This time of year, a lot of us are already considering what we want to “spring clean” at home to create a new, fresh start. When it comes to your organization’s data, you shouldn’t be thinking any differently! Clean data is one of the most useful resources for a business. Data can provide powerful insights into consumer behavior and help improve analytics, strategies, and goals for virtually every department, from marketing to customer support. On the contrary, “dirty” data creates many challenges for teams and as well as result in millions of lost revenue for the company.
If your organization doesn’t take the time to clean data or ensure data quality now, no one will see the benefits of this valuable consumer information or use it to their advantage. Thankfully, there is software available that makes collecting clean data easier than ever. But before you start implementing new tools, it’s a good idea to “spring clean” data currently in the system. Data clean up not only will improve the quality of the data you already have, but will provide you with a framework for keeping new data clean as it’s collected in the future.
In this blog, you’ll learn about the importance of data cleansing with a checklist of six essential steps to ensure your organization is collecting high-quality, clean data.
What is data clean up?
Data clean up, or data cleaning, is the process of removing or replacing incomplete, duplicate, irrelevant, or corrupted data from a database or CRM. In other words, you’re essentially “tidying up” or spring cleaning “dirty” data to ensure this information is accurate and consistent. Data cleaning may seem like a daunting task at first, but it’s an essential process if your organization wants to leverage the important information you’ve collected.
Establishing a proper data cleansing routine can save your organization significant time and money, reduce security and compliance risks, and help your team be more efficient and productive. Maintaining clean data means your organization will always have the right data for analysis and decision making, no matter how much is collected or from what source.
How to complete a data clean up
Understanding why your organization needs certain data is crucial for understanding what information you actually need and how best to ask for it. Before your data cleanse, consider these questions:
- Is my organization currently collecting the right data for our goals?
- Are we collecting the least amount of data we need to meet these goals?
- Could our teams be more strategic in how we’re asking for data?
Once you have established an understanding of what clean data means for your organization, you can move forward with a data cleanse.
Step 1: Eliminate duplicate data
Duplicate data makes it difficult to accurately analyze information, causes confusion and bottlenecks, and creates wasted space within your database. Collecting large amounts of data from various sources is often the cause of duplications. Without routine cleaning or standardization of these processes, you’ll end up with multiple copies or siloed variations of the same information.
Deduplicating data is essential to ensure that your team is able to properly analyze this information and maintain high-quality reporting. Remove instances of duplicate data first, then make a plan for streamlined data collection processes in the future. These processes should include standardizing form fields and objects to ensure data entering your CRM is correct and high-quality.
Step 2: Repair incorrect data
Structural data errors include misspellings, inconsistent naming conventions, incorrect word use, capitalization errors, and more. While these errors may seem obvious to humans, databases or applications won’t be able to decipher these errors and will skew results. This can cause several challenges with data if such fields like dates, address, or phone numbers have no standardization.
To keep your data clean, you must correct all issues and inconsistencies in data sets. Making these repairs ensures that the information in your CRM is accurate and usable, whether for prospecting, sending direct mail, providing customer support, and more. Once this incorrect data is repaired, continue to standardize these data types, including using validation to ensure that data is correctly entered at the source.
Step 3: Handle missing data
Missing data is a problem for several reasons, including distorting analysis, hindering communication, and loss of quality insights. This can occur when a customer fails to input data into a field that should have been required or validated but was not (such as an email address). This loss of information can skew reporting, but it also presents an opportunity to discover patterns in this missing data.
Before deleting or fixing missing data, first analyze the field(s) to determine patterns. If the same survey question or contact field continues to remain blank, it’s possible these fields are not necessary or need to be updated. When this isn’t the case, determine if the missing fields should be deleted or manually filled with a predictable response. However, it’s important that you try to retain as much of the dataset as possible to maintain reporting accuracy.
Step 4: Remove irrelevant data
Irrelevant data is any information that is not necessary to meet your goals or does not assist in the problem you’re trying to solve. If a form or survey includes fields or questions that don’t make sense for the overall purpose, users may avoid answering them or provide incorrect information. Missing or incorrect data will further skew results and hinder proper analysis.
First, determine the purpose of collecting data. Then, reevaluate your form to make sure you are only asking for the information you absolutely need to meet this goal. All other fields can be eliminated to avoid confusion or data collection overload. Only asking for the information you need not only provides a better experience for customers, but it also ensures that you receive clear answers that are easy to analyze.
Step 5: Filter outlying data
Outliers in data are pieces of information that fall significantly outside the normal range. Though this extreme data is generally less common, it still has the potential to negatively affect reporting and analysis. Such an extreme may be due to incorrect data entry or unintentional mistakes by the customer. It may be tempting to ignore or remove outlying data, but it’s good practice to analyze the information first.
The presence of outliers in data sometimes provides valuable information, so be sure to first analyze the information for patterns or trends. You may find a niche or new area of focus that your organization can benefit from. If a data outlier is simply the result of input error, this data can be removed. Instead, you can modify the data collection process to help avoid this mistake in the future.
Step 6: Validate and QA data
The final step of the data cleansing process is validation, which double checks that the previous steps are complete and no duplication or errors remain. This ensures that the data is clean and high-quality, with the right standardization in place to keep data collection clean in the future.
To validate and QA your data, first be sure that the data makes sense and follows all standardization rules (such as capitalization, abbreviations, spellings, etc.) you have set in place. If the current data passes these validations, it should have the correct structure and consistency. Clean data is then ready for analysis and reporting and will provide the correct results your organization needs to make better decisions.
Start building better data clean up habits
Data cleansing is a necessary step in ensuring your organization has the right information to make strategic decisions, achieve aggressive goals, and ultimately increase revenue. A data cleanse should not be a one-time process, however. It’s important to conduct routine cleaning and data checks to keep data as high-quality and accurate as possible. Establishing standards with your organization for data collection are great ways to start building better data cleaning habits.
Using an all-in-one data collection platform like FormAssembly eliminates manual data entry processes and improves data quality from the source (web forms). With advanced Salesforce integration, FormAssembly makes it easy to send data to Salesforce to create or update records for any standard or custom object. Prefill capabilities also ensure that any data collected through FormAssembly forms is accurate or can be updated by a customer and synced back to Salesforce dynamically.
With FormAssembly, your organization not only improves the efficiency of data collection processes, but creates a standard for collecting clean, high-quality data from the beginning. Curious to learn how much time, money, and resources your organization could save with FormAssembly? Calculate your potential cost savings with FormAssembly’s ROI Calculator at the link below.