Data Cleansing: What It Is & Why It Matters
Data plays a crucial role in today’s business environment. Without it, businesses would lack the tangible insights and knowledge needed to make informed business decisions, perform key operational functions, plan ahead for the future, and more.
But, simply having data on hand is not all that’s needed to aid successful operations. Businesses need to have access to quality, clean data that is comprehensive and reliable. This is easier said than done, which is why data cleansing has become so important for businesses today.
As you continue reading through this article, we will define what data cleansing is, expand on why it’s a crucial business function, and walk you through the data cleansing process so you can see how to apply it in your own business.
Understanding Data Cleansing
Data cleansing, also referred to as data cleaning or data scrubbing, is the process of taking incorrect, duplicate, and unstandardized data and making it accurate, uniform, and complete. There are a number of key steps that make up the data cleansing process, which we will describe in more detail below.
Dirty data is an issue for a number of reasons. Namely, it can become difficult to work with, and it can also provide you with inaccurate or inconsistent insights if the data is incomplete or incorrect. This can lead to incorrect customer records, faulty applications, and inaccurate outputs from applications and tools.
Overall, data cleansing is used by businesses in all industries to improve the quality of their databases or other data sets, and make them more reliable, accurate, and consistent for better decision-making. Plus, it plays an important role in a business’s data management system, helping them to prepare data sets for use in business intelligence and other applications.
Benefits of Data Cleansing
As one could assume, there is a wide variety of benefits that your business can take advantage of once you’ve implemented data cleansing into your processes. Here are a few of the main advantages you’ll find.
Better Data Reliability
Possibly the most important result of data cleansing is that you are left with data that is more reliable. You can leverage this data for more accuracy in your customer targeting, better marketing and sales efforts, more accurate forecasting, marketplace analysis, risk management, and more. With data you can trust, the types of insights you can pull from data sets become practically endless.
Improved Decision-Making & Performance
As we briefly mentioned above, having clean data at your disposal means you can gain more accurate business insights from it–giving you a competitive advantage in the marketplace. Knowing that you can rely on the data is one thing, then putting it to use to generate accurate insights is where the value of data cleansing really comes into play.
Thus, clean data supports enhanced decision-making and forecasting abilities, improving the business’s overall strategy and performance. In fact, using clean data has helped companies meaningfully improve the ROI of their marketing efforts and made their campaigns more effective.
Enhanced Operational Efficiency & Cost Savings
Every business is constantly looking for ways to become more efficient and tap into cost savings without compromising the quality of their operational performance. With data cleansing, you can take advantage of both.
Data cleansing helps businesses prevent erroneous and costly mistakes from occurring down the road. Saving the business both valuable time and money, the data cleansing process gives businesses accurate insights the first time, without making them re-do the process or correct mistakes produced by faulty data later on.
By taking the time to clean data once, data scientists and other professionals can turn their focus to other strategic tasks that move the needle forward for the business.
Stronger Compliance Measures
With consumer data protection and privacy laws evolving at a rapid pace today, data cleansing will help a business ensure they are not breaching consumer protection laws like the EU’s GDPR and California’s Consumer Privacy Act.
Data cleansing will help reveal whether you’re adhering to customer contact permissions, helping you avoid hefty fines if you’re unaware and continue sending emails to contacts who have opted out. Plus, you can catch any other possible infractions if you are storing customer data that you shouldn’t.
Key Steps of the Data Cleansing Process
With a better understanding of what data cleansing is and why it’s important, let’s walk through what the process looks like in further detail. Data cleansing is generally performed by data scientists, engineers, or data analysts; however, other business professionals may take part in some or all of these aspects as their company undergoes data cleansing measures.
1. Preliminary Assessment & Identification of Problems
To begin the data cleansing process, the data sets in question are assessed for quality and accuracy. At this point, data scientists can determine what the major issues are with the data, if they haven’t been identified already.
This will help the team create a strategy for cleansing the data, and help them understand the scope of their work and the amount of resources they will require to remedy the problem.
After this preliminary assessment, the team will get to work, walking through some or all of the following steps depending on the unique issues of the specific data set.
2. Resolving Missing/Incomplete Data Sets
One major issue that can arise in data sets is that there is missing or incomplete data. As a result, the data is not entirely reliable as is. This may not be the case for all dirty data sets, however, it tends to be a common issue.
Thus, data scientists will need to seek out the missing data points manually, using proprietary methods, or existing technology to complete the data set and fill in any holes that exist.
3. Eliminating Redundant/Duplicate Information
Another issue that makes data dirty is that there is duplicate or redundant information in the database. Through the data cleansing process, any duplicates are discovered and eliminated. The resulting data set is streamlined, accurate, and only contains the data points that the business deems valuable and useful.
4. Correcting Inconsistent Data
When data is collected over time and input into a database by different users, it’s easy to see how it could become inconsistently documented, labeled, and stored. Thus, standardizing data sets is a key part of the data cleansing process. When the data is more uniform, it becomes more workable and easier to draw insights from.
The resulting data set has a more predictable format and it becomes much easier to search for relevant data the team may need to reference in the future.
5. Verifying Data Accuracy
Lastly, a comprehensive data cleansing process should end with a final verification and validation of data accuracy before it is turned loose for the team’s use. Data quality specialists should do one final pass-through of the data to ensure it meets the company’s internal quality standards and formatting.
Overcoming Common Data Cleansing Challenges with Outsourcing
While necessary, the data cleansing process can get to be quite intricate and complex. In turn, the process can become very overwhelming for teams who are unprepared or unfamiliar with these frequent challenges.
If a company wants to take on the data cleansing process on their own, they should be prepared for:
- Dealing with large and complex datasets that have a multitude of issues (missing data, duplicate data, incorrect data, etc.)
- Managing data cleansing with limited resources and tools
- Taking the time to perform data cleansing on an ongoing and regular basis as they collect more data
Instead, many companies turn to outsourced partners for their data cleansing needs. Outsourced teams are often experts in the field and have the proper tools and resources to properly perform this process. These teams will work diligently to ensure you’re left with clean, reliable data that you can use for business intelligence purposes.
Plus, companies can save time and money by working with an outsourced team given the time-intensive and tedious nature of data cleansing. Many teams who try to take on the data cleansing process themselves quickly realize that their internal team does not have the capacity to deal with such a meaningful task.
Working with outsourced teams is cost-effective and much more affordable than expanding the internal team. So, companies that rely on outsourced partners for data cleansing end up with quality data sets, without making their profitability suffer for it.
Wrapping up Our Discussion on Data Cleansing–Outsource with Assivo
To be successful in today’s competitive marketplace, businesses need to have access to clean and reliable data to help them stay agile, make quick business decisions, and respond to changing market trends. Thus, data cleansing will only become a more pertinent and common practice as our world becomes increasingly digitized.
Given the intricate nature of the data cleansing process and how resource intensive it can be, working with outsourced data cleansing providers is the smart way to go. Specifically with Assivo, our clients benefit from our years of experience, the proprietary tools that help us work efficiently, and our custom-tailored model that fits your business’s unique needs.
Contact Assivo today to learn more about our data cleansing service and see why hundreds of companies have trusted us as their outsourcing partner.
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