5 Biggest Data Related Mistakes That Can Ruin A Business


Data Discovery & Profiling Capability

Everything has turned digital nowadays: the internet pervades every aspect of our lives, all our personal information is present on social media, and industry 4.0 is only bound to make us more reliant on binary code. In this digital age, data has become the new oil, an asset whose full value we’re yet to realize.

Like industrialists in the 19th century, companies have been clamoring to reap the largest possible benefit from this powerful resource, hoping that it will give them a competitive advantage in the market.

Some companies have been exploring how AI and deep learning could enhance their operations, whereas others have been ardently searching for the best data scientists in the market.

However, during this gold rush, some companies have been making dangerous mistakes, which is understandable since we are all in uncharted territory. With that in mind, companies that want to gain a competitive advantage should learn from their mistakes as well as those of others.

Five biggest mistakes companies make when handling data

Here are some most common mistakes that businesses encounter when collecting, managing, and analyzing data:

#1 Missing the entire point of data analysis

There is a purpose behind data collection and analysis: finding actionable insights that can improve the bottom line. Yet, as simple as this may seem, some companies may lose sight of it as they race to “get in” on the latest data analysis trends.

 Here are a few common ways businesses miss the bigger picture:

  • They rush into data analysis without taking a beat first to figure out their goals and objectives. Without a clear understanding of the challenge the business is trying to overcome through data analysis, businesses are liable to fumble around, squandering valuable resources.
  • They over-analyze or fall into what is commonly known as analysis-paralysis. Companies can make this mistake because they are dealing with a massive amount of data or because they want to be certain about any of the insights they find. In either scenario, analysis paralysis is deadly: in addition to stalling key projects and holding off important decisions, which can be costly, over-analysis runs into the problem of diminishing returns, where the more a company analyzes the data, the less valuable the insights become.
  • They don’t have key metrics in place. Any company that adopts a data strategy needs to develop in tandem a test to see how efficient this strategy is. Hence, companies should put in place KPIs to track their progress and ensure that their data strategy is bringing them a lucrative return.

#2 Gathering bad data

When it comes to data analysis, the quality of your conclusions and assessments rests on the quality of the data you collect. The expression data scientists use is “garbage in, garbage out.”

Data can be bad in one of two ways: being inaccurate or being outdated. Inaccurate data might come from an unreliable source. Outdated data, regardless of its source, is data that isn’t only a bit old, but that also provides outdated insights that are worthless today; these insights might revolve around obsolete trends, for instance.

Even when the data collected is of the highest quality, it is useless if it is irrelevant to the analyst’s goals.

As a result, to ensure that the data collected is of the highest quality, analysts should start by defining their goals first, then curating the data they need accordingly.

#3 Underestimating the required costs

Implementing a data strategy can be a costly endeavor, and if a company doesn’t invest all the requisite resources, the entire strategy is liable to fail.

Here are a few of the costs companies should be aware of:

  • Technical costs: When a company decides to gather large amounts of data, this requires a significant investment in infrastructure. A company can either decide to use cloud technology as the basis for their infrastructure or decide to build the entire thing in-house, which offers more control but can be more costly.

In addition to the hardware, companies have to invest in the appropriate software. This means that companies have to consider which tools to use to gather data, analyze it, and to extract predictive insights from the data.

  • Costs associated with training and skills development: Once a company has a data strategy in place, it will need its employees to know how to use it. This will require training.

What’s more, companies should consider hiring a business intelligence team whose sole purpose is to analyze the data and share the insights gleaned with the relevant departments. This team would also be responsible for looking after the quality of the data and making sure that best practices are always followed.

  • Costs that come with change: For many companies, implementing a data strategy means changing the company culture from one where decisions are made by instinct to one where decisions are supported by insights gleaned from the data. There will probably be other changes with a new strategy as it takes people a while to acclimate to new circumstances.

#4 Overestimating their capabilities

Big data is a new field, and any company planning to explore this pond is bound to make several mistakes along the way. Consequently, it is always good to dip your toe first and start small so that the errors can be contained.

That said, some companies overestimate their capabilities and try to dive in headfirst. For instance, a company could overwhelm themselves by collecting raw data indiscriminately. They believe that every morsel of data has value, so they don’t let any of it go to waste.

The result is that the company may stall their decisions, given that they are having a hard time sifting through the raw data, let alone analyze it.

#5 Being negligent with the data

A lot of the data companies collect will pertain to their customers. Ergo, there are privacy and security issues involved, and any company that isn’t careful may lose this valuable information or, worse, have it stolen by an unscrupulous entity.

Therefore, to live up to the trust and confidence placed in them by their customers, companies must invest in security, have a clear stance on ethics and privacy, and offer their customers complete transparency on how the data is being used.

There are other things companies can do to improve their security. For starters, they can invest in offering their employees cybersecurity training; after all, the biggest liability in any company is always human.

Also, companies want to have redundancies in place in case one of their databases gets corrupted somehow. Moreover, should something happen to the data, companies should report this to their customers.

Companies should also look into applying a top-down security infrastructure instead of a siloed system, as a siloed system may cause interoperability issues between different applications.
Despite their best efforts, companies are still liable to get hacked, which is why companies need to prepare for the worst. Otherwise, one data breach could be the death of a business.

Data is everywhere

Data isn’t just changing the way we do business; it is changing the very fabric of our lives. It is behind revolutionary new technologies, such as AI, deep learning, and predictive maintenance.

It is raising new questions that older generations never had to grapple with, something that is exemplified time and again every time a company suffers a breach. Over and above, we are only just scratching the surface, and how later generations use data will make our current efforts seem paltry in comparison.

Companies should brace themselves for a change in tide, a change from gut-feeling to data-backed decisions. This means companies should always keep the bigger picture in mind and avoid performing data analysis for its own sake.

Also, companies want to avoid underestimating the costs or overestimating their capabilities, both of which could lead to problems. And, most important of all, security and privacy are pertinent issues that cannot be ignored.

Ashley Wilson is a content creator, writing about business and tech. She has been known to reference movies in casual conversation and enjoys baking homemade treats for her husband and their two felines, Lady and Gaga. You can get in touch with Ashley via Twitter

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