With the internet producing quintillions of readily available information per day, you could be forgiven to think that data is losing its value. Apparently, data is one of those weird commodities that go up in value the more they are available, or perhaps we haven’t produced enough to attain the demand-supply equilibrium.
Virtually all companies making good in various niches are where they are because of data. They know not only where to find the precious commodity but also how to harness it to understand their markets better and stay ahead of their rivals.
Proper data management is the key to data-driven success. Just like the internet doesn’t reward you for the sheer ownership of a website (that used to work a couple of decades ago), simply having data in your hands won’t give you the edge on your competitors. You ought to know what to filter out and what to do with what you are left with. More importantly, you need to understand that some of the data you have is sensitive and needs to be protected and kept away from cyber crooks.
Data management is a combination of functions and factors that work to ensure that the data available in corporate systems is accessible, accurate, and safe. Individual specialists and IT teams implement the bulk of the work that goes into data management, but regular business users may be involved in some parts of the procedure.
The availability of enormous amounts of data comes with one major downside: management difficulty. So much information is being pumped in that finding the crucial bits and working on their quality is extremely difficult.
The quality of the data you have will be reflected in the business decisions you make both in the short run and in the long run.
Data quality will make or break your business, as the insights you get from it dictate the business moves you make. The higher the quality of data a company has in its hands, the better the results its campaign strategies are going to produce.
In a word, data quality is the whole multi-faceted process of styling data to align it with the needs of business users. A business can optimize its performances and promote user faith in its systems by working to improve the following six metrics of data quality:
Bad data is inaccurate, unreliable, unsecured, static, uncontrolled, noncompliant, and dormant.
While poor data can be a significant threat to data-driven brands, from another angle, it can be seen as a market gap and an opportunity for businesses to improve. Let’s take the example of a self-driving vehicle that makes use of artificial intelligence (AI) and machine learning to find directions, read signs, and maneuver streets. If the car lulls the user into driving into a traffic snarl-up, we can say that the data that led to that is inaccurate and unreliable. This will take a toll on the car maker’s reputation, especially if it happens to more than one person. They must be quick to redress the issue, or it will ultimately cripple the company and create an opportunity for rival businesses to rise and fill the void.
Certainly, no one wants to go to the wall in such fashion, and businesses will work their heads off to improve the quality of their data as they seek to make good in a rather competitive digital age.
Why AI/ML Based Data Quality makes sense?
Impacts of Poor Data Quality
Steps to collect High-Quality Data
How to Build Your Data Quality Team
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