Episode Title : Aligning data processes, management & tools in a CDO role
Episode Summary: Business Intelligence has evolved significantly over the years. In Gen 1, BI was predominantly owned by IT. In Gen 2, starting in 2000 or so, business users have gotten involved with self-service analytics. Going forward in 3rd gen, the focus will be on controlling and managing the backend of data management & governance and liberating the front-end of analytics & visualization with democratization of data. Joe DoeSantos, CDO of Qlik, has seen this evolution with multiple companies. Given that data scientists and other analysts will be needing raw data as opposed to processed data, the job of a CDO should be to catalog raw data at speed and allow analysts and data scientists to analyze as quickly as possible. To enable this, AI models can be used to enable a fast data cataloging at speed. With self-service analytics, there will be more and more need to manage & govern AI models along with data to ensure that the outcomes are ethical.
1:30: Role of Chief Data Officer. CDO should be an enabler instead of a Janitor who only cleans up after the fact. Role should be about solving big problems.
03:00: Qlik started as a data visualization company but now an analytics company. CDO at Qlik is different from other organizations because people at Qlik are already data literate. So the challenge is to liberate the data in a meaningful way and get out of the way.
05:20 : Evolution of BI. First generation BI – only for IT; Second generation of BI – Tableau, Qlik – Empowerment of BI; 3rd generation of BI – Control the backend and liberate the front end; Careful and thoughtful generation of Data.
08:00: Analogy of liquor control and drug control. Equivalent of Oxycontin is PII; The future belongs to companies who can at-speed understand data, catalog it, manage it and get out of the way.
10:00 : Data scientists want raw data. So we need to think about managing raw data and making it available. Need to detect raw data at speed.
13:00 (Headliner): Data consumers don’t understand data governance and vice versa. So we should start talking business language to connect these disparate groups.
15:50 (Headliner): Role of AI with respect to self-service analytics. First task being done is identifying patterns of data. Role of AI in this is identifying type of data as birth date for example. Second task is the AI models grabbing the appropriate data sets they need in unsupervised learning.
20:30 (Headliner): If AI is for automation tool for data stewards to put raw materials on the shelf, that’s great. There should be governance for the consumption side as well for model control. Example is assessing if the algorithm is right for the targeted use.
24:00 : Who should be owning governance for different phases of data lineage starting from where the data is sourced to where different algorithms are governed? Is CDO the right person? Goal should be to make it faster to set the policies and controls.
Resources mentioned in this episode:
Podcast website: https://DataTransformersPodcast.Com
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