How Can Augmented Analytics Benefit Your Role? A little over a year ago, a Gartner report stated that “augmented analytics is the future of data and analytics.” In just a short time, the future has arrive
copyright by tdwi.org
Thanks in large part to the use of AI and the rise of augmented analytics, business users are closer than ever to their data and, more important, to actionable insights. The wall between the answers and the people asking the questions has toppled.
In a nutshell, augmented analytics uses machine learning and natural language processing to automate data analysis and the presentation of insights. By combining artificial intelligence (AI) with business intelligence (BI), non-technical users can now automate major aspects of their data analytics.
Augmented Analytics Has Transformed How Users Approach Data
Augmented analytics platforms benefit end users on two fronts.
First, much like we all interact with Google and Siri, non-technical users can easily interface with augmented analytics solutions by asking questions directly and getting answers instantly, drastically reducing reporting time and accelerating strategy and performance.
Second, technical analysts and data scientists are suddenly freed from running routine and basic reports, empowering them to charge ahead and tackle more complex queries and data science projects.
Speaking to the latter point, I previously touched on this new frontier of analytics in my previous Upside article , “Why Your Top Analysts Are About to Quit — And How to Keep Them.”In fact, augmented analytics can help many different types of users across the enterprise.
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Benefits for Data Scientists
The core benefit of augmented analytics for data scientists and similar positions is simple: they are freed up to solve more complex problems.
I spoke to a data scientist who built an impressive churn-prediction algorithm and bemoaned the fact that all he did now was run it for various products, channels, and customers, preventing him from working on his next project. He was stuck.
Enter augmented analytics.
Rather than running repetitive reports and answering basic queries, your analysts — like this data scientist — can solve problems with advanced AI and machine learning. In turn, that advanced work gives the company a leg up on the competition. It’s a win for all involved.[…]
read more – copyright by tdwi.org
How Can Augmented Analytics Benefit Your Role? A little over a year ago, a Gartner report stated that “augmented analytics is the future of data and analytics.” In just a short time, the future has arrive
copyright by tdwi.org
In a nutshell, augmented analytics uses machine learning and natural language processing to automate data analysis and the presentation of insights. By combining artificial intelligence (AI) with business intelligence (BI), non-technical users can now automate major aspects of their data analytics.
Augmented Analytics Has Transformed How Users Approach Data
Augmented analytics platforms benefit end users on two fronts.
First, much like we all interact with Google and Siri, non-technical users can easily interface with augmented analytics solutions by asking questions directly and getting answers instantly, drastically reducing reporting time and accelerating strategy and performance.
Second, technical analysts and data scientists are suddenly freed from running routine and basic reports, empowering them to charge ahead and tackle more complex queries and data science projects.
Speaking to the latter point, I previously touched on this new frontier of analytics in my previous Upside article , “Why Your Top Analysts Are About to Quit — And How to Keep Them.”In fact, augmented analytics can help many different types of users across the enterprise.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Benefits for Data Scientists
The core benefit of augmented analytics for data scientists and similar positions is simple: they are freed up to solve more complex problems.
I spoke to a data scientist who built an impressive churn-prediction algorithm and bemoaned the fact that all he did now was run it for various products, channels, and customers, preventing him from working on his next project. He was stuck.
Enter augmented analytics.
Rather than running repetitive reports and answering basic queries, your analysts — like this data scientist — can solve problems with advanced AI and machine learning. In turn, that advanced work gives the company a leg up on the competition. It’s a win for all involved.[…]
read more – copyright by tdwi.org
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