6 Steps to Ensure and Sustain Data Quality

For any business, data quality issues are not a scope to deal with. Data is the most valuable asset that is going to use to manage business operations.

And for any functioning, the quality of data is a must. To process further with your raw data, you must be aware of the sources of data for better business analytics.

So, the trust generated for regulatory compliance initiatives focusing more to integrate and access data from various sources to solve data quality problems. A question arises is how to ensure data quality. Lets’ see major 6 steps to ensure the quality of the gathered data:

Step 1 — Definition

Business data quality makes a huge difference to achieve the predefined business definition. Data owners play a role in impacted business documentation and processes.

Step 2 — Assessment

Once the definition is defined, the collected data can be assessed for further processing. Assessing data against multiple dimensions asks for completeness of the data and related attributes along with the consistency of data sets, and more.

Step 3 — Analysis

Analysis can be done on multiple aspects. One section for analyzing is the gap between DQ business goals and current data. Root causes for inferior data quality is another data analysis, and it goes on.

Step 4 — Improvement

Any business process relays on improvement and here comes the design and development process to be improved based on the analysis. The plans include wireframe, resources, timeframes, and costs.

Step 5 — Implementation

Before implementing the solution, one should go with the comparison of both technical and any business process related changes. It helps to ensure the associated changes as per the plan.

Step 6 — Control

Data quality measures and verification are the two key aspects that should be checked and controlled at intervals of time for data consistency to achieve business goals.

Why data quality is important

If the quality of data is inadequate or lacks its importance, no customer is going to trust your services. To stand and compete in the market, data efficiency and quality matter a lot.

Data quality management frees up data teams to focus on processed data and to get productive output at the end of the day.

For example, data analysts and researchers can focus more time to process user data and can spend time helping business users to take advantage of the data quality for best business practices.

In a nutshell, data quality is a continuous process for every upcoming date. It’s not a one time project for the entire organization to achieve data-driven results. With an appropriate focus from the top, Data Quality Management can reap rich dividends to organizations.