Data Transformers Podcast

Episode Title : Iceberg strategy for Chief Digital/Data Officers

Episode Summary:

Nowadays, there are a lot of expectations of Chief Data Officers for both short term and long term. One way to manage the expectations is to have a two-track strategy. CDOs need to have a list of items that are of value to business stakeholders in the short term and also have a long term roadmap. Krishna Cheriath, CDO of Zoetis, the largest animal health company in the world, has been experimenting with Iceberg strategy with great results.  Krishna is an advocate of every employee being a digital citizen with a certain expectations of them and also with a need to be more aware.

1:16: Krishna Cheriath is the Data and Analytics leader at Zoetis, world’s largest animal health company. Through Data and Analytics, there is a unique opportunity to optimize R &D, optimize sales, operations, manufacturing and supply chain.

4:52 (Headliner): Nowadays, there are a lot of expectations coming in as CDO. There should be a two-track strategy for CDOs. one is to practice established credible partnerships and with business leaders across different areas and find where you can demonstrate value fast. Simultaneously, lay out a pragmatic roadmap, evolve the data and digital fabric in a better direction.

7:04 (Headliner): Data and Analytics don’t progress in a company without a coalition developing around it. Needs to be proficient leading from the front and leading from the behind.

9:50 (Headliner): Iceberg strategy. Focused on the things that are above the waterline for all of those stakeholders or in business areas. How can data analytics optimize drug, drug development etc.

11:30 (Headliner): Every employee is a digital citizen. So there are certain expectations of every employee and a certain awareness that each should have. Every employee should be cyber-aware.

15:58: Need to recognize digital means different things to different people. A sales person looks at digital differently than a recent employee.

17:54: Example of accomplishing rapid results is having a data lab. Started with a few data initiatives and ended the year with 440.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.

01:47: Role of data strategy is always in the context of a business strategy. It doesn’t exist by itself.

02:39: What was once people, process, technology is now people, process, technology, and data. Data strategy works hand in hand with technology strategy.

05:30: To build a data strategy that works for all the initiatives is possible but organization will not wait for all the elements such as standards, principles etc. to fit in. So it is better to plan for the org but build tactically.

07:48 (Headliner) : six pillar approach to building data strategy. (1) Understanding and creating the vision (2) people and culture (3) Operating model (4) Data platforms, tech and architecture (5) Data excellence (6)

09:54: Start with an assessment of the capabilities. Based on the assessment, you start filling the gaps.

14:52 (Headliner): Difference in working as a consultant versus as an employee to put a strategy together is that you have to move faster as a consultant.

19:23: As a fundamentalist, always try to get basics right first.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Episode Title : The pillars of a successful data strategy – Jennifer Agnes

Episode Summary: Data strategy can’t live by itself. It needs to be driven by a business strategy. A six pillar approach to data strategy will stand the test of time. 1) Understanding and creating the vision (2) people and culture (3) Operating model (4) Data platforms, tech and architecture (5) Data excellence (6) . Jennifer Agnes, who was implementing data strategy within the corporations, is now helping companies as a consultant. For any work, an assessment is the first step. The gaps from the assessment will direct the next steps.

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.

Episode Title : Storytelling ABOUT the data is as important as storytelling WITH the data

Episode Summary:

The concepts behind master data have been around for a very, very long time. Which means the businesses won’t function well without implementing master data. Scott Taylor, the Data Whisperer, believes that it is more productive to talk to management about data than the processes behind it. The business side is more interested in the WHY side of data. Why are you telling me about this? Why are we funding this? What does it matter to me? So there is always that gap between requirements/implementation versus strategy/rationale. Storytelling is very hot right now. But most of the storytelling is focused on data analytics, visualization, charts etc. But not many are focusing on storytelling of the data management itself. Telling stories of the data is as important as telling stories with data. Data management is about determining the truth. So instead of saying garbage in garbage out, be strategic about the gaps in that truth.

1:25: Scott, the Data Whisperer, came up with his moniker to calm things down just like a horse whisperer does. With all the noise around structured/unstructured data etc.

2:27: Scott’s focus is on the strategic value of data management. The concepts behind master data have been around for a very, very long time. Which means the businesses won’t function well without implementing master data. But it is more productive to talk to management about data than the processes behind it.

7:25: The business side is more interested in the WHY side of data. Why are you telling me about this? Why are we funding this? What does it matter to me? So there is always that gap between requirements/implementation versus strategy/rationale.

8:28: Key is to make the business folks understand that the systems they rely on data and the data in the demos they see are perfect data. For real life systems, actual data is a lot more messy.

11:04 (Headliner): Storytelling is very hot right now. But most of the storytelling is focused on data analytics, visualization, charts etc. But not many are focusing on storytelling of the data management itself. Telling stories of the data is as important as telling stories with data.

14:05 (Headliner): Data management is about determining the truth. So instead of saying garbage in garbage out, be strategic about the gaps in that truth.

16:30: A CDO should be as articulate as a CMO who talks about brand equity and brand governance etc. Over the years, CIO forgot about that middle initial ‘Information’ and focused so much on tech and infrastructure.

18:38: Scott talked about his unique way of communicating using puppets and humor to drive home the message. Finding different ways to stay on the message but convey in different ways.

Resources mentioned in this episode:

Scott Taylor Youtube channel: https://youtube.com/playlist?list=PLashWxBySOAOKUtvn2NTBQeENJTLB8NoS

Scott Taylor LinkedIn: https://www.linkedin.com/in/scottmztaylor/

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.

Episode Title : Security, Privacy, Integrity, Transparency for AI Systems

Episode Summary:

As AI and its subset Machine Learning systems continue to increase in breadth and depth around us from systems being used in courts around the country to assist in determining length of incarceration to connected systems to home based devices such as Alexa, Siri and Google home – one glaring gap and risk is that of security in the development of these systems. Traditional security SDLC is not going to be sufficient to identify security, privacy vulnerabilities in these systems.

Artificial Intelligence systems require a different approach that includes the traditional security methods such as access control etc but more, a lot more – I am proposing a model that aims to build 4 critical components as a part of the build process. Security, Privacy, Integrity and Transparency so we can ensure we have secure, resilient systems with outcomes that we can trust.

Youtube link: https://youtu.be/VSQN3ck4Gxo

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.

Episode Title : Ethical considerations for companies in implementing AI

Episode Summary:  Artificial Intelligence deployments are at an early stage almost akin to E-Commerce deployments were 15 years ago. The terminology is still being understood and normalized. Fion Lee Madan of Fairly AI goes over the need for fairness for AI based on her observations in personal life. Similar to DevOps for e-commerce, there is need for ML ops and model ops for AI as well. Unfortunately, business decision makers focus on ROI first with governance second. Regulation can help balance the equation.

02:00: One of the sources of inequality could be lack of data itself. Or incomplete data. Or lower income people not having access to technology or the internet itself.

05:00 Focus of Fairly AI is to be horizontal but initial focus is Financial services which is more ready than other industries.

07:30 (Headliner): AI is currently the same state as e-commerce was 15 years ago. For example: Buy Vs. Build decisions; DevOps used to be the thing for e-commerce; ML Ops and Model Ops is very reactive. So we need Analytics Ops.

10:40: Need for explainable AI. There is a lot of need for explainable AI. We should make AI more transparent.

13:00: Business decision makers focus on ROI. But data scientists may not be so. Need to fix that gap. Business users need to understand the tradeoffs of more accuracy versus more cost.

15:00: Companies first need to focus on their data. Ethics need to be considered even before design is concerned. Should look at something like AI readiness planner to deal with ethical considerations.

19:00: AI Governance – Reputational harm should be one of the main concerns of any organization. Other than that regulation is the way to ensure that governance is in the front burner.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.

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

Data Transformers Podcast

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.