant teams and departments, highlighting any common errors or issues that need attention.

Leverage Basic Data Integration Tools

If you don’t want to go down the MDM route, there are a number of data integration tools that can help you  consolidate data from different sources. This can provide a more unified view of your data, albeit a less sophisticated one compared to MDM solutions. Data integration tools are designed to combine data from different sources, bringing it into a single, cohesive view. This process involves extracting data from its original sources, transforming it into a consistent format, and loading it into a target system or database. There are different types of data integration tools that you may want to consider:

• ETL (Extract, Transform, Load) Tools: These extract data from various sources, transform the data into a suitable format, and load it into a central repository. Examples include Talend, Informatica PowerCenter, and IBM DataStage.
• Data Replication Tools: These tools replicate data from source systems to data warehouses in near-real-time. Examples include Oracle GoldenGate and Qlik Replicate.
• iPaaS (Integration Platform as a Service): These cloud-based platforms offer a suite of tools and services for integrating applications and data. Examples include MuleSoft Anypoint Platform, Dell Boomi, and Zapier.

Implement Simple Automation Tools

Basic automation tools are software applications designed to automate routine tasks involved in data entry and processing. They can handle repetitive, rule-based tasks, reducing the need for manual input and minimizing the risk of human error. Here’s an overview of the types of automation tools you might find useful:

• Robotic Process Automation (RPA): RPA tools use software bots to mimic human actions in completing digital tasks. Examples include UiPath, Blue Prism, and Automation Anywhere.
• Form and Data Entry Automation Tools: These tools automate the process of entering data into electronic forms or databases. Examples include tools like Typeform, which can automate the collection and entry of survey data, or Microsoft Power Automate for simpler workflows.
• Spreadsheet Automation Tools: Tools like Google Sheets and Microsoft Excel offer features (like macros and formulas) that automate data calculations, sorting, and organization.

The Limitations of Non-MDM Approaches

While it is undoubtedly possible to improve data quality without using an MDM solution, there will be a number of restrictions and challenges that are either made much harder, or aren’t possible at all, without one.

As your business grows, the volume and complexity of data increase. Non-MDM methods may struggle to scale effectively, leading to potential data quality issues. It is also very difficult to achieve a consistent and accurate single view of any domain without an MDM solution – as that is what they are specifically designed to do. As the volume and variety of data increases, so this problem becomes more and more challenging to address. Managing complex data relationships and hierarchies is also more problematic without the sophisticated capabilities of an MDM solution.

There’s also no getting away from the fact that non-MDM approaches will require more manual intervention and effort. As well as the time drain, these methods are also more prone to human error.

In the long-term, any organization with a sizeable amount of enterprise data to manage will need to invest in an MDM solution. Using data to help achieve impactful outcomes requires robust data governance, data quality, consistency, and compliance capabilities across the entire organization. MDM solutions are also designed to scale with your business, handling increasing volumes and complexities of data efficiently.

While the initial investment in MDM might be higher, the long-term benefits in terms of reduced errors, improved decision-making, and operational efficiency will outweigh the costs.

Improving data quality without an MDM solution is a bit like patching a leaky boat – it works for a while, but eventually, you'll need a sturdier vessel. MDM solutions offer the comprehensive, scalable, and efficient approach needed for long-term data quality and business success, ensuring that your data becomes a valuable asset, and not a liability.