Big Data Challenges and How to Deal with Them

big data challenges woman in front of numbers

Big data challenges are serious issues faced by nearly all organizations. Before we delve deeper into the topic, it is practical that we first comprehend what big data is. Big Data defined by one business may not fit into the definition of the same for another. 

At the same time, big data defined by a small company may not fit into a large corporation’s definition of big data. For this reason, it would not be practical to define big data in terms of bytes.m

What is Big Data?

Most experts define data using the three Vs. – Volume, Velocity, and Variety. However, over the years, big data has transformed the face of businesses across many sectors. The positive impact of big data on companies is an irrefutable fact.

Today, companies can gather and evaluate vast amounts of data that can unlock unmatched potential. Big data solutions can help forecast market trends, discover new demographics, and achieve greater heights of success with comparatively lesser effort.

Challenges Associated with Big Data

Yet, big data analytics has a few challenges. Some of the big ones are:

Management of Huge Volumes of Data

The speed at which big data is being created surpasses the rate at which computing and storage systems are being developed. It is becoming a colossal problem for organizations to handle unstructured data. With the volume of unstructured data growing every year, the challenges are proliferating. 


Businesses can leverage AI-powered unstructured data analytics tools to manage problems better. These tools are specially created to help users get detailed insights into unstructured data. Businesses can generate meaningful information from large volumes of unstructured data created daily using AI algorithms.

Shortage of Data Scientists

With more businesses using big data to manage their business, the demand for data scientists has skyrocketed in recent years. Companies need experienced and well-trained data analysts to effectively handle big data and its analysis. Those who have data scientists on their rolls find it difficult to retain them. Training of entry-level technicians is extremely expensive. 


Organizations are exploring self-service analysis solutions powered by machine learning, AI, and automation. These systems tune to extract meaning from data and involve minimal manual coding. 

Data Governance and Security

Big data involves collecting and collating data from multiple sources. Many of these sources use specialized data collection methods and have distinct formats. Also, it is very likely that companies may experience inconsistencies in data even with comparable value variables.

Adjustments have to be made when dealing with such a situation to get the appropriate outcome. This process is called data governance. 

Indeed, there is no denying that the accuracy of big data is questionable. Some types of data may contain wrong information and even duplication and contradictions. Certainly, such inferior quality data cannot be of any value. So, organizations need to focus on big data’s consistency, quality, and accuracy. 


Data cleansing techniques are available on the market precisely to deal with such problems. The company must make sure its big data has a proper model. They must also identify unwanted data and place them in the purging automation system before the data collection process.

Data Security and Integrity

Data security and integrity create yet another big data-related problem faced by many organizations. The presence of a large number of channels and interconnecting nodes may put the data at high risk. Hackers with sophisticated tools can take advantage of a vulnerability in the system.

Also, the nature of data can result in huge losses, even from a minor mistake. Organizations must stringently adhere to best security practices and incorporate data security tools, including automated pen-testing tools, in the data handling systems. 


It is imperative to prioritize security over anything else while handling big data. More attention must be focused on data security and integrity right at the stage of designing the data system. It might not be a good move to wait until later stages to incorporate data security aspects.

Big Data Handling Costs

The management of big data is a hugely expensive aspect for any business. Companies looking to use an on-premises solution must invest heavily in new hardware, specialized skills, and power. Also, the additional cost of developing, configuring, and setting up the system exists. Ongoing maintenance means another cost-contributing factor. 


Your big data spending will correspond to your organization’s business goals and precise technological needs. Certainly, if flexibility is a priority, it’s better to look at cloud-based big data solutions. On-premise setup is recommended for firms with stringent security requirements. Organizations can also choose hybrid solutions. In this solution, portions of data store and process in the cloud.  The other part is securely placed away on-premises. This solution is also cost-effective to some extent and hence practical to use.

Data Integration

Big data integration involves incorporating data from various resources into one version. They can be helpful to everyone in the organization. Handling a surge of data from multiple sources, in different forms, and from other software systems can be pretty challenging. 

Using multiple and exclusive data processing platforms makes the task even more challenging for organizations. It is important to simplify the IT infrastructure for easy data handling, and smooth data process flows. 


Organizations often want to take the manual means. It may look like an easy and cheaper option. But in the long run, it will not be cost-effective. The better option is to choose advanced automation tools with pre-built APIs, databases, and files.


As it is evident, there are a host of compelling challenges that organizations have to deal with while using big data. At the same time, there are practical and easily applicable solutions also available to handle these challenges. These challenges may vary from time to time and from one organization to another. The key to the success of big data handling is to apply the solutions by keeping the company’s goals and technological needs in mind.

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