AI capabilities such as machine learning, natural language processing and computer vision and to some degree other technologies like AR and VR are poised to augment analytics activities like preparing data and identifying insights. Augmented analytics will enable companies to run more efficient and effective analytics departments and internalize data-driven decision making and enable employees to become citizen data scientists.
What is augmented analytics?
Cognitive or AI-driven or augmented analytics all mean modern analytics: analytics that leverages the latest advances in AI algorithms such as deep learning. When you google these terms, you may find slightly different explanations
Augmented analytics techniques help identify patterns in large and complex data sets and improve the decision-making process. These techniques combine a variety of advanced technologies such as semantics, machine learning, and Natural Language Processing (NLP) to improve how people explore and analyze data.
Why is it important now?
There is an increasing need for democratization of analytics.
Though more organizations want to increase their data-driven decision making, data scientists are a scarce resource. McKinsey Global Institute estimates that there will be a shortage of approximately 250,000 data scientists by 2024 in the United States.
Companies can make up for this scarcity by enabling their less technically savy employees to act as citizen data scientists. Previous generation of BI tools remained difficult to use and only 21% of employees have adopted BI technologies because it is hard to use for non-technical employees according to Gartner research VP, Cindi Howso, . With augmented analytics, businesses can adopt an analytics solution that gives all citizen data scientists easy access to insights.
The high cost of analytics is another reason that makes augmented analytics the next step in the evolution of analytics. Data scientists are expensive to hire. Augmented analytics can fulfill businesses’ needs with less investment in talent.
How is AI contributing to augmented analytics?
Recent development in machine learning and AI enable analytics to be
- more efficient thanks to automation
- more accessible since they can process natural language queries relieving users from the need to learn yet another programming or query language
- more powerful since previously difficult to analyze data such as text and videos are now easily analyzable
How does it work?
AI-driven analytics has three layers: First, data is automatically taken from multiple data sources and prepared for analysis. Then, a machine filters data rows using machine learning algorithms for in-depth data analysis to uncover insights. After analysis, analytics software may translate the machine’s findings into words and phrases with Natural Language Generation (NLG) to provide reports to the organization.
Augmented analytics has three interfaces for users:
- Search driven analytics: Instead of data scientists extracting insights from data, citizen data scientists can ask questions by using everyday language. Software returns with results in various formats (reports, dashboards etc.) to answer the user’s query.
- Auto discovery: Augmented analytics applications can automatically find actionable insights and inform users about them. For example, millimetric.ai helps users automatically identify issues, trends and actionable insights from their website visitors.
- Visual analytics: With the rise of AR, companies can use advanced visualizations to explore their data visually
How are cognitive/augmented/AI-driven analytics different?
All of these terms mean the same thing.
Industry analysts and pundits are using different expressions to mean modern analytics, analytics that leverages the latest development in technology. Since 2010s the most relevant event for analytics was the rise of deep learning and AI. Advanced analytics in 2020s means analytics that leverages the latest AI algorithms along with other currently less impactful technological advances such as augmented reality.
Augmented analytics was put forward by Gartner, cognitive is used a lot by IBM and now if you read up pundits with limited business or technology experience, they will give you elaborate explanations of how these things are slightly different. They are not. However, if you want to be amused by people trying to find different exotic words to describe the same thing, just google things like “cognitive computing vs AI” etc.
What are the ideal use cases for augmented analytics?
Analytics can be applied to any business problem and augmented analytics is no different. Some example applications include:
- Predictive analytics in demand planning: Large amounts of historical data can be automatically analyzed for accurate forecasts
- Anomaly Detection: ML software such as IBM Streams and DataTorrent helps businesses discover anomalies so organizations can perform real-time fraud detection and prevention.
- Customer Insights: Domo’s AI for Business Dashboard scales with the size of a company and extracts data from external sources such as social media to gain insight into customers, sales, and product inventory
- Merchandising automation: The Apptus eSales solution automates marketing based on an understanding of consumers. It unites Big Data and machine learning to discover the products that may attract potential online customers searching.
What are the leading platforms for AI-driven analytics?
|Name||Status||Number of Employees|
|Alphine Data Chorus||Private||11-20|
|Einstein Analytics by Salesforce||Public||10,000+|
|Microsoft Power BI||Public||10,000+|
|Oracle Analytics Clous||Public||10,000+|
|SAP Analytics Cloud||Public||10,000+|
|TIBCO Software Inc||Private||1,001-5,000|
Tableau is one of the leaders in the augmented analytics market, and it generated $ 1.2B revenue in 2018
If you are interested in AI and analytics here is our recommended research for you:
- How is AI revolutionizing analytics?
- How are citizen data scientists democratizing analytics?
- Top +100 AI use cases
Sources that are not quoted above: