AI, Data and Decision Making
This roundtable will look at how the availability and usability of data today is changing the nature of decision-making in business. It’s changing the quality and speed of decision-making, and also often where or what level in the organization the decision is made. As part of this, we will examine the emergence of artificial intelligence (AI) and machine learning (ML) and the implications of this for analytics, predictive capabilities and decision-making.
The technologies that underpin modern society (e.g. IoT and sensors, social media) continue to drive a massive increase in data of all shapes and sizes, from internal to external, structured to unstructured, hard numbers to sentiment analysis. Even since we last addressed this issue, the amount of data has multiplied many fold, often exceeding our ability to manage it and make use of it. It could be argued that this is true in many areas of the enterprise, and that we all have real challenges around people, process and technology associated with the management, analysis and usage of data. Roles and skills are changing in enterprises, data-science organizations are being created, and the apparent need and desire for the democratization of data, it’s availability to all, is changing how we function. We will address these challenges by discussing questions such as:
- What has changed in the types and volume of data in the last three years, and in what parts of the enterprise is this most prevalent? What has changed in how we manage, analyze and use the data? Is the availability of data outpacing our ability to harness and analyze it, and if so, how do we figure out what to focus on?
- Have we made real progress in enabling development of insights, especially predictive insights?
- How can/are machine learning (ML) and artificial intelligence (AI) help(ing) advance automation and replace human involvement in decision-making? In what areas in particular id this happening (e.g. chat bots w/customer service)?
- Where it doesn’t, or can’t yet, how do we enable all levels of the enterprise to make faster and better decisions with the data, to gain new insights? How do we organize for this, how do we pick what to go after?
- How are you using data to get closer to your customers and/or collaborate with them? How are you thinking about data ownership with partners and customers/end-consumers?
- How are we organizing differently to take advantage of the data we have and make sure the right capabilities are in the right place in the organization?
- How are your talent needs changing? Are they changing just in a need for additional data scientists, or also a different education for many in your enterprise? How and where are you sourcing this talent from? To what degree can/do you help grow it?
- How do you deal with the need for sharing data more, the democratization of data, so that people can make better and faster decisions, while at the same time protecting data better, especially key data, crown-jewel data? What do we do for data security outside the traditional data management paradigms?
- How do we wrestle with the associated ethics, privacy, data protection, etc. issues at a time when trust in digital assets seems to be going down? How is regulation affecting your management and usage of data (e.g. GDPR)?