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When Big Data Goes Wrong: 3 Common Issues and Possible Solutions

Nastel® Technologies
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Our shared future has always been profoundly enigmatic. Hoary seers from days of yore would never have predicted everyday life as it is now. It would have been impossible to guess most of what has already happened in the 21st century. Peering into crystal balls would have proved equally futile. Even an attempt to make well-educated guesses about possible issues with big data would likely have been way off the mark.

 

For example, we used to imagine a world full of technicolor space suits and flying cars. Instead, today’s generation has found itself embracing an entirely novel technology. Hoverboards may look cool, but it’s reliable data sets that will serve to propel us through the 21st century.

 

Behold big data, a remarkably intricate system of highly complex data sets. These massive data sets reveal patterns about politics, healthcare, human behavior, and everything else in between. The advent of the internet and smart technology has exponentially sped the process. Today, big data has utterly transformed the way that we cull and interpret information.

 

Nowadays, an estimated 2.5 quintillion bytes of new data is generated every single day. And this number continues to grow.

 

However, despite the multitude of benefits and fascinating insights we’ve managed to cultivate from big data, there are still a number of issues that come with its application. It’s naively optimistic to believe that big data is always going to be our ally. That being true, it’s prudent to be both alert and vigilant as to its potential downsides, too. With that in mind, here are three of the more common pitfalls common to our era of big data along with possible solutions.

 

1. The Human Element
One of the most intriguing aspects of big data is that it’s relatively impartial. After all, it’s just information. It’s neither inherently good nor bad.

 

Still, while the data itself is primarily factual, that doesn’t change the fact that it requires error-prone human beings to interpret it. This is where a myriad of problems can begin to arise. For starters, human beings — unlike big data — come with ingrained biases, whether they realize it or not. While interpreting data, hidden prejudices tend to seep out.

 

Conversely, while big data merely reports information, it still takes people to translate it into something viable. When this happens, critical mistakes can be made, potentially nullifying the findings. One way to help minimize this issue is by requiring cognitive bias training for everyone who is given access to big data. In turn, it’s possible to mitigate these all-too-human errors. Working toward that end improves the quality of information gleaned from the data.

 

2. Handling Worthless Data
Another glaring issue that can arise with big data is the fact that it merely serves as the messenger. Big data collects information and shares it with invested and curious eyes. Unfortunately, data sets lack the ability to determine what is worthwhile and what is arguably junk. Because of this, it’s easy to lump so-called “good” data in with inconsequential information. This frustrating reality makes it difficult to perform a reliable analysis.

 

One way to help prevent a veritable heap of messy, useless data is to ramp up the training of those who have the skills to comb through it. Of course, this training can be costly, and humans are prone to errors.

 

Another option could be to create more effective and reliable data analysis systems. A two-pronged approach — marrying both a human and an artificial intelligence element — can help make sure that these massive amounts of data are properly sifted and assimilated.

 

3. Serious Security Breaches
In today’s largely interconnected society, there’s a very real risk of big data leaks exposing confidential information that should never have been revealed. Big data is used in a wide variety of industries and sectors. These include healthcare, real estate, politics, and banking.

 

Just one security breach could quickly spell a massive violation of protected health information (PHI), financial ruin, and even humiliating political downfalls.

 

It’s impossible to overstate the importance of advanced security measures. This is especially true when it pertains to sensitive information hidden within big data archives. Whether you’re the type of person who instinctively understands information security, or you need an SPF record explained to you, it’s all but impossible to deny the value of implementing these security measures when handling large data sets. By doing so, you help ensure the privacy of the information contained within them.

 

Handling Big Data Safely
Ultimately, the use of big data can completely transform the way we understand and interpret complicated systems. The impressive benefits that can come with its use arguably outweigh the potential risks.

 

However, it’s still wise to approach big data with more than a hefty grain of caution. While big data isn’t necessarily dangerous, in and of itself, what we glean from it does come with possible issues. Being alert to these potential problems, though, can help bolster the value of its contents.

 

Whether you’ve fully embraced big data and everything it can do for society, or you’re still somewhat reticent about its application, there’s no arguing that it’s changing the world and here to stay. By recognizing the innate issues that accompany it — and implementing safeguards to help reduce them — big data can very well be the boon to society that we all hoped it would be. And that is, no doubt, more than any soothsayers or clairvoyants could have ever hoped to predict.

 

This article originally appeared on techreport.com, to read the full article, click here.

Nastel Technologies, a global leader in integration infrastructure (i2) and transaction management for mission-critical applications, helps companies achieve flawless delivery of digital services.

 

Nastel delivers Integration Infrastructure Management (i2M), Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers.
The Nastel Platform delivers:

  • Integration Infrastructure Management (i2M)
  • Predictive and Proactive anomaly detection that virtually eliminates war room scenarios and improves root cause analysis
  • Self-service for DevOps and CI: CD teams to achieve their speed to market goals
  • Advanced reporting and alerting for business, IT, compliance, and security purposes
  • Decision Support (DSS) for business and IT
  • Visualization of end-to-end user experiences through the entire application stack
  • Innovative Machine Learning AI to compare real-time to the historical record and discover and remediate events before they are critical
  • Large scale, high-performance complex event processing that delivers tracing, tracking, and stitching of all forms of machine data
  • And much more

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