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

No organization can function without data these days. With huge amounts of data being generated every second from business transactions, sales figures, customer logs, and stakeholders, data is the fuel that drives companies. All this data gets piled up in a huge data set that is referred to as Big Data.

This data needs to be analyzed to enhance decision making. But, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data science professionals, validating data, and accumulating data from different sources.

We will take a closer look at these challenges and the ways to overcome them.

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. 

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.

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.

Also Read: Job Oriented Courses After Graduation

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.

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.

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 an inappropriate technology. As a result, money, time, efforts and work hours are wasted.

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.

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. Securing data

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

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. 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.

Conclusion

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

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