Don’t Make These Six Big Data Mistakes

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Click to learn more about author Evelyn Johnson.

Why do big data projects fail?

They do; that’s for sure.

Gartner estimated that 60 percent of big data projects fail to achieve their desired objectives. A year later, they revised this figure to 85 percent, admitting they were “too conservative” with the original estimate.

So, going back to the original question — what’s the reason so many big data projects are unsuccessful?

Well, there is a combination of reasons. Most of the time, technology is not even the main culprit.

Let me explain.

Mistakes That Can Make Big Data a Big Failure

Not to be clichéd, but big data is reshaping the modern world. Its fingerprints are in every sector, from retail to healthcare and beyond.

The term “too big to fail” takes on a whole new meaning when applied to big data.

The scale of such projects often overwhelms organizations to the point where they avoid these initiatives altogether. Losing investments on Data Science projects can deal a major blow to corporations.

But not all big data projects are created equal and are doomed to fail. In fact, if you avoid the following mistakes — big data can become your biggest asset.

1. Focus on Short-Term Gains

Our era is not called the Information Age for no good reason. Every important decision, be it from a government institution or a corporate entity, has to be backed by relevant and authentic data. Gone are the days when intuition and personal experience drove decision-making.

Companies now recognize that the data collected from customers has immense value. It’s an asset that needs to be managed effectively. This can happen by using technology to simplify data collection, auto-scaling for managing variable data volumes, and enabling AI, all while keeping some space for customization.

What most firms tend to do is focus on short-term value from these tools and ignore everything they would gain in the long run. This leads them to miss out on some major benefits down the road.

2. Lack of Focus on Visualization

Sometimes, data scientists don’t dedicate much time to understanding the data through a variety of visualization techniques. These tools can play a significant role in helping businesses provide insights at a quick pace.

Appropriate visualizations are important for modeling exploratory data analysis, monitoring this analysis, and illustrating the result.

Without this, even the best machine learning models will not be able to salvage your big data projects.

Most data scientists prefer chart-like visuals due to their aesthetic appeal instead of taking the characteristics of their dataset into consideration and deciding accordingly.

Companies should hire data scientists who realize the goals of visualization along with its basic principles.

3. Not Having a Central Authority

The accuracy and quality of the data is a recurring issue with many firms. No matter how “big” the data is, it’s of no use when riddled with inconsistencies and duplications.

One way to maintain high standards of data collection is to establish a central oversight for this purpose. This way, any duplications, bad input, and incorrect columns usage can be avoided altogether.

So either set up a committee or give this role to a seasoned professional. In any case, this authority should have a mandate to keep the data clean and train employees who will utilize it.

Data hygiene is something that deserves special attention, and organizations shouldn’t shy away from investing extra in such a critical function.

4. Going Too “Big”

This might seem a tad bit ironic, but it’s something that needs to be said; big data doesn’t always have to be big. Yes, the whole point is to examine large chunks of datasets to unveil patterns, obtain insights, and base your future decisions on them.

However, collecting everything that can be collected can complicate business functions. When irrelevant data is retained, it creates what’s known as data saturation — a situation where heaps of data are stored, and it’s almost impossible to organize it and gain meaningful insights.

So it’s important to go in with a well-devised plan from the very start. Your Data Strategy should align with the overall business objectives of your company. Collect purposeful and strategic information, and it will lead to some helpful discoveries.

Again, a central authoritative body should be tasked to ensure redundancy in data collection and help identify the main goals and related datasets.

5. Seeking Answers Without a Question

To quote David Copperfield, “We live in the world our questions create.” Knowing the questions you need answered is a vital part of Data Science. It’s the first step after which one collects appropriate datasets and then gathers results.

Jumping on the data without any questions makes way for analysis results that have little value. A better approach is to have clear objectives and questions along with few hypotheses to attain these objectives.

If you don’t know what you want, be ready to receive nothing. Big data empowers you to seek new answers to old and new questions. All these answers are received by merging datasets that have never been merged before. Curiosity is the primary driver in the whole process.

6. Fitting Big Data in the Same Infrastructure

Big data has its own sets of requirements, which include different mechanisms for authentication, data isolation, access, and administration of environments when compared to conventional organization functions.

To put it simply, organizational procedures need to change in order for big data to work. Adding it in an existing environment is only a recipe for disappointment.

The operation processes will need to be adjusted for big data to work. Otherwise, your company will end up with extremely complex and inefficient architecture.

Success will be achieved when you will take a holistic look at what your organization needs, get the entire company involved, and execute the plan in phases.

All this needs to happen while you move towards reference infrastructure, which was built in the initial strategy.

While change is indeed difficult, it’s necessary while undertaking big data implementation.

Conclusion

Different technologies are helping businesses save money and opening new doors of revenue. Along with artificial intelligence (AI), the Internet of Things (IoT), and virtual reality (VR) — Data Science has emerged as a technology that’s completely transforming how businesses operate.

Big data is helping decision-makers understand past trends and equipping them to make better decisions in the future. However, it’s not some magic trick that can fix all decision-making woes.

For it to become an asset to your organization, big data should be treated as a tool, along with appropriate context and relevant business cases.

Since data helps you understand past trends for future use, it’s only fitting that you understand the common big data implementation mistakes before going ahead with the project.

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