Data cleansing and data enriching are key elements in data management, with profound effects on your company’s ability to make informed and strategic business decisions.
You’ve probably heard the terms data cleansing and data enriching many times. But what exactly do they mean, and how do they relate to each other?
In this article, we will examine the difference between the two concepts and why you need to be on top of both.
Table of Contents
- Data Cleansing vs Data Enriching – How Do They Differ?
- Why is Data Cleansing and Data Enriching Important?
- Data Cleansing: Making Sure Your Data Is Accurate
- Data Enriching: Making the Most of Your Data
- How Often Should You Clean and Enrich Your Data?
- Common Mistakes and Challenges
- In Conclusion
Data Cleansing vs Data Enriching – How Do They Differ?
So, what is the difference between data cleansing (or data cleaning) and data enriching (or data enrichment)?
The answer is quite intuitive. While data cleansing focuses on getting rid of inaccurate data and keeping everything updated, data enriching is all about enhancing your data in different ways, such as combining data from various sources.
Data cleansing is the process of ensuring the data you have is correct and of high quality.
Data enriching is the process of enhancing that data in different ways to make it more useful.
Why is Data Cleansing and Data Enriching Important?
A lack of data is very rarely a problem for companies today. In fact, most businesses are drowning in data, while not always knowing what to do with all of it.
Already in 2008, SiriusDecisions noted that despite the importance of data to the business, “the databases of B2B organizations are akin to an attic filled with contents that over time have not been properly labeled, managed and maintained.” They went on to point out that, unlike the stuff we keep in our attics, management of customer and prospect data can be what ends up either making or breaking your bottom line.
For data to be useful, it needs to be neat and tidy. Verified and optimized. But more often than not, companies struggle to keep it that way.
According to a survey by Experian, 95% percent of organizations state that their businesses suffer from poor data quality, resulting in wasted resources and additional costs. In the same survey, 69% of respondents claim that flawed data actually hinders them from providing an excellent customer experience.
While big data was once considered the arena of analysts and developers, today, it concerns everyone in your organization. Every department is collecting vast amounts of data daily, and you need to manage all of it. Marketing, sales, support and development are all depending on healthy, reliable data – for strategic decision-making and in their day-to-day operations.
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Data Cleansing: Making Sure Your Data Is Accurate
Data cleansing and data enriching are crucial steps in the process of Data Quality Assurance (DQA). As companies strive to develop and act on data-driven insights, they need to go through the motions of the Data Quality Assurance to avoid incorrect data leading to costly mistakes.
The process of data cleansing is often the first step, and as such, a very critical one. The purpose is to identify any gaps and discrepancies in the raw data so that you can discard all invalid data points.
We can use customer data as an example:
Let’s say you have an email list, compiled through digital marketing campaigns. In this instance, data cleansing would mean removing all the weird, fake email addresses from your list, and erase any duplicates. When you have identified and removed all redundancies and inaccuracies, you’ll be ready to move on to the next step: data enriching.
Related Reading: Top 10 Data Cleansing Tools
Data Enriching: Making the Most of Your Data
Once you know that your raw data is accurate, it’s time to start putting it to use. Data enriching, or data enrichment, refers to the process of augmenting your raw data to make it more useful. You can do this in a number of ways. One of the most basic and common ways is by combining data from different sources.
If we continue using our example with the email list, the next step after cleaning your list would be to enrich it. This is something you can do, for instance, with data from your CRM system, or perhaps with data purchased from a third party supplier. Enriching email addresses with data on things like role, industry and full name will make your email list more valuable.
With every new layer of information you add, your data becomes a better representation of the reality you’re aiming to represent and analyze.
There are several processes for data enrichment, using a range of different tools depending on the goal and the use case. A data enrichment process can, for example, make use of an algorithm to correct spelling mistakes or typographical errors in a database. Data enrichment can also be a matter of simply adding information from different sources to a unified data table. Other methodologies for data enrichment include extrapolating data and using fuzzy logic to get the most out of a given raw data set. These are all variations of data enrichment.
How Often Should You Clean and Enrich Your Data?
Data is not unlike fresh produce; it doesn’t age very well. If you consider the fact that every year up to 18% of all telephone numbers change, up to 21% of CEOs shift, and up to 60% of employees get new roles within their organizations, it becomes evident that Data Quality Assurance needs to be an ongoing process.
Data cleaning and data enriching is not something you can do once and then it's finished. Ideally, you automate these processes so that they take place continually, preferably in real-time.
Common Mistakes and Challenges
Staying on top of the ever-increasing amounts of data can be challenging. Here are three things to keep in mind as you strive to optimize your data management:
Don’t Keep Your Data in Silos
A common mistake is to let data stay stuck and siloed in different databases. The marketing department have their marketing automation system; sales have their CRM, and customer support has yet another system. If these systems are not integrated, it hinders everyone in the company from seeing the full picture. Bringing this information together, thereby creating a 360-degree view of the customer and situation, usually results in vast improvements. Research indicates that companies choosing to integrate their databases across departments enjoy conversion rate increases of up to 12.5%.
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Don’t Hang On To Data You Don’t Need
There is often a tendency never to get rid of data after going through the effort to collect it. From a compliance perspective, however, this can be problematic. By removing data that is obsolete (for example people who have unsubscribed to your email list), you keep your company compliant with regulations such as the GDPR that came into effect in May 2018.
Keep Cleaning and Enriching Your Data Over Time
Research indicates that B2B data deteriorate and decay at a rate of 70% per year, which means that if you don’t continuously clean and update your data, you risk setting your sales and marketing team up for failure in 7 out of 10 interactions. Just-in-time data cleansing refers to when companies clean and review their data on a project or campaign basis. This can be an effective way to ensure all data is up to date and accurate.
- Data cleansing is the process of ensuring the data is accurate and of high quality, while data enriching is about enhancing the data in different ways to make it more useful.
- While data cleansing is about removing data that is obsolete, wrong, or redundant, data enriching is about adding data points from other sources to create a full picture.
- Data cleansing needs to take place first, before the process of data enrichment it started.
- The result of data cleaning is updated with reliable raw data. The result of data enriching is data augmented with additional layers of information.
- Both data cleansing and data enriching need to be done continuously, to keep your data clean and as useful as possible.
In this article, we’ve discussed the difference between data cleansing and data enriching. Both concepts are vital parts of data management and data quality assurance, helping to sanitize and fact-check all your data as it moves down the data pipeline.
Effective and continuous data cleansing and data enriching are prerequisites of meaningful analytics and data-driven business decisions.
The Xplenty data integration platform enables you to cleanse and enrich your data, transforming it to the required target format. The platform allows for repeatable, transparent data pipelines that you can run in-house with ease – without sacrificing data integrity or data quality. The data is reliable, easy to access, and transformable into insights you can use to run your business.
Xplenty’s scalable platform allows you to quickly and easily benefit from the opportunities offered by big data without having to invest in hardware, software, or related personnel.
Schedule a demo with Xplenty and learn how we can help you manage your data cleansing and data enriching processes.