The term “augmented analytics” has become popular in the business intelligence (BI) and analytics community. But what is augmented analytics? And how can it help managers make better decisions?
We'll answer these questions and more. You'll get a manager-level crash course in augmented analytics. By the end of this article, you should have a solid understanding of what augmented analytics is, and how you can use it to improve your decision making.
What is augmented analytics?
Augmented analytics is a term used to describe the use of machine learning to automatically analyze data, then provide insights that humans can act on.
It’s sometimes also referred to as “cognitive analytics” or “analytics 2.0.”
How do augmented analytics work?
Augmented analytics rely on machine learning algorithms.
Machine learning is a type of Artificial Intelligence (AI) that allows computers to learn directly from data – without the need for a human to programming them. (That is, there's no need for a human to explicitly tell the computers what to do.)
Machine learning algorithms can automatically detect patterns in data, then use that to make predictions about future events.
What are the benefits of augmented analytics?
There are several benefits of using augmented analytics, including:
1. Automation of tedious and time-consuming tasks. Augmented analytics can automate the more rote aspects of data analysis, freeing managers to focus on the higher-level considerations, including a lot of domain-specific considerations.
2. Improved accuracy. By using machine learning algorithms, augmented analytics can provide more accurate insights than humans alone could. Computers are much better at crunching numbers than humans are. And humans are better at interpreting the underlying meaning of those numbers.
3. Increased speed. Augmented analytics can help managers make faster decisions by providing real-time insights. Imagine automatically refreshing dashboards that you can reference whenever you need to make a decision.
4. Increased scalability. You can easily scale up your augmented analytics to support larger organizations, or incorporate a broader array of data sources.
Which Industries Use Augmented Analytics?
Here are a few examples of industries that have embraced augmented analytics:
Augmented Analytics in Retail
In the retail industry, you can use augmented analytics to automatically analyze customer data and identify trends. If you can anticipate what customers are likely to buy, you can make better decisions about product assortment, pricing, and promotions.
Augmented Analytics in Manufacturing
In the manufacturing industry, managers use augmented analytics to automatically analyze data from sensors and machine data to identify issues and optimize production.
Imagine a factory where you know the exact state of every assembly line, the output of every human worker, or even the power usage of individual machines. Imagine being able to identify bottlenecks or waste through real-time dashboards.
There is a lot of room for applying Augmented Analytics tools to save money. So we see manufacturers like Toyota, Apple, and Airbus all making heavy use of these tools.
Automated Analytics in Healthcare
In the healthcare industry, doctors and managers can use augmented analytics to automatically analyze patient data, identify vital sign trends, and improve patient care.
Healthcare in particular has a lot of room for improvement in understanding medical records, and making better decisions based on available data. Augmented Analytics can give hospitals a big leg up.
Automated Analytics in Financial Services
There is perhaps no more obvious use of automated analytics than in finance. Numbers are an intrinsic part of moving money around between investors and companies deploying that money.
Because finance involves money, it is a magnet for fraudulent activity. Automated Analytics can help banks and other parties prevent fraud. They can also help governments prevent money laundering.
Automated Analytics in Marketing
Marketers can use augmented analytics to automatically analyze customer data to identify trends and improve marketing campaigns.
When you are spending money on advertising, even marginal refinements can improve your bottom line.
In short: Automated Analytics can help managers focus on what they do best, while offloading the grunt work to computers
Thanks for taking time to read my article. I hope you have fun exploring the world of Automated Analytics. If you're looking to harness technology tools, you can learn some new programming and computer science skills here on freeCodeCamp.org.