Master Data Management & Machine Learning: How ML Can Improve Your Data
In today’s data-driven world, managing organizational data can be a messy and tedious task. However, Master Data Management (MDM) is a process that helps organizations manage their most critical data, providing a single, accurate, and consistent view of data needed for core business decisions. Increasingly, organizations are turning to machine learning (ML) technology to power their MDM efforts. This article explores three significant ways in which ML can help organizations get more from their master data, including automating data cleansing and enrichment, finding hidden data relationships and trends, and keeping data secure. With the use of ML algorithms, organizations can improve data quality, identify patterns and correlations that humans may miss, and ultimately make better business decisions.
If you’ve ever worked with organizational data, you know it can be messy. Master data management is a process and set of tools that helps organizations manage their most critical data.
It provides a single, 360-degree view of data that is needed for core business decisions, and ensures that the data is accurate, complete, and consistent across all systems.
There are many ways to approach master data management, but increasingly, organizations are turning to machine learning technology to power their efforts.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that allows computers to learn from data without being explicitly programmed. In other words, machine learning algorithms can automatically improve given more data.
That’s what makes master data management machine learning software so powerful. It can help organizations keep their master data accurate and secure, while identifying patterns and correlations that humans may easily miss.
In this article, we’ll look at three major ways in which machine learning can help you get more from your master data, while keeping your staff free to focus on more substantive tasks–such as finding new ways to make use of all those data-driven insights.
Automate Data Cleansing and Enrichment
Machines are simply better than humans at processing large volumes of data quickly and accurately. Many organizations use machine learning tools to automate data cleansing and enrichment, both fundamental components of master data management.
Data cleansing is the process of correcting errors and inconsistencies in the way data has been logged, while data enrichment refers to filling in missing values, or improving the granularity of data by adding details and context.
Both tasks are extremely tedious, time-consuming, and error-prone when performed manually, especially for organizations that store data across multiple systems. But with the advent of master data management machine learning software, they can be made efficient and accurate.
For example, if your master data includes a customer domain, a machine learning algorithm could cleanse duplicate entries, incorrect spellings, or outdated contact information.
The software could also be used to enrich customer data by adding missing information, such as demographic details. This would allow for the creation of more robust and targeted marketing campaigns, among other advantages.
Enriching and cleansing data is an iterative process: as it analyzes more and more data, the software will become sharper at identifying and correcting errors, and learn to add increasingly detailed and accurate information.
Find Hidden Data Relationships and Trends
In addition to improving the accuracy and quality of your data, machine learning can help you understand it.
A major advantage of master data management is that by synthesizing large amounts of master data, organizations can reveal relationships and trends that would otherwise be hidden.
Through data categorization, clustering, or predictive models, among other functions, master data management machine learning software can find patterns that human observers may miss.
For example, if an organization has data sets on customer purchases, demographics, and social media activity, machine learning can be used to find relationships between these data sets.
This might reveal, for example, that customers who purchase a certain product are also more likely to follow a certain social media influencer. This type of information can inform the creation of more effective marketing and sales campaigns.
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In addition to finding hidden relationships, machine learning can also be used to identify trends. For example, if an organization has data on customer purchases over time, machine learning can tease out patterns in customer behavior.
You may learn that certain products are becoming more popular, or gain insight into how customer behavior is changing–insights that can be used to improve a variety of business strategies and processes.
By finding hidden relationships and trends in master data, machine learning can help organizations make better decisions about where to allocate resources and how to target their efforts.
Keep Your Data Secure
As data becomes more valuable, it also becomes more vulnerable to theft and misuse. Data breaches and cyber attacks are growing more common and increasingly sophisticated.
If you’re handling sensitive data, such as customer credit card information or personal health information, you need to be extra careful to protect it.
These days, it’s not enough to just have a firewall and antivirus software; you need to constantly monitor your data and look for signs of unusual activity. Machine learning can help you do this by analyzing your data for patterns and anomalies.
For instance, machine learning can be used to detect data that is highly vulnerable and needs enhanced protection. Or, it may notice changes in data that could indicate that someone has tampered with it.
You will then be able to take steps to protect your data, such as changing your passwords or increasing security measures.
In the past, master data management was a time-consuming and expensive process that required a dedicated team of experts. Machine learning is changing all of that, allowing organizations to manage even highly complex, fast-growing data sets with ease.
Machine learning can be used to improve the quality of data by identifying and correcting errors, filling in missing data, and standardizing formats. It can also predict future trends and help businesses make better decisions.
Finally, it can improve the security of your data by detecting anomalies and potential threats.
All told, master data management machine learning software can save a lot of time and effort that would otherwise be spent on manual data entry, analysis, and maintenance.
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.