It’s amazing how fast data is now able to be collected. In fact, with such an abundance of data, big data is growing faster than ever and leading to many successful innovations across industries. But, do you know what are the Big Data Challenges?
Organizations like yours have to keep up with all these changes, whether they’re introducing artificial intelligence or harnessing the power of machine learning, to continue growing and staying competitive with others in your field.
While that all sounds reasonable, working with all the data you collect can also be troublesome. It is normal for companies to run into challenges when trying to use the data they’ve collected, especially if they don’t have a solid data strategy.
The benefits of accessing and using it are huge, but you still have to have the infrastructure and ability to integrate it into your daily work.
Do you want to know more about the big data challenges that you may run into as you create your big data strategy? Here are some important issues to keep in mind.
Top 10 Big Data Challenges
There are dozens of challenges that you could run into as you work with big data strategies. From collecting too much data to running into data silos, you have a lot to look out for.
We’ve put together this helpful list of 10 of the greatest challenges, so you can prepare to handle them if they become a problem for your business. By identifying the possible issues now, you can avoid serious issues that could negatively impact your business in the future.
1. Finding and fixing data quality issues
Data quality is one of the most important things to keep in mind when you’re collecting data for your projects. You want to be sure your system collects accurate data that is still valid while removing data that no longer applies.
Your data lifecycle starts with the collection phase. During this phase, you’ll want to know that your data is being collected from the correct sources at the right time.
Next, you need to be sure that it is stored in the right place and is accessible for analysis.
Maintenance, the third stage of the data lifecycle, is when you or your automated processes can review the data that is present and make sure that it is available to the right teams when they need it. You’ll need to validate the data and move it to the correct location.
Fourth, you have data usage, which is the stage where you can access data and make informed decisions based on the information in front of you. You can see that if any of the previous three steps have errors, you could be making decisions based on faulty data.
The fifth stage of the data lifecycle is data cleaning, and it is also important for finding and fixing data quality issues.
During this stage, you’ll delete, destroy, purge, or archive data depending on its value and if it is still accurate. Additionally, since storing data can get expensive, you’ll want to take part in this part of the lifecycle regularly to keep down the cost of data storage.
Beneficially, you’ll save money by doing this, but you’ll also be sure that the data you keep is of a higher quality and still important for your projects.
2. Long system response times
When you input data into your system, you want it to be processed quickly. When you want something analyzed or want to draw up a form, you need the data to be ready for export.
Unfortunately, long system response times can occur because of the expansive nature of data on the cloud. Real-time delays can cost you, though, especially when a report is due immediately.
How can you fix this issue?
Start looking into how your data is organized as a first step. Re-engineering the way data is stored could keep the data you want closer to the surface, so you can quickly grab it.
Another option is to look for a different data system that can be scaled beyond what this one is capable of. For instance, if your current data solution has reached its scalability limit, it may be that your company has simply outgrown that software or platform.
3. Dealing with data integration and its complexities
One of the biggest issues that firms run into is that to use data you have to be able to integrate it. Big data platforms help by being able to store large amounts of data for your company. It’s important, though, that this data is easy to access.
There are different ways to store your data. You could use a catch-all repository on the cloud, for example, to be sure it’s always available in one centralized location.
If you’ve ever tried to merge old databases with a brand-new SaaS tool, you know it’s a dance of mismatched formats, legacy weirdness, and, sometimes, just sheer stubbornness from IT. Getting all those streams to play nicely so you don’t spend more time cleaning up errors than analyzing anything—that’s no small feat. In 2025, vendors keep promising frictionless workflows, but in reality, there’s always at least one oddball system that refuses to cooperate, making data integration a kind of full-time puzzle-solving gig.
And underneath the technical headaches, there’s the human side nobody warns you about—people with a little too much attachment to their favorite tools or a workflow they swear “works just fine.” Convincing them to switch or standardize is less about software and more about negotiation (often over bad coffee at 4pm). More often than not, integration comes down to buy-in: get the humans aligned first, or you’ll still have data islands no matter how fancy the tech stack is.
It’s easy to underestimate just how much manual effort is still involved. You’d think by now, with all the automation talk, that half this work would be push-button simple. But almost every major integration project I’ve seen this year ends up coming down to someone building spreadsheets by hand just to crosswalk mismatched fields. The real irony is, even the best APIs don’t save you when no one can agree what “customer name” actually means between systems.
Sometimes folks get so caught up wrestling with formats and field mappings that they overlook the bigger issue—whether the data itself even belongs together in the first place. It’s not just a technical merge, it’s a philosophical one. And now and then, once everything is loaded up, you realize the analytics you were after don’t quite work because you mashed up apples and oranges. That aftermath, the sense of “wait, did we actually solve the real problem?”, is pretty common. At least in practice, no integration toolset replaces a clear discussion up front about what you’re really after.
4. Scaling big data systems while being cost-efficient
Big data systems are great because they are often easy to scale, but you have to have your plans for keeping track of data and cycling old data out.
That’s why your team has to determine the types of data you’ll collect, how it will be stored, and how it will be used before implementing a data system.
For example, you may want to use a repository in the cloud, but when doing so, it could make more sense to have Parquet files to store like data together.
If you have no method of organizing your data, you could find that it’s much harder to retrieve what you need and that it’s harder to manage your data as you continue adding more when your company grows. (As an added benefit, keep in mind that Parquet files generally have a greater performance-to-cost ratio than CSV dumps).
5. Expensive growth due to increased storage needs
With such an abundance of data, it’s easy to save more than you are right now once you convert to a cloud-based data solution. The cloud makes it easy for companies to save more granular data, but in doing so, they may need much more capacity than they planned for.
What does that mean? It means more expenses. Costs can quickly grow as your company realizes the need for more data storage space.
To help avoid this, you do need to implement fine controls over queries, so unnecessary data isn’t saved but your necessary data is stored exactly where you need it.
6. Trouble with data governance
Another thing to watch out for is trouble with data governance. As your big data applications grow, it can become harder to manage governance issues.
You need to use built-in governance rules from the start of any new data process, so you don’t accidentally hinder the kind of data access you were looking for.
7. Expensive maintenance
Maintenance is also an expense that you have to keep in mind with big data. Any system maintaining your data has to be kept in working order. You need to be sure that the infrastructure is sound and that the technologies aren’t outdated.
If you find that the technology is outdated, you may want to update to faster, cheaper methods of storing, analyzing, and processing your data.
If costs are high, looking into a cloud-based platform may be a better solution, since they tend to offer pay-as-you-go options. Or, if you find that your system has too much to offer for what you want to do with it, it may be time to downgrade to something simpler to save money.
8. Inaccuracies when analyzing data
Another problem some people run into is receiving inaccurate analyses from their data. There are normally two reasons for this:
- Poor quality source data
- System defects
If there are errors or defects, you can expect that there will be poor results. Make sure to test your platform and verify each part of the development to identify problems and ensure your data is handled correctly.
9. You’re struggling with silos
Another problem you may run into is trouble with silos. Data silos slow everyone down, because they limit access to your data.
Storing your data on separate databases is the most common cause of data silos, so consider upgrading to a cloud-based platform with a centralized storage area for your data.
10. Unprotected, unsecured data
Finally, remember that your data is important and needs to be secured. If the platform you’ve decided to use doesn’t have good security, your system will be open to viruses, malware, and external infiltration.
Wrap Up for Big Data Challenges
There are many big data challenges that you can run into as you build your data strategy. It’s necessary for you to think about the way you collect, store, manage, use, and delete data, so you can keep that data up to date while also being sure it is still available to those who need it.
