Seven Big Data Challenges and How to Solve Them

Organizations can get stuck at the early stages of Big Data projects because they might not be aware of Big Data challenges and how to solve them. We will take a look at some of the most common scary Big Data challenges and how to solve the challenges if we ever encounter them. 1st Big Data Challenge: Lack of In-Depth Understanding of Big Data One of the major Big Data challenges is the lack of proper understanding of Big Data. Some employees in an organization might struggle to understand the proper meaning of Big Data, which can lead to such an organization lacking the needed Big Data initiatives that will help with decision-making. If an organization does not have a sufficient understanding of Big Data basics and the benefits it offers, there’s a high tendency of their Big Data adoption projects failing. The Solution: The top management at every organization should adopt a Big Data culture. This involves arranging Big Data workshops and training for staff at all levels. Employees can also enroll in Big Data Online Course or register for Big Data Hadoop Certification. This will encourage the acceptance and proper understanding of Big Data at all levels, which in turn promotes change. 2nd Big Data Challenge: Difficulty in Managing Big Data Growth Just as the name implies, Big Data can experience huge growth over a period of time. When this happens, it can be quite difficult to handle the massive data growth. Also, adjusting to this Big Data growth turns out to be a serious challenge that needs to be addressed. From Big Data online training research, it is estimated that the amount of data stored in IT data centers will continue to accumulate for years to come. So, figuring out how to manage Big Data growth storage will become a pressing challenge that needs to be addressed and solved. The Solution: The best way to solve a Big Data growth challenge is to learn how to manage large data sets. Some techniques can be deployed such as tiering, deduplication, and compression. When data is compressed, the number of bits in the data is reduced which will then reduce the data size. Deduplication of data involves removing unwanted data duplicates, while the tiering technique involves storing data in different cloud storage tiers. A Big Data tool like Hadoop can be used to solve this challenge. The Big Data Hadoop Certification will be useful for this issue. 3rd Big Data Challenge: Lack of Skilled Data Professionals Big Data is a modern-day technology that requires the control of skilled data professionals. They include Data Analysts, Data Scientists, and Data Engineers. These are certified data professionals with Big Data online training and certification. They have the experience of handling sophisticated Big Data tools and they are in high demand across the industry. The Solution: A good way to clear this roadblock is to encourage employers to focus on hiring skilled data professionals. Companies can also support the professional development of employees that work with data by enrolling them in Big Data online training. They can also pay for a Big Data Hadoop Certification to help their employees remain loyal to the course of Big Data culture. 4th Big Data Challenge: Confusion with Big Data Technology Selection There is a massive variety of Big Data technologies available in the market. This means it can be difficult to determine the right technology to use for a particular Big Data project. Confusion might set in when differentiating the technology to use in storing data or the one that will offer the fastest speed. Is it Spark, HBase, Cassandra, or Hadoop? This can be a serious challenge. The Solution: The best way to solve this Big Data challenge is to seek professional help. You can hire the services of a Big Data consultant or expert who will guide you on how to use the various Big Data technologies available in the market. Another good strategy involves getting in-depth knowledge of Big Data technologies from a Big Data online course to understand them better. 5th Big Data Challenge: Big Data Security Breaches One of the most daunting challenges of Big Data lies in how to secure a huge data set. Many organizations focus more on gathering data, analyzing data, and storing data that they leave data security for later. If data sets are not properly secured, hackers can breach through and steal vital information. Companies and organizations can lose a lot of money if this happens. The Solution: A good way to solve Big Data security challenges is to put data security first. Data security is very important across every stage of Big Data projects, especially at the design stages. Another effective way to solve data security challenges is for companies to hire security professionals. Other steps that can help include Data Encryption and Real-Time Security Monitoring. 6th Big Data Challenge: Difficulty in Data Integration from Variety of Sources Data can be integrated from a variety of sources but it is a task that can be quite challenging. For instance, data can be integrated from sources like emails, customer logs, data reports, social media platforms, and even financial reports. The integration of data is vital for data analysis, so it is important to learn data integration for compiling reports and data analysis. The Solution: The best way to solve data integration issues is to purchase the right tools. Several tools can be used for data integration from a variety of sources; they include Microsoft SQL, IBM InfoSphere, CloverDX, Centerprise Data Integrator, Xplenty, QlikView, and Oracle Data Service Integrator. You can check out the Big Data online training course to learn how to use these tools. 7th Big Data Challenge: Big Data Handling Expenses Handling Big Data involves spending a lot of money. These expenses include power supply, new hardware, and recruitment of data professionals. If the software is newly developed, a lot of money will go into configuration and maintenance. Whether a company is using an on-premises data solution or a cloud-based water solution, it is important to learn how to handle the costs. The Solution: A good way to reduce the cost of Big Data projects is to analyze company needs and deploy the best Big Data solutions for the project. Applying Big Data on-premises solutions, cloud-based solutions and hybrid solutions will go a long way to reduce the Cost of handling Big Data in organizations. These are cost-effective solutions that will help you handle data properly. 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