How To Use Augmented Data Science & Why Is It Important For Business?

By Kimberly Cook |Email | Mar 18, 2019 | 3624 Views

Augmented Data Science is integrated into the average enterprise, the domain of data science proliferates as does the knowledge and understanding of analytical techniques

If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for automated machine learning (AutoML) and leverage sophisticated analytical techniques and algorithms in a guided environment that uses auto-recommendations and suggestions to lead users through the complex world of data science with ease and intuitive tools.
Augmented Data Science is integrated into the average enterprise, the domain of data science proliferates as does the knowledge and understanding of analytical techniques. Citizen X roles, like the much-discussed Citizen Data Scientist, Analytics Translator, Data Translator, Citizen Integrators and Citizen Developers will emerge, cascading knowledge and leveraging power users as liaisons with IT and data science staff. The propagation of these tools throughout the enterprise will improve decisions, planning, and competitive advantage.

Augmented Analytics includes Assisted Predictive Modeling, Smart Data Visualization, Self-Serve Data Preparation, Clickless Analytics, NLP Search Analytics, Automated Machine Learning (AutoML), which enables faster, or accurate analysis across the organization optimizes resources and improves the value of each team member. Business users can merge core business knowledge and skill with critical analysis and share, collaborate and advance ideas, innovations and issue resolution.

The popularity and availability of intuitive, guided, Advanced Analytics allows for new analytic insights. As business users adopt and become comfortable with advanced data discovery and advanced analytics tools, that no longer require the skills of IT or a data scientist. This democratization of data analysis tools frees the data scientist and IT teams to focus on core tasks and on those strategic analytical projects that require 100% accuracy and refinement. Business users can perform analysis and use this analysis on a daily basis without delay, thus increasing the return on investment, and the accuracy of decisions and supporting data.

In a report published by Gartner on October 31, 2018, and entitled 'Augmented Analytics Is the Future of Data and Analytics', Gartner analysts provide the following strategic assumption: 'By 2020, automation of data science tasks will enable citizen data scientists to produce a higher volume of advanced analysis than specialized data scientists'.
Augmented data science automates and simplifies analysis with machine learning so implementation, training, and adoption of these tools are rapid and successful. Users do not need the skills or knowledge of a data scientist. Instead, they can leverage the easy-to-use augmented analytics tools that provide guidance, suggestions, and auto-recommendations to ensure that the data retrieved, analyzed and presented is the right data in the right format to produce clear, concise results.

To put it simply, augmented data science lends a helping hand to users and to the enterprise that wishes to streamline the analytical process and provide every team member with access to the sophisticated tools they need to produce results without delays, complication or advanced skills.

Source: HOB