Over the past few decades, there’s been a major shift in the way companies provide products and services, launch marketing campaigns, deliver the customer experience, and drive sales. The reason for this major shift is the Fourth Industrial Revolution, which is also affectionately known as industry 4.0.
Industry 4.0 is different from previous industrial revolutions as those were about the physical abilities of machines while Industry 4.0 is all about artificial intelligence and the ability of machines to think. One of the best things about the latest industrial revolution is the use of big data to get insights from seemingly obscure data. Continue reading this beginner’s guide to big data to learn more about how data science is changing the way we use information.
What in the world is big data?
Big data isn’t as complicated as it may sound. In fact, the chances are that your business is already using it to a certain degree to get insights into your market or customers. However, what makes it so different now is the way data scientists and analysts use historical data to find future trends.
As you heard when you were a little kid, knowledge is power, and data analytics is knowledge. Data analysts put a lot of hard work and hours into big data discovery to find metrics that tell a story about the company’s productivity and profitability. Look at it this way: We’re used to data telling us what something is, but with analytics, we can learn why something is, when that something is liable to reoccur, and how to prevent it from happening or use it to your advantage.
How do you get actionable insights from raw data?
One of the main things about data analytics that causes people to break into night sweats in the daytime is the abundance of numbers. Data science is all about using numbers or raw data to find trends that directly affect key performance indicators and profits. It’s the job of data engineers to develop machine learning algorithms that can adapt to changes in real-time and up to data scientists to interpret data and put it in a form that business users can understand.
The main way of putting raw data into a format everyone can understand is through processes known as data visualization. Data visualization is the practice of using infographics to interpret the insights gained from data discovery.
Even companies like MenoFit use data visualization to show how their proprietary blend of probiotics, prebiotics, vitamins, and herbs as part of a healthy diet affect women as opposed to the placebo. So you can see, even though data analysis can be difficult to understand, with infographics, anyone can understand the stories the metrics tell.
The purpose of big data is to describe, predict, and prescribe.
The main functions of big data discovery are to describe, predict, and prescribe events that directly affect your bottom line, key performance indicators, and market demand. After big data discovery, machine learning algorithms go to work looking for metrics that describe what’s going on what your business.
Once you know what’s going on currently, a robust data science platform will predict what’s going to happen next through a process known as predictive analysis. Then, using prescriptive analysis, the analytics platform comes up with solutions for the problem when it arises again.
The use cases and benefits of big data are countless.
You’d be surprised to learn how much data analytics have become a part of our society. Law enforcement uses predictive analytics to prevent crime sprees and professionals sports teams use in-depth analytics to gain a competitive edge over their rivals and help develop their players. So, how can analytics help your business?
One of the most common ways companies use data analysis is to predict changes in demand to prevent over or underproduction of goods. They also use analytics to optimize their supply chains and find the materials they need at better prices.
As you can see, the possibilities with big data are endless. You can find as many uses for business intelligence as there are numbers on a business intelligence report, meaning you don’t even have time to count the ways data science can help improve your company.