Iceberg strategy for Chief Digital/Data Officers

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.

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The pillars of a successful data strategy – Jennifer Agnes

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.

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Storytelling ABOUT the data is as important as storytelling WITH the data

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.

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Security, Privacy, Integrity, Transparency for AI Systems – Pamela Gupta

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 – Pamela is 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.

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Ethical considerations for companies in implementing AI

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.

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Aligning data processes, management & tools in a CDO role

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.

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Data Transformers Podcast

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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.