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Common Pitfalls When Utilizing Big Data

December 21, 2020 by Rupesh Kumar

There are many advantages to utilizing big data, such as finding failures in real-time, understanding the data behind what drives your market and improving the customer experience. However, there are also a few pitfalls you should avoid.

Statista predicts the big data and business analytics market will reach $274.3 billion by 2022. Although large corporations initially took on the use of complex data sets, smaller companies now see the advantage of warehousing information and tapping into data for market research.

There are some drawbacks to keep in mind when utilizing big data. Avoid these common pitfalls if you want to make the most of your investments and efforts.

Data

Not Having an Objective

Why do you want to delve into the world of big data in the first place? If you want your investment to have a good return, you must know what you want to get from the effort. Do you want better customer relationship management? Perhaps you want to study patterns and send salespeople to areas not currently covered. Knowing what you expect to gain from data storage helps you choose a platform that works best for your purposes.

Messing Up the Migration of Data

If you’re like most companies, you probably have tons of data stored on in-house networks. When you’re ready to organize it from all the various departments, it is difficult to know what to keep and what to discard. Migrating the necessary data over to a cloud can be quite a challenge. Hire someone experienced in migrating data and put department chairs in charge of sorting through what information needs uploading.

You have to take the time to assess the usage of any data you’re sending over to the cloud. Will customers need access to old files? Are there any reports leadership or salespeople run that might require certain data in the future? A data consultant can help you determine what to keep and save you money in the long run.

Forgetting Others Have Big Data

It’s easy to narrow your focus so much that you only run reports based on your big data. However, you can learn a lot from companies that aren’t your competition but have a similar target audience. Sharing data helps you develop more robust buyer personas and better refine marketing efforts to match your typical customer.

Look at public information and social media players and study the behavior of your customers. Gathering as much information as possible allows you to make predictions about consumer behavior in the future. Knowing what users might do helps you mitigate risk and prepare for upcoming changes in the marketplace.

Ignoring Artificial Intelligence

Machines are smarter than ever before. If you aren’t using artificial intelligence (AI) in your analytics, you’re missing out on an opportunity to understand your customers better. When you pull from your big data, you can request a report predicting everything from how popular a new product might be to how much your customers will spend in the coming year.

You can also utilize AI to pull data and write emails to your top customers or remind salespeople of significant events. The uses for AI in business intelligence (BI) and analytics is just coming into its own. Expect to see even more advances as natural language patterns become more predictive and machines become faster.

Not Preparing for Growth

You want to invest in a data platform that scales up as your company grows. A small Excel database might give you a start on keeping records, but it isn’t going to be powerful enough to keep up as you gain employees and customers. Instead, look for a platform that has different packages and can grow as your brand expands.

The bigger your company becomes, the more storage space you’ll need. You may also need a third-party service that understands the importance of cybersecurity and regulations in the industry.

Not Looking at Applications

Some BI solutions only work with specific software. Before you choose where to store your data, understand which programs work. The last thing you want is to migrate all of your data to a service only to realize it doesn’t work with your favorite third-party software. Pay careful attention to the requirements of the program you choose.

Missing a Data Overseer

Many companies complain about a lack of oversight and reliability in data. After all, the information you use is only as good as the amount and the detail punched into the system. Before investing in big data, you should appoint someone to be in charge of your data operations.

A data manager can offer procedures for inputting data, check in with department heads to ensure everyone stays on task and create systems for making sure the info is accessible and understandable across channels. Having oversight prevents data duplication and faulty input into the wrong columns and keeps everything clean and tidy. Your data manager should also offer regular workshops and training so employees know what the company expects for recordkeeping.

Choose Flexibility

In addition to the pitfalls to avoid, businesses should look for flexible data solutions. If you get locked into an internal architecture because you’re on a tight budget, you’ll find it gets outdated quickly. People are migrating to the cloud because the cloud-based systems morph and mutate as technology changes. Even though it might cost a little more upfront, look for a solution viable for the next five to ten years as opposed to storage you’ll have to update in a year or two.

Lexie is a digital nomad and graphic designer. She enjoys hiking with her goldendoodle and creating new cookie recipes. Check out her design blog, Design Roast, and connect with her on Twitter @lexieludesigner.

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Filed Under: Business, Featured, Technology

About Rupesh Kumar

Rupesh Kumar is a systems architect, full-stack engineer, and the founder of Best Free Web Resources (est. 2010). He oversees BFWR physical hardware testing laboratory, benchmarking consumer GPUs and Apple Silicon for local AI inference. Learn more: Hardware Testing Lab & Editorial Standards → · Follow on X (@bestfreewebres) →

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