In this article, we are discussing the challenges businesses face in managing big data and how big data analytics and its associated tools solve this issue.

Introduction to Big Data & Big Data Analytics

Businesses, especially large-scale ones, need to store huge amounts of data. For example, Instagram has tons of data to store – including the billions of past and upcoming posts, the likes and comments on each post, and user information like profile data. It also tracks how people use the platform (known as consumer behavior) to recommend reels to them.

An e-commerce site like Amazon also stores data about their millions of customers – like their searches, purchases, wishlist, etc. – to recommend products to them. Plus, it also keeps track of its own inventory and stocks.

In this way, several other platforms like Facebook, Netflix, and YouTube, also operate on massive amounts of data sets and still work efficiently. How?

So the secret to managing and analyzing such massive data sets is big data analytics. But first, what is big data?

Big data and its challenges

Big data, in simple words, are large complex data sets that grow continuously. They are often described using the 5 V’s:

• Volume: Volume refers to the massive size of data sets that modern businesses use.
• Velocity: Velocity refers to the speed at which big data grows. For example, Instagram has 1000+ new posts every second.
• Variety: Variety refers to the various types of data that businesses acquire from different sources and formats like emails, social media posts, audio, images, etc.
• Veracity: Veracity refers to the accuracy and credibility of data as it can often be inaccurate, false, or incomplete.
• Value: Value refers to the usefulness of data and how much it benefits the business.

Big data, due to its huge size, poses a variety of challenges which are as follows:

• Firstly, storage is a major concern with big data as it keeps on growing from terabytes to even petabytes and exabytes.
• Big data can be either structured, semi-structured, or unstructured. This diversity makes it difficult to analyze using traditional technologies.
• Maintaining security and privacy across huge data sets is also difficult.
• With such massive amounts of data, inaccuracies like false data, duplicate data, or conflicting data are inevitable.

With all these challenges, it is clear that analyzing big data manually or using traditional techniques is quite inefficient and impractical.

Hence, specialized tools and technologies are implemented for analyzing big data, giving rise to the term big data analytics.

What is big data analytics

IBM defines big data analytics as “…the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights.” It involves the following steps:

• Data collection: The first step of big data analytics is to collect data, which can be found in structured, semi-structured, or unstructured form.
• Data processing and cleaning: This step involves organizing the acquired data, removing any duplicate data, and formatting the data properly.
• Data analysis: Now that the data has been organized, it becomes much easier to analyze it in various ways. For example, it can be used to create graphs or predict trends.

Next, let’s discuss the types of big data analytics…

Types of big data analytics

Based on its function, big data analytics can be divided into 4 types:

1. Descriptive analytics: Descriptive analytics describes past data in ways. It is used to summarize historical data, generate reports, or help interpret unstructured data through visualization (as in graphs). It answers the question ‘What happened?’ It is used by businesses to analyze their traffic and engagement on their website or social media handles.
2. Diagnostic analytics: Diagnostic analytics helps diagnose problems and the cause of certain events. It answers the question ‘Why did it happen?’ For example, if a company is witnessing lower sales, diagnostic analytics can help find its cause by analyzing factors like ratings, customer reviews, competition, etc.
3. Predictive analytics: Predictive analytics, as the name suggests, uses AI, machine learning, and data mining techniques to analyze past data to predict future trends. It answers the question ‘What might happen?’ By predicting consumer and market trends, it helps businesses make informed decisions and stay a step ahead of their competitors.
4. Prescriptive analytics: Based on the data provided by the previous three types, prescriptive analytics helps to find the solution to a business problem or help make a decision as per upcoming trends. It answers the question ‘What to do about it?’ It is used by the airline industry to adjust ticket prices as per customer demand, weather, destination, and other factors.

Big data analytics tools

The following are 3 of the popular big analytics tools:

• APACHE Hadoop: Hadoop was developed in 2005 and is one of the most popular and widely used tools for big data analytics. It is a free Java-based open-source platform that is used by large companies like Amazon, Microsoft, Uber, etc.
• Tableau: Tableau, launched in 2003, is an analytics tool that helps businesses create interactive data visualizations like graphs and charts. In addition to descriptive analytics, it also helps recognize patterns and predict trends. It has various products like Tableau Prep, Tableau Server, and Tableau Desktop to suit diverse business needs.
• Power BI: Power BI is a cloud-based analytics tool developed in 2014 by Microsoft for the Microsoft ecosystem. Like Tableau, Power BI also offers data visualization features. It has a Q & A feature powered by Natural Language Processing (NLP) which allows its users to question their data.

Advantages of big data analytics

• Saves time: Big data analytics tools help businesses collect and analyze vast amounts of real-time data from various sources. Thus, it saves time and helps businesses make quicker decisions.
• Improved decisions: Along with speed, it also helps businesses make calculated decisions. Predictive analytics, in particular, help with risk assessment and forecasting trends to help businesses devise marketing strategies.
• Customer satisfaction: Big data analytics can also be used to better understand customer needs and pain points. These insights can then be used by businesses to improve their existing products and even create new ones, thus improving customer experience.

Conclusion

Handling and analyzing huge amounts of ever-growing data sets can be time-consuming through traditional methods. Big data comes with several challenges like storage problems, security concerns, and possible inaccuracy. Thus, modern technologies like Hadoop and Power BI are the only way through which large companies can analyze vast amounts of past and real-time data efficiently and stay ahead of their competition.