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]

Contents

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

Defining Augmented Analytics

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]

There are three aspects to democratising data:

  1. Data Parameterisation and Characterisation.
  2. Data Decentralisation using an OS of blockchain and DLT technologies, as well as an independently governed secure data exchange to enable trust.
  3. Consent Market-driven Data Monetisation.

When it comes to connecting assets, there are two features that will accelerate the adoption and usage of data democratisation: decentralized identity management and business data object monetization of data ownership. It enables multiple individuals and organizations to identify, authenticate, and authorize participants and organizations, enabling them to access services, data or systems across multiple networks, organizations, environments, and use cases. It empowers users and enables a personalized, self-service digital onboarding system so that users can self-authenticate without relying on a central administration function to process their information. Simultaneously, decentralized identity management ensures the user is authorized to perform actions subject to the system’s policies based on their attributes (role, department, organization, etc.) and/ or physical location. [10]

Use cases

References

  1. 1 2 3 4 5 Sallam, Rita; Howson, Cindi; Idoine, Carlie (July 27, 2017). "Augmented Analytics Is the Future of Data and Analytics" (PDF). Gartner.
  2. "Definition of Augmented Analytics - Gartner Information Technology Glossary". Gartner.
  3. Pribisalić, Marko; Jugo, Igor; Martinčić-Ipšić, Sanda (2019). "Selecting a Business Intelligence Solution that is Fit for Business Requirements". Humanizing Technology for a Sustainable Society. pp. 443–465. doi:10.18690/978-961-286-280-0.24. ISBN   9789612862800. S2CID   202767869.
  4. Ghrab, Amine; Romero, Oscar; Jouili, Salim; Skhiri, Sabri (2018). "Graph BI & Analytics: Current State and Future Challenges". Big Data Analytics and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 11031. Cham: Springer International Publishing. pp. 3–18. doi:10.1007/978-3-319-98539-8_1. hdl:2117/127964. 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". 8 February 2019. Retrieved 2019-07-22.
  7. "What is natural language generation? | Narrative Science" . Retrieved 2019-07-22.
  8. "Definition of natural language query".
  9. Marr, Bernard (July 24, 2017). "What is Data Democratization? A Super Simple Explanation and The Key Pros And Cons". Forbes.
  10. "Democratisation of Object Data within the Telecoms Sector".
  11. 1 2 Ghosh, Paramita (June 20, 2018). "Augmented Analytics Use Cases". Dataversity.
  12. 1 2 Howson, Cindi; Richardson, James; Sallam, Rita; Kronz, Austin (February 11, 2019). "Magic Quadrant for Analytics and Business Intelligence Platforms" (PDF). Gartner.