Data fuels machine learning. Without timely, accurate data, machine learning models do not perform well and can give results that are misleading or fail to deliver value. It is vitally important that companies source and prepare data in the right manner - even though the process can require a vast amount of preparatory work and specialist expertise that may appear daunting. Companies should not even consider embarking on a machine learning project before completing this process.
***Available only to Supply Chain Exchange members and invited guests.
In this roundtable, participants will explore the challenges of acquiring the right data as well as the work needed to turn data into the fuel that will run machine learning models efficiently. We aim to address these questions among others.
- How do you collect the right data?
- How do you visualize data?
- How do you prepare the data for analysis?
- How do you make decisions about the governance and structure of data?
***This MIT CTL Roundtable is exclusively for members of the MIT CTL Supply Chain Exchange and invited guests. If you are not a member of the Exchange and are interested in attending this roundtable as a potential SCE member, please contact email@example.com.
MIT CTL Event and Travel FAQ
|Tuesday, October 15|
|Welcome and Introductions|
|I: To Be Announced|
|II: Managing Transformation and Change through Use-Case-Based Prioritization|
|III: Future State Data Governance Structures and Policies|
|IV: Data Storage|
|Closing: Key Take-Aways from Day 1|
|Wednesday, October 16|
|Recap of Day 1 and Introduction|
|V: Collecting the Right Data|
|VI: Understanding Your Data|
|VII: Data Visualization|
|VIII: Data Wrangling|
|Closing: Key Take-Aways from Day 2|