Effective data cleansing is crucial for your business’ success! You must be on top of your data and ensure that it is accurate and up to date so that your business can make good decisions. If you don’t have good data hygiene, the insights you receive from that data will be flawed as well, leading to poor decision-making.
What is Data Cleansing?
Data cleansing is the process of fixing and removing incorrectly formatted, incomplete, inconsistent, inaccurate, and/or duplicate data from a dataset. This could be done simply to conduct proper data analysis and improve decision-making within your organization. Many companies regularly cleanse their data to improve their business operations. Data cleansing may also be done however, as a step within the software implementation process to prepare your data for the new system that your company is putting in place, such as an ERP system for example. Data cleansing is ultimately a vital step when conducting a data migration and cannot be ignored.
Garbage In, Garbage Out (GIGO)
The concept “garbage in, garbage out” refers to the fact that bad input will always result in bad output. This term originated in the field of computer science, referring to the fact that the results of a computer program are only as good as the data that the program is fed. If the input data is garbage, then the output data will be garbage as well no matter how good the computer program is. When it comes to data analysis, it doesn’t matter how good your reporting and analytics are if the data you are analyzing is flawed.
How is Data stored?
There are many ways that your business’ data can be stored depending on the size of your business and the systems you have in place. If you are a small business, you may store your data manually within spreadsheets either stored physically within filing cabinets or electronically on Excel documents. Otherwise, you might have multiple places where you store your data using a combination of software like QuickBooks, spreadsheets, and an inventory management system (etc). Then, if you are a growing business, you would use an all-in-one ERP system that helps you centralize all your data onto a single system.
Different Types of Data Errors
Outdated or Obsolete Data
This is data that’s not up-to-date or relevant. This could be information that is no longer in use and has been replaced with new information.
This is the absence of values within a data set. Missing data will disrupt your data analysis as you won’t have a complete set of data to work with.
This is when two or more identical records of data exist. Duplicate data makes it difficult to determine which record is correct and leads to inaccurate insights.
Inaccurate data inputs are probably the most common type of data error. Inaccurate data occurs even with small mistakes and typos or putting decimals in the wrong place for example.
Incorrect Data Formatting
When you enter the correct information in the wrong column or field, you have what’s known as a formatting error. These types of errors are especially difficult for your staff or systems to catch.
Characteristics of Good Quality Data
Reliability is a term used when you have multiple sources of information. You will know that data is unreliable if another source contradicts it. For example, if a shipment arrived on January 20th in one data source but arrived on January 28th in another data source, it should be deemed unreliable and should not be trusted.
Good quality data must be up to date. You need to update your data frequently, and preferably in real-time for your business to be able to make relevant decisions. On the other hand, out-of-date information will lead to your company making the wrong decisions.
High-quality data is complete, in the sense that it allows you to find all the information that you are looking for. Say for instance you are looking at when a shipment occurred and find data about the name of the customer and the month of delivery, but you do not have information about the exact date. In this case, your data would be incomplete as you don’t have all the information that you need.
Benefits of Data Cleansing
By having clean data, your business will be able to make informed decisions. Your reporting and analytics are only as good as the data that's used, which is why quality data is critical for good decision-making.
Data Integration (Streamlined Business Processes)
Cleansing your data throughout your system(s) makes it easier to integrate data from various sources. It’s much easier to ensure consistency between your inventory, accounting, and customer data and connect these processes together when you have clean data.
In industries with regulatory requirements, having clean data is critical to ensuring compliance, such as the pharmaceutical industry for example. Cleansing data helps to ensure that your company is meeting industry standards and improves your compliance reporting.
Although data is crucial for reporting, it’s also important for your day-to-day operations. If your warehouse team has bad operational data, they will make mistakes as it pertains to warehouse processes like receiving, picking, packing, shipping, inventory management (etc).
Cleansing data can ensure there are no overlaps or duplicates. Ensuring that each data entry is a unique observation will prevent your analysis from being skewed.
Data Cleansing Best Practices
Identify How Data Cleansing Will Help Business
Before embarking on a data cleansing project, it’s important to identify the business objectives of the project and have a clear purpose in mind. Pick specific areas of your business that will benefit the most from data cleansing. For example, you might predict that cleansing your data will help you to improve inventory management reporting to stock the right amount of inventory and improve customer satisfaction. Having a clear picture of business objectives will motivate you to carry out the project to completion and to come up with a routine for data cleansing.
This is the process of checking your data to ensure that it is valid. You will check the data that has come into your systems and measure it against the rules and criteria that you’ve established. This should not be a manual process, but your team will instead have to prepare a script to check whether your dataset agrees with the rules you come up with. This will help you find out which data entries don’t align with your established rules and standards. During the data validation process, you might perform the following checks:
Establish specific formatting rules when it comes to data entry. For example, you might establish a rule to store dates in “DD-MM-YYYY” format and will have to check that all dates are in this format instead of “YYYY-MM-DD”.
Your company could use codes to keep track of products, suppliers, customers (etc). Ensure that all codes are valid and properly formatted by creating rules based on the characteristics of valid codes.
Writing scripts to check the data entries make logical sense is something that should be done to check for errors. If you are a company that buys and sells products, for example, the date that you ship a product must be later than the date that you receive it into your warehouse.
You will want to check fields to ensure that they have the correct characters based on the rules you define. For example, you might require all fields with names to not contain symbols or numbers.
Checking for uniqueness ensures that the same data is not entered into the field multiple times and will help you eliminate duplicate records.
Outliers will skew your results and give you an incorrect understanding of your data. An important part of the data cleansing process is identifying outliers and removing them from your analysis.
Regularly Maintain Data
You need to cleanse your data regularly, depending on the size of your company. Whereas bigger companies should cleanse data 4-5 times a year, smaller businesses can get away with doing it on an annual basis. This is important to ensure that all the data entered your system within a year is clean, and to adhere to changing regulations.
Fix Data at The Source
Fixing data at the source is important when dealing with data that contains errors related to duplication, incompleteness, inconsistency (etc). Correcting data at the source will help you figure out why errors are occurring to determine how to fix them. You should then use that information to create a standardized procedure to ensure only high-quality data enters your system moving forward.
Create Data Cleaning Documentation
Documenting your data cleansing efforts is important for future reference and for employees, stakeholders, and data experts within your organization to understand the process. During future data cleansing projects, your company will now be able to refer to previous documentation to either replicate those actions or use that information to reinforce decisions. Within the document, you should record objectives, steps, and challenges and explain your thinking so that it’s easy to understand. Provide evidence, examples, and visual aids to improve the quality of your documentation.
Fill in Missing Data
There may very well be required fields within your system that are missing such as an email, company name, first name (etc). During a cleanse, it’s important to go through records to find where data is missing and fill it in. Finding this information might prove difficult but is possible with the help of third parties.