Many businesses are striving to become more data-driven. After all, with more data at their disposal, businesses should be able to make more informed decisions. Businesses are even investing large sums of money into data gathering and analysis technology – but is it worth it?

This article looks closely at the recent hype businesses have about becoming data-driven. We’ll also examine why this is a challenge for many businesses. Despite investing in and using the right tools, such as web scrapers and residential rotating proxies from a provider like Smartproxy, bad data can still negatively impact your business’s performance.

We’ll cover the following topics related to bad data:

  • Why the hype about becoming data-driven?
  • What is bad data?
  • What causes bad data?

Why The Hype About Becoming Data-Driven?

Becoming data-driven has been an objective for many companies in the last few years. Technology and business are constantly evolving, and industries are becoming more competitive. It stands to reason that if you have more information at your disposal, you can make better decisions than your competitors. Not only will you be able to outperform the competition, but you’ll be able to make better investments, break into new markets, and test innovations more quickly.

This reasoning makes sense, so businesses are taking active steps to become more data-driven. From investing millions in data collection and analysis to appointing data management teams, businesses are spending a lot of time, money, and energy on getting data that they can use to improve their performance.

Unfortunately, some challenges hamper this progress in the form of bad data. Using web scrapers alongside residential rotating proxies is fine for collecting data. Unfortunately, the data collected was originally written, edited, and uploaded by a person. This means there can be many mistakes, from typos and grammar to more serious misleading statistics and facts. In a recent survey, many organizations believed that poor quality information cost the companies up to $15 million a year in losses, although 60% weren’t sure exactly how much they were losing.

What Is Bad Data?

Bad data is wrong, false, or inaccurate information.

Real-Life Examples Of Bad Data In Action

Over the last few years, there have been many situations where bad data was used, and the organizations responsible were called out for this. Some of the most notable took place during the recent Covid-19 pandemic, where the spread of misinformation reached epic proportions.

One example of such a situation occurred in Florida. State Senator, Steve Glazer, was demanding that his state go back to a strict lockdown over concerns of rising Covid cases in the area. During this period, the state ramped up testing healthy people(either asymptomatic or who tested negative). At the time, they reported that they’d done 6,778,304 tests; of those, only 425,616 tested positive. However, the Senator used this number as the basis for his reasoning. This was despite numerous doctors, epidemiologists, virologists, and immunologists stating that the most important factor was the mortality rate – not the number of positive cases.

Similarly, the county of Georgia posted a chart on its website to show the five counties with the highest number of Covid-19 cases. However, they structured the chart to further their own agenda rather than accurately depicting the cases. For one, the original chart shows the dates corresponding to positive tests in the X-Axis – however, when you look closely, these dates are not in chronological order. This makes it look like the numbers for each county are steadily declining when in reality, they aren’t. Another issue is that the counties don’t appear in the same order at each date but are again arranged in descending order which makes it look like the number of cases is dropping.

A more recent and unrelated situation occurred in 2021 when Fox News broadcaster, Tucker Carlson, brought up a graph. During the segment, the broadcaster showed a graph depicting how the number of Americans identifying as Christian had dropped. The graph showed that in 2009 77% of the population identified as Christian, whereas in 2019, the number dropped to 65%. It’s not that big of a drop, but the way they presented the cart, made it look much bigger than reality. They did this by starting the Y-axis at 58%, which made the gap appear much bigger than if they’d started at 0% – which is usually the best way to accurately present percentage-based statistics.

What Causes Bad Data?

It can be created in a few different ways, some of which include:

  • User mistakes such as spelling, grammar, using the wrong formatting, etc
  • Creating multiple copies by working on many systems simultaneously
  • Poor software quality
  • Technical faults
  • Changes made to the source
  • Wrong facts or statistics used – either intentionally or unintentionally
  • Individuals pushing their own agendas by spreading fake news

Final Thoughts

With privacy concerns becoming stricter and businesses either losing money on bad data or struggling to get the benefits they expected from data, this data-driven fad might vanish completely in the next decade. It would likely be replaced by a more efficient way of managing data to ensure accuracy and reliability.