Tech – Disruption 3 – Big Data
Sloppy or stellar – some companies are better positioned than others for data management.
Maria Pacella, SVP, Private Equity and Portfolio Manager of the Pender Technology Inflection Fund, continues her conversation with Rich Miller, Silicon Valley veteran, about data management. Rich has an interesting take on data as an asset that needs nurturing, feeding and care, in order to fulfill its potential. Some companies are well positioned, and some have a way to go, especially against the backdrop of increased regulation. Rich provides insights around how AI machine learning can be applied to data analysis and predictions, and touches on the difference between data science and data engineering. He gives the example of the challenges and outcomes of a recent project.
00:16 – Maria introduces her guest for this episode – Rich Miller.
01:01 – Rich explains the importance of having the ability to retain control and ownership over data, and the impact it has on the economics of data and information.
04:28 – What are enterprises doing today to care and feed and manage their data? What could they be doing better?
07:28 – Rich raises another issue that enterprises face – the challenge of determining data jurisdiction, particularly in cases where regulations are different.
09:32 – What are Rich’s thoughts on applying AI machine learning on data to make predictive decisions down the road? Where we are in the sequence of that?
14:25 – Data scientists are a big part of AI machine learning and analytics. How important is it for them to also have domain experience?
16:52 – How important is it to properly scope or define the problem, to be able to ask the right questions?
18:24 – Only 15% of big data projects make it to production. Where do companies falter?
22:12 – Rich talks about one of his recent projects with “broken data” and how he helped them fix it to achieve better bottom line results.
26:29 – Watch out for the next and final episode of the series to find out more about the Internet of Things, edge computing and anomaly detection.
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