7 Tips for Improving Data Quality Management Today

Essentially, data quality is a measurement of the data’s ability to perform the function bestowed to it by its native organization. There are a series of factors that can determine whether or not the data is ready to perform its duty. Some of these components include its relevance and recency, to its overall completeness and accuracy.

What is Data Quality Management?

Managing the quality of data is a process with multiple steps to ensure that the data being used by the organization is feasible for proper use. There are many important aspects of the management process to ensure high quality data. This includes cleansing the data, utilizing quality improvement methods, setting performance targets and defining rules by which the data is to be used. Many organizations today are looking for ways to improve their management of data, and there are concepts that can be used to help facilitate that process.

Here are seven tips for improving the management of the quality of data today.

1. Prevent Duplicate Records

One of the most expensive problems that any organization can face when it comes to data is duplicate records. These can be seen commonly in email addresses, but regardless of where they are found, it would be wise to delete them if they are discovered. If this problem persists, the duplicate record can take on a life of its own and be conflated with data that is relevant.

2. Ensure Sales Team is Aware of Data

The sales team plays an invaluable role in any business. They are the ones that are responsible for updating information and creating certain leads. However, they also need to be trained in ensuring that the data they enter is consistent and accurate. People on the sales team may oftentimes enter incomplete data due to a misunderstanding or gap in communication in regards to its use.

3. Normalize The Data

Normalizing the data will help create a singular standard by which the data can be judged. Data often comes from multiple sources which can cause potential discrepancies in its interpretation. A data standardization system should be set up via a smart campaign or an external data normalization tool.

4. Cease CRM Sync Fails

Sync failures can be pointed to a litany of issues, and this can cause new information to be potentially lost. This can include data field updates and other pertinent information to business operations. These problems in particular can be very costly to fix. Alleviate these problems by setting up a smart campaign via your support engineer.

5. Build a Data Quality Firewall

Building a firewall is very important to protecting the quality of data as well. A firewall utilizes specialized software in order to keep the data free from errors. Since data is an asset both practically and financially, safeguards must be put in place to protect it at all costs.

6. Develop Metadata Management

Having a metadata management process is also important because the sources of data can have a variety of sources. In this situation, other organizations may misinterpret certain parts of the data because of its ambiguous background. Centralizing management is crucial in reducing inaccurate misinterpretations.

7. Identify All Data Elements

Elements of data gradually accumulate over time, but you can save yourself a lot of money and time by having an updates list of all of your data elements. This can be called a “data dictionary” of sorts. This will also help those who develop databases and data fields understand its contents.

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