Augmented Analytics

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Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist[1]. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper[1][2].

Augmented analytics is based on business intelligence which is realized with OLAP tools.[3] In the graph extraction step, data from different sources are investigated.[4]

Defining augmented analytics

  • Machine Learning - a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules.[5][6]
  • Natural Language Generation (NLG) - a software capability that takes unstructured data and translates it into plain-English, readable, language.[7][8]
  • Automating Insights - using machine learning algorithms to automate data analysis processes.[1]

Data democratization

Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making and uncovering opportunities hidden in data.[9]

Use cases

Agriculture - Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions.[10]

Smart Cities - Many cities across the United States, known as Smart Cities collect large amounts of data on a daily basis. Augmented analytics can be used to simplify this data in order to increase effectiveness in city management (transportation, natural disasters, etc.).[10]

Analytic Dashboards Augmented analytics has the ability to take large data sets and create highly interactive and informative analytical dashboards that assist in many organizational decisions.[11]

Augmented Data Discovery - Using an augmented analytics process can assist organizations in automatically finding, visualizing and narrating potentially important data correlations and trends.[11]

Data Preparation - Augmented analytics platforms have the ability to take large amounts of data and organize and "clean" the data in order for it to be usable for future analyses.[1]

Business - Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions. [1]

In the media



  1. 1.0 1.1 1.2 1.3 1.4 Sallam, Rita; Howson, Cindi; Idoine, Carlie (July 27, 2017). "Augmented Analytics Is the Future of Data and Analytics" (PDF). Gartner.
  3. Pribisalić, Marko; Jugo, Igor; Martinčić-Ipšić, Sanda (2019). "Selecting a Business Intelligence Solution that is Fit for Business Requirements". pp. 443–465. doi:10.18690/978-961-286-280-0.24.
  4. Ghrab, Amine; Romero, Oscar; Jouili, Salim; Skhiri, Sabri (2018). "Graph BI & Analytics: Current State and Future Challenges". Big Data Analytics and Knowledge Discovery. Cham: Springer International Publishing. pp. 3–18. doi:10.1007/978-3-319-98539-8_1. ISBN 978-3-319-98538-1. ISSN 0302-9743.
  5. Pyle, Dorian; San Jose, Cristina (June 2015). "An executive's guide to machine learning".
  6. "What is Augmented Analytics (And How Can it Help?) | AnswerRocket". Retrieved 2019-07-22.
  7. "What is natural language generation? | Narrative Science". Retrieved 2019-07-22.
  8. "Natural-language generation", Wikipedia, 2019-07-03, retrieved 2019-07-22
  9. Marr, Bernard (July 24, 2017). "What is Data Democratization? A Super Simple Explanation and The Key Pros And Cons". Forbes.
  10. 10.0 10.1 Ghosh, Paramita (June 20, 2018). "Augmented Analytics Use Cases". Dataversity.
  11. 11.0 11.1 Howson, Cindi; Richardson, James; Sallam, Rita; Kronz, Austin (February 11, 2019). "Magic Quadrant for Analytics and Business Intelligence Platforms" (PDF). Gartner.

External links

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