Big Data has proven to be a revolutionary step in the data processing. It has revealed multiple patterns and trends associated with human behavior and interactions. However, this data processing system has shown some big data challenges when it comes to volume, variety, and velocity. In this blog, we will explain the challenges associated with big data and how to overcome them. 

To start, let us go through an overview of big data. Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. The following aspects characterize big data:

• Volume: Volume is like the base of big data, as it is the initial size and amount of data that is collected. If the volume of data is large enough, it can be considered big data. What is considered to be big data is relative, though, and will change depending on the available computing power that’s on the market. In recent times, social media platforms, e-commerce websites, the Internet of things, and other popular data-gathering platforms produce voluminous data daily. As a matter of fact, the overall data gathered in 2022 was 44 times more than in 2009. 

• Value: Value refers to the value proposition that the company adds by using big data. It refers to the best value proposition that the organization can drive from the data that they have gathered. Data in itself is not information, the value of data depends on the insights that an organization can derive from the available data. 

• Veracity: Veracity refers to the accuracy and quality of the data. The data gathered might have some missing pieces or might have been adopted from an unreliable source. A data set with missing pieces can have a negative impact as it might lead to harmful or negative insights. 

• Velocity: As the name suggests, velocity defines the speed at which an organization or platform produces data. Social media platforms, e-commerce websites, and other such platforms generate data at a remarkable speed. 

• Variety: Big data handling is challenging in itself. It becomes even more challenging when it is in different forms, like emails, images, presentations, videos, and data stored in Relational Database Management System. The large variety in which the big data is available, makes it challenging to process the data and get insights. Variety is one of the important characteristics of big data as the processed data is not always in one form. 

Now that you understand what Big data is and how it is characterized, let’s take a look at some of its challenges and how you can solve them. 

Big data challenges

Here are 7 major big data challenges that you need to be aware of, including tips on how to overcome them. 

1. Insufficient understanding of big data

Big data is like the new kid on the block. He is intriguing and has taken the whole block for a spin but remains mysterious. Organizations are yet to understand Big data thoroughly. Without complete understanding, the adoption is messy, and so are the results. The projects based on Big data might see a lower success rate with a lack of understanding of the concept. Then, there is the challenge of acceptance. Employees overtime have become accustomed to a style of working and processing data. So, adoption and lack of understanding are major challenges that Big data still faces. 

The solution you can implement:

The best way to implement big data successfully in your team will be by following a trickle-down approach. It should be first accepted by the upper management. The IT department can also organize learning sessions with the team to seamlessly integrate the technology. Having a better understanding will promote acceptance of the best utilization of the technology. 

2. A vast variety of big data technologies

There are multiple technologies attached to big data. Questions regarding the application of Apache Spark or Hadoop Map Reduce, application best suited to save the data – Cassandra or HBase? These are all genuine questions that can be quite confusing without a technically sound knowledge of such applications. The situation can be worse if you end up choosing an application that is not appropriate for your organization and requirements. 

The solution you can implement:

If you are new to Big data, it is best to seek professional help. Hiring an expert can be quite helpful. Furthermore, you can try to understand all available options in detail and then move ahead with the choice that is best for your organization. 

3. Big data security

Organizations often deal with sensitive data that they would not want to become public. However, only a few organizations spend on additional protocols like data encryption, data segregation, access authority, etc. Big data is often used for the sake of business optimization and left unchecked. Stolen data and records can cost an organization substantially. 

The solution you can implement:

The solution for data security is simple. Implement more protective measures like:

1. Hiring more cybersecurity professionals
2. Implementing data encryption and segregation
3. Setting up identity and access authority controls
4. Opting for big data security tools like IBM Guardian

4. The complexity of managing data quality

While using big data to get the appropriate results, you need to sample data from various sources. This data is not always in a similar format. Making sense of different forms of data is the first challenge. The other challenge is the reliability of the data. The data presented has to come from an authoritative source else you will spend a considerable amount of time cleaning data. 

The solution you can implement:

There are multiple big data technologies dedicated to data cleaning. You need to start by comparing your data to a single point of reference. You must also try to analyze big data by bringing it in a similar format. Investing in ad-hoc data management solutions can also come in handy. 

5. Lack of skilled big data professionals

Running big data tools is not an easy task. It requires an understanding of multiple applications and data visualization. Driving valuable insights from the vast pool of data is a task in itself. There is a lack of a talent pool to match the requirements. Due to a lack of skilled professionals, the salaries of big data professionals are skyrocketing. Such increasing salary expectations can drive up the internal budget of the organization. 

The solution you can implement:

To deal with the issue of depleting talent pool, organizations will need to increase their hiring budgets. Other than that, another option will be to opt for internal training. Upskilling your existing employees can be helpful in keeping budgets in check. Implementing technological solutions can also come in handy as software with analytical solutions can be used while sticking to budgetary measures. 

6. The tricky process of converting big data into valuable insights

Big data works on data that is presented to it. And, the dataset might not always be up to the mark which can lead to some issues. For instance, the best-selling product on your e-commerce website is shoes. But a famous and influential celebrity like Ronaldo uploads an image and is seen wearing a new cap matching his shoes and T-shirt. Now, obviously, a viral trend would bring in a lot of people on the website looking to buy all three – cap, T-shirt, and shoes. But as your data predicted only shoes, you do not have the other two products in stock and your customers might go back empty-handed and upset. 

The solution you can implement:

Big data is an excellent and efficient tool if used correctly. While presenting it with data set you need to consider all the aspects that can impact your business. With the example above, the business needs to consider social media impact, competitor analyses, and much more. Obtaining such third-party data does come at a cost but is crucial for your success. 

Conclusion

Big data presents a great opportunity for growth but comes with a bunch of challenges. However, tackling these challenges with the right solution is the key to an excellent performance. To keep these challenges in check, you must:

• Facilitate adoption of the technology with the help of the upper management.
• Choosing the right software and technology suitable for your business. 
• Implementing additional security measures to keep the data safe
• Maintaining the data quality through data cleaning
• Hiring the best engineering talent or helping internal employees upskill to use the technology efficiently
• Considering data from multiple sources even third-party platforms

Author Bio: 

Palashy Mathur is a wordsmith who brings life to mundane topics. She has worked in collaboration with many brands and facilitated their growth. As a journalist, she believes in being a jack of all trades and a master of one. In her leisure time, she cherishes reading fiction and jumps at every opportunity to talk about Politics & World Economics.