11 Big Data Challenges and Solutions

Photo of author
Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

Spread The Love

What are the big data challenges that businesses confront, and how can they be addressed? In the age of big data, the volume, velocity, and variety of data being generated have created a new set of challenges for businesses and organizations. From managing data to analysis and storage, big data presents a unique set of obstacles that require innovative solutions.

Typical big data challenges include the growing volume of data, multiple data silos, poor data quality, the lack of processes and systems, complex data integration, security concerns related to data breaches and accessibility, insufficient data governance, the lack for data automation, and complex data analysis.

Businesses all around the globe are becoming increasingly reliant on data to operate their day-to-day operations and make educated business decisions. With so much data being created, it has become challenging to manage big data throughout the enterprise, which may be distributed over different geolocations and using tens of business lines applications.

Implementing best practices for big data, following latest trends, and understanding the main big data components can help organization overcoming the challenges associated with it and increase your big data management project’s success while enjoying all the rewards.

We are all aware that data is the new oil for any organization and made positive impact on businesses. Unlocking its worth may result in tremendous opportunities for companies, which is why it is vital to have well-defined big data plans in place in order to face the most difficult challenges that big data implies.

Big data challenges

Big Data Challenges and how to address them

You could encounter lots of barriers while you use big data strategies. You have a lot to watch out for, including gathering too much data and encountering information silos.

The most common big data challenges are:

1- Sheer volume of data

Every day, it is estimated that 2.5 quintillion bytes of data are created, and guess what? The majority of this data is generated by various types of enterprises. As a result, the organization now faces new challenges in terms of obtaining, maintaining, and generating value from data.

With the advancement of digital technologies and the widespread use of mobiles and tablets, the amount of data being generated is growing at an exponential rate. Traditional data management systems can’t cope with this high volume of data.

Organizations need to have a robust and scalable infrastructure to be able to manage and store such large volumes which usually involves implementing special techniques and technologies such as Hadoop or cloud-based storage solutions.

Successfully addressing these challenges requires a combination of scalable infrastructure, distributed computing frameworks, efficient data transfer mechanisms, and effective data curation strategies.

2- Data silos

Data silos pose significant challenges in the realm of big data due to the inability to consolidate all data in one source for data analysis. With this issue in mind, the decision-making process that the higher management of your organization is seeking from the data stored on your premises will be incomplete. This negatively impact the ability to achieve a holistic view of the organization, inhibiting the potential for gaining meaningful insights and making data-driven decisions.

In addition, having multiple data sources scattered across your organization and departments will create redundancy in storage hardware thus increasing the cost.

To effectively leverage the power of big data, one of the most critical tasks to do is to break down these silos and help provide a workplace where data is managed in a centralized way with maximum security.

3- Data quality

Data quality is one of the most critical factors that can positively or negatively impact the reliability and trustworthiness of the insights that should be derived from your data.

However, it is super hard to achieve high data quality with the current growth rate and the multiple sources available. Organizations should overcome some barriers related to data inconsistency, incompleteness, inaccuracies, and duplications to ensure that decisions are made based on high quality data.

Poor data quality can lead to erroneous conclusions, flawed predictions, and unreliable insights, which can have severe consequences for organizations relying on big data analytics.

Addressing these challenges requires implementing robust data governance practices, data cleansing techniques, data validation processes, and data quality monitoring frameworks to ensure the integrity and usability of the data. For more info about this topic, you can check out this guide describing the top tips to improve data quality.

4- Data variety

During the normal operation of any business, data is being collected and analyzed from a wide range of sources including text documents, images, videos, social media feeds, log files, and more. Each data type brings its own set of challenges in terms of processing, storage, and analysis.

The key to succeed is to understand the complete types of data that you are dealing with and define the best way to handle each type.

5- Data integration

Data integration is one of the key pillars to overcome in order to have an effective big data implementation. The main purpose is to have the ability to consolidate data across multiple repositories and systems in one of the best business intelligence tools to be able to slice and dice it for advanced analytics.

This might require lots of efforts at the beginning of the implementation but without it, you will not be able to manage the complete view of your data.

6- Lack of talent

Big data requires specialized skillset and expertise to effectively harness the potential of large-scale data. It involves working with complex data sets, the usage of advanced technologies and techniques, experience with machine learning and artificial intelligence.

The demand for data scientists, data engineers, and data analysts surpasses the available talent pool, leading to fierce competition for skilled individuals. Additionally, these skilled employees need to have the needed patient to always stay up to date in order to remain valuable.

Organizations are struggling to find and retain these qualified individuals who are ready to help store and analyze their data to identify opportunities and gain insights about their daily operations. Bridging the talent gap necessitates investments in training programs, educational initiatives, and creating an environment that attracts and nurtures data-driven talent.

7- Security concerns

As all organizations are moving to the cloud to reduce infrastructure costs and the need for dedicated employees for this task, there are some security concerns that business dealing with sensitive data are confronting.

Without the ability to address these concerns and specially that the number of data breaches cases is increasing daily, these businesses won’t be motivated to invest more in big data.

8- Data governance

Data governance is in charge of establishing rules and regulations for an organization’s information state. Due to the nature of big data, the ability to govern these large volumes of data become more and more complex.

Almost consistently a new rule or industry specific regulations are being added by governments or organizations to provide a roadmap on the best methods to manage customers and users’ information with care.

Read more about the difference between data management and data governance.

9- Data automation

Data automation presents a significant challenge for big data due to the complexity and scale of data processing tasks. Big data environments involve enormous volumes of data that require efficient and timely processing to derive insights and drive decision-making. Automation plays a crucial role in streamlining data-related tasks, such as data ingestion, data integration, data transformation, and data analysis.

Taking this into consideration, automating data related tasks is extremely efficient but challenging to implement as it involves the availability of scalable infrastructure and technologies to drive the data processing workflows.

10- Data analysis

Analyzing big data represents lots of opportunities to better understand how your business is operating and how your customers are interacting with your brand. However, having the ability to process and visualize these huge number of data is certainly not an easy task.

It requires advanced skillset, technologies, and infrastructure to be able to effectively analyze data without any impediments.

11- Going from unstructured to structured Data

According to existing studies, more than 80% of data collected is unstructured, which is a major problem.

Unstructured data includes text documents, social media posts, images, videos, and sensor data, among others, which lack predefined organization or format. Extracting valuable insights from unstructured data requires transforming it into a structured format that can be easily processed and analyzed. However, this conversion process poses several challenges.


In conclusion, the growth of big data has brought significant improvements to businesses and organizations, but also presents a host of challenges. However, by utilizing innovative technologies, organizations can overcome these obstacles and turn big data into a powerful tool for driving business success.

Leave a Reply