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Post-Transformation Data: How to Validate and Improve Data Quality

The world of business intelligence runs on data. But more than just cold, hard facts, the data that businesses use needs to be precise, accurate, and, most importantly, valid. Bad data is a notorious problem in the world of business. After all, if a business prides itself on being data-driven, then using incorrect data or misinterpreting outliers as truths, then it could rapidly maneuver down the wrong pathway of action. 

Typically, bad data costs organizations an average of $12.9 million each year, with poor decision-making resulting from low data quality impeding the company’s chances of growth. In order to make the most of data, businesses have to put in place structures and data quality control checks that ensure all final data products are as powerful and precise as possible. 

In an ideal world, all the data we use would arrive in a structured format that allows us to rapidly embark on analysis. However, the opposite is generally true, with the vast majority of collected data being unstructured. Without extensive checks for data quality, a few poor points of information can diminish the accuracy of entire data sets. 

Thanks to the extensive data architecture pipeline, we’re now able to transform unstructured data into a more flexible and usable format. However, this still doesn’t guarantee the cleanliness and validity of data. In this article, we’ll dive into the world of data transformation, discussing the post-transformation data checklist and pointing out core strategies that your business can use to improve the quality of your data.

Let’s dive right in.

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What Does “Good” Data Look Like?

 In the world of data analytics, the actual pieces of information that businesses use in analysis can vary incredibly. One business might simultaneously use written data, numeral data, voice recordings, social media data with natural language processing, and even video recordings. With the sheer extent of formats that data can take, understanding what good data looks like can pose a challenge.

 High data quality, despite its format, rests on a few core aspects: 

        Error-free – Data should never contain typos, format issues, or errors in its structure that could lead to problems in analysis.

        Unique – Data that is duplicated wastes processing time and power, and can lead to inconsistencies and weighting issues in larger data sets.

        Useful – No matter how perfect data is, if it doesn’t have anything to do with your business goals, then it isn’t an effective use of resources.

        Consolidated – Creating accurate data systems also extends to where you store this data. If you’re storing information across distinct systems, there is a higher chance of duplication or redundancy. 

A high baseline quality of data is vital for businesses that want to take their data-driven decision-making to the next level. Discover and neutralize problem areas in order to drive engagement, boost results, and empower all data-related employees.

Strategies to Improve the Baseline Quality of Data in Your Organization

 Improving the baseline quality of data won’t happen overnight. Unfortunately, many of the processes that are most impactful when it comes to data quality come from policy and regulatory changes that you enact in your business. Over time, once you have included the following strategies in your processes, you’ll start to notice a considerable difference in data quality.

Here are a few core strategies for improving your business’s baseline data quality.

Establish Data Quality Guidelines and Standards Across Your Organization 

Data quality changes won’t occur unless data-related business departments actually understand why data quality is so vital. In order to point data analysts in the right direction, be sure to clearly outline what data quality should look like in your business.

Outlining core characteristics of quality data and writing a data and analytics guide that covers the speed, format, manner, and maturity of your post-transformation data quality will come in handy. Once this core governance is a part of your organization, all employees will be able to start taking the necessary steps toward improving data quality.

Data Profiling is Key

Without profiling your data, you’ll never know how many errors are slipping through the cracks. Be sure to create a series of data profiling filters and layers that seek to identify incorrect data against your current organization’s standards.

Frequently run data profiling sessions to spot errors and then fix them as quickly as possible. The first few times you do this, you may have a great deal of work, but each positive change will lessen the load and help to increase the baseline quality of data.

Make use of DBT Data Quality Checks 

Data build tool (dbt) is a leading open-source command-line tool, allowing businesses to employ complex data transformations using Python. As a leading transformation tool, many organizations will use this as a core part of the data pipeline architecture. By implementing a further step of data quality checks, you can ensure dbt data quality.

There are a few core steps to implement dbt data quality checks in your data infrastructure: 

        Locate – First of all, your organization should define the core areas of data that you actually want to test. Testing for average data quality across specific views or tables will help you to orientate your checks.

        Define – Next, determine the specific tests that you want to run on your data. Depending on typical inaccuracy or errors you find in your data, like missing values, incorrect typing, or a lack of completeness. Use can use queries in SQL to determine these tests.

        Configure – Once you’ve established which data you’ll test and the specific tests you’ll run, configure your dbt project to include these data quality checks

        Test and Reconfigure – Run the specific data quality tests that you have outlined, checking data to ensure that it meets all of your data quality standards.

Initiating frequent testing will ensure that all of the data that comes through your data transformation pipeline is as precise as possible. The earlier you start these processes, the higher the chance of catching errors and routing them out will be. 

Final Thoughts 

Data quality is the silent killer of business analytics. No matter how extraordinary a data analyst is, if they’re working with data that doesn’t give the full picture or contains mistakes, they will be unable to effectively produce insight. In order to keep data quality as high as possible, organizations should endeavor to put in place standards and checks throughout their transformation and analysis processes. 

By using the strategies outlined in this list, businesses will be able to increase the average quality of the data they use, driving their data-driven potential to a whole new level. Protecting and ensuring data quality is one of the most important aspects of data analysis, yet one that is continually overlooked.

Focus on the small details and create data quality checks to scale your data analytics without dampening its efficiency. 

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