Das Wie und Warum der Datenbereinigung

First, What is Data Cleansing?

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies. By cleansing data (removing duplicates, correcting inaccuracies, and filling in missing information), organizations can improve operational efficiency and make more informed decisions.

Data Cleansing vs. Data Cleaning

The terms “data cleansing” and “data cleaning” are often used interchangeably, but they have subtle differences:

Data cleaning refers to the broader process of preparing data for analysis by removing errors and inconsistencies.
Data cleansing is a more specific subset that focuses on correcting or deleting inaccurate records to improve data integrity.

For instance, in accounting data cleansing, finance teams might remove duplicate transactions, correct misclassified entries, or update missing financial details to ensure accurate reporting. In contrast, data cleaning in a data science workflow might involve removing irrelevant data points, formatting inconsistencies, or standardizing variable names to prepare a dataset for analysis.

Benefits of Data Cleansing

Messy data slows everything down—bad decisions, wasted time, and frustration all stem from inaccurate information. Cleansing your data ensures that what you’re working with is reliable, so your team isn’t second-guessing reports or scrambling to fix errors. Here’s why taking the time to clean your data pays off:

Smarter Decision-Making – When your data is accurate, your insights and strategies actually make sense.
Less Wasted Time – No more chasing down errors or fixing duplicates. Clean data keeps things moving.
Better Customer Relationships – Accurate info means more personalized marketing and fewer embarrassing mistakes.
Staying Compliant – Regulatory requirements? Handled. Clean data keeps you on the right side of the rules.
Saving Money – Bad data costs businesses big. Cleaning it up now avoids expensive mistakes later.
Stronger Security – Outdated or irrelevant data is a liability and cleaning house reduces risks.

But what happens when businesses don’t clean their data? The costs—both financial and operational—can add up fast. Let’s take a closer look at just how expensive “dirty data” can be.

How Much is “Dirty Data” Costing You?

According to The Data Warehouse Institute (TDWI), “dirty” data is costing US companies around $600 billion every year in lost revenue, missed opportunities, and ill-informed strategic decision-making. Data is “dirty” when it is incorrect, incomplete, irrelevant, duplicated, missing, or improperly formatted.

For many organizations dirty data is caused by:

Collecting data haphazardly over the years through multiple sources.
Using older technology or systems that can’t keep up with current data demands.
Trying to condense large, complex datasets into a more manageable form.
Integrating systems with duplicated or mislabelled data.
Linking different data sets after a business merger or acquisition.
Users who lack understanding of data systems and how to use them.

Air Your Dirty Laundry, Don’t Wait for “Clean Data”

You may be thinking, “When is my data ‘clean-enough’ to be useful?”

The hard truth is that your data is in a constant state of decay. Achieving clean data is a time-consuming and ongoing journey. There are many tactics to help you realize this goal, but it could take several months, if not years, for your data to be considered “clean.”

That’s why we believe in using your data now, and we aren’t the only ones.

According to research completed by the University of Texas, increasing data usability by 10% would boost annual revenue for Fortune 1000 companies by more than $2 billion.

The Case for Publishing “Dirty Data” Early

Don’t wait to publish your data. A significant benefit of publishing your data early, through a platform like Calumo, is that it makes people more accountable. Share the data cleansing load with those responsible for collecting it, allowing you to use your data to better effect sooner.

Increase Staff Accountability

More often than not, finance teams find it difficult to engage the business and drive accountability in the data collection process. Publishing and sharing your data, even before it is 100% clean, helps educate employees as to the importance of accurate information and increases ownership of the numbers. Automated reminders from your CPM system can highlight missing data to ensure best practice across your organization.

Identify and Correct Inconsistencies Sooner

The use of reports, visualizations, and dashboards in sharing information helps with quick distribution of data, so that the wider business can easily identify and investigate inconsistencies or anomalies.

Teams responsible for inputting data will see the roll-on effect sooner rather than later and be able to make changes to correct current and future mistakes.

Clean Data, First Time

Longer-term, those involved in data collection will be more deliberate in their data entry, because they’ll know what information is important. The Six Sigma practitioners refer to this as “First Time Right” and the benefits of even striving for it are immense.

Before putting the data to use, you need to develop a strategic framework to determine potential data security risks and applicable compliance requirements, working with our professional consultants and a solution like Calumo can help you address these points.

4 Data Cleansing Tactics

Keeping your data cleansed isn’t a one-time fix. It’s an ongoing effort that requires strategy, consistency, and company-wide commitment. Without proper processes in place, errors, duplicates, and inconsistencies will continue to creep in.

The good news? With the right approach, you can create a data culture that prioritizes accuracy and efficiency. Here are four essential processes with steps to help maintain high-quality data across your organization.

1. Shift Organizational Data Culture Through Clear Communication and Leadership Buy-In

The most effective way to ensure clean data is to make it a priority across your business. Leadership buy-in is essential.

Clearly communicating why clean data is important to all staff will help shift your organization’s data culture. Make ‘data management’ a recurring agenda item in weekly meetings or stand-ups, encouraging staff to raise issues or concerns.

Arrange meetings with data collectors to discuss best practices and provide regular updates on any process or system changes.

2. Provide User Training and Education

Providing training and education not only encourages system adoption among staff, it also ensures complete and accurate data entry from the outset.

Training should include practical skills for how and what data needs to be entered, but also information on what constitutes clean data.

Regular, periodic training of all staff, not just new staff, eliminates any bad data habits, like corner-cutting, that may have been learned. Here, you can also teach best practices, such as updating any incorrectly formatted data and checking for existing data prior to creating new entries.

3. Configure your Database or System

Limit potential errors by configuring your database or system to only accept data that is the required type and format. You can do this using the following methods where appropriate:

Set up mandatory data fields to ensure all critical information is complete and accurate.
Create a drop-down with set entries so users can’t enter irrelevant content.
If you know that a field requires a certain amount or type of characters, limit the field size to the type and maximum number of characters, so users can’t enter additional information.
If multiple users access your data, keep it secure by providing access rights for users appropriate to their role.

4. Assign a Data Champion

Assigning a dedicated person to administer your processes will help to maintain data consistency and make your database easier to manage. As part of their role, the data champion not only monitors and cleans system data, but improves data collection processes.

Your data champion can help empower staff to adopt best practices and ensure leadership buy-in.

Keep your data clean by upgrading your systems to a robust CPM solution, like Calumo, to support your data collection and analysis processes.

Which Comes First, Data Cleanse or CPM solution?

A question we hear every day from our prospective customers is: “Is it worth implementing a CPM system if our database is not in good shape and needs a cleanse?”

Anyone who has undertaken a data cleanse in preparation for a database migration knows that it can be complicated, time-consuming, and expensive–no small task. It needs to be thorough and process-driven, or you will just end up back where you started.

Your team needs time to understand trends, isolate errors, control data entry points, cleanse the data, redevelop strong processes, and then test and repeat. What you see as a sequential process is, in fact, an iterative, endless process. But please don’t let that deter you, we’re here to help.

What many people don’t know is that the right CPM solution can significantly expedite your data cleanse process. This is especially true in the early stages, where oversight of your current data set can really help your team understand trends and isolate errors. This early insight is essential for long-term success.

Data Cleansing Steps and How CPM Can Help

A CPM solution can help at different stages of your data cleanse process.

Step 1: Inspection

At this stage, data is inspected to detect unexpected, incorrect, missing, and inconsistent data. A CPM solution brings together disparate data into a single platform, providing a single source of truth. Your team can inspect the full data set more easily, which makes it easier to identify trends, inconsistencies, and missing data.

Data Profiling

A statistical summary of your data helps assess its quality. Not only does the process of implementing a CPM solution help with data profiling, CPM solutions can also identify, flag, and report outliers and errors.

Visualizations

Visualizations present data in a way that makes it easy to understand by everyone, not just finance experts. Interactive reports, dashboards, and other tools allow your team to analyze data quickly to find values that are unexpected and thus erroneous.

Step 2: Cleaning

Cleaning data comes next and involves fixing or removing the anomalies discovered. A CPM solution can automate and standardize this process. By reducing manual manipulation of data, you also reduce the likelihood of errors. Configuring your system and standardizing data entry fields can prevent duplicate data and inconsistent formatting, which can all affect the quality of your data.

A CPM solution ensures that any changes made to your data are reflected across the board, so everyone is working from the most up-to-date information. Real-time data entry also helps keep your data clean and up to date. Built-in Calumo features, like writeback, also allow you to update source systems, so that data is clean everywhere.

Step 3: Verifying

The resulting data needs to be inspected to verify correctness. A CPM solution allows you to publish reports and distribute information so that data can be analyzed and verified by your team.

Features like drill-through and drill-to-transactions make it easier for your team to verify data and investigate anomalies.

Step 4: Reporting

Using CPM tools, you can track and report on changes made to your data. Calumo features, including embedded commentary, allow system users to explain changes, anomalies, or their investigation findings within the one platform. You can also set automated reports to highlight missing data, which ensures that data is updated and increases the accountability of your teams.

Make Data Cleansing a Priority, Not a Problem

Let’s be honest—data cleansing isn’t the most exciting task, but it’s one of the most important. Without clean, accurate data, decision-making becomes a guessing game, efficiency takes a hit, and costly mistakes pile up. But keeping your data in check doesn’t have to be overwhelming. By making data quality a priority, providing the right training, optimizing your systems, and designating a data champion, you can build a smarter, more reliable data strategy.

And you don’t have to do it alone. A powerful CPM platform like Calumo takes the heavy lifting out of data cleansing, helping you automate key processes, maintain accuracy, and keep your business running smoothly.

Ready to stop fighting with messy data? Let’s chat—our data specialists are here to help.

Additional sources

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