Augmented Analytics: Complex work made simple
Anyone can use AI and ML to gain information with augmented analytics.
Defining augmented analytics
Augmented analytics uses artificial intelligence (AI) and machine learning (ML) tools to assist you with tasks across the entire AI life cycle. It breaks down the limitations of business intelligence (BI) and brings forward insights from data using AI and machine learning.
Gartner describes augmented analytics as “the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”
Rather than building AI and ML models from scratch, you’re interacting with them to better inform your decisions, automate your workflows and even create other models.
Five advantages of using augmented analytics:
1
Automated Explanation
2
Automated Insights in Reports
3
Automated Forecasting
4
Automated Outlier Detection
5
Trusted, Easy-to-Understand Results
Learn more about how augmented analytics can empower your organization.
To learn more, read about augmented analytics on sas.com.
Experiencing augmented analytics
Imagine at mealtime, you stand in your kitchen pantry and write down all your ingredients. You can make all sorts of meals with them, but which one? You may have certain preferences, there may be meals you don’t like, or there may be nonsensical food combinations. Wouldn’t it be great if an intelligent assistant looked at that list for you and suggested relevant meals and explained why?
Take this concept and apply it to your data. What’s interesting in my data? What connections are there between groups? What data is important?
That’s what augmented analytics does for you.
Over the past two years, 88% of organizations have increased their use of augmented analytics with 36% using it and an additional 40% planning to do so in the next twelve months.
Gartner Market Guide for Augmented Analytics
Why augmented analytics matters to business analysts
In a typical case, decision makers need to regularly review content to monitor a process, make a future decision or simply be informed of how their business is doing today.
Often, new questions require changes to the report or a consult with a data scientist. While this reaffirms that the report is leading the decision maker down the path of data-driven insights, such changes can take days, weeks or even months of data analysis, redesign and testing.
Augmented analytics benefits BI and advanced analytics users by taking away complex or time-consuming ways to find answers in your data and automating the process.
According to Gartner, “by 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.”
Why augmented analytics matters to data scientists
It is designed to bring self-service analytics and insights to users while reducing the time data scientists spend on tedious tasks so they can spend more time modeling.
Data scientists can save time and focus on deeper, more complex tasks without getting bogged down by ad-hoc requests from business users. With augmented analytics, more people in the organization can gain insights for themselves with ease. This enables data scientists to dedicate their attention to modeling, programming and development work.
According to Gartner, "by 2025, context-driven analytics and artificial intelligence (AI) models will replace 60% of existing models built on traditional data.”
Learn more about how augmented analytics can empower your organization.
To learn more, read about augmented analytics on sas.com.