Friday, April 16, 2021
Digital transformation is being affected in nearly every industry but accomplishing true transformation requires not only technological advancement but reformation of culture and processes to embrace that transformation. At the core of digital transformation is a shift toward data-driven decisioning and action. The tools and capabilities that are produced by data scientists can be very powerful, but without the data translator, without someone to link projects to business initiatives and strategies, achieving enterprise transformation and AI adoption is impossible.
A discussion of how roles and skills are evolving across the analytics and data science space. Points out some important new roles that are growing and talks about how to adapt your organization to the trends.
Constantly questioning whether the value of the work you do as a data professional really justifies the salary they're paying you? That concern is more than valid, but what if I told you that there is a proven process for making triple sure that your data projects result in business success for your company? In this brief session, you’ll learn how to build an analytics strategy framework from the ground up. If you’re in the conception/kick-off phase of a data project, and are wanting to develop an evergreen strategy for analytics, data management or even machine learning products, this session will show you how to get started doing that right away. This session is for data professionals who want a head-start on creating data strategies; whether that be an AI strategy or just a general data strategy - this session will help you craft a strategic framework so you can start taking measures to ensure that your data projects produce greater business ROI.
What you’ll get:
- A Clear Understanding Of The Value And Necessity For An Evergreen Analytics Strategy Framework: To create an evergreen analytics strategy framework, you first need to understand what it is and how it’s beneficial, so I’ll break that down for you.
- Steps That Go Into Creating Your Own Evergreen Analytics Strategy Framework: You’ll come away with all the steps and ingredients that go into creating an evergreen analytics strategy framework.
- A Proven Data Strategy Framework You Can Use Starting Today: Bypass all the hard work of developing your own framework, and just use mine. I’ll be providing that to you for free in this brief session.
You might have heard that there are only three things that matter - execution, execution, and execution. But how do you bridge the gap between strategy and execution?
We all make decisions. All the time. Especially leaders.
We all want those decisions to be “evidence-based” and are investing resources and efforts in building the data infrastructure, teams, and functionality of our organizations. But how can data and data scientists inform not only the company’s direction and strategy, but also support its execution?
In this talk, I will present two quick case studies that demonstrate how data can lead to successful execution. The first of which is a past case in which a reliable causal analysis was instrumental to setting the direction of a young mental health startup and securing a 45 million dollar investment in its Series C. The second is a present example of a social impact organization setting its course to reach diverse audiences supporting people who learn and think differently in all areas of life and shape the world for difference.
Every business is dependent on several processes to deliver value. In this presentation we will see how to use AI that can learn and trigger actions that will help organizations optimize their processes.
Humans are biased creatures. Therefore, data around human behavior will reflect these biases. As AI solutions are by design built to pick up on complex patterns, biased data left unchecked will propagate through the AI solution, potentially leading to unintended and inequitable outcomes. As more and more decisions with increasing impact are automated through AI, this problem becomes increasingly front and center. However, there are steps that can be taken to mitigate bias throughout the process – beginning with the way the initial question is framed all the way to data and outcome evaluation. This talk will focus on ways to address unintended consequences and ethical considerations throughout the data science process from project inception to operationalization using a combination of thoughtful investigation and machine learning-based techniques.
Meet and greet with Dr. Tom Miller, faculty director of the data science program at Northwestern University
Why Can’t My Data Scientists and the Business Stakeholders Understand Each Other? Bridging the Gap Between the Business and Data ScienceJoin on Crowdcast
Businesses are hungry to transform into data driven organizations. However, despite hiring top data science talent, businesses often encounter challenges implementing data science solutions. This talk will give concrete advice about how executives can structure their organizations to reduce the frustration and failure that often comes with data science projects. Executives and managers will come away with concrete practices they can adopt to make their own data science projects succeed.
The importance of data quality is often undermined in the rush to acquire and process as much data as possible. Data science and plethora of opportunities it presents including decision making, compliance and confidence is only as good as the data itself. This is a talk that deconstructs the good, bad and ugly of data quality.