Understanding Data Quality: What is it and Why is it Important for Businesses?
- Sales and marketing departments lose as much as 550 hours and more than $32,000 per sales representative using bad data. (DiscoverOrg)
- Businesses lose more than 20% of their revenue on account of poor data quality. (KissMetrics)
- Employees lose as much as 50% of their working hours due to mundane, unfiltered data quality. (MIT Sloan)
As data levels up the playing field for companies to make critical business decisions or make software solutions driven by modern-day algorithms, there has been pressing needs for “great/ good” data quality to make different business functions click.
But what exactly is “great/ good” data quality, and how do companies differentiate good data from mediocre or bad ones?
Today, with this article, we discuss data quality and how good data quality plays an enriching role for businesses.
What Exactly Is Good Data Quality?
Good data is data that satisfies certain conditions, the primary being accuracy. This ensures smoother transactions, followed by higher accuracy in predictions.
In contrast, bad data represents data sets that involve inaccurate information, duplicate data, conflicts in data types, or absence of data consistency and standards, to name a few.
In the age of AI and machine learning, data plays a pivotal role, and hence, the quality of data cannot be compromised. This, in turn, has led to the evolution of software solutions and frameworks that make cleaning the data a piece of cake. This also eliminates the errors stemming from human interference.
For instance, in a research commissioned by Experian Data Quality in 2013, the top reason found for data inaccuracy was human errors. An astounding 59% of cases resulted from human errors. Other leading factors include lack of communication between different business units and unstructured or lack of clear data strategy.
Fortunately, most of the problems have been automated since then, resulting in better fulfillment of data needs.
Importance of Good Data Quality for Business Houses
They say that data is the new oil and today businesses can’t be surer of it.
Currently business houses employ systems/ processes that are fueled by data and churn out business-oriented solutions.
Whether it is employed to target potential customers or leverage data to feed AI models with meaningful data, the solutions are endless and hence the demand for good quality data is at all-time high.
Some of the leading examples of the use of good data quality in the industry are as follows:
- · Reduction in cost of identification and fixation of bad data.
- · Improvement in business decision-making process followed by an increase in sales.
- · Improvement in internal processes, allowing businesses to have a competitive edge over rivals.
- · Promotion of data-driven decision-making process instead of gut-based.
- · Rightful engagement of different teams.
While good quality data delves deep into businesses, data collection plays as the entry point. This determines the quality of data. Companies spend hundreds of thousands of dollars to move the needle in the right direction.
This highlights the importance of high-quality data and how unsurprisingly good data matters to companies of all sizes.