Data cleansing, also known as data cleaning or scrubbing, is the process of identifying and correcting (or removing) inaccuracies, inconsistencies, and errors in datasets. Critical to data management because “dirty” (obsolete, inaccurate, irrelevant) data leads to faulty analyses, misguided decisions, and poor business outcomes.
These days, every organization relies on data to keep things running smoothly. Whether it’s managing inventory, developing new products, understanding customers, or handling billing and logistics, data is at the heart of it all. Keeping your data clean isn’t just a good idea—it’s essential for staying profitable and growing your business.
How It Gets “Dirty” in the First Place
Before we dive into how Datagence ensures reliable data, let’s talk about why it’s so crucial. Data comes from all over the place—customer interactions, websites, databases, surveys, manual entries, and even new systems and technologies. With so many sources, it’s easy to see how data becomes dirty in the first place.
This data can come with a host of problems, including:
- Duplicate Entries: Multiple records for the same data point can skew analysis.
- Inconsistent Formats: Different date formats (e.g., “01/02/2024” vs. “2024-02-01”), units of measurement (e.g., meters vs. feet), or naming conventions can make comparisons tricky.
- Missing Data: Incomplete entries can lead to flawed conclusions or introduce bias.
- Outliers: Unusually high or low values that don’t fit the pattern of the data.
- Typos and Errors: Human input errors can creep into datasets, particularly in manual data entry.
These challenges can lead to a nightmare scenario for everyone in the organization, not just data analysts. When data isn’t cleansed, it becomes unreliable, potentially leading to wildly inaccurate results that hinder effective job performance across the board.
Imagine this single example anyone can relate to:
A Sales team member reaches out to a contact for a check-in. They are no longer with the company. The sales team member enters all the new information into the CRM. It just so happens, the contact was also the billing contact. While the CRM has the updated information, unless the billing system and CRM are unified and updated in real-time, the next bill will go to an address that may or may not be checked regularly, delaying the payment of an invoice.
For anyone who has experienced this simple scenario, you know all the different ways this one thing can reverberate through an organization and the problems it can cause.
Now multiply this scenario by every record with the potential to be obsolete – the issues are exponential.
The Data Cleansing Process
Data cleansing isn’t a one-size-fits-all process. The specific steps vary depending on the data source, the tools used, and the problem at hand. However, some common steps in the process include:
- Removing Duplicates: Data cleansing tools or scripts can identify and eliminate repeated records. Imagine if you had a list of customer email addresses, and the same person appeared five times due to a glitch. Cleansing would eliminate those duplicates, leaving only one unique entry.
- Handling Missing Data: One of the trickiest parts of data cleansing is dealing with missing values. Sometimes, you can simply discard incomplete records, but in other cases, you might want to fill in missing information using averages, estimates, or machine learning models.
- Correcting Inaccuracies: This involves fixing data that’s flat-out wrong. For example, if you notice that someone’s birth date is listed as “02/30/1985” (an impossible date), you’d flag and correct that entry.
- Standardizing Data: This means making sure all data follows a consistent format. For instance, ensuring that all currency amounts are in USD or converting all dates to a single, agreed-upon format.
- Validating Data: After cleansing, data is often validated to ensure that it meets the required quality standards. This could involve running tests to ensure the data fits within expected ranges or distributions.
Before addressing the aforementioned and other critical aspects of data cleansing, it is essential to standardize and unify incoming data streams. This ensures that all data is interconnected, allowing for real-time updates across the board whenever a change occurs.
Why Does It Matter?
So, what’s the big deal? The reasons are as numerous as the data sets themselves. The principle of “garbage in, garbage out” is a stark truth in data science. When data is messy or inaccurate, any analysis, reports, or models derived from it will be equally flawed. In our data-driven world, poor data quality can result in missed opportunities, subpar customer experiences, and even financial losses. Ensuring data is clean is just the starting point; what organizations truly need is reliable data across the board.
Dirty data has deep and adverse consequences across an organization.
- Operations: bad decisions, increased costs, compliance and legal risks, inefficient resource allocation, missed opportunities, challenges implementing automation and AI, etc. etc. etc.
- Sales: wasted time on bad leads, lower conversion rates, bad forecasting, compliance risk, increased churn, negative impacts to ABS, etc. etc. etc.
- Marketing: inaccurate targeting, misleading analytics and reporting, decreased campaign efficacy, poor personalization, higher bounce rates and low deliverability, compliance and privacy risks, wasted resources, damage to brand, difficulty scaling, etc. etc. etc.
Final Thoughts
Data cleansing is not sexy. However, the impact of what reliable data delivers is undeniable. Whether you’re handling small datasets or massive amounts of big data, cleansing ensures your information is accurate, consistent, and actionable. In the end, clean and reliable data gives businesses the freedom to operate with confidence.
Learn more about Datagence Data-Quality-as-a-Service (DQaaS) and how our solutions can impact Migration, Marketing Data Management, and Operations data.
