To give an insider’s perspective on use-cases for Machine Learning and Data Science –including the pitfalls– I spoke with Mark McQuade, Practice Manager of Data Science and Engineering at Rackspace Technology, to talk us through what’s possible and what he calls the end-to-end data journey.
We also discussed a few ways companies can use machine learning:
1. Transcribing audio and pulling text out for analysis.
2. Conversational chatbots for contact centers–front-end for an agent.
3. Forecasting things like sales, trends, and predictions.
4. Sports– Evaluating athletes, identifying winning tactics in-game
5. Package routing for speed, efficiency, and fuel savings.
6. Entertainment– Personalizing content recommendations, auto-generation, and personalization of thumbnails.
7. Media– Location scouting for movie productions, movie editing.
8. Streaming services– Using historical viewing data to predict when it makes sense to cache servers for faster load times during expected peak demand.
The limit of what is possible with data and machine learning has to do with an organization’s needs, imagination, and budget.
In Summary: Whether you are looking to use data for forecasting, risk mitigation, marketing, product development, or entertainment– the opportunities are limitless!
You just need to work through the four-step end-to-end data journey:
1. Centralize your data, or leverage a datalake.
2. Cleanse all data.
3. Determine use cases that give you an ROI (or other non-monetary return).
4. Move your data development projects into production.
The key is starting with a clean data set and a clear purpose in mind.
Armed with that knowledge, you simply need to consider whether you want the expense and management of implementing a data strategy internally or leveraging the expertise and economies of scale of a partner.