Nowadays, no organization can function without data. Data is the fuel that drives businesses, with massive amounts of data generated every second from business transactions, sales figures, customer logs, and stakeholders. All of this data accumulates into a massive data set known as Big Data. It brings with it its own set of Big Data processing challenges.
This data must be analyzed in order to improve decision-making. However, there are some Big Data challenges that businesses face. These include data quality, storage, a lack of data science professionals, data validation, and data aggregation from various sources.
We will look more closely at these Big data processing challenges and how to overcome them.
Challenge #1: Lack of proper understanding of Big Data
Companies frequently fail to understand even the most fundamental concepts, such as what big data is, what its benefits are, what infrastructure is required, and so on. A big data adoption project without a clear understanding is doomed to fail. Companies may waste a lot of time and money on things they don’t even understand how to use.
And if employees do not understand the value of big data and do not want to change existing processes to adopt it, they can resist it and hinder the company’s progress.
Solution:
As a significant change for a company, big data should be accepted first by top management and then down the ladder. Therefore, IT departments must organize numerous training and workshops to ensure understanding and acceptance of big data at all levels.
To increase big data adoption, the implementation and use of the new big data solution must be monitored and controlled. Top management, on the other hand, should exercise restraint because it can backfire.
Challenge #2 Problems with data growth
One of the most pressing challenges of Big Data is appropriately storing all of these massive sets of data. The amount of data stored in corporate data centers and databases is rapidly increasing. As these data sets grow exponentially over time, they become challenging to manage.
Most of the data is unstructured and originates from documents, videos, audio, text files, and other sources. This means they cannot be found in databases. This can present significant Big Data analytics challenges that must be addressed as soon as possible, or the company’s growth will be hampered.
Solution
Companies use modern compression, tiering, and deduplication techniques to handle these large data sets. Compression reduces the number of bits in data, decreasing its overall size. The process of removing duplicate and unwanted data from a data set is called deduplication.
Data tiering enables businesses to store data in various storage tiers. It ensures that the data is stored in the most appropriate location. Depending on the size and importance of the data, data tiers can be public cloud, private cloud, or flash storage.
Challenge #3 Confusion with Big Data tool technologies
It’s easy to get lost in the vast number of big data technologies on the market. Is Spark required, or will the speeds of Hadoop MapReduce suffice? Is Cassandra or HBase better for data storage? It can be challenging to find the answers. It’s even easier to make poor choices when swimming in a sea of technological possibilities without a clear idea of what you need.
Solution:
If you are new to big data, it is best to seek professional assistance. You could hire an expert or turn to a vendor for big data consulting. In both cases, collaborative efforts will allow you to develop a strategy and, from there, select the appropriate technology stack.
Challenge #4 Lack of data professionals
Companies require skilled data professionals to run these modern technologies and Big Data tools. These experts will include data scientists, analysts, and engineers with prior experience working with tools and making sense of large data sets.
Companies are facing a shortage of Big Data professionals. Data handling tools have evolved rapidly, but most professionals have not. Actionable steps must be taken to close this gap.
Solution
Companies are spending more money on recruiting skilled professionals. However, they must also provide training programs for existing employees to get the most out of them.
Another critical step that organizations take is to purchase data analytics solutions powered by artificial intelligence/machine learning. These tools can be used by professionals who are not data scientists but have a basic understanding of the subject. This step allows businesses to save a significant amount of money on recruitment.
Challenge #5 Data security
One of the most difficult challenges of Big Data is securing these massive amounts of data. Unfortunately, companies are frequently so preoccupied with understanding, storing, and analyzing their data sets that they postpone data security. However, this is a bad idea because unprotected data repositories can be breeding grounds for malicious hackers.
A stolen record or a data breach can cost a company up to $3.7 million.
Solution
Companies are hiring more cybersecurity experts to protect their data. Other measures taken to secure data include:
- Encryption of data
- Data separation
- Identity and access management
- Endpoint security implementation
- Security monitoring in real time
- Make use of Big Data security tools like IBM Guardian.
Challenge #6 Salary Increases for Skilled Big Data Professionals
Big Data salaries have risen significantly. For example, the average salary for an entry-level prominent data engineer (1-3 years of experience) is ₱568,057. A senior-level engineer of big data (8+ years of experience, on the other hand, earns an average salary of ₱1,013,381.
Solution: To address the talent shortage, organizations have had to increase their budgets and efforts to recruit and retain employees. As a result, these organizations are training their current employees to have the skills from within the company.
Some are also looking to technology, such as self-service analytics solutions with machine learning capabilities. These solutions are intended for use by professionals who do not have extensive knowledge of data science. It is thus possible to achieve big data objectives without spending money on big data experts.
It is critical to stay competitive in today’s data-driven economy. While Big Data challenges can arise at any stage, it is critical to recognize that everyone approaches them differently. In addition, Big Data has an infinite scope, which makes it ever-changing.
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