Big Data Analytics Services and Why It is Important 2022
26 January 2022
Big Data analytics is a technique used to discover useful insights, including obscure patterns, unidentified connections, market trends and consumer preferences. Big Data analytics provides various advantages, such as better decision-making and preventing fraudulent actions, among other things.
Importance of Big Data Analytics
At the present, Big Data analytics is the driving force behind everything we do online, in every sector.
Consider the streaming music service Spotify as an instance. The company boasts nearly 9 million users who produce an enormous amount of data daily. With this information the cloud-based platform produces suggested songs using a sophisticated recommendation engine based on shares, the number of shares, likes as well as search history and many more. The key to this is the methods, tools and frameworks that result from Big Data analytics.
If you're an avid Spotify user and you are a Spotify user, then you have been to the most recommended section, which is based upon your interests, your past history as well as other factors. The recommendation engine utilizes data filtering tools that gather data and filter it with algorithms. This is exactly what Spotify does.
However, let's go back to basics first.
What is Big Data?
Big Data is a large number of data sets that cannot be stored, processed or analyzed using conventional tools.
There are now thousands of sources that generate information at a speed. The data sources are available all over the globe. Some of the most significant databases of this data are social media networks and platforms. Let's consider Facebook as an example. Facebook generates more than 500 Terabytes of data each day. This includes photos and videos, messages and much more.
Data can also be found in different formats, including semi-structured, structured data, as well as unstructured data. For instance, in the regular Excel sheet, it is classified as structured data, with an established format. Emails, on the other hand, are considered semi-structured while videos and images are classified as unstructured data. All of this data makes Big Data.
However, Big Data in its pure form is of no benefit. Let's be aware of Big Data Analytics.
Let's take a look at the four benefits of Big Data analytics.
Benefits & Advantages of Big Data Analytics
1. Risk Management
Use Case: Banco de Oro is one of the Phillippine banking firms, that makes use of Big Data analyses to detect irregularities and fraudulent activities. The company uses it to identify a set of suspects or the root causes of the issues.
2. Product Development and Innovations
Use Case: Rolls-Royce, one of the world's largest producers of engines used by airlines as well as military forces around the world employs. Big Data analyses are used to assess the efficiency of the designs for engines as well as if there's any need for improvement.
3. Quicker and Better Decision Making Within Organizations
Use Case: Starbucks uses Big Data analytics to make strategic decisions. For example, the business uses it to decide whether a specific location will be suitable for a brand new store or not. They'll look at a variety of variables, such as accessibility, population, demographics, the location of the site and many other factors.
4. Improve Customer Experience
Use Case: Delta Air Lines utilizes Big Data analysis to improve customer service. They analyze tweets to discover the experience of their customers about their travel plans, delays, and the like. The airline recognizes bad tweets and then takes the necessary steps to correct the issue. In publically addressing these issues and providing solutions, it can help the airline to build positive relationships with its customers.
The Lifecycle Phases of Big Data Analytics
Let's take a look at the way Big Data analytics works:
Stage 1 - Evaluation of the business case - The Big Data analytics lifecycle begins with a business plan, that outlines the purpose and purpose behind the analysis.
Stage 2 – Data Identification - In this stage, a wide range of sources of data is identified.
Stage 3 - Filtering Data - The data identified from the previous stage has been processed here to eliminate the corrupted data.
Stage 4 - Data extraction - Data that isn't incompatible with this tool can be removed and transformed into a format compatible with the tool.
Stage 5 - Data aggregation - This stage is where data that share similar fields across multiple datasets are combined.
Stage 6 - Data analysis - Data is analyzed by using statistical and analytical tools to uncover important data.
Stage 7 - Visualization of data - Utilizing tools such as Tableau, Power BI, and QlikView, Big Data analysts can make graphic representations of the analysis.
Stage 8 - Final Analysis Results - The final step in the Big Data analytics lifecycle, which is where the outcomes of analysis will be released to business stakeholders, who can be able to take appropriate actions.
Different Types of Big Data Analytics
Here are four types of Big Data analytics:
1. Descriptive Analytics
This consolidates past data in a format that anyone can easily understand. This aids in the creation of reports like those of a business's sales, profit, revenue etc. Additionally, it aids in the calculation of social media's social media-related metrics.
Use Case - Dow Chemical Company analyzed its historical data to improve facility utilization throughout the office and lab. By using analytical descriptive data, Dow was able to determine spaces that were not utilized. The consolidation of space allowed the company to save almost US 4 million annually.
2. Diagnostic Analytics
This helps to determine what caused the problem in the initial place. Techniques such as drill-down as well as data mining and data recovery are just a few examples. Companies employ diagnostic analytics since they offer a deep insight into a specific problem.
Use Case - A company's report indicates that sales have dropped however; customers are still adding items to their carts. This could be due to many reasons, such as the form did not load properly the shipping price is too high, or there are insufficient payment options. That's why you should utilize diagnostic analytics to pinpoint the cause.
3. Predictive Analytics
This kind of analysis looks at the past and present data to predict the future. Predictive analytics utilizes information mining AI or machine-learning to analyze the present data and predict the future. It helps in forecasting trends in the market, customer trends, and more.
Use Case - PayPal determines what types of measures they will need to implement to protect their clients from fraudulent transactions. Utilizing prescriptive analytics, PayPal makes use of all historical data on payments as well as the user's behaviour information and develops an algorithm to predict fraud.
4. Prescriptive Analytics
This kind of analytics recommends the best solution for a specific issue. Perspective analytics is a combination of predictive and descriptive analytics. The majority often, however, is based on AI and machine learning.
Use Case - Prescriptive analytics may be used to boost the profits of an airline. This kind of analysis is utilized to create an algorithm that can automatically adjust flight prices according to a variety of variables such as demand from customers as well as weather, destination holidays, the price of oil.
Big Data Analytics Tools
Here are a few of the essential tools for big data analytics.
Hadoop - helps in organizing and analyzing data
MongoDB - is used for datasets that are frequently updated
Talend - is a tool to integrate data and manage
Cassandra - is an open database that can handle large amounts of data
Spark - is a software used for real-time processing as well as for analyzing large quantities of data
STORM - an open-source real-time computational system
Kafka - is an open-source streaming platform that can be used to store fault-tolerant data
Big Data Industry Applications
Here are a few areas where Big Data is actively used:
Ecommerce - Predicting the trends of customers and optimizing pricing are just some ways that E-commerce makes use of Big Data analytics
Marketing Analytics - Big Data analytics helps to generate high-return marketing campaigns, resulting in increased sales
Educational - used to design new courses and enhance existing ones per market demands
Health - With aid of the medical history of a patient, Big Data analytics is utilized to determine how likely they will be to suffer from health problems.
Entertainment and media - Use to comprehend the popularity of music, shows, films and much more to offer an individual recommendation list to its customers
Banking - Income and spending patterns can help determine the probability of selecting different banking options, such as credit and loans
Telecommunications - used to predict capacity on the network and improve the customer experience
Government - Big Data Analytics for Government Big Data analytics helps governments in police enforcement, among others.