In today's competitive world companies are evolving very swiftly. And they are heavily employing technology as a tool in this expansion. As all companies are going digital to show and increase their presence, they are ending up creating more and more data, which needs some space to store and getting hard to consume. This enormous amount of data has started to create a nuisance in the form of Bad Data.
What is Bad Data?
Bad Data or Poor Data means false information or inaccurate data that can be created by duplication of data, wrong formatting or by an uncomplicated error of typos.
Bad Data can turn into an expensive mistake for your business. It can be a difficult problem to deal with. A recent survey shows that bad data costs companies an average of $9.7 million per year.
What are the Causes of Bad Data?
Bad data is mainly a corruption of data that includes error done during writing, reading, copying or saving data. With a large amount of data created every day the chances of producing poor data has increased largely. Using poor quality software can also damage your data quality, resulting in bad data.
Many ways can create bad data. A few reasons that lead to data corruption are:
- Making mistakes: Mistakes made during the migration of data from old system to new system like leaving an extra copy, typos, wrong formatting, spelling error, etc., can corrupt data.
- Working on multiple systems: The chances of corrupt data are reasonably high if you are working on multiple systems. Since systems relying on multiple users, this process can end up creating unwanted copies.
- Using poor software quality: Poor software quality is also a major concern that affects the data quality.
- Technical faults: Bad data can happen very easily because of technical flaws that end up creating bad data.
- Changes made to Source: Any changes made to the source system can also produce poor data.
Impacts of Bad Data Quality on Business:
- A decrease in growth & productivity: Business suffers a lot due to poor data quality. It affects their growth and productivity. It takes a lot of efforts to neutralize it's after effects.
- Time-consuming and an expensive process: Bad data can be a costly mistake. It evolves very quickly and becomes a hard task to clean the mess and locate the original file. Cleaning of bad data can be very exhaustive as it requires a lot of research and time.
- Influences decision making: Bad data can affect your decision making process as you won't be able to differentiate between correct data and incorrect data, which may affect your business badly.
- Creates unwanted copies: Bad data deviates the focus of entrepreneurs by creating numerous copies.
- Delays due to poor data: Poor data can cause a delay in your work by showing you different results on different systems for the same search.
- Bad relationship with customers: Your relationship with your customers can also get affected badly as you won't be able to guide your customers due to poor data.
How can you improve poor data?
Poor data quality can affect your business very badly that can impact your performance and efficiency severely. It is highly advised that you should curb this problem as soon as it rises. Controlling it on early stage will save you money and time both.
- Do Regular Check: You can control poor data quality by implementing a regular quality check.
- Utilize technology: Data management with the help of experts and modern technology can also help you in curbing poor data quality.
- Eliminate extra copies: If your work requires working on multiple systems, make sure that no extra copy remains on any system.
- Check twice: And most importantly always double check your data entry work for any possible error. Checking your work twice will cost you some time, but it will save a lot more.
Data has become an essential part of our modern world. An enormous amount of data is produced every day and every second. With more and more businesses opting for the internet to help them meet and fulfill the demand of their consumers, it is highly unlikely to see any reduction in data growth in the coming future.
Bad data and the problems that come with it are here to stay for now, and the only thing that we can do is deal with it.