Modern world entrepreneurs rely on data heavily for successful business ventures. However, innumerable issues can still cause it to fail. Good entrepreneurs may be able to identify a lot of them. However, only a few point fingers at bad data.
According to IBM's estimate, the US lost $3.1 trillion yearly due to bad data. This mind-boggling factor is why bad data visualization and data quality improvement should be every business's primary concern. Nearly 2.5 quintillion bytes of information are generated every day. Naturally, bad data gets mixed up with useful data. Thus, ensuring you spend money on good data is precious!
Suppose a company's internal department Y is on a deadline to do their jobs. When department X gives them faulty information, department Y takes up dual responsibility. In addition to performing their own tasks, they now also correct department X's mistakes. As a result, a few unnoticed issues seep into the consumer's deliverables. These issues infuriate customers and deteriorate the company's brand image.
The above case is just one among many others!
Bad data has a devastating influence on businesses. For those who need clarity on bad data's definition, here's what we consider bad data to be.Bad data is characterized as: -
Now, let us compute how much bad data actually costs you. Here is some information that will shock you: -
These statistics reveal a clear pattern of losses due to poor data quality. As stated above, 2.5 quintillion bytes of data are generated every day. This exponential data generation only exacerbates bad data issues.
Nowadays, all companies use data to make big decisions about their business. Heavy reliance on data puts businesses in vulnerable positions when bad data is used.
Thus, poor quality data harms all facets of businesses.
However magnanimous bad data issues may be, they are solvable. Here is a list of steps (recommended by HBR) that aid businesses fix bad data: -
1.Confessing having bad data issues: Every solution begins with an honest acknowledgment. Fixing bad data is no exception.
2.Focusing on data exposure to external agencies: Meticulously monitor systems to ensure sync with the latest data for your customers, regulators, and other agencies.
3.Formulate and execute advanced data quality programs: Ensuring quality data filters is a viable long-term option to prevent future poor data quality issues.
4.Carefully assess the way you treat data: Deep dive into current data management practices gives a good insight for future optimization purposes.
Though steps 3 and 4 seem simple, they are tremendously complicated and need expert assistance. Be assured that this investment will be an asset moving forward.
Data is the New Oil - Adeola Adesina.
This quote, though true, is somewhat incomplete. Not all information is useful and some even harm companies. Bad data wastes time, resources, and most importantly, revenue! Good data, like oil, should go through various purification processes to be deemed useful.
Data cleaning may seem like a big business game. However, all businesses lose revenue due to bad data. Start-ups dealing with aggressive burn rates should not compromise on data. Doing this will ensure they never become cash-flow positive and grow. Good quality information is difficult to extract and retain. However, as proved above, this aspect of your business is too critical to ignore. So, seriously consider investing in good data processes. After all, bad data does mean bad data out eventually. We promise it will be worth your while!
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