Data is the foundation of every piece of information. With it, making decisions is like a cakewalk. Without it, it’s challenging to reap benefits. In the nutshell, you should have well-managed data resources to get insights.
Before managing them effectively, it’s a necessity to clean your data. This practice would require the right practices to ensure high quality.
Let’s understand what high-quality data means.
Understand the Definition of High-Quality Data
The records having minimal to zero errors and are updated, accurate, fresh, and relevant are high-quality data.
These are the most important aspects of forming high-quality:
Timely & Up-to-Date: The data collection that is not months or years old represents high quality.
Reliable and Trusted: The records that come from trusted resources are the most reliable and qualitative.
Privacy and security: The pool of information that complies with internal and external regulations, like GDPR and privacy policies are useful.
High returns on investing: If the price of your data or insights’ sales is more than that of its processing or cleansing price, it’s the best data strategy.
Why is There a Big Struggle to Achieve High-Quality Data?
Businesses actually have tons of data to deal with, and these tons continue to multiply over time. However, some tools, like Trifacta, Winpure Clean & Match, and more, are evolved to clean and manage data efficiently.
But still, the struggle is as-is. This is simply because of unstructured data lying in a complex form. Various industries face gaps in skill sets, data management, and their integration.
Let’s dig these gaps a little deeper.
Complex Data Structure Makes It Hard to Understand
However, it seems like a bed of roses to collect and cleanse data. But actually, it’s not that easy. Mainly, there are five different stages-Create (via data capturing, extraction), store (for cleansing, enrichment, and quality assessment), use (to make decisions), share (for making an impact), archive (redundant data entries), and destroy obsolete or bad data.
If any of these steps does not complete in time, something adverse happens. The businesses that have gaps in these phases certainly face problems sooner or later.
One more thing is that data continue to get richer. It’s not static, but changeable because of these reasons.
Multiple Types of Data Sets
The problems turn double by discovering the fact that a data set is not sufficient. If you want to drive transformation and analyse insights for strategy or business intelligence, you require a pool of data.
Find which type of data can help you to get insights and drive decisions. If you combine onboarding data with attrition details, you will find better answers to your questions.
But, the biggest challenge is the spread and unstructured database. It adds more pain to your struggle because each data set has its own life. It decays quickly. Moreover, if the collection took more than the usual hours, the report won’t paint the actual picture.
Filling The Industry Skill Set Gap
In the digital data world, hiring an expert or skilled professional is indeed expensive. If you delete any figure or even a zero, the result won’t be reliable or trustworthy. The entire report might be wrong or misinterpreted.
This incident won’t let the manager trust your report. Here, you lose. The distrust is the real cost of errors that you pay later.
It’s expected that the top-down hierarchy can easily work with data systems. It can find every crucial thing to understand your business and its complexity. But, the reality is the opposite to it.
You need a professional who has the ability to design a data collection system and understand the voice of information or data. Unfortunately, such professionals are just a few countable ones in the market. This is why companies struggle to onboard the person with the required skill sets.
Data can come from anywhere, be it from mobile, websites, or data vendors. Likewise, their cleansing and processing are carried out by tools. Unfortunately, there is no reporting or processing tool that is like a one-size-fits-all system. In other words, multiple tools are there, but they are not able to integrate seamlessly.
Here, the solutions architect or data engineers should have the answer to these questions:
Are there sufficient security arrangements and if you have the skills to secure them?
Do you know the best practices for data integration?
How can you track tools?
How can you come out of any problem related to data integration? What challenges can be there?
Are your legacy systems capable of integrating data resources and cleaning them all frequently together?
Treat Your Data As Your Property
This part is dedicated to solutions that organizations seek for treating errors or bad data.
It’s compulsory to keep your unstructured datasets in a disciple. Ensure that it is guided by principles of timeliness, benchmark quality, data privacy or security, and compliance. Here are the steps that can guide you to treat your data well:
Develop ownership of keeping it clean
There may be multiple stakeholders who use or consume your data. Find out what they expect and what your objective is.
Monitor with tools
You may hire the best data cleansing companies in the United States or deploy automated tools for monitoring data mining and cleansing like SAP Data Intelligence or Informatica Data Quality.
Make data pipeline failsafe
Discover what challenges can disturb data quality and what issues your quality analysts face during the entire workflow. Set realistic goals for them to achieve in the end.
Ensure maximum production
Errors are inevitable. But, you can determine the pattern of errors. Once understood, your team can easily run a query to find specific erroneous patterns and then, fix them all in no time. You may deploy some tools to automate data scraping and cleansing tasks.
There are many best practices for data cleansing. The best thing for ensuring the cleaning of data and managing their quality is to treat it like your own property. Invest in automated tools to make cleansing easier. Fill dots in data integration and quality management.