What is Data Cleansing, and How Should I Approach It?
Your business relies on clean data every day. Every one of your business processes, from top-level strategy to daily operations, relies on trustworthy information you can use to set well-informed, intelligent goals.
It’s worth the time to be proactive about data cleansing. If you’re just reacting to data errors as they appear – or worse, ignoring them altogether – you’ll likely pay for it down the line. The costs of dirty data can include inefficiency, lost revenue, poor business decisions, and stalled growth.
By setting business rules for data cleanliness and determining the processes that support it, you can avoid the unnecessary busy work that goes along with remediating poor data quality.
What is High-Quality Data?
It’s best to understand clean, high-quality data by describing its evil twin: dirty data. Dirty data is information that is unusable because it contains inaccuracies, typographical errors, or missing information. Most of the time dirty data can be classified as outdated or obsolete which offers no value.
On the other hand, quality data is relevant, clean, and useful. It is:
High-quality data is critical to every business process. Imagine that your report on customer age demographics comes back saying that they’re all over 100 years old. Searching the data manually, you discover that every customer’s birthday was input as Dec. 13th, 1901. Not only is your data worthless, it’ll take a ton of time to track down every single correct birthday.
Humans and machines are both error-prone, which means that no data can be perfect. However, data cleaning can mitigate the effects of dirty data.
What is Data Cleaning?
Whether you’re looking to create sales reports, business strategy proposals, help customers, or launch a marketing campaign, you’ll need clean data. Data cleaning is the process of preparing data for use by putting it in a standardized format and correcting inaccurate, duplicate, and incomplete information.
Before you start cleaning your data, you’ll need a clear understanding of the level of quality required, both for the project and for your organization’s standards.
Gartner estimates that in 2022, 70% of businesses will be tracking data quality levels. They do so to define what good data means for them, and use it to guide how they manage data going forward.
According to Gartner, having consistently trustworthy data also trains your organization to rely on it. When your employees don’t have to constantly worry if the data is correct, they can use it to make informed decisions and guide future goal-setting.
How to Begin Cleaning Data
To determine the standard of data quality that your business needs to function, you need to first assess how quality data will lead to the results you’re looking for.
Here are some questions to ask yourself before you start:
- What type of data am I using?
- What format is my current data in?
- In what other formats do I store my data?
- How will aligning this data in one format benefit my business?
- How will this alignment help my business run more smoothly?
- How will this data affect my business outcomes?
- How can the data be improved?
Data cleaning isn’t always enough by itself. If you’re using data sets from different tools, you may find yourself struggling to make sense of everything.
To present irregular data in a way that is clear, concise, and clean, you may need to take additional steps.
Data Cleaning or Data Transformation?
If you’re dealing with irregular information from multiple data sources, improving it often requires both data cleansing and data transformation.
If you’re planning to run a digital ad campaign targeting former customers of the past ten years, the data you need will likely be scattered across various spreadsheets and legacy tools. Before you can clean it, you’ll need to figure out what format it needs to be in to achieve your final goal.
Both data cleansing and data transformation are important when dealing with irregular data from multiple sources. If you’re not processing it properly, you’ll end up with data full of structural errors and missing information.
Going through this process will help you determine how much you and your colleagues trust your current data and how your business can benefit from an improvement in data quality.
What are the Steps in the Data Cleansing Process?
Once you understand your objective, it’s time to figure out your approach. Here’s what the data cleansing process looks like step-by-step.
1. Assess Your Tools
Make sure your cleansing tools are up to the task before you dive in. Are you manually adjusting spreadsheets? Does your point-of-sale system help you clean up customer data? Are you using multiple databases?
2. Find the Right Master Data Management System
A good master data management (MDM) system integrates the data from the many tools a business uses into a single platform. It comes with out-of-the-box data cleaning solutions that legacy tools do not have.
3. Choose Your Team
Determine who will be responsible for managing your data. You may manage it yourself, find folks within your organization who have the skills, or hire a data scientist.
4. Set a Data Quality Standard
Determine how your organization will value high-quality data. Attend data quality webinars, create internal documentation, and set up a way to profile data.
5. Profile Your Data
Data profiling is a technology that uses statistical methods to identify data quality issues. Profiling your data will help you determine how to clean your data.
6. Commence the Clean!
There’s a lot to keep track while you clean your data, so here’s a checklist:
- Remove duplicates.
- Identify and locate missing data.
- Identify and correct inaccurate and out-of-date data.
- Standardize data to one format.
- Determine and remove irrelevant data.
7. Quality Check
Establish a process for checking your data quality. How does it perform after cleansing? What did the cleansing overlook?
Look at the definitions your organization established for quality data. Does the current quality meet your standards?
8. Establish an Ongoing Data Cleaning Process
You’ll need to ensure new data is profiled and cleaned continuously. For example, an MDM system will keep new data aligned with your standard of data quality. Modern MDM systems like Reltio use machine learning to monitor data quality, reducing the time it takes to do certain processes from weeks to hours.
Of these steps, hiring a data expert and finding the right Master Data Management tool are the most pivotal to long-term success.
How Can a Data Scientist Help?
To set and achieve data quality goals, you may want to consider hiring or contracting a data scientist. Data scientists have devoted their lives to making sense of messy facts and figures. These experts use statistical methods bridge the gap between the data your organization holds and the business decisions it affects.
There are also citizen data scientists that you may already know or have on hand. They may not focus on data in their regular roles, but they have the technological chops to become data “power users” in your organization.
Data Cleaning in Different Industries
While information differs in each industry, none are immune to dirty data. Organizations must consider their approach to data as it pertains to the constraints of their industry.
Gartner suggests financial companies must balance the difficulties of cleaning huge amounts of financial data with an acceptable amount of dirty data.
Some financial data might not need to be fully cleaned to be useful. Therefore, it’s wise to create a framework to identify the most important data to clean.
Healthcare data is typically cleansed on patient registration or patient index systems. However, HealthIT.gov recommends extending cleansing to systems that share data with patients or internal stakeholders. It also recommends creating a system where data issues can be escalated and fixed as quickly as possible.
Because it powers competitive customer service, organizations in the retail industry need to focus on accurate customer data. But how can this data be tied to product information like SKUs?
Master data management can help connect these datasets. Modern MDM solutions have tools for profiling and cleaning product information and connecting it to customer identities.
Data Cleansing Ensures Trust in Data
When the task of data cleaning seems tedious or overwhelming, it’s important to remember the benefits of data management. Don’t fret—there are many tools to help you along your path to data excellence.
You can hire a data scientist, train internal stakeholders to become data power users, or update your cleaning tools. Regardless of how you go about it, establishing trust in quality data is your biggest priority.
Of the listed methodologies, the best way you can clean, standardize, and elevate your data is with a Master Data Management system. Not only can a modern MDM system use machine learning to evaluate the quality of data, it can then take that data and provide overarching insights to drive your business forward.
Implementing an MDM system can help your organization learn to prioritize and trust its data. To see it in action, request a demo.