How to Avoid 10 Big Data Mistakes

Big data brings a new wave of opportunities for businesses of all sizes and types. It’s heralding in a new era of understanding about organizations, their nuances, their opportunities, and their challenges. But it’s also a highly complex and, in some cases, a confusing field for many, due to its many layers.
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An intricate area, big data can be highly beneficial for any business — as long as it’s leveraged correctly. At BairesDev, we have extensive expertise in big data, helping organizations devise strategies and solutions for making data-informed decisions. We also work with them to avoid these 10 common big data mistakes.

1. Believing You Need to Use All the Data

Companies collect huge amounts of raw data every day. But with so much information at their disposal, it can be difficult to determine which data is valuable to them. It’s important for businesses to separate the quality data that can help them make informed decisions from the one that’s not useful and that might even hinder their efforts.

Moreover, data must be cleaned and mined to glean insights that the company can actually use. Often, the information in its rawest form isn’t all that helpful. Remember, there is such a thing as bad data, which can negatively impact your organization.

2. Not Appointing a Data Officer or Team

Data science is a niche field for a reason — not everyone is an expert at it. Rather than assuming your current staff can handle the data analytics, it’s wiser to appoint a specific data officer or team. Employ individuals who have a background in data science and know-how to gather, clean, mine, manipulate, and make informed decisions about how to best use it.

This officer or team will serve as the go-to source for big data, helping your company understand how data will best inform and back your efforts.

3. Not Having a Proper System in Place for Managing Data

In addition to having a point person who is responsible for big data at your organization, there should also be a system in place for managing, storing, and using the information you collect. Work with your data team or officer to establish a logical structure for handling the data the business generates to ensure that it’s managed and used correctly and efficiently. 

4. Failing to Rely on the Cloud

Even in the digital age, some companies are still relying on outdated storage solutions to collect and maintain their information. These aren’t only less efficient than newer alternatives but also less secure. 

Cloud solutions are ideal for storing and managing data, as well as making sure of the proper accessibility for anyone that needs it. Cloud-based solutions are also significantly more secure than individual servers. Plus, they can scale with your business, growing with your company as needed.

5. Investing in Fancy, Expensive Tools You Don’t Need

Some organizations always want to be at the cutting-edge of technology, so much so that they invest in expensive tools that aren’t actually useful to them. This is certainly true of big data tools. For example, a business that doesn’t actually generate huge quantities of data may readily want to pay for data warehouse technology when they don’t need it. After all, warehouses won’t solve many problems or store certain materials.

Instead of looking to the next big tools, consider what your business really needs. It might not be fancy, expensive equipment.

6. Ignoring Security

Security risks are inherent to any technology. Because big data and technology are intertwined, it’s important to pay close attention to the risk of data breaches and other threats. These measures might include:

  • Granting access only to those who legitimately need to use the data
  • Implementing multiple-factor authentication systems
  • Using the aforementioned cloud storage systems

Business leaders should be aware that data can be widely accessible to people from many different locations and that no system can keep them 100% protected. For that reason, they should also have measures in place for addressing and minimizing breaches if they do occur.

7. Thinking Small Picture, Rather than Big Picture

It’s called big data for a reason. While it is, of course, important to focus on the nitty-gritty and use data to inform your small-scale initiatives, it’s also a good idea to think big picture, too. Ensure each and every decision you make is backed by data, including your long-term goals and objectives. This might include scaling your organization, developing company-wide strategies, and so on.

If you ignore the big picture while getting wrapped up in the smaller pieces, you could be missing out on real opportunities.

8. Following Trends Rather than Common Sense

There’s a lot of hype surrounding big data. But the trends you see in the media aren’t always the best strategies for your business. Rather than trying to align your methods with the latest fads, work with your data analytics team to determine the best way forward for your particular organization. While you, of course, shouldn’t turn to outdated strategies, you shouldn’t necessarily embrace a trend just because it’s making headlines.

9. Not Using Your Data Enough

Data shouldn’t just sit there — it should be used. The vast majority — if not all — of the decisions you make should be backed by the data you collect. So, don’t just let it stay in a repository. Act on it so you can achieve real, measurable results. It has the power to completely revolutionize your business.

10. Over-relying on Data

At the same time, not every piece of data has all the answers. It’s critical to recognize the limitations of big data and know when to use it — as well as when not to use it. Moreover, bear in mind that data can indicate correlations without proving causation, and sometimes, it may be necessary to gather more information before making decisions.

Are you looking to take your big data game to the next level? BairesDev can help you create data management systems to help you make better-informed decisions. Learn more by contacting us.

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