In the rise of the information age, the massive proliferation of data has made data cleansing an increasingly difficult challenge. This is mainly because people and companies are producing more and more data, which causes the volume of data to be processed to grow exponentially.
Data alone offers no benefit, so everything we can do to leverage it must start with a data strategy that gives data cleansing the leading role it deserves.
Why is it important?
The importance of data cleansing lies in the role of data as raw material in business processes today. Poor-quality data can cause many errors in a company, resulting in wasted time, money, and other resources.
As companies become increasingly dependent on data and the volume of information being generated grows exponentially, the consequences of data errors can be catastrophic. That is why company data must maintain the necessary quality to work as a reliable and solid starting point for today’s businesses.
What is data cleansing?
Data cleansing is identifying erroneous or inaccurate data to modify or delete it. The criteria for defining what erroneous or inaccurate data should be detailed in the company’s data management strategy. This ensures a standardized and consistent process.
Modern technology offers multiple options for managing and, in many cases, automating data cleansing. However, the initial definitions for determining data quality standards remain crucial to ensure that the chosen technological solution functions smoothly.
In other words, having a well-established data cleansing process guarantees quality. It’s worth noting that data cleansing is a continuous process, as errors can occur each time data is created, transformed, or processed.
With a well-implemented ongoing process, data will have the required quality across every area of the company that uses it.
What are the most common errors?
Many types of errors can occur when working with data. Here are some of the most common and their implications:
- Obsolete data: Data that, due to age or nature, no longer provides any benefit to the company. As a result, it unnecessarily consumes storage resources and increases the likelihood of compromising the integrity and reliability of the entire data set.
- Duplicate data: Data that appears more than once in data warehouses, without serving a backup function. These are often the result of disorganized data management or incomplete changes in information architecture.
- Inaccurate data: Data that, from the beginning, is incomplete, contains errors, or is inconsistent. Failing to correct this data compromises the integrity and reliability of the entire data set.
How to carry out effective data cleansing
From this point forward, we’ll share key points you should keep in mind to ensure that data meets the necessary standards:
- Implement strategies at the source
Many poor-quality data points originate from human error, particularly when data entry is handled by people, such as on a web form.
An effective strategy to reduce errors is to apply validation rules on input fields. These may include character limits, numeric vs. alphanumeric formats, etc. This will reduce the chances of low-quality data entering the system.
2. Monitor data across the entire digital ecosystem
Even with source-level strategies in place, some data may still lack the necessary quality, as data may be altered every time it is processed or transformed. For this reason, it is crucial to create strategies throughout the data lifecycle to preserve data quality.
3. Cleanse the data
Since data has a lifecycle, we must define how long we need to store it and what resources to assign. If data becomes obsolete, it is crucial to have a plan, like managing solid waste so it doesn’t pile up in our homes or businesses.
With cleansed data, you can maintain a precise data ecosystem with optimal performance, as it won’t be bogged down with outdated information.
Who is responsible for data quality?
Everyone in the organization shares responsibility for maintaining data quality, from the person entering the data into the system to those managing its storage and distribution.
However, the data manager can help the organization define, lead, and coordinate data quality strategies and actions.
This person is primarily responsible for defining the types of data to be collected, identifying the tools to do so effectively, creating management policies, and reviewing them periodically.
The data manager must also oversee the training of team members so they follow the defined guidelines accordingly. This helps reduce human errors, maximizes the use of selected tools, and boosts organizational effectiveness.
If you’re a data manager or in a related role, we recommend exploring DataGate Orchestration Platform, a platform designed to manage company data in a centralized and efficient way.
In conclusion, data cleansing is a cross-functional, continuous process that requires clear definitions from company leadership and the involvement of all team members to deliver results that enhance business competitiveness.