Big Data Analytics: Impact of Low-quality Data on Business Performance
With an increase in the adoption of data-driven systems, processes, and strategies among businesses, the need of maintaining data quality becomes even more crucial. However, as the data volumes steadily increase, it is challenging for businesses to maintain the quality of the critical data that drives decisions.
Massive volumes of data generated from the business systems in every second result from the increase in a wide range of technologies such as user-generated content, affordable electronic devices, and mobile technologies, machine learning and artificial intelligence, and the Internet of Things, etc.
The number of data that is 'created, captured, copied, and consumed in the world' is expected to increase at a breakneck pace. According to IDC, a well-known market research company, the amount of data that is going to be created in the next 3 years will surpass all of the data that has been created over the last 30 years.
Moreover, the number of data created in the next 5 years will be more than 3 times than the previous five.
Low-Quality Data is Expensive?
The impact of bad data on the bottom lines of business is starting to get attention from the business leaders. After conducting a survey, the firm Gartner found that the majority of companies strongly believe that data with bad quality is the cause of an average of $15 million losses per year. According to Gartner, approximately 60% of the survey's respondents did not even know how much they spent as a price of using bad data because they did not even measure it in the first place.
In another research carried out by Forrester Research, enterprise systems with low-quality data are responsible for the poor productivity of business leaders, since they have to make sure that the data remains accurate by vetting it continuously. 
Theoretically saying, the increase in data quality accessibility could bring benefits to businesses. However, data access will not be able to provide the lift in the practical situation.
Mistake Happens, Along With Algorithmic Bias
Take the airline industry as an example, travel experts take advantage of problems that are frequently caused by poor quality data to hunt down 'mistake fares' through booking sites. Mistake fares result from low-quality data that come from different sources. A recurring pattern is made to look like a series of disconnected, one-off accidents. Mistake fares have been generated by human errors, currency miscalculations, and glitches in software applications.
How To Maintain Data Quality
Businesses can minimize or even neglect the impacts of bad data by following the following steps:
- Focus on larger patterns.
Organizations should stop believing that this is just simply a detached, one-time event when low-quality data negatively affects the companies' operating systems. The organizations' executives should take a closer look to investigate carefully the larger patterns in play.
Organizations should make sure that the tools that are used to monitor and measure the quality of data are always ready. These tools could be a great help for companies to avoid poor quality data problems that could be potentially lurking in the operating systems.
- Replace obsolete tools that cannot keep up with modern problems.
The impacted organizations most likely prepared some standard IT and Application Performance Management tools to cope with each bad data-driven issue that was listed above; however, bad data could still slip past them. Any sort of degradation in infrastructure or applications was not even detected by available monitoring tools.
Businesses need to adopt modern data management tools in which visibility can be provided into the whole data lifecycle, in order to reduce the damage that is caused by bad data.
- Treat your data stack as critical infrastructure.
When more data-driven is being adopted by businesses, the quality of data plays an important role in the success of the companies. Companies need to start treating their data as a mission-critical asset.
Modern data architectures (such as ELT-based ones) and technologies (such as data pipelines) should be adopted. Moreover, data management tools that are able to keep data under observation across all the assets should be utilized, whilst AI and/or ML are ideally embraced to find problems automatically, without human interference.
It is not an easy job to resolve the problem that is caused by low-quality data any time soon; however, as the organization follows those three steps that were listed above, it will be able to find out bad data and reduce its impact on the operating system before it negatively affects the revenue or publicity of the company.
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