Share this on:
Augmented analytics, also known as AI Analytics, is a branch of analytics that’s backed by natural language processing, machine learning, and artificial intelligence technologies.
Gartner was first to come up with the term “augmented analytics.” Machine learning automates and takes care of analytics processes including data preparation, data visualization, and insights generation. Natural language processing enables users to get answers related to their data.
If you are wondering how augmented analytics is different from traditional analytics – we have the answer. Augmented analytics does not require users to be experts in working with data. You do not need to be a data analyst, data engineer, or data scientist to leverage augmented analytics.
With AI analytics, non-technical/business users can easily use data to make decisions. We could say that augmented analytics is best for businesses that are looking to incorporate self-service analytics into their ecosystem.
Benefits of Augmented Analytics
Apache Spark follows a hierarchical architecture that’s based on a primary and secondary node setup. At the center you’ll find the Spark Driver which is the primary node. It interacts with the Cluster Manager to handle resource allocation and distributes tasks to the secondary nodes in the cluster.
Once the processing is done, the driver will send the results to the client application. Whenever any Spark application runs, the driver initiates SparkContext. It interacts with the cluster manager to distribute tasks and track their execution across worker nodes.
1. Automates Data Management
Be it data preparation or data discovery – augmented analytics helps automate all. There are many AI-powered business intelligence platforms that enable you to use machine learning to perform data cleaning activities.
Machine learning scans data to identify patterns, relationships, anomalies, and generates insights with great speed.Augmented analytics saves data analysts from manual work by cleaning, standardizing, and enriching data automatically. Algorithms continuously monitor data quality activities and flags duplicates and other data quality issues.
Augmented analytics empowers users with natural language querying. They can ask questions in simple English and receive quick, instant responses. There’s no coding or SQL required.
2. Democratizes Data
Also mentioned earlier, augmented analytics does not require its users to be experts when it comes to working with data.
Individuals from any domain can interact with data and get involved in data analytics activities.
Result?
Enhanced data literacy across the organization.
With augmented analytics, working with data is easier than ever.
3. Improves Predictions and Decision-Making
Augmented analytics uses artificial intelligence to understand data patterns and guarantees more accurate predictions than traditional analytics.
Users get faster insights and results as prediction making processes are automated.
Users have better ownership of data analytics.
They can leverage augmented analytics to unify critical information into dashboards and reports.
This, in turn, makes complex data easy to understand and helps make quick and more informed decisions.
Augmented Analytics Use Cases
Augmented analytics is used across domains like manufacturing, sales & marketing, HR, and supply chain.
Manufacturing
Analyze sensor data in real-time to predict equipment failure
Sales/Marketing
Unified view of customer profiles, campaign assessment, quick identification of cross-sell and upsell opportunities
HR
Use historical data to predict employee churn, sentiment analysis to retain top-performing employees
Supply Chain
Real-time inventory analytics, detect bottlenecks
