Big data is a hot topic in the business world, with compelling statistics and examples cited at every turn. But what about real-life big data mistakes? What’s the impact of these missteps on businesses large and small? The following examples can help you improve your own company’s big data strategies by showing you what big data mistakes that cost companies millions.

1. Forgetting What Your Data is Used For

The term “big data” applies to any collection of data that’s so large it necessitates new technology to store and analyze it. It’s important to remember, though, that the size of the data isn’t what makes it valuable or useless—it’s how you use it.

Data that is too large to store or process with traditional techniques can be a gold mine if you’re studying trends and patterns.  On the other hand, if you’re trying to draw conclusions from your data and find that there’s too much noise to detect a signal, you might have a big problem.

The best way to avoid this pitfall is by being clear about your goals going into the Analysis process. Have a reason for collecting all of this data before you start gathering it.

2. Expecting Too Much, Too Fast

Many companies jump on the big data bandwagon expecting immediate results. Unfortunately, this is rarely the case—the technology required to run analyses on huge datasets simply doesn’t exist yet.

That said, just because you can’t analyze all of your company’s data now doesn’t mean you can’t start storing it. Even if your big data analysis platform is in its early stages, the more historical data you collect the better off you’ll be when it’s ready to go live.

3. Ignoring Security Concerns

When it comes to their data, companies often put blinders on and ignore issues of cybersecurity until something bad happens.

This is a mistake that many companies have made in the past, but it’s especially costly when big data is involved. Big data isn’t just a matter of larger datasets and faster processing speeds—it also involves more moving parts in terms of storage and transmission, not to mention a higher risk of privacy violations if security measures aren’t put in place.

4. Ignoring The Small Picture

Another common mistake when it comes to big data is the failure to consider all of the factors that go into a given analysis.

For example, what happens if you’re not able to gather enough data from a source before it becomes outdated? What if your sampling doesn’t reflect the diversity of your actual customer base? These are small data problems, but they can cause big data analyses to fail.

5. Analyzing the wrong data

A major downfall of big data is analyzing the wrong subset of your big data collection.

There are two ways this can happen—analyzing too much or too little of your dataset, which both lead to the same result.

If you analyze too much data, your results will be unreliable, which defeats the purpose of having so much data in the first place. If you analyze too little data, you won’t have enough viable conclusions to make any actionable decisions based on your results.

6. Not Factoring in “Noisy” Data

Noise is the equivalent of static in the online world. It’s just more of what you don’t want, and it can affect your big data results.

For example, when companies are looking at historical trends in their supply chains, they need to make sure that any outliers—new suppliers or manufacturers who are producing faulty products, for example—aren’t overwhelming the signal of what’s happening with the rest of their supply chain.

7. Assuming That Bigger is Better

Bigger isn’t always better when it comes to big data analysis.

There are times when companies need to look at smaller samples of data to understand complex trends—for example, trying to determine why a certain product is experiencing a sudden spike in demand.

The solution: look at the data and come up with a hypothesis rather than collecting more and more information that doesn’t give you actionable results. Sometimes less is more when it comes to big data analysis.

8. Not Testing Your Assumptions

Before you start distributing your big data analysis platform to a wider audience, do a test run on a subset of users.

If you go straight from the test run to full-scale implementation, you might not get an accurate idea of how well your platform works. Any problems with the platform will impact a large number of users who expect it to work flawlessly.

Testing your assumptions is the only way to be sure that the results you’re seeing are accurate and relevant—and it’s also an opportunity to iron out any bugs in your system.

9. Not Recognizing The Limits of Big Data Analysis

The biggest mistake companies make when it comes to their big data analysis platforms is failing to acknowledge what they don’t know or can’t know.

For instance, if you have a large dataset but no context for interpreting all of those numbers, you won’t get very far in terms of true insights. You need both for your company to see real value from implementing a big data strategy.

10. Not Changing Your Actions Based on Your Results

Finally, the biggest mistake companies make with their big data analysis is failing to change their behavior or relationship with consumers as a result of what they find out about consumer demand and preferences.

For example,  you might find that certain products are experiencing a sudden spike in demand. If you don’t change your product mix or marketing efforts to make those products more readily available to consumers, you’re not seeing the value of your big data analysis efforts.

Companies must find ways to incorporate their big data analytics into the way they do business and change how they interact with their consumers as a result.

Conclusion

There are so many potential pitfalls for companies trying to implement big data analysis, but the good news is that these mistakes can be avoided.

Remember, the more streamlined your data analysis process is, the better your results will be.

The key is having a solid strategy in place, knowing what results you want to get out of your insights and how you’re going to get them—and more importantly, taking action on those results.

Zara Raza is the Head of Marketing at Sunvera Software. She has written several technology blogs covering anywhere from software to emerging tech trends.

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