<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=306561&amp;fmt=gif">

Video: Data Conversations Over Coffee with Mishumo Dzhivhuho

Written by Craig Steward

Video: Data Conversations Over Coffee with Mishumo Dzhivhuho

Written by Craig Steward on Jun 22, 2020 6:49:16 AM

Data and Analytics

This episode covers the ethical use of customer data in AI models and pandemic response. 

Speaking to Mishumo Dzhivhuho I get a lot of insight into how data science teams can use customer data within the confines of data privacy and protection regulations.

Mishumo is a Senior Data Science Manager at MTN - a company which has access to large volumes of customer data...including location intelligence.

He speaks about how to use this information ethically to build better models, offer products to customers and to work with government on pandemic response. 

We have a bit of laugh at the end about how governments can use drones to ensure social distancing...great movie scene. 


DataCon Africa: Live is a 100% virtual conference and will connect Africa's most progressive data analytics leaders with the world's most forward-thinking solution providers, set against a backdrop of cutting-edge content that you cannot find anywhere else. At home, in the office or on the road.


Data Quality Training Online 

About the Course

In an age where data analytics is used to drive decision making across every facet of a business ensuring the quality of your data is paramount to your organisation's success. The adage "garbage in, garbage out" may be cliched but it describes, perfectly, the critical importance of high quality data being fed into sophisticated models. 

Corinium has partnered with InfoBluePrint to bring you the most comprehensive data quality management course delivered through a state-of-the-art webinar platform. 

The course structure is 8 modules spread over 4 days by our leading trainers - Diana Joseph and Joe Newbert. The 8 modules are:

  1. Data Quality Awareness
  2. Data Quality in Context
  3. Data Assessment
  4. Business & User Requirements
  5. Data Measurement
  6. Data Correction
  7. Problem Prevention
  8. Context of Data Management


Related posts