Top 10 Big Data Challenges and Solutions

Top 10 Big Data Challenges and Solutions

Are you unable to use all of your data? You may be facing some of the biggest big data challenges that can prevent you from fully utilizing this resource. You are not the only one.

Understanding your enemy is key to winning the war. Let’s look at the top big data challenges and the solutions that can help us solve them. First, let’s review what big data is and give you some examples and business cases. Big Data Challenges are the difficulties that arise when processing, storing and analyzing large volumes of data. These challenges can include data volume, data velocity, data variety, and data complexity.

What is big data? And why is it important?

Big data is the exponentially increasing volume of data that exists within an organization in many formats and often comes from multiple sources. It is multiform, massive, and scattered all over. These characteristics are the reason for most of our big data problems that we will discuss in this post.

An inventory spreadsheet from the previous year? Not big data. data warehouse management includes employee performance records, inventory files, facility usage data from smart lights and power management systems, location trackers, heatmaps, and warehouse management data. This is the one.

Big data is a significant factor in how organizations make decisions, design products and manage their businesses in almost every industry.

Major retail brands such as Nike, for example, use big data technologies to track consumer trends and adjust their marketing campaigns and product strategies. Tesla and other disruptive brands have broken the monopoly, building their entire businesses on data. Tesla products, from self-driving cars and roof tiles to roofing tiles, rely heavily on data at a large scale.

Every other major brand in every key industry, from Maersk in shipping and Netflix in streaming, is using its resources to effectively run operations. Despite all the investments and the abundance of tools available, only a small percentage of companies are able to extract value from their data.

Big data presents a number of challenges. These include technological, organizational, as well as operational, constraints. Let’s break these problems down into smaller, more manageable issues and provide actionable solutions.

Also read: Big Data Analyst: What it is, Tools, Career and Salary

Top 10 challenges of big data and Solutions

1. Unmanageable volume

Big data lives up to its name. Companies have terabytes, if not managed well, exabytes, of data. This data is constantly growing and can quickly get out of control. Businesses can’t keep up with the growth without adequate infrastructure, computing power, or architecture. This means they miss out on opportunities to extract value from data assets.


  • Use management and storage technologies to address the ever-increasing volume of big data and the challenges associated with managing it. You have to make sure that your choices are compatible with your company’s goals and needs, regardless of whether you use cloud or on-premises hosting. On-premise hosting won’t scale quickly – you will need to increase your infrastructure and admin staff – but it is a great choice if your data needs are very specific., whether public, private, or hybrid cloud solutions offer you incredible flexibility and resources to manage any data volume. This is especially important if you don’t have the computing power to do it in-house. However, the cloud has a recurring price.
  • Create A scalable architecture with tools that will allow you to adapt to growing data volumes without compromising your integrity. The following example shows how Azure Cosmos DB was used to create a scalable data processing platform for Oxford University Press.

2. Bad data can lead to poor results

The biggest challenge of big data is poor quality, which costs the US more than $3 trillion annually. How can so much money be wasted? Inefficiency and poor-quality data can lead to mistakes, inefficiency, or misleading insights that in the end translate into business costs.

For example, a company may not be able to match one customer to the correct order because of an incorrect entry. Bad data can also cause it to be costly and end up costing millions.

What is bad data? The quality of the entire set may be compromised by duplicate, inaccurate, incomplete, inconsistent, or missing data. Even small errors or inaccuracies can lead to big data problems. It is important to monitor its quality. It could cause more harm than good.


  • Good data hygiene starts with having the right people and processes in place to manage the data. It is important to establish appropriate data governance. This will determine the tools, procedures, and protocols for data management and access control.
  • You can create an efficient process to cleanse, filter, sort, enrich, or otherwise manage data with modern data management tools. You probably have all the tools that you need if you use one of the most popular cloud solutions.
  • You need to be able to sort and peel data according to your business goals. You may need to hire business people to determine requirements for data quality.

Also read: What are the Challenges of Network Security Management

3. Multiformat deals

It may shock you to learn that most of the data ( 80-90% according to ) we have are not structured. It doesn’t belong in a database table (emails or customer reviews, etc.). It’s likely unstructured, or semi-structured. Another challenge with big data is how to combine heterogeneous data into a format that meets your business intelligence requirements and matches the requirements for the tools you use to analyze, visualize, predict, etc.


  • Learn how to use the latest data processing technologies and tools to format unstructured data and draw insights from it. You may need to use multiple tools to analyze data if you work with different formats (e.g. text and image recognition engines that are based on machine learning) and extract the information that you require.
  • Create or adopt custom applications to accelerate and automate the conversion of raw data into valuable insights. Your business’s unique needs will determine which application you choose, as well as the nature and source of your data.

4. Multiple sources and integration hurdles

You would think that more data is better. In many cases, however, more data does not equal more value unless you are able to combine it for analysis. Integrating diverse data sets and creating connections that lead to insights is one of the biggest challenges in big data projects.

This makes it a double-edged challenge. You must first decide when it is a good idea to combine data from different sources. For instance, if you want to get a 360-degree view of customer experience, You need to gather reviews, sales, and performance data for joint analysis. The second step is to set up a space and a collection of tools for integrating and preparing the data for analysis.


  • Create an inventory to understand where your data is coming from, and to determine if it makes sense for you to combine it for joint analysis. This task is largely a business intelligence task because it’s the business people who must understand the context and determine the data they need in order to achieve their BI goals.
  • Data integration tools can be used to connect data from different resources, such as files, databases, and data warehouses, and prepare them for big data analytics. You can use Microsoft, SAP, Oracle, or other specialized tools for data integration, such as Precisely and Qlik, depending on which technologies you have in place.

5. Data projects and infrastructure are expensive

Executives in the US admit it to 50% and 39% in Europe. A limited IT budget is one of their biggest obstacles to maximizing the value of their data. Implementing big data is costly. This requires planning and significant upfront costs. It may not pay off immediately.

The infrastructure also becomes more important as the data volume increases exponentially. It can be easy to lose track of your assets and the costs of managing them. Flexera estimates that up to 30% of money spent on cloud computing is lost.


  • Monitoring your infrastructure continuously can help you solve many of the cost-related problems that big data is causing. Effective DataOps and DevOps practices can help you keep track of the resources and services that you use to store, manage and analyze data. They also identify cost-saving opportunities and balance scaleability expenses.
  • When creating your data processing pipeline, consider the costs early. Are there duplicates of data that increase your costs? To optimize your data management costs, can you group your data according to business value? Are you using data archiving or forgetting? These questions will help you to create a solid strategy and save tons of money.
  • Choose cost-effective instruments that meet your needs. Cloud-based services are typically provided on a pay-as-you-go basis, meaning that your expenses will be directly affected by the computing power and services you use. The number of big data solutions is growing rapidly, allowing you to combine and choose.

6. Scarce big data talent in-house and in the market

Talent shortage is one of big data’s most difficult and expensive problems. There are two reasons. It’s becoming increasingly difficult to find skilled tech talent for a project. Demand for data scientists, engineers, analysts, and other technical specialists is already higher than the supply. As more companies invest heavily in big data projects and compete for top talent, the demand for specialists will skyrocket.


  • Partnering with a reliable and experienced tech provider is the best way to address a talent shortage. This will allow you to quickly fill the gaps for your big data or BI requirements. If you are unable to hire in-house, outsourcing your project could save you money.
  • Your team and you know your data better than anyone. You can upskill your existing engineers and keep the talent in-house.
  • Make visualization tools and analytics available to non-tech experts within your company. Your employees should be able to access insights and integrate them into decision-making processes.

7. Slow time to insight

Time to insight refers to how quickly you can get insights from your data before they become outdated or old. The slow time it takes to see big data is due to inefficient data management strategies and cumbersome data pipelines.

This parameter is critical for certain business cases more than others. For example, by comparing consumer behavior analysis based on quarterly data with IoT real-time data analytics for monitoring equipment, you can see how critical this parameter is. While the first can handle delays of days, or even weeks, the second can be prone to serious problems if there is even a slight delay.


  • Partnering with a reliable and experienced tech provider is the best way to address a talent shortage. This will allow you to quickly fill the gaps for your big data or BI requirements. If you are unable to hire in-house, outsourcing your project could save you money.
  • Your team and you know your data better than anyone. You might consider upskilling your engineers in order to acquire the required competence and keep the talent in-house.
  • Make visualization tools and analytics available to non-tech experts within your company. Your employees should be able to access insights and integrate them into decision-making processes.

Also read: Top 10 Open Source Big Data Tools and Software

8. No clear understanding of how to get and use insights

One thing is to extract insights. It’s another to put them to work. The second is the most important. If it doesn’t, your whole big data strategy could fail because it won’t produce any returns.


  • Make a business case for your project. Get business people involved to help you understand the data and what you can do with it.
  • Use advanced analytics to discover new ways of understanding insights and make them easily accessible for everyone in your organization.
  • Modern visualization tools, dashboards, and interactive experiences are available to drill down, analyze insights, create reports, and communicate data throughout the organization.

9. Security and compliance

Compliance and defense consume more than a third of the big data budget according to a NewVantage survey. This is not surprising considering the growing pressure from strict privacy regulations as well as big data security risks.


  • Use big data to improve your strategy, planning, and design. Treating it like an afterthought can lead to serious data problems and huge fines.
  • Check your data and sources against compliance requirements that are applicable to your niche or your location e.g., GDPR in the EU, HIPAA and HITECH Act for healthcare data in the US, etc.

10. No one-size-fits-all solution for all data needs

There is a large market for platforms, cloud suites, and AI services analytics, visualization, and dashboarding tools that can cover all your needs. Every single big data project we have worked on required a customized approach to selecting the right services and strategies to deliver actionable insight on time and within budget.


  • Do a technology assessment to evaluate available solutions and tools in relation to your business goals, objectives, infrastructure, budget, and scaling requirements.
  • Hire an experienced big-data service provider with both technical expertise and development resources to help you select the right tools for your project and implement them. Then, optimize and maintain it based on changing requirements.
Written by
Denis Bitson

Denis Bitson is content editor of The Next Trends. He is passionate about sharing his technical knowledge through engaging blogs and articles. Enthusiastic about exploring the latest gadgets and indulging in video games.

Leave a comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Articles

Jobs Lost To Technology

Top 10 Jobs Lost To Technology: Threat by AI, Technology and Automation

Numerous jobs have been lost to technology over the years, including those...

Tech News Apps

Top 10 Tech News Apps for Latest Technology News

You can get the latest tech updates, as well as popular tech...

5 Best Practices for Building a Data Warehouse

5 Best Practices for Building a Data Warehouse

Data warehousing can be a powerful tool for creating a vault full...

Top 10 AI Algorithms You Should Know

Top 10 AI Algorithms You Should Know

Artificial intelligence (AI), from self-driving vehicles to multimodal chatbots, is advancing rapidly....