What Exactly Is Augmented Analytics?

Augmented Analytics is the forthcoming disruption of Data and Analytics marketplace. It’s an approac...

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Posted by Dave Food on Jun 25, 2019 4:46:04 PM
Dave Food

Augmented Analytics is the forthcoming disruption of Data and Analytics marketplace. It’s an approach that automates insights using Machine Learning (ML) or similar technologies, changing how Analytics content is improved, used and shared through your enterprise

 The idea behind Augmented Analytics is that companies and stakeholders can use ML, Deep Learning (DL) and Artificial Intelligence (AI) to improve the Analytics process and add capability

 10 Capabilities empowered by Augmented Analytics:

  • The democratisation of Big Data management makes it possible that those not so-tech-expert users such as Citizen Data Scientists, deserve equal rights and opportunities to put into operation Augmented Analytics.
  • Augmented Data Science and ML reduce the need for specialised skills to produce, operate and run Advanced Analytic models.
  • When Augmented Analytics is run by more people empowered to use that data, those scientist drive business forward. It seems that best engineers and programmers are not tied enough to the core goals of the enterprise, even though they might count on the right expertise and importance.
  • Augmented data preparation must be enhanced to extend data quality, modelling, data profiling, harmonisation, manipulation, and cataloguing, changing all outlooks of Data Management, encompassing database and considering automating data integration and data lake administration too.
  • It is as a segment of Analytics and BI, which empowers business users and Citizen Data Scientists to discover, visualise and report significant outcomes without the need to build patterns or writing algorithms (segments exceptions, cluster, predictions.)
  • Augmented Analytics makes it possible to consider more hypotheses and the recognition of hidden patterns.
  • It represents the adoption of specific technologies, as data preparation, data management, modern Analytics, Business Process Management, Process Mining and Data Science platforms.
  • It will also be embedded in enterprise areas, such as the HR, Procurement, Finance, Sales, Marketing, Customer Service, and asset management departments.
  • It will elevate not just those tasks of analysts and data scientists, but also the results and programs of all employees.
  • Augmented Analytics empowers the implanting of an Automated Data Scientist role in any Autonomous Things.

Ten steps to take

·       Implement Augmented Analytics as part of a digital transformation strategy.  Automate all your Data Science tasks.

·       Enable the Citizen Data Scientist to fulfil the shortage-high-cost of Data Scientist.

·       Take advantage of the use of Citizen Data Science to balance and work together with current-user of Analytics and BI.

·       Carry out a program for Citizen Data Scientists from present roles, such as Business and Data Analysts.

·       Promote to better-skilled productivity alongside Citizen Data Science by outlining and affording management for the collaborations and duties of both fields.

·       Build trust and provide evidence of the value.

·       Keep an eye on the Augmented Analytics capabilities of modern Analytics and BI, and Data Science platforms.

·       Evaluate languages in use, the incorporation of Augmented Analytics with current tools and the AI control processes.

·       Open up to opportunities to use Augmented Analytics to balance current Analytics and BI, Data Science proposals, and inserted Analytic apps.  

·       Develop a strategy to tackle the impact of Augmented Analytics on current data and Analytics competencies, functions, responsibilities and talents. Expand funds in data learning

CONCLUSIONS:  Augmented Analytics will be the focal point of Data and Analytics systems by 2020. It will be a crucial element of Analytics embedded in Autonomous Things interacting with users — especially Autonomous Assistants using conversational interfaces.  

The use of edge technology like these means avoiding error; it will cut the time users spend exploring data, allowing them more time to work on the most relevant insights. Moreover, automation of Data Science tasks will enable Citizen Data Scientists to generate a higher level of Advanced Analytics than specialised Data Scientists. 

 

Dave Food

Prophetic Technology


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