Are you up for losing $15 million or more annually?
Don’t get us wrong. It’s just the facts. For example, if you’re still not taking your data cleaning process seriously, you must get ready for the consequences you may face.
According to Gartner reports, organizations lose approximately $15 million annually due to poor data quality.
Maintenance, Repair, and Operations (MRO) needs a reliable and clean form of data to function properly. Any sort of data being inaccurate, obsolete, irrelevant, or redundant could lead to informed decision-making and higher costs for operations within an organization.
However, manual data cleaning can be a monotonous and error-prone task. But then what’s the solution?
Automation it is.
Clearly, Automated data cleansing is the modern solution for making the best use of your data for decisions that power your business. But Automation isn’t just to make the process simple but to add accuracy, reliability and cost-effectiveness.
So, let’s cut to the chase now.
What is Data Cleansing?
Data cleansing, data scrubbing or data cleaning, whatever you may call it, is a way to make your data free from corrupted, inaccurate, irrelevant or incomplete records.
Obviously, you don’t need such data which is making your overall decisions affected.
MRO data cleansing allows you to make decisions regarding maintenance schedules, supplier management, inventory control, and cost optimization with a systematically managed and clean data.
But why is this cleaned data so important?
Why you just can’t go without a data cleaning process for your MRO organization?
How this cleaned data help make the best decisions?
Eager to know the answers to all these questions? There you go!
Significance of Data Cleaning in Maintaining Data Quality
Having validated, updated and secured data is just as important for a business as cleaning data from invalidated, obsolete and compromised entries.
Either way, the goal is to ensure decisions are based on accurate, up-to-date data.
Here’s what data cleaning does.
- Helps reduce processing times and streamline operations by cutting irrelevant and redundant data
- Helps save operational costs by improving data quality and usability ultimately saving you from 12% of revenue loss annually
- Helps harmonize datasets from multiple sources to build uniformity and compatibility for downstream processes
- Helps remove redundant records that can skew analysis or lead to inefficiencies in operations.
- helps Fill in missing fields to create a complete dataset
- Helps Identify and update obsolete data to maintain relevance.
- Helps remove errors in data entry that are often overlooked.
Is Master Data Cleansing Different from Regular Data Cleaning?
Generally considering these both as the same wouldn’t be wrong. However, they differ in terms of scope and complexity.
Master Data Cleansing is inclined towards critical business information such as customer records, supplier details, product information, and other key entities that are essential for operations.
Alternatively, Regular data scrubbing or cleaning includes a broader range of data types such as transactional, operational or any dataset that needs to be cleaned for day-to-day operations.
In terms of process, master data cleansing comprises comparatively more complex methodologies than regular data cleaning. Establishing templates for standardization, validating against external reference datasets, and implementing governance frameworks are a few things to name when it comes to implementing master data cleaning.
Best Practices for Automated Data Cleansing In MRO
Every business requires consistent data quality for its Maintenance, Repair and Operation (MRO) activities. But as said earlier, the manual data cleaning approach fails to meet the standard.
Therefore, data cleansing tools and software have become the need of the hour for MRO tasks to run smoothly. However, This is where it gets interesting: There are certain ways and practices to automate your data cleaning process, and if not done right, they can lead to inconsistencies, inefficiencies, and more errors.
Here’s what you need to do:
Set Clear Data Quality Rules
Define validation rules for critical fields like part numbers, manufacturer names, and maintenance schedules to ensure consistency across all datasets, driving better data quality and data automation.
Then move forward with defining validation rules for part numbers and categories, manufacturers and supplier names, and maintenance schedules to avoid the efforts and time being wasted doing this each time.
Implement Comprehensive Data Profiling
So, here you will need data automation software to assess the structure, completeness, and quality of datasets. This stage helps locate patterns or anomalies that need attention before cleansing begins.
Starting without it would cause duplicate records or missing supplier information that could disrupt procurement or maintenance planning.
Use Machine Learning for Pattern Recognition
Machine learning algorithms are the best and perhaps the fastest way to detect patterns and anomalies in large datasets.
You can use them to identify duplicate entries with slight variations alongside predicting missing values based on historical trends or similar records.
Even complex tasks like deduplication and error correction become a breeze with machine learning, which would be nearly impossible to do manually.
The plus point is that it improves over time as it learns from new data which makes it an essential tool in data automation particularly for MRO.
Integrate Cleaning Processes into ETL Workflows
Now there comes a technical part. You need to embed automated data scrubbing directly into your Extract, Transform, Load (ETL) workflows to ensure data is cleaned as it moves between the systems.
Also, automate tasks like standardizing formats e.g., units of measurement, removing duplicates, and validating against predefined rules during data migration or integration.
This allows real-time cleansing and prevents poor-quality data from entering critical systems like CMMS (Computerized Maintenance Management Systems) or ERP platforms. In a nutshell, it makes the overall MRO processes to run consistently without the typical roadblocks.
Establish Constant Data Governance
Once done approach is not for data cleaning. Instead, implementing a solid data governance framework is what works to maintain quality over time. Assign roles such as data stewards to monitor and enforce compliance with established standards.
Audit regularly to identify emerging issues and ensure alignment with organizational goals. This ongoing process is important to maintaining a high level of master data cleaning and ensuring that your data automation tools continue to deliver accurate and reliable data.
Putting this theoretical information aside, does this all actually make a difference in the real world? Let’s see it together.
EY's Transformation of MRO Material Master Data
A leading energy firm partnered with EY to tackle inefficiencies caused by duplicate MRO data that cost them wasted capital and poor decision-making.
EY implemented the AI data cleansing solution, processing over 100,000 datasets using algorithms and industry-standard templates to enforce data standardization and governance. This improved collaboration between maintenance, procurement, and field teams while reducing costs.
As a result of these efforts, the company anticipates saving up to 10% of its annual MRO spend over the next three years through cost avoidance, reduced inventories, and enhanced productivity. Furthermore, they are cleansing their material master data sets in preparation for migration to SAP S/4HANA, ensuring they maximize their investment by migrating with clean material ledger capabilities.
How Net Results Group is a Best Bet for Automated Data Cleansing?
Net-Results Group’s data cleaning services specializes in tools like MRO3i™ which allows us to standardize, cleanse, and enrich data while eliminating inconsistencies that lead to costly delays and inefficiencies.
Our data governance solutions help businesses maintain long-term data quality, reducing the need for repeated cleansing while improving inventory management and operational productivity.
But that’s not all, we are always on the verge of making MRO processes simple and easier for businesses that are already been dealing with so much and couldn’t afford to let bad data slow them down.
