Tips to Improve Data Quality Including Best Practices

EM Admin
Jan 4, 2023
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It’s found that individuals create 70% of data, but enterprises are responsible for storing and managing 80% of it. It may not be good enough to make decisions that can produce results. Moreover, bad data costs US entrepreneurs a loss of $600 billion annually, which is colossal.

Best Practices to Deal with Bad Data or Bad Quality

Tips & Best Practices to Improve Data Quality in 2023

Analyse Your Data

Benchmark quality is really significant to keep your data useful. You have to focus on its consistency while analysing the following things in your records:

Assess what data you gather/ do profiling
Think about where they would be stored
Authenticate who can access them
Segment structured and unstructured data & more.

Define Standards for Data Quality

Here, the point of concern is which data standards are a good fit for your company, or which quality metrics are superb & acceptable for it. This is all for introducing maximum accuracy and relevancy in your database.

Here, the noteworthy thing is to define different quality standards in accordance with the different types and uses of datasets.

Fix Errors Up Front

Data quality management is all about consistently monitoring and correcting errors or oddities. This process would directly impact data entry solutions. This is simply because your warehouse or virtual database would ingest correct entries at the point of entry. Quality management solutions, especially profiling, data enrichment, & structuring, minimise the scope for inaccuracies or incomplete records before storing them.

Remove Data Silos

There are several aspects of data quality. One of the best of them is removing silos. Silos are like keeping records in any department or physical location on your desktop. Maintaining records in this manner can never allow you to understand your business efficiency. Nor can you use them. This practice can be disastrous. The real motto of storing any piece of information is to draw understanding. If it’s not comprehensive, there remains no meaning in storing it.

So, centralize the entire database. Then, validating them under the data quality management system won’t take much time.

Make Data Accessible to All Users

Data silos are good for nothing because data analysts, strategy-makers, or any user, unfortunately, fail to access valuable data. This is where quality steals the limelight. With benchmark quality, accessibility is also vital. If their profiling & structuring is excellent, their accessibility can be defined to potential users.

Cloud servers and Google Drive are perfect examples or practices to improve data quality in 2023, which allow users to access their messages, emails, photographs, or anything from any location.

Gather Correct Records

Various organizations focus on collecting data for different purposes. But, do all collect accurate and niche-based datasets?

This is a big question. You can never achieve results unless you have input the right set of records for analyses. So, it’s extremely important to decide beforehand which data you need via research. It should resonate with your goals or what you want to achieve through it. Then only, filtering results would be awesome.

Come up Auto-Selection of Defined Records

Think of a scenario wherein you run an online survey for primary research. Your target audience fills in the location as Philipines, Filipines, and Philippines. The first two entries are certainly incorrect.

This happens because users don’t have a choice to auto-select the location, which leads to errors. Here, predefining values in some common fields of your online questionnaire can do the magic. Let users have the option to select the alternative from a drop-down list. It helps you to have a clearer version of the records.

Define Robust Security of Your Data

Unauthorised access can be disastrous. Scammers can attack your confidential personally identifiable information or sensitive data via bots. Here, stringent privacy rules and policies can prevent your data from their reaching. These policies can never allow cyber spies to attack your precious data. Setting up policies is not sufficient. You need to take serious action to ban unauthorised access. Strengthen the firewall of your company’s server to ensure that no suspicious attempt could damage the integrity of your data. Cover it with two or three-way of authentication.

Data-driven Culture Should Be There

When you use data to measure your performance and make further decisions at every level of your organisation, it’s called a data-driven culture. Simply put, you make decisions on the basis of available facts. This can happen when you emphasize the quality. Make sure that the top-down hierarchy or every employee is putting effort into it. Start training them about key quality metrics and how to manage their files effectively. It will introduce everyone to put effort into quality.

Keep Quality Under-check

You need an experienced quality analyst to oversee your data quality management. Data keep on adding and replacing the obsolete ones. It’s necessary to do so. With that flow, inconsistencies and errors also get added over time. This is why you need a qualified expert to review it regularly and integrate tools or modern approaches for a benchmarked quality. Besides, he can train your staff on how to better themselves. This is the best practice for improving quality in 2023.

Regulate Quality Check

If you want quality to be there, following quality tips or conducting regular reviews is a must. With them, you can find if there is any progress in your data quality and complementary solutions. Keep a track of it, which helps you to analyse it later. If it shows any difference, keep on reviewing it. Otherwise, upgrade rules for quality management.

Have Robust Quality Management Solution

This is mainly related to data monitoring. A person cannot keep his eyes open round o’clock to monitor imperfections. Here, you can rely on smart tools, which can analyse your data and highlight issues to fix quickly. This is how you can fight the problem of bad data without doing spadework.

Data

Data Entry