How AI can rescue your BI "situation"
Author: Krishna Kallakuri
Even with a few credible upstarts in the past couple years, Self-Service BI still seems to be dominated by some large players that also require some very large and "very ongoing" commitments with systems that tend to be complex by design.
The sad truth is that despite these massive investments, adoption rates are in the 5% range, with only a fraction of those being regular or "power users". Here we thought the whole premise of Self-Service BI was "self-service" for business users! Users that don't know how (or care) to program, don't understand data warehousing, engineering, modeling, etc. but have the data and the right questions!
It shouldn't come as a surprise considering the countless hours required to
manually analyze, interpret, and communicate data insights, to then be left with the big question after this massive effort: "What does all this mean for me? Without insights that are actionable, contextual, and articulated for the user’s specific business scenario, businesses are still left with making "courageous decisions". To further complicate things, put a collection of visualizations in front of a collection of users and you’ll get just as many meanings. Varied understanding, analytical skills, and biases can lead to confusion without a clear and consistent narrative.
This gap between knowledge generation and decision making can be helped by AI systems built to apply reasoning, to explain what all this knowledge means at scale.
But wait, even AI has it's "little secrets" and is no Panacea; it is only one small but very important part of a greater cognitive framework, one that places “top-down” and “bottom-up” knowledge on equal footing with a business-first approach. Speaking of the "optimal approach", at diwo we're making a quantum leaps from data-driven insights and creating new knowledge, to the realm of decision making.
What we call "Cognitive Decision-Making" starts with a platform that works continuously to sense a full range of business situations before they occur, explain their potential impacts, and recommend interactive strategies to address them, all while guiding implementation. This cognitive framework is conceived to maximize the human-machine symbiosis by suggesting and optimizing decisions by initiating conversation in a natural language, rather than simply providing the raw materials for making them. By focusing on solving specific business scenarios, the system can learn to improve with time, while providing user-specific solutions and scalability by tackling one area at a time.
A cognitive framework that combines 10+ technologies for a seamless analytics solution, it also leverages existing BI and analytics assets to provide value on day one- no complicated deployment cycles to worry about.
See some real-world scenarios of Cognitive Decision-Making in action at getdiwo.com/solutions
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