Data is transforming the world and industries across the globe. The revolutionary change the data has brought has sent a ripple effect across the industry spectrum. Dealing with a large volume of structured and unstructured data requires meticulous work and precision. Data scientists and Big Data analytics work rigorously to derive useful insights. And for this, they have to rely on different tools. Big Data has many benefits, as it improves decision-making, develops new products and reduce costs. However, we cannot undermine that there are challenges in dealing with large volumes of data. In this blog, we are going to explore details about the challenges of Big Data and how to solve Big Data challenges. Read ahead to know more.
What is Big Data?
To combat the challenges of Big Data, it is important to understand what is Big Data. The term refers to any processed information in the form of a character or symbol that is recorded on media or transmitted via electronic signals. This is the raw data that is available in the repository.
Here are some of the major challenges of Big Data:
1. Lack of Awareness of Big Data
One of the primary challenges that organizations face is the comprehension of Big Data. They fail to gain expertise in its storage, processing and sources. Data professionals know the same, but a similar ideology doesn’t follow throughout the hierarchy. For example, if employees do not understand the significance of data storage, they may not keep a backup of sensitive data. This will eventually lead to a loss of information.
Hence, it becomes significant for organizations to ensure that Big Data workshops and seminars. This training includes awareness about Big Data, its significance and how to handle the information. A basic understanding of data concepts should be a part of employee training.
2. Data Storage
Another common challenge is storing a huge volume of data. We are creating more data than ever and so, the database of the companies is growing rapidly. As the data sets continue to rise exponentially, it becomes extremely important to ensure its apt storage. Most of the data is unstructured and is available in the form of either documents or texts, or audio. This can pose huge Data Analytics challenges. The solution to this lies in reliance on advanced technologies for data storage.
Companies need to adopt modern technologies like tiering, deduplications and compression of data. This helps in reducing the size and also filters out the relevant information.
Data tiering allows the company to store data for different years. Public cloud, private cloud, and flash storage are common options for data tiering.
3. Data Security
Data security is also a point of concern. Since we receive massive amounts of information, storing it correctly is paramount. The increasing volume of data also increases the threat of cyber-attacks. Hence the solution lies in adopting security measures that can reduce unauthorized access to the information. Installing firewalls, two-factor authentication, and other methods can help enhance data security.
4. Integration with existing systems
Integrating Big Data with existing systems can be a significant challenge for organizations. Many businesses and organizations have existing systems and processes in place for managing data. This includes customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and legacy databases. These are designed to handle structured data and may not be scalable enough to handle large volumes of data.
In many cases, data from different sources may be in different formats, making it difficult to integrate and analyze. In addition, duplicate or redundant data can lead to inconsistencies and errors in analysis.
Accessibility of data is yet another challenge that organizations face. It requires developing a comprehensive data management strategy that includes access controls, data governance policies, and user training programs. Organizations must also ensure they have the right tools and technologies to manage and analyze Big Data effectively.
5. Talent shortage
As organizations increasingly turn to Big Data to drive innovation and growth, they face a major challenge: a shortage of skilled talent with the necessary expertise to manage and analyze Big Data effectively. Big Data requires specialized skills in data science, statistics, machine learning, and programming, which are in high demand but in short supply.
Big Data requires specialized skills and expertise. Organizations must recruit and retain qualified professionals who can manage, analyze, and interpret the data.
Wrapping it up !!!
In conclusion, Big Data presents both opportunities and challenges for organizations. To leverage its full potential, organizations must address these challenges and develop a comprehensive strategy for managing and utilizing their data effectively.
Pickl.AI offers a unique learning platform where you learn about the different Data tools, industry best practices. And at the same time, you also get exposure to real-world case studies. Eventually, all this helps you understand the complexities of handling real-world data sets, churning them and deriving insights. So, join Pickl.AI today to learn more about the latest tools, technologies and trends in the world of data.