Data helps brands understand their customers, communicate more effectively, and drive new business opportunities. Now, companies are exploring how to democratize their data and align their organizations around how they innovate, manage, and activate on customer insights.
But, none of that is possible without clean data to power your systems.
In order to successfully deploy a customer data platform (CDP), you must have clean data to feed into it. Clean data is also needed to train artificial intelligence to ensure algorithms are delivering insights securely and accurately. In turn, data cleansing should be viewed as a primary goal during the CDP implementation process.
What is Data Cleansing?
Data cleansing, also known as data scrubbing or data cleaning, is the process of fixing or removing incorrect, incomplete, duplicate, corrupted, or poorly formatted data in a data set. The process involves identifying data errors, and then fixing or deleting that data. Data cleansing is a subset of an organization’s overall data management strategy and process.
Data cleansing may be necessary for a variety of reasons. One of the most common is for resolving issues that arise during the data consolidation process. When data is integrated across sources or systems, information can be sometimes be duplicated, mislabeled, or corrupted. In short, your data can be dirty.
The problem with dirty data is if it is fed into other systems it’s going to result in skewed and inaccurate results. These inaccuracies can undermine trust in the analytics companies are using to make data-driven decisions.
It can also impact the bottom line. According to Treasure Data research, poor-quality data can result in inaccurate targeting, reduced productivity, and wasted marketing spend.
Understanding Data Cleansing
Having data scattered across multiple silos is one of the largest challenges for data cleansing. Data needs to be centralized into a database like a CDP or other data management solution so appropriate data management standards can be applied. For larger companies, or companies dealing with huge data sets, data can often be a mix of structured, unstructured, and semi-structured data, making integration a bit challenging.
What are the Attributes of Clean Data?
There are a variety of standard clean data attributes that organizations use to track and measure data hygiene. The ones you decide to focus on will depend on your particular business, strategy, customers, and industry. Some clean data attributes include:
- Accuracy: How close your data is to its true value.
- Completeness: How complete your data set is.
- Consistency: How consistent your data is across your data sets.
- Uniformity: How uniform your data is against common measurements.
- Validity: How your data conforms to rules.
How to Clean Your Data
There are many different techniques and technologies deployed for data cleansing. What mix you use will be germane to what types of data you have and need to manage. Here are a few basic steps to ensure data hygiene is consistently applied and validated.
- Inspection and Auditing. Organizations must understand the data they have, and what shape it is in before using it
- Cleaning. De-duping your data sets is one of the first steps to getting your data clean. Fixing structural errors and scrubbing irrelevant data points is part of this step as well. Any missing data also needs to be identified and added.
- Validation/QA. The data needs to be inspected after the cleaning process to ensure the data conforms to data governance standards.
Keep Your Data Clean
Keeping your data clean is a requirement for modern marketing. It may not seem sexy at first, but when you consider all the systems that clean data feeds, it should be top of mind for forward-looking CIOs, CDOs, and CMOs.
Dirty data will lead to redundancy and wasted effort and resources, inefficient operations, and poor decision making. And, don’t expect to deploy advanced marketing technology solutions effectively without having your data cleansed and scrubbed first – doing so would just be a disservice.