The Top 10 Big Data Challenges – And How to Overcome Them

Big Data challenges are much concerning for the business. Big data can be overwhelming to deal with, but it doesn’t have to be as challenging as it seems at first glance. 

Big data is a term that has been thrust into the spotlight in recent years. It's a topic that has been discussed by business leaders and analysts alike, but what exactly is it? And how can you use it to your advantage? We're here to answer these questions and more by diving deep into 10 common challenges faced by companies looking to leverage big data for their own benefit.

Table Of Contents

• What is Big Data?
• Lack of Big Data skills
• Busting Business Myths About Big Data
• Lack of Management Buy-in
• Poor Corporate Strategy
• Security and Privacy Issues
• Information (Un)Governance Issues
• An Incomplete Landscape of Tools
• The Skills Gap in Analytics Professionals
• Lack of Infrastructure and Talent Support
• Conflicting Objectives
• Conclusion

What is Big Data?

Big data is a term used to describe the ability to collect, store, and analyze large sets of data. As the amount of information stored in digital form continues to increase exponentially, so does its potential power as a tool for solving problems. This can be especially true when dealing with complex systems that are difficult or impossible to solve using traditional tools.

Big Data has become an essential component of modern life because it allows us to better understand customers' needs—and how those needs change over time—as well as their preferences based on past experiences or habits (e.g., what kind of coffee they prefer at Starbucks). The 5 V's of big data or its characteristics encourage business to adopt this technology.

Here are the top Big Data Challenges and some solutions

1. Lack of Big Data skills

The lack of Big Data skills is a very concerning big data challenges. The lack of Big Data skills is a real problem. If you’re not skilled in data management and analysis, it will be hard for you to solve the problem.

• This is why we need more people who understand the technology involved in big data—but also people who can use that technology in innovative ways.
• The second way is to implement training programs that teach people how to use the technology and how they can analyze its results.
• The third solution is to hire more people with Big Data skills.

Now that’s not something companies can easily do—but it’s definitely an option worth considering. In fact, most companies are already doing it!

2. Busting Business Myths About Big Data

When you think of big data, what comes to mind? The image of a giant server farm somewhere in the middle of nowhere? Or maybe an entire city filled with sensors measuring everything from air quality to traffic flow to energy consumption in order to save money on energy bills.

But while all these things are true, they don't tell us everything we need to know about big data and its potential benefits for organizations. In fact, large amounts of data are just one aspect that makes up this term—there's also the speed at which your organization can analyze them (i.e., "big" here isn't limited by physical size), their variety (which can come from multiple sources such as social media posts or customer interactions), and their volume: how much information exists within each system?

Why does this matter? Because if you're going through all this trouble just so you can avoid making mistakes or improve efficiency by analyzing existing processes more effectively then why not just build another system instead?

The way to solve this problem is by doing Two things: 

First, you need to understand what makes up big data. 

Second, once you know where all of these pieces come from and how they're connected, then you can start building new processes that leverage them.

3. Lack of Management Buy-in

Lack of management buy-in is a common problem. It is natural  big data challenge. Many companies are reluctant to invest resources into big data, because they don't have the support from their executive team or board members.

Why is this important? Because without management buy-in, you'll struggle to scale your solution and get results fast enough to justify the expense involved in doing so. You may also find yourself unable to move on from one project after another because there's no easy way out of the current one once it's underway—you'll be stuck with an expensive infrastructure for years!

How do you get management buy-in? 

The first step is having an open dialogue about why it's important for them—and then showing them how this will benefit them personally (or at least indirectly). Once that happens, teams can work together as partners toward achieving common goals: 

• Improving customer service processes across multiple departments; 
• Reducing costs by automating manual processes; 
• Improving employee productivity by using data analysis tools like predictive analytics software packages such as Tableau Desktop Data Visualization Software For Businesses In India With A Free Trial Version That Helps You Get Started Quickly Without Spending Any Money On Software Licenses Or other things like those here that can help you get started.

4. Poor Corporate Strategy

One of the dangerous challenges of big data is Poor Corporate Strategy. In this case, the company doesn’t have a clear vision or goal. It also lacks a clear mission and strategy for achieving it. If you want your employees to be successful in their roles, then they need to know where they are going and how they will get there. Without this information, it’s impossible for them to make any meaningful progress toward achieving their goals and objectives.

A good way to avoid this type of problem is by providing regular updates on your company’s progress toward its goals. You can also use data analysis tools to track performance metrics over time, so that you have an idea if you are making headway in the right direction. Not just measuring success is important; it’s also setting up a framework for doing so.

Once you set up a framework for measuring success, you can use it to track your progress over time. This will help you determine if the changes that you are making are having an actual impact on company performance. If they aren’t, then it may be time to rethink them.

5. Security and Privacy Issues

Security and Privacy Issues are the vital challenge for big data. It is important to note that security and privacy issues are at the forefront of big data. Security and privacy are not just concerns for businesses, but also for consumers. While this may seem obvious, it's worth considering that many people have little or no knowledge about how their data is being used by companies or governments—or even what kinds of information those entities have access to. This lack of understanding can lead them down dangerous paths when attempting to protect their own personal information online.

In order to ensure your company stays ahead in this race towards greater transparency and accountability (and therefore success), you need an effective way to address these areas:

• Security and Privacy Issues
• Ensure Data is Accurate, Complete and Correct
• Make sure Your Data is Obtainable When Needed
• Don't Let Your Data Get Corrupted or Lost (Back it Up!)
• Ensure that Your Data is Secure
• Encrypt Everything

6. Information (Un)Governance Issues

One of the most difficult problems that big data creates is around information governance. This issue has to do with how we manage and use data – both internal and external, current or historical – for the purpose of decision-making. When you’re dealing with so much information in one place, it can be easy to make mistakes or even cause harm if you don’t know what you are doing!

In order to solve this problem, there are several things you need to consider:

• Managing data quality: Data has to be of high quality in order for it to be useful. You can’t use bad data – so how do we ensure that what goes into our system is accurate?
• Managing data governance: How do we decide who gets access to certain types of information? Who has power over what kind? How do we keep track of who does what with this resource (auditing)?
• Managing data security: How do we secure the information from unauthorized
• Managing data privacy: How do we ensure that no one violates anyone else’s privacy?
• Managing data lineage: How do we track all of this information as it moves from place to place, person to person and system to system?

This problem can be solved by implementing a big data governance strategy and tools. These will help with decisions about who has access to what type of information, how we audit those decisions, how secure our systems are so that only authorized people can get access to the data and how we track its movement.

Data governance is not just about making sure that information is protected, however. It also means ensuring that it’s used in a way that makes sense for the business. For example, if there are multiple data sources in one system — such as customer satisfaction surveys, website analytics and call center metrics — they should be integrated so that they can be used together to give a complete picture of customer needs.

7. An Incomplete Landscape of Tools

You might think that big data is a new concept, but it's actually been around for a long time. The term was coined by Google in the early 2000s and has since become an umbrella term for large volumes of data that can be collected from multiple sources.

Big Data is used in many different fields including:

• Business intelligence (BI) – this involves using analytics to help companies better understand their customers' preferences, needs and trends;
• Healthcare – healthcare organizations collect large amounts of patient records and other health information to improve outcomes through better diagnoses;
• Science & Technology – scientists use big data tools like machine learning algorithms or image recognition software tools to analyze massive collections of photographs taken over time periods ranging from seconds up through several years;

This problem can be solved by using big data tools, which allow users to analyze these massive volumes of information and create meaningful insights.

The next generation of big data will be a huge challenge for companies and organizations. It is imperative that they are prepared with tools that enable them to analyze these new types of data sources efficiently.

8. The Skills Gap in Analytics Professionals

Another big data challenge is the skills gap in analytics professionals.

There are a number of skills that all data scientists, engineers and architects should have. This includes:

• An understanding of technology trends and innovations. For example, you need to know how machine learning is evolving in order to stay ahead of other companies' use cases or products that may become available as a result.
• A basic knowledge of statistics, which helps when it comes time for you to make decisions based on data analysis (e.g., "Should we launch this new feature?").

Further down the line, if your organization wants its employees who work with big data analytics professionals on their team—or even if it doesn't—you'll want them all at least familiar with some basic concepts like graph theory or network analysis; these are necessary for building effective models from large datasets without getting lost in technical details or formulas (which could otherwise lead them into trouble).

Of course, before any of these specific skills can be acquired through training programs or mentorship opportunities, each organization needs to hire for these roles first. But hiring managers can't just hope that there will be enough experienced applicants out there to fill their needs—instead they need proactive strategies:

• Partnering with other companies in order to expand your reach and find the best talent available.
• Looking into current employees who can be trained on big data skills, or even considering recent graduates who may not have experience but have studied relevant subjects.

The skills gap isn't going to be solved overnight, but if your company starts taking steps now to fill its roles with qualified people then they'll have an edge over competitors who wait until after they've already missed out on good opportunities.

9. Lack of Infrastructure and Talent Support

As you can see, the lack of data scientists and technical infrastructure are two big challenges for big data that must be overcome if you want to be successful with big data. However, there are ways in which these issues can be solved:

• Hire more people who know how to use algorithms and machine learning techniques on a daily basis (this will also help with the other challenges).
• Build out your own internal team of engineers who specialize in using these tools for engineering applications such as building mobile apps or web sites.

Big data is a big challenge. There are many different issues that need to be addressed when it comes to implementing this technology within an organization. However, if you're able to overcome these obstacles and make changes within your own company then there's no reason why this shouldn't work out for yourself as well as other businesses in the future.

10. Conflicting Objectives

This is one of the most challenging big data challenges because it requires you to make decisions that may not be in your organization’s best interest. For example, if you have two different departments within your company that have very different goals and objectives, then you will have to decide which department should be given access to the most data. If one department wants to monitor its sales performance while another wants to analyze customer behavior patterns, then how do they both get what they need from their data? This can lead some organizations down a rabbit hole where resources are wasted on projects that no longer serve any purpose or impact positively on business results (e.g., getting rid of unused equipment).

To overcome conflicting objectives, you must first decide what your company’s main objective is and then make sure everyone in your organization understands this goal. Then, ensure that each department has the resources necessary to achieve its own objectives while still aligning with corporate goals.

Conclusion

Big data is becoming increasingly important in today’s business world. However, with this increase in importance comes a number of challenges. It’s important to be cognizant of these challenges, but it’s also important to remember that they are not insurmountable. In fact, they can be overcome with the right approach and tools. It may take time, but companies will see success as they learn how to leverage big data effectively in their organization.

In this blog post, we discussed the top 10 big data challenges and provided solutions for each. By understanding and preparing for these challenges, you can ensure that your big data initiative is a success.