#208 Monetizing Data & AI | Vin Vashishta from V Squared, & Tiffany Perkins-Munn from JPMC
Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash?
Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.
Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications.
As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's d
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from DataCamp · DataCamp · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
SQL Server Tutorial: Date manipulation
DataCamp
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
R Tutorial: Adding aesthetics to represent a variable
DataCamp
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
Python Tutorial: Preparation for modeling
DataCamp
Python Tutorial: Machine Learning modeling steps
DataCamp
R Tutorial: The prior model
DataCamp
R Tutorial: Data & the likelihood
DataCamp
R Tutorial: The posterior model
DataCamp
R Tutorial: An Introduction to plotly
DataCamp
R Tutorial: Plotting a single variable
DataCamp
R Tutorial: Bivariate graphics
DataCamp
Python Tutorial: Customer Segmentation in Python
DataCamp
Python Tutorial: Time cohorts
DataCamp
Python Tutorial: Calculate cohort metrics
DataCamp
Python Tutorial: Cohort analysis visualization
DataCamp
R Tutorial: Building Dashboards with flexdashboard
DataCamp
R Tutorial: Anatomy of a flexdashboard
DataCamp
R Tutorial: Layout basics
DataCamp
R Tutorial: Advanced layouts
DataCamp
Python Tutorial: Time Series Analysis in Python
DataCamp
Python Tutorial: Correlation of Two Time Series
DataCamp
Python Tutorial: Simple Linear Regressions
DataCamp
Python Tutorial: Autocorrelation
DataCamp
R Tutorial: The gapminder dataset
DataCamp
R Tutorial: The filter verb
DataCamp
R Tutorial: The arrange verb
DataCamp
R Tutorial: The mutate verb
DataCamp
R Tutorial: What is cluster analysis?
DataCamp
R Tutorial: Distance between two observations
DataCamp
R Tutorial: The importance of scale
DataCamp
R Tutorial: Measuring distance for categorical data
DataCamp
Python Tutorial: Plotting multiple graphs
DataCamp
Python Tutorial: Customizing axes
DataCamp
Python Tutorial: Legends, annotations, & styles
DataCamp
Python Tutorial: Introduction to iterators
DataCamp
Python Tutorial: Playing with iterators
DataCamp
Python Tutorial: Using iterators to load large files into memory
DataCamp
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
SQL Tutorial: Tables: At the core of every database
DataCamp
SQL Tutorial: Update your database as the structure changes
DataCamp
Python Tutorial: Classification-Tree Learning
DataCamp
Python Tutorial: Decision-Tree for Classification
DataCamp
Python Tutorial: Decision-Tree for Regression
DataCamp
Python Tutorial: Census Subject Tables
DataCamp
Python Tutorial: Census Geography
DataCamp
Python Tutorial: Using the Census API
DataCamp
R Tutorial: A/B Testing in R
DataCamp
R Tutorial: Baseline Conversion Rates
DataCamp
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
R Tutorial: Introduction to qualitative data
DataCamp
R Tutorial: Understanding your qualitative variables
DataCamp
R Tutorial: Making Better Plots
DataCamp
SQL Tutorial: OLTP and OLAP
DataCamp
SQL Tutorial: Storing data
DataCamp
SQL Tutorial: Database design
DataCamp
Python Tutorial: Introduction to spaCy
DataCamp
Python Tutorial: Statistical Models
DataCamp
Python Tutorial: Rule-based Matching
DataCamp
More on: ML Maths Basics
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
Python Programming Course in Delhi
Medium · Python
Choosing the Right Architecture: A Software Engineer’s Field Guide to Neural Networks
Medium · Data Science
Chandra OCR 2: When Open Source Reads What Others Miss
Medium · Machine Learning
The hidden value of teaching ML to Non-ML teams
Medium · Machine Learning
🎓
Tutor Explanation
DeepCamp AI