Big Data Challenges & Solutions

1. Lack of knowledge Professionals

To run these modern technologies and large Data tools, companies need skilled data professionals. These professionals will include data scientists, data analysts, and data engineers to work with the tools and make sense of giant data sets. One of the Big Data Challenges that any Company face is a drag of lack of massive Data professionals. This is often because data handling tools have evolved rapidly, but in most cases, the professionals haven't. Actionable steps got to be taken to bridge this gap.


Companies are investing extra money in the recruitment of skilled professionals. They even have to supply training programs to the prevailing staff to urge the foremost out of them. Another important step taken by organizations is purchasing knowledge analytics solutions powered by artificial intelligence/machine learning. These Big Data Tools are often suggested by professionals who aren't data science experts but have the basic knowledge. This step helps companies to save lots of tons of cash for recruitment.

2. Lack of proper understanding of Massive Data

Companies fail in their Big Data initiatives, all thanks to insufficient understanding. Employees might not know what data is, its storage, processing, importance, and sources. Data professionals may know what's happening, but others might not have a transparent picture. For example, if employees don't understand the importance of knowledge storage, they could not keep the backup of sensitive data. They could not use databases properly for storage. As a result, when this important data is required, it can't be retrieved easily.


Big Data workshops and seminars must be held at companies for everybody. Military training programs must be arranged for all the workers handling data regularly and are a neighborhood of large Data projects. All levels of the organization must inculcate a basic understanding of knowledge concepts.

3. Confusion while Big Data Tool selection

Companies often get confused while selecting the simplest tool for giant Data analysis and storage. Is HBase or Cassandra the simplest technology for data storage? Is Hadoop MapReduce ok, or will Spark be a far better option for data analytics and storage? These questions bother companies, and sometimes they're unable to seek out the answers. They find themselves making poor decisions and selecting inappropriate technology. As a result, money, time, efforts, and work hours are wasted.


You'll either hire experienced professionals who know far more about these tools. Differently is to travel for giant Data consulting. Here, consultants will provide a recommendation of the simplest tools supporting your company’s scenario. Supporting their advice, you'll compute a technique then select the simplest tool for you.

4. Integrating Data from a Spread of Sources

Data in a corporation comes from various sources, like social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. Combining all this data to organize reports may be a challenging task. This is a neighborhood often neglected by firms. Data integration is crucial for analysis, reporting, and business intelligence, so it's perfect.


Companies need to solve their Data Integration problems by purchasing the proper tools. A number of the simplest data integration tools are mentioned below:

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

5. Securing Data

Securing these huge sets of knowledge is one of the daunting challenges of massive Data. Often companies are so busy in understanding, storing, and analyzing their data sets that they push data security for later stages. This is often not a sensible 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 knowledge breach.


Companies are recruiting more cybersecurity professionals to guard their data. Other steps taken for Securing Big Data include: Data encryption Data segregation Identity and access control Implementation of endpoint security Real-time security monitoring Use Big Data security tools, like IBM Guardian.

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