Top 6 Major Challenges of Big Data & Simple Solutions To Solve Them

No organization today can operate effectively without data. Data, generated incessantly from various sources like business transactions, sales records, customer logs, and stakeholder interactions, serves as the driving force behind companies. This colossal collection of data is what we refer to as Big Data. However, working with Big Data presents significant challenges.

For career professionals aspiring to excel in this field, it’s essential to recognize these major challenges of Big Data. These challenges encompass issues such as data quality, storage, a shortage of data science professionals, data validation, and the integration of data from diverse sources.

Exploring and overcoming these challenges is a crucial aspect of managing and deriving value from Big Data. This data needs to be analyzed to enhance decision making. To gain a competitive advantage in this data-driven era, consider exploring our free courses, which can help you navigate and conquer these hurdles effectively.

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What Is Big Data?

Big data encompasses the vast and intricate datasets that permeate today’s digital landscape. It derives from sources such as social media, sensors, and business transactions, boasting immense volume, high velocity, and diverse data types. From my firsthand experience, I understand that analyzing big data unveils invaluable insights, empowers informed decision-making, streamlines processes, and reveals crucial patterns and trends. As mid-career professionals, grasping this concept is imperative in our data-centric business environment. It opens doors to a multitude of opportunities and, as I’ve encountered, presents us with the challenges and complexities that come with harnessing the potential of big data across diverse industries.

The Four ‘V’s of Big Data

The Four ‘V’s of Big Data are key attributes that describe the nature of large-scale data sets: 

• Volume: This refers to the sheer size of data, often exceeding the capacity of traditional databases, as it accumulates rapidly from various sources. 
• Velocity: Denotes the speed at which data is generated and must be processed, particularly important for real-time analytics and decision-making. 
• Variety: Encompasses diverse types of data, including structured, semi-structured, and unstructured data like text, images, videos, and more. 
• Veracity: Addresses the reliability and accuracy of data, acknowledging that big data can contain errors and inconsistencies. 

Big data comes from many sources like social media, sensors, and transactions. However, it brings unique challenges known as the “4 Vs”: Volume (amount), Velocity (speed), Variety (types), and Veracity (accuracy).

Comprehending these ‘V’s is essential for professionals to harness the potential of big data for improving organizational performance and competitiveness. 

Challenges of Big Data

Many companies get stuck at the initial stage of their Big Data projects. This is because they are neither aware of the challenges of Big Data nor are equipped to tackle those challenges. The challenges of conventional systems in Big Data need to be addressed. Below are some of the major challenges of big data in business and their solutions.

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Let us understand them one by one –

1. Lack of proper understanding of Big Data

Companies fail in their Big Data initiatives due to insufficient understanding. Employees may not know what data is, its storage, processing, importance, and sources. Data professionals may know what is going on, but others may not have a clear picture.

For example, if employees do not understand the importance of data storage, they might not keep the backup of sensitive data. They might not use databases properly for storage. As a result, when this important data is required, it cannot be retrieved easily.

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Solution

Big Data workshops and seminars must be held at companies for everyone. Basic training programs must be arranged for all the employees who are handling data regularly and are a part of the Big Data projects. A basic understanding of data concepts must be inculcated by all levels of the organization.

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2. Data growth issues

One of the most pressing challenges of Big Data is storing all these huge sets of data properly. The amount of data being stored in data centers and databases of companies is increasing rapidly. As these data sets grow exponentially with time, it gets extremely difficult to handle.

Most of the data is unstructured and comes from documents, videos, audios, text files and other sources. This means that you cannot find them in databases. This can pose huge Big Data analytics challenges and must be resolved as soon as possible, or it can delay the growth of the company. 

Solution

In order to handle these large data sets, companies are opting for modern techniques, such as compression, tiering, and deduplication. Compression is used for reducing the number of bits in the data, thus reducing its overall size. Deduplication is the process of removing duplicate and unwanted data from a data set.

Data tiering allows companies to store data in different storage tiers. It ensures that the data is residing in the most appropriate storage space. Data tiers can be public cloud, private cloud, and flash storage, depending on the data size and importance.

Companies are also opting for Big Data tools, such as Hadoop, NoSQL and other technologies. 

This leads us to the third Big Data problem.

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3. Confusion while Big Data tool selection

Companies often get confused while selecting the best tool for Big Data analysis and storage. Is HBase or Cassandra the best technology for data storage? Is Hadoop MapReduce good enough or will Spark be a better option for data analytics and storage?

These questions bother companies and sometimes they are unable to find the answers. They end up making poor decisions and selecting inappropriate technology. As a result, money, time, efforts and work hours are wasted.

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Solution

The best way to go about it is to seek professional help. You can either hire experienced professionals who know much more about these tools. Another way is to go for Big Data consulting. Here, consultants will give a recommendation of the best tools, based on your company’s scenario. Based on their advice, you can work out a strategy and then select the best tool for you.

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4. Lack of data professionals

To run these modern technologies and Big Data tools, companies need skilled data professionals. These professionals will include data scientists, data analysts and data engineers who are experienced in working with the tools and making sense out of huge data sets.

Companies face a problem of lack of Big Data professionals. This is because data handling tools have evolved rapidly, but in most cases, the professionals have not. Actionable steps need to be taken in order to bridge this gap.

Solution   

Companies are investing more money in the recruitment of skilled professionals. They also have to offer training programs to the existing staff to get the most out of them.

Another important step taken by organizations is the purchase of data analytics solutions that are powered by artificial intelligence/machine learning. These tools can be run by professionals who are not data science experts but have basic knowledge. This step helps companies to save a lot of money for recruitment.

5. Security

Securing these huge sets of data is one of the daunting challenges of Big Data. Often companies are so busy in understanding, storing and analyzing their data sets that they push data security for later stages. But, this is not a smart move as unprotected data repositories can become breeding grounds for malicious hackers.

Companies can lose up to $3.7 million for a stolen record or a data breach.

Solution

Companies are recruiting more cybersecurity professionals to protect their data. Other steps taken for securing data include:

• Data encryption
• Data segregation
• Identity and access control
• Implementation of endpoint security
• Real-time security monitoring
• Use Big Data security tools, such as IBM Guardian

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6. Integrating data from a variety of sources

Data in an organization comes from a variety of sources, such as social media pages, ERP applications, customer logs, financial reports, e-mails, presentations and reports created by employees. Combining all this data to prepare reports is a challenging task.

This is an area often neglected by firms. But, data integration is crucial for analysis, reporting and business intelligence, so it has to be perfect. 

Solution 

Companies have to solve their data integration problems by purchasing the right tools. Some of the best data integration tools are mentioned below:

• Talend Data Integration
• Centerprise Data Integrator
• ArcESB
• IBM InfoSphere
• Xplenty 
• Informatica PowerCenter
• CloverDX
• Microsoft SQL
• QlikView
• Oracle Data Service Integrator      

In order to put Big Data to the best use, companies have to start doing things differently. Addressing these Big Data challenges as soon as possible is crucial. This means hiring better staff, changing the management, reviewing existing business policies and the technologies being used. To enhance decision making, they can hire a Chief Data Officer – a step that is taken by many of the fortune 500 companies. 

Technologies needed to meet the challenges of big data include distributed storage systems, real-time processing frameworks, data integration tools, and advanced analytics platforms.

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Big Data Analytics Challenges in Different Industries

Big Data challenges are there in every industry and are very common. Here are some of the challenges of conventional systems in big data and their solutions. 

Big Data Challenge in Healthcare

• Boost effectiveness of diagnosis.
• Predictive Analysis can be used to find trends that were previously classified.
• Delivering digitised findings to medical professionals.
• Providing healthcare and preventative medicine.
• Real-time monitoring can become prominent. 
• To suggest a Prospective and Prescriptive Modeling System for doctors in order to close the complexity for a precise diagnosis.
• To create a data transfer and interchange framework to give the patient individualised treatment.
• To create an appropriate technology powered by AI for combining data from several sources.

Solution 

• Prescriptive and Predictive Analysis

Utilising the information gleaned from the patient’s records, the transmission of data and accessibility were developed to offer the patient individualised treatment. AI can store all medical records in the same place. It can also increase the rate of accurate diagnosis. 

• Text Analysis

The General Health Records (GHR) database, compiled by gathering medical reports, is utilised to develop the algorithm. These reports are then digitalised so that the analysis can be considered. 

• Genomic Data Analysis

Genomic data analysis thoroughly explains the connections among various genetic tags, alterations, and states. It has the potential to significantly aid in developing many genetic medicines to treat diseases. 

Big Data Challenge in Security Management

• Sensitivity to generating fake data.
• While “points of access and exit” are frequently guarded, your system’s internal security may not be.
• Granular Access control challenges.
• Protecting and securing data. 

Solution – 

• Centralised Management

Centralised key management is more efficient than distributed or application-specific key management. Security keys and audit logs can be accessed from a single point in centralised management systems. Companies handling sensitive data need reliable key management systems.

• User Access Control 

Basic network security tools include user access control. Big data systems can suffer a great deal from improper access control measures. Role-based settings and policies are the foundation of a robust user control policy. With policy-driven access control, complex levels of user control, such as multiple administrator settings, are automatically managed to prevent insider threats.

• Encryption

Several big data encryption tools can help in handling large volumes of data. This is the reason why companies encrypt their data, both machine-generated and manual. 

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Conclusion

But, improvement and progress will only begin by understanding the challenges of Big Data mentioned in the article.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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