In a 2020 survey, 77% of American organizations indicated that they value data-driven decision-making.

But how reliable is all that data?

If you were to take stock of all the data your company is keeping on partners, suppliers, vendors, contractors, customers, and products, would you be confident that it’s all consistent? If not, then you’re far from alone!

Many companies struggle with maintaining consistent names in their systems. For example, you might notice a problem with different company names being used for the same supplier. A company logged as “Biggles Engineering Solutions” in one department’s database might be “Biggles & Co” in another – think about other shifts in word order, abbreviations, misspellings etc.

One of the extreme cases we have observed in the insurance industry was a company with over 800 unique case insensitive spellings!

This can cause all sorts of problems. If you’re trying to coordinate an emailing campaign to all your suppliers, you might end up addressing some of them twice and others not at all.

Or, when trying to track down a payment, you might waste valuable time looking under the wrong name.

Inconsistent names for entities can also prevent you from gaining a holistic view of what’s going on in your company, causing you to miss connections, trends, opportunities, and threats. It leads to fragmentation in your reporting and forces BI teams to keep adding filters and reporting workarounds instead of working with actual data analysis to gain insights.

But rather than chasing after errors and trying to clean up data manually, there’s a way to automate the process – and keep things from getting messy again in the future.

Manual Name Normalization and Deterministic Algorithms

Data teams and admins usually try to go after the first available solution. Usually it’s spreadsheets – but Excel is the wrong tool for name normalization and data linkage. Data validation functions in Excel, VLOOKUP, fuzzy matching algorithms, RegEx rules – this is neither a maintanable nor scalable way of fixing data in the long run.

The problem is that formulas and standard ways of solving that through programming are deterministic approaches to name normalization. This way of dealing with disparate records is inflexible, because it usually requires closely matching variants to produce reliable results.

Real-world data is unstructured, and people get oustandingly creative with manual data entry.

Manual normalization of names may be enough if you are dealing with small data sets and a limited number of data sources. However, it quickly collapses in an enterprise-scale setting where hundreds if not thousands records come in daily.

Thing Bigger with Master Data Management

Master data management is a system, strategy, and a set of processes that provide a single, accurate, and up-to-date view of your master data; i.e., data that is core to your organization’s decision-making processes. It can be used to clean up and standardize data as it’s being entered, and to keep it consistent going forward.

Taking a strategic approach to master data management, following the basic steps below, can help you to avoid the issues caused by inconsistent data. It can save you time and effort, and make your data more accurate and reliable.

1. Decide What Master Data Is

Your first step is to decide what qualifies as “master data” for your organization. What information is driving your high-level business decisions, or used in inter-departmental processes?

The scope of master data varies for every company, but it often includes data on customers, products, suppliers, and employees.

Being clear on what is master data and what isn’t will allow you to stay focused as you set up your new processes and systems.

2. Identify Your Data Sources

Next, you’ll need to identify all the locations where master data is being logged – the departments and teams where it lives, and the (possibly multiple) vendor and third party administrator systems being used to host it.

This may take some digging, but it’s important to give yourself a full picture of where your data is coming from before you can hope to fix any inconsistencies.

3. Cleanse and Standardize Your Master Data

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A major component of master data management is improving the accuracy, consistency, and completeness of data so that it can be used to provide valid insights and power decision-making.

Going back to your problem with different company names being used across databases, it’s decision time. You’ll need to track down the inconsistencies and decide which is the correct name for this and all other “entities” (people, places, and things being tracked) in your master data.

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Only once you’ve decided on the correct names for your entities, you can standardize them. This means creating a consistent format for how the data is entered. For example, you might want to use all uppercase letters for company names. Establishing proper data governance practices is vital for improving and maintaining quality of your data.

This will help to ensure that the data is consistent across all databases and can be easily recognized. It will also make it easier to search for and find the data you need.

Now, you’ll need to embark on a process to fix all data entries that deviate from your new standardized formats, as well as fill in any missing information and correct other types of inaccuracies.

If you have a limited number of data sources, you may be able to get away with manual data entry and cleanup. But if you have numerous sources across a variety of systems, you’ll likely want to purchase or develop a data linkage software solution that can quickly and easily bring your data into line.

4. Establish Data Standards Across All Systems

Of course, a one-time cleanse of your data isn’t enough. You’ll need to take steps to ensure your new standards are upheld going forward.

One way to approach this is to develop a set of data standards that must be followed by all systems. This can be a daunting task, but it’s necessary to ensure that your data remains clean and consistent.

Your data standards should cover all aspects of data, from how it’s entered into the system to how it’s stored and accessed. They should also be designed to be flexible, so that they can be easily updated as your needs change.

Enforcing data standards can be a challenge, but there are a few things you can do to make it easier.

First, make sure that everyone who needs to use the data understands the standards and knows how to follow them.

You can also consider using automation to enforce the standards. For example, you can develop a script that checks for compliance with the standards and alerts you if there are any problems.

5. Implement a Master Data Management Solution

Once you’ve cleansed your data and established standards for how it should be handled, you’ll need a way to manage it going forward. This is where master data management solutions come in.

This solution is a platform that provides a single, centralized repository for all of your data. It gives you complete control over your data and how it’s used. It functions as a source of truth, allowing you to track changes to your data and ensure that everyone is using the latest version.

Your central repository could take a variety of forms, such as a data lake, data warehouse, or cloud-based solution. But it should make sense for the amount and type of data you handle, be accessible to those who need it, and offer scalability and flexibility as your organization grows.

A best practice is to create an interdepartmental team to govern the implementation and long-term management (such as periodic auditing and cleansing) of your master data management solution. This governing body should be made up of representatives from all departments concerned with the master data, and be empowered to assign roles and responsibilities.

6. Employ Machine Learning for Name Normalization

Remember the part about deterministic ways to link  data? Machine learning presents a different approach, a probabilistic method of linking data.

Essentially, machine learning links records based on confidence scoring, while also being able train itself to be better and better based on name normalization tasks completed in the past. It’s flexible, smart, and can sustain your data normalization as your organization grows.

Excel and standard programming approaches based on rules can only somewhat add efficiency to name normalization. Often they leave you with a lot of mislinked records to fix, or are unable to handle cases in which a few records match a single entity. This is not an issue with machine learning.

The best part? Machine learning doesn’t have to take away your control over how data is linked. From our experience with RecordLinker, we took guided, semi-automated approach to linkages, which leaves your data admins with options to approve and edit linkages, or even create new canonical records.

Recommended Reading About Data Normalization

Master data management and scalable naming normalization are complex topics. Approaching MDM is not just about implementing a bunch of solutions, rules, and automations. It requires a shift in the organization’s way of approaching data to succeed and turn it truly data-driven. Here are several articles to help you plan and put proper data management practices in place:

Name Normalization Wrapped Up

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Master data management is a critical part of ensuring that your data is clean, accurate, and consistent. By taking steps to establish data standards and implement a master data management solution, that old problem with different company names tying up your data will become a thing of the past.

RecordLinker uses Machine Learning to normalize records across your data systems!

RecordLinker uses Machine Learning to normalize records across your data systems

Interested in improving the quality of your data, but don’t have the time or resources to create a master data management program from the ground-up? RecordLinker is here to help. Our data integration and management platform can quickly connect your disparate data sources, identify and deduplicate records, and keep your data clean and up-to-date.

To learn more about how RecordLinker can help you improve the quality of your data, request a free demo!

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