What are the hidden costs of bad data?
There’s a huge amount of free data out there, but as we all know, there’s no such thing as a free lunch. What are the hidden costs with all of this free data?
One thing to be aware of is that the data might have been captured unethically. Another potential issue is that procedures may not have been put in place to ensure a lack of bias. Finally, if the data was not built for your requirements, then you will likely end up fitting your models to the data rather than the data working for you.
The bottom line is:
“The problem is that not all data is good data – and if it is not used or managed correctly, it can lead to poor outcomes with potentially catastrophic consequences. These consequences not only impact the population and individual businesses, but also the wider economy.” – source
In this article, we’re going to look at the true costs of bad data, and how you can start your model the right way.
Ultimately, many organizations don’t realize the value of their data – meaning they don’t realize how bad data is able to negatively impact their finances and their business. According to IBM, bad data is said to cost the US economy alone around $3.1 trillion dollars each and every year. Experian has then gone on to provide additional research, revealing that bad data causes 88% of American companies to lose 12% of their annual revenue.
Evidently, bad data can harbor catastrophic results for a business – especially young startups.
Bad Decision Making
Bad data can lead to bad decision-making, which then leads to impacting
the business in a negative way. These impacts can be on marketing, relationship with your customers, and even employee efficiency – which, as a business, is detrimental to an organization.
For example, during the recent covid-19 pandemic, infection rate data has been crucial for hospitals and healthcare organizations to ensure they have the ability to cope with the number of cases.
Granted, many businesses don’t have to deal with the same life or death consequences as the healthcare sector. However, bad data for all businesses can mean loss of jobs, loss of revenue, and serious damage to your brand and reputation.
Quality over Quantity
Harboring bad data within your organization can cause a major strain on time and finances. As a lot of data is collected by humans, there is plenty of opportunity for error. If the data is needed within a certain time frame, you may find the data is tampered with, and individuals make corrections in order to complete the collection process.
According to Forrester, a third of data analysts spend 40% of their time validating their data before it can be used to inform data models and decision-making. Ensuring your data is without bias and concluded for absolute accuracy is the only way to conduct data analysis.
The cost of bad data can often mean a damaged reputation. As a business, the last thing you want to do is gamble with your reputation. A strong company reputation takes a lifetime to build, and only seconds to destroy.
Often, companies will experience problems within the day-to-day runnings of their business – customer & employee satisfaction, poor financial performance – but not realize the answer is within the data.
Customers, displeased with the company’s performance, may then turn to social media – leaving poor reviews and testimonials. This will eventually destroy the trust between business and customer, and lead to less revenue earned.
Refining Your Model
Working with a reputable and ethical source, from the beginning, will eliminate any risk of bad data within your AI data collection. Twine can help build you a dataset, with none of the repercussions of bad data.
From our global marketplace of over 400,000 diverse freelancers, we are able to provide a wide selection of data to build your model from the ground up. Our team will work on audio and video datasets, entirely customized to your requirements.