Big Data is one of the most popular words in the IT industry today. In the digital era, Big Data is a great asset that a business can own. But this data cannot be processed, stored and analyzed using traditional tools. So, in this article, let’s learn about what Big Data Analytics is and why it matters.
I. What is Big Data Analytics?
Big Data Analytics is the use of advanced analytic techniques for huge data sets including structured, semi-structured and unstructured data, from different sources and in different sizes from terabytes to zettabytes.
Specifically, big data analytics refers to the methods and tools used to collect, process, and derive insights from data sets. Such as information about market trends, customer preferences,…
Big Data Analytics helps process large amounts of data and provides valuable information – Image: eduba.com
II. The importance of big data analytics
Today, big data analytics has become an essential and useful tool for businesses of all sizes in a variety of industries. Every business pursues big data to derive valuable insights from large amounts of raw data.
So big data analytics helps organizations mine their data, transform data into information, information into insights. As a result, businesses can identify new opportunities, make smarter business decisions, operate more efficiently, become more profitable, and have happier customers, and more. These benefits can provide a competitive advantage over competitors.
Additionally, businesses rely on big data analytics to save time, costs, and better manage risk.
Learn more about Big Data applications here.
Big data analytics brings countless benefits to businesses – Image: recosense.com
Practical examples
- Netflix is a prime example of a well-known brand using big data analytics for targeted advertising. With more than 100 million subscribers, the company has collected huge amounts of data. Netflix will send subscribers recommendations for the next movie they should watch. This is done by analyzing the user’s previous search and viewing data.
- Amazon has leveraged big data analytics to enter a vast market. Through data analysis, businesses can understand how customers buy groceries and how vendors interact with grocers.
III. Different types of Big Data analysis
1. Descriptive analysis
Descriptive analysis is a useful technique for keeping up to date with current trends and company performance. It simplifies data and summarizes it into a readable form.
After identifying trends and insights with descriptive analysis, you can use other types of analysis to learn more about what’s causing those trends.
For example, Dow Chemical Company has used descriptive analysis and tracked data in the past. Thanks to Descriptive Analytics, the company can recognize underutilized space. They then promote the use of facilities around their office environment. This has saved the company about US$4 million annually.
Descriptive analytics is useful for uncovering patterns in a certain customer segment – Image: analyticssteps.com
2. Diagnostic analysis
This is the type of big data analysis to answer the diagnostic question “Why is that happening?”. With this type of analysis, you can understand the reasons for certain events related to your customers, employees, products, etc.
In business, diagnostic analysis is useful when you are studying the reasons for the churn metrics and usage trends of loyal customers.
For example, for a social media marketing campaign, you can use diagnostic analytics to measure posts, mentions, followers, page views, reviews, and more. There can be thousands of online mentions distilled into a single view. From there you can see what worked in your past campaigns and what did not.
3. Predictive analytics
This type of analysis looks at historical and current data to make predictions about the future, like customer trends, market trends, etc. To get the best results, it uses predictive tools and models such as data mining, AI and machine learning technologies. This is one of the most widely used types of analysis today.
For example, PayPal is a company that provides online payment services. They used all past payment data and user behavior data to predict customer fraudulent activities.
4. Prescriptive analytics
Prescriptive analytics derives results from descriptive and predictive analytics. This type of analysis prescribes the solution to a particular problem. It relies on rules to prescribe a certain path of analysis and uses insights from data to suggest the best move for the company.
Example: In the healthcare industry, you can better manage patient numbers by using prescriptive analytics to measure the number of obese patients. Then, add filters for factors like diabetes and LDL cholesterol levels to determine where to focus treatment. The same prescriptive analysis model can be applied to almost any industry problem.
There are 4 common types of Big Data analysis – Image: designveloper.com
Conclusion
In summary, above are explanations of what big data analytics is, its importance, and different types of analytics. With the continued growth of the amount of data globally, the use of big data analytics also grows. Almost every industry, from banking to government, education to manufacturing,… can apply it to solve challenges and make better decisions.
If your business is still not taking advantage of your existing data source to operate more efficiently. Let’s start learning and using big data analysis services from today. At BAP Software, we have staff with extensive experience in building big data systems to analyze the necessary information for sales, marketing, business models, etc.
