Big Data refers to handling vast amounts of data that require storing and analysing information on multiple data sources. However, handling such a huge quantity of data daily has enormous challenges. Store and analysing large, fast-growing data sets are among big data challenges. These challenges are real implementation hurdles and must be dealt with agility and great smartness.  Unfortunately, these challenges must be solved before big data can provide us with accurate predictions. Big data technology may also fail if it is not handled properly, resulting in unpleasant outcomes.

In this article, we will discuss the challenges of big data in detail and ways to combat them. Let’s proceed.

Challenges of Big Data

1.    Choosing an appropriate Big Data Tool

Many companies often get confused while selecting the most effective tool for massive Data analysis and storage. HBase, Cassandra, Hadoop MapReduce, and Spark are the most used tools. It is often difficult to find the most appropriate option in a market with so many options, and they are often unable to do so. They find themselves making poor decisions and selecting inappropriate technology. As a result, money, time, effort, and work hours are wasted.

What can you do?

To combat this problem and find a viable solution, you must hire experienced professionals with extensive knowledge of these tools. Some tech consultants are experts in this field and can recommend the appropriate tools to support your company’s goals. Their advice will help you compute a technique and select the simplest tool for your organisation.

2.    Having a poor understanding of sourcing and utilising massive data

Insufficient understanding of Big Data causes many companies to fail in their initiatives. Data scientists do not know about big data, its storage, processing, and sources. Often, data professionals know about big data but cannot effectively communicate the concept to others on the team. If employees do not understand the importance of knowledge storage, they may be unable to back up sensitive data. They could not use databases properly for storage. As a result, when this critical data is needed, it is difficult to retrieve.

What can you do?

Companies need to hold big data seminars and workshops for all employees to solve this challenge. There is a need to arrange training programs for all workers handling data regularly and working in the vicinity of large data projects. Knowledge concepts must be ingrained at all levels of the organisation.

You can also check out this Big Data Course or other related certification programs to train your employees. The best way to equip your employees with relevant big data skills is by getting them certified. The big data certification programs of well-established institutions offer a strong foundation and advanced understanding of cutting-edge technologies such as Apache Hadoop, Spark, etc.

3.    Finding and fixing data quality issues

Quality data management is a key concern for many companies. If data quality issues creep into big data systems, analytics algorithms and artificial intelligence applications can produce inaccurate results. If data management and analytics teams attempt to pull in more and different types of data, these problems can become more significant and more challenging to audit.

What can you do?

Duplicate entries and typos are common and inevitable mistakes encountered during data sourcing. To ensure the quality of the data they collect, it is advisable to use an intelligent data identifier to identify duplicates and minor variations and report possible typos. As a result, sourced data will be more accurate, and insights generated through analysis will be deeper.

4.    Generating relevant business insights

The data teams use big data technology to generate insights that will help them understand market trends and implement strategies that will benefit businesses. Big data tools and applications help generate useful business insights for organisations through KPI-based reports, identifying useful predictions, or making different strategic recommendations. But the data collection process can be very challenging. Constant updating is crucial to maintaining the integrity of an enterprise’s data stores. Big data integration strategies must be dedicated to integrating various data sources to accomplish this.

What can you do?

A strategic approach is recommended to maximise ROI on data integration projects. These actions require input from business analytics professionals, statisticians, and data scientists with Machine Learning expertise.

5.    Handling the noise

There are two types of errors that can occur in data sets: Noise and Bias. Noise is found in every type of data set, and it’s not something we can completely eliminate. It comes from unpredictable or uncontrollable sources, and it has no meaning for the information collected by a machine. It just increases the complexity and decreases its quality. On the other hand, the biases come from humans or non-living entities like incorrect sourcing, faulty tools, etc. It also occurs because enterprises tend to overemphasise technology without considering the context of the data and its business value.

What can you do?

Teams must be built to demarcate who will refine the data and how clearly. Collaboration between those closest to the business problems and those closest to technology is crucial for managing data risk and ensuring proper alignment. One can rely on data sourcing agencies to ensure data accuracy and authenticity.  Simple end-to-end use cases can also be built to get early wins, understand limitations, and engage users.

6.    Ensure the security of the data

Companies often put data security on the back burner because they are so busy analysing, storing, and understanding their datasets. An unprotected data repository can be hacked, but that’s not a good idea since it can serve as a breeding ground for malicious actors.

What can you do?

Cybersecurity professionals are being recruited by companies to protect their data. Other steps taken are data encryption and segregation, implementation of endpoint security, and using big data security tools.

So, to conclude

Without a doubt, data is one of our generation’s most important resources. We can use this information to predict the future and make decisions with more accuracy. Since the rise of artificial intelligence, machine learning, predictive analytics, and other heavily data-driven fields, there has been an increased demand for professionals who are well-versed in data science. We hope the solutions mentioned above against each big data challenge were relatable to you and will be helpful for your organisation in the long run.