(a) What is Decision Sciences and how important is it?
Increasing number of businesses are focusing on analytics driven decision support across all facets of business where experts have to take decisions based on bits of information.... Analytics is now where IT was in the eighties:
- Business + Technology allows us to simply automate
- Math + Business allows us to more cogent arguments at the board room
- Math + Technology allows us operate proactively with anticipation
- Math + Business + Technology allows us to execute better
Math + Business + Technology + Behavioral Science will let us develop nudges (cognitive repairs) against bias that we as human beings are gifted with
Decision Sciences is a combination of Math + Business + Technology + Behavioral Science to help make data driven decisions.
(b) Key Takeaways for audience
1. Understand what this nascent industry called ‘decision sciences' and the opportunity that lies ahead of us
2. A perspective on the vision for the industry in 2015.
(c) Session Structure: 60 min presentation, with a PPT. No moderator, only introducers (1 min.)
1. Macro trends seen in the industry
2. Why decision sciences?
3. A framework for decision sciences
4. Where do we see this going? A vision for 2015
Presentation points to be covered:
- Macro Trends in the industry:
- There exists an incestuous cycle between data, applications, techniques and technologies...leading to data explosion, expanding applications, complex technologies, new analytical techniques
- Big Data, High Performance Computing, Intelligent Systems, Usability & Visualization
- Why decision sciences
- Elaborate the need for data driven decision making for businesses
- Many new business problems have come up, new business models, new customer psyche - a "new normal" has emerged post the economic downturn
- With the explosion of data, companies have to adopt decision sciences as a competitive advantage
- Framework for decision sciences
- DIPP Framework
- Creation vs Consumption of analytics
- Decision sciences will lead to accelerated hyper-competition.
- Creation of analytics will become more commoditized while organizations compete on consumption of analytics.
- Premium for disruptive innovation will increase as we will see more continuous incremental innovation.
- New data providers will emerge focusing on intelligently interpreted data - especially for social media analysis, location specific data, etc.
- The equivalent of a "Chief Analytics Officer" position will emerge ...these people will be from diverse backgrounds
- Analytics Education will be formalized
- Open Source platforms will gain prevalence, Analytics-as-a-Service (AaaS) will be more common place
- Understanding of human biases will be needed to develop cognitive repairs or nudges that enable better decision making
- With blurring of value chain boundaries and emerging business models a new era of convergence in the use of analytical techniques will come into play.