Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis that is normally done by an analyst or data scientist. It is a combination of data science and artificial intelligence.
According to Gartner, Augmented analytics is 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 analyse data in analytics and BI platforms. It also augments data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.
As the modern data stack evolves, Augmented Analytics levels up insights generation by incorporating AI and Machine Learning in traditional BI. As more and more data is collected and transformed throughout the data pipeline, augmented analytics enables data consumers to make sense of this data and turn it into insights. It augments the largely manual and bias-prone process of preparing and analysing data, and sharing findings. It also fills the technical skill gaps in the analyst population and helps them spend the bulk of their time interpreting insights and deriving recommendations and actions. In essence, analysts can now control a lot more of the data value chain and increase their impact on organisations
Augmented Analytics solutions follow the two core principles of the modern data stack:
On an organizational level, Augmented Analytics addresses a number of problems currently faced across organisations:
Problem 1: It takes days to find answers to business questions, which is often too late to take action
Solution 1: Increasing speed to insight
Problem 2: Analysts do not know where to look in the data
Solution 2: Providing a comprehensive picture
Problem 3: Insights are not contextualised or easily consumable
Solution 3: Prioritising actionable insights that move the needle
Problem 4: High dependency on data teams to conduct analyses
Solution 4: Improving data-democratization
It’s one thing to ask “What are sales for Category A?”, which most tools in the current data stack can easily answer. Questions like “Why are sales declining for Category A?” are much more complex, requiring more processing power and machine learning capabilities, which are at the forefront of modern advancements in data and analytics. And that is where Augmented Analytics for businesses steps in.
When it comes to Gartner’s definition, there are three key components of Augmented Analytics that businesses should understand:
1. Machine Learning
Machine Learning (ML) programs are capable of adapting to different uses without being explicitly programmed to do so. In practice, this means machines process a ton of data until they get really good at completing tasks. ML can apply statistical models to business data and identify trends that directly impact the bottom line.
2. Natural Language Generation
Natural-language generation (NLG) refers to the process that translates a machine’s findings into words and phrases that humans can understand. NLG is not just about communicating data trends effectively, but also about transforming intangible algorithms into something human, so business users can internalize and apply the insights they’re receiving
3. Automating Insights
Data-driven insights determine business strategy. The combination of machine learning and NLG allows businesses to automate the labor-intensive process of analyzing data and communicating important findings to business people.
Amongst the many use cases of Augmented Analytics, they are most commonly found across the following industries:
Examples of use cases by industry and function
Here are some amazing companies in the Augmented Analytics.