Since data is being produced in abundant quantities, firms are not missing even a single opportunity to make the most of their data. But you can never be successful with your data management strategy if you don’t focus on data quality.
Before data science, data quality was related only to the reports sent to external or internal clients. Nowadays, modern technological solutions like machine learning demand much training data, and analytics are always hungry for datasets that can add value. Therefore firms are adopting new ways of adopting datasets or data sources.
But the primary goal of a company should be to create an effective pipeline that allows the firm to build and sustain better data quality right from the beginning. This means that data quality cannot be improved just by isolating the issues and fixing them. Instead, all the firms should always focus on producing good quality data in the first place.
This blog post is intended to make you aware of the different methods used by companies for ensuring and sustaining data quality.
Control on incoming data and rigorous data profiling
In a vast majority of cases, it is the data receiving that becomes the root cause of bad data. Most companies source the data from locations that are not in the control of the department or the company. The data can be collected from other firms or obtained from a third-party software solution. Therefore, since data quality cannot be ensured in the first place, every company needs to have a rigorous data quality control of all the incoming data.
In such cases, a good data profiling tool can come in handy. Such tools can be used to analyze the data patterns and formats, data value distributions, completeness of data, and even the consistency of data on every record. But you must ensure that both data profiling and data quality alert systems have been automated as it helps maintain consistency and encourages better data quality checks.
Designing the data pipeline with precision
In the world of data, duplicate data means a set of data has been sourced from the exact location, using the same logic by using different people. This is usually done for a wide array of downstream purposes. But when duplicate data is created and used for any analysis or study, it is likely to go out of sync, leading to different results.
If you want to avoid this, then you have to focus on creating an impeccable data pipeline. The data pipeline needs to be clear in business rules, data modeling, data assets, and even architecture. In addition to this, you also need to promote effective communication and enforce proper data sharing in the entire organization.Incorrect data flowing can impact negatively. Some companies provide services such as an integration platform and data comparison, which works best to identify data differences quickly.
Maintaining accuracy when it comes to data requirements.
One of the most crucial aspects of data quality is to fulfill all the requirements and deliver the data to the users and clients for what the data is intended for. But this is not going to be as simple as it sounds. Properly presenting the data is not that easy, and even the requirement should capture all data scenarios and conditions. Moreover, you have to focus on clear documentation of all the requirements.
Here, the business analyst is going to play a vital role in requirement gathering. The different skill sets of a business analyst, like understanding the current system and familiarity with the client, help them speak both sides of language.
Working on data quality control teams
Two types of teams will help you with data quality checks, and you should always work on forming these two teams.
Whenever there is any change in the software and program, you can rely on the quality assurance team. This team will keep a tab on the quality of the data in the most precise manner possible. This team will perform rigorous change management, and it is essential for all those organizations that go through transformational change.
Production quality control
Based on the type of organization, this team doesn’t need to be a separate team by itself. In many cases, it can simply be a function of the Quality Assurance or Business Analyst team. But make sure that this team is entirely familiar with the business rules and requirements.
If you want to embrace the data-driven era, then you can never overlook data quality. Spending time, effort, and money on poor quality data will not give any results, and it might push you in the wrong direction as well. Therefore, use the tips mentioned in this blog post to maintain better data quality through better quality checks.
Mt. Airy Technologies, Inc. is one such company that provides an Integration platform and data quality check technologies.