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Quality of data matters the most in every context of business operation at present. High-quality data means more value output, whereas low-quality data simply indicates business loss. Earlier, businesses used to invest in manual data quality enhancement programs to retrieve the best output. But now, manual checking of each datatype is challenging and time-consuming. So, data quality automation has taken over the place.
Automating data quality operations (it includes many things) can save time and budget. Let this blog help you understand all the processes to simplify data quality automation. First, start with the concept!
Meaning of Data Quality Automation
Data quality automation simply indicates using technology and tools to automate data quality enhancement functions. Basically, it automates the functions of data cleaning, validation, and enrichment. Therefore, it pushes accurate, complete, and consistent data into the system.
Automating the functions of data quality saves time and costs. Everything is done via tools or API (Application Programming Interface). At present, big companies like Teradata, IBM, Databricks, etc, use data quality automation tools and techniques to maintain their data quality parameters.
Data Quality Automation Includes
Data quality improvement measures include various processes and sub-processes. In other words, you need to go through a lot of detailed processes (lengthy or medium) to keep the quality of your data intact. Let’s check here what is included in the data quality improvement strategy.
Data cleansing
Clean data is the ultimate need for maintaining your smooth operation. However, maintaining clean data is a challenge because data gets outdated over time and becomes junk. When it turns into junk, it becomes unusable. Thus, we have to chuck it out of the database and allow better (updated) data to enter the database.
Data cleansing is a process of removing junk data from the database. When automation takes place, every single dataset gets relocated to a dedicated place after a set date for cleansing.
Data validation
Every single data your team collected from messy databases is not usable. It needs further validation. Simply means, you have to validate each datapoint to check whether it is representing a true value or not.
Data profiling
Opening up every dataset every single time when you need some specific data is not possible. A summary of each dataset can help skim through the data points to reach your intended dataset. Data profiling is more like an index of the database.
Data deduplication
One data can appear multiple times in the database due to a mistake or any other reason. To remove the extra data, you need to locate the other data and then delete it. Data deduplication helps remove the additional data from your database.
Data standardization
Maintaining data in the right format is essential, and you cannot mess with it. It’s obvious that you cannot store Excel data in PDF files until you know the data conversion processes. Keeping data in a standard format always works in favor.
Data enrichment
During data cleaning or deduplication, major datasets are getting removed due to poor quality. So, data enrichment fills the gaps with updated data. It fills up the vacuum space with quality data.
Why do you need Data Quality Automation?
Automaton means the involvement of machines in the work, so manual efforts can be channeled in the right way, not in mundane tasks. Data quality improvement strategies will get performed into the system automatically once you integrate data quality automation. It provides you with the following benefits;
Data reliability & accuracy increase
Automation can bring a constant flow into the system without interruption. On the other hand, the accuracy of the data will also increase. You can rely on your data if you set the right data quality automation parameters.
Develop business intelligence
With the incorporation of the right automation tools and strategy, you can build a business intelligence system. This system can ensure the data you use for making strategic decisions is clean and consistent, without any errors. Therefore, the decisions you make can bring excellence into your operations.
Better ROI
Instead of doing the mundane tasks of eliminating errors from the corrupt files manually, automation provides you with freedom. The return on investment is higher than double when you automate data quality matters.
No risks of poor data
There is nearly no chance of making mistakes with poor data. But that’s possible only when the automation process takes over everything beautifully. That’s also too rare; you need manual support at some point in case. With the help of human + automated data enrichment, you can fix the quality of your data to the optimal level. The chances of getting poor records are less in this case.
Best practices of data quality automation
Set clear goals and expectations
Bringing a data quality automation system into operation takes clear planning and an execution strategy. You need to set clear goals and expectations before you start anything. Parameters like consistency, accuracy, completeness, etc, matter here to set your goals.
Clean your data
To automate your data quality functions, first, you need strong support for clean and updated data. It required your data to go through all the processes like cleansing, standardization, enrichment, etc, before incorporating the automation. Bypassing all these essential steps can harm your automation journey at some points.
Establish data governance policies
Data governance plays a crucial role in data quality management. It brings accountability and responsibility into the data quality system. Make sure you follow all the data standards and data stewardship practices when you implement data quality automation into your system.
Fix your tools or tech stack
Ultimately, you have to decide which tools you are going to use for this automation integration project. You should always go with the needs of your organization because that is your top priority here. Factors like scalability, user-friendliness,and ease of integration are the matters you need to focus more on.
