What Is the Biggest Problem With Big Data?
To shed light on the biggest challenges in Big Data, we’ve gathered insights from eleven top CEOs and COOs. From standardizing data for actionable insights to addressing data privacy concerns in Big Data, these leaders share their firsthand experiences and solutions to the most pressing issues in the field. Dive into their expert perspectives to better understand and navigate the complex world of Big Data.
- Standardizing Varying Data Points
- Extracting Actionable Insights From Big Data
- Understanding and Exploring Big Data
- Cleaning and Managing Data
- Assessing Data Reliability and Security
- Ensuring Data Quality and Veracity
- Navigating the Overwhelming Nature of Big Data
- Addressing the Shortage of Data Professionals
- Integrating and Analyzing Diverse Data
- Finding Patterns Amidst Big Data Noise
- Addressing Data Privacy Concerns
Standardizing Varying Data Points
For us, it’s about making sense of it all. What good is a trillion data points if they don’t help us make better decisions? We’ve been working on standardizing common construction tasks as “Line Item Templates,” but we’re having to sort through millions of data points.
We can take samples from 100 different users, all regarding the same task, and all 100 samples will have a huge variance. So, figuring out how to clean it up to standardize it in a way that’s useful has got to be the greatest challenge we’re facing.
Extracting Actionable Insights From Big Data
Actually, getting actionable insights out of it is something that I see on a fairly regular basis, even to this day, despite this being a well-known and understood problem for years at this point.
Having a vast quantity of data is useless if you cannot derive accurate, actionable insights from that data. This is why data scientists and analysts always have to be working hand-in-hand in any organization that plans to leverage big data.
Understanding and Exploring Big Data
When it comes to big data, the biggest problem is that it’s hard to know what you’re working with. When you’re dealing with a data set, there are so many different ways to look at it and explore it that there’s always going to be something new. And that can be great—but it can also mean that if you’re not careful, you might miss something important.
For me personally, this has been a challenge. When I first started working with big data, I wanted to make sure that I was getting everything out of my data sets as possible. But I soon realized that if I kept trying new things constantly, then I wouldn’t have time for anything else!
So now I try to make sure that every time I pull up a new dataset or create a new visualization, I take some time to look back at my previous work and see if there’s anything else I can learn from it.
Cleaning and Managing Data
Cleaning data is hard. To be honest, that is the understatement of the year. Anyone in data science will tell you they spend a lot of time in the clean-and-triage mode with data.
Somewhat paradoxically, the best companies will be dealing with some of the messiest raw data sets as input. Knowing how to clean and manage raw inputs is a true competitive advantage.
Assessing Data Reliability and Security
The biggest problem with big data is how much, how fast, and how different it is. Our team often struggles with the huge amount of data that is produced today, which can make it hard to collect, store, and analyze data in a useful way.
It becomes very difficult to ensure that information is correct and reliable. We’ve also had problems with data privacy and security because we have so much information. This has led to increased risks of data leaks and cyber threats.
Also, handling different kinds of data, from organized to unstructured, is challenging and requires advanced tools and skills. In our company, we’ve learned that getting the most out of big data requires strong strategies for data control, quality assurance, and security. This makes it difficult to navigate through the data landscape.
Ensuring Data Quality and Veracity
The predominant quandary in the realm of Big Data pertains to data quality and veracity. The proliferation of data sources has led to challenges in data validation, integration, and cleansing, resulting in the “garbage in, garbage out” conundrum.
With voluminous datasets, ensuring data accuracy and reliability becomes intricate, necessitating robust data-governance frameworks, automated data profiling, and advanced anomaly detection mechanisms to fortify decision-making processes based on accurate insights.
Navigating the Overwhelming Nature of Big Data
The biggest problem with Big Data is its overwhelming nature, akin to trying to drink from a firehose. Imagine you’re at an all-you-can-eat buffet with an endless array of dishes, but you can only take a few bites from each plate before it’s replaced with something new.
Big Data inundates us with a deluge of information, making it challenging to sift through and extract meaningful insights. It’s like searching for a needle in a haystack, where the haystack keeps growing and changing.
This inundation can lead to information overload, making it difficult for individuals and organizations to harness the true potential of the data and turn it into actionable knowledge.
Addressing the Shortage of Data Professionals
There’s a massive shortage of big data professionals. Over the last couple of months, many in the big data industry have been struggling to recruit qualified big data professionals. This is often attributed to the constant evolution of the big data industry, while data professionals aren’t. If this problem is familiar, consider investing in AI tools that streamline data processes. Anyone with basic big data knowledge can typically operate these tools, saving money on recruitment.
Scott Lieberman, Owner, Touchdown Money
Integrating and Analyzing Diverse Data
In my experience, the biggest problem with Big Data is the fact that we don’t have enough of it. It’s not that we don’t have enough data—we have tons of data. It’s just that we have so many different systems, and they’re all collecting their own kind of data.
And then there are all these different ways to analyze and interpret it too, so if you’re trying to make sense of all this information, you’ve got to be careful about what questions you ask first, or else you’ll get overwhelmed by the sheer quantity of possible answers.
The thing about Big Data is that you need a lot of patience for it; what looks like an answer might just be a red herring, and then suddenly everything goes dark again.
Finding Patterns Amidst Big Data Noise
One of the biggest problems with big data is that it’s easy to get lost in the noise. It’s easy to think that you’re making progress when really, you’re just going around in circles.
With so much information coming at you all the time, it’s easy to believe that you’re getting somewhere, but really, all you’re doing is running in place. You see something interesting and try to pursue it, but then another interesting thing pops up, and you go after that instead.
Before long, your whole day has passed, and nothing has been accomplished—yet you feel like you’ve made progress because of all these different threads of thought.
The key is not just looking at the trends—it’s looking at patterns. Big data doesn’t have to be overwhelming if you take a step back from it every once in a while and look at what’s happening overall—what are people doing? What do they want? How can we make our product better serve their needs?
Addressing Data Privacy Concerns
One of the biggest concerns and problems with big data is privacy. Data privacy is a major concern that arises from big data. Companies or businesses that use big data are under the radar for unethical practices.
Users claim that they don’t know how much of their personal information is being used by these companies. Even if we don’t enter our data and give consent, we are still being micromanaged based on our behaviors and preferences. So, data privacy is a major concern for users when it comes to big data.