Data Transformers Podcast

Episode Title : Focus on Business Use Cases First – Greg Coquillo

Episode Summary:

With so much hype about machine learning, people think every problem needs to be solved and can be solved with ML. In this episode, Greg Coquillo goes over the importance of separating use cases where ML can be beneficial and use cases where just a rule-based approach might work. Greg talked about re-learning statistics, probability, and data science to apply strategically in his job. Greg is taking his team to using ML where it makes sense such as classification so ML can do lot more and lot faster than humans. The episode also covered the importance of transparency in AI/ML and how documentation is key to driving that transparency.

02:29: Machine Learning helps with classification as humans can’t do as accurately. And classification is very important because a mis-classified object, it may not go through all compliance tasks. And it could be costly as well.

06:04 (HEADLINER): Not every process needs machine learning. Sometimes, a rule based algorithm is just fine. So it is important to separate the processes that need ML and others that don’t.

10:07 (HEADLINER): In identifying use cases best for AI, start small. Then understand the pain points in their specific department. If ML is a solution for that problem, understand where the data is coming from.

12:42 (HEADLINER): Started learning data science because of curiosity. Greg mentioned that he is re-learning what he has learnt in college such as statistics and probability.

16:59: To make a real business impact, both technical teams and business teams need to stretch and understand each other. Given that data science is probabilistic, business needs to explain if they are comfortable with that uncertainty.

20:32 (HEADLINER): Driving transparency is very important in data science and documenting and enforcing the discipline of documenting is crucial.

Resources mentioned in this episode:

Connect with Greg: https://www.linkedin.com/in/greg-coquillo/

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 : Impact of COVID-19 on digital transformation of education and health

Episode Summary: COVID-19 has accelerated digital information by multiple years in many industries and especially in education and health. UC Irvine Vice-Chancellor Tom Andriola was in the middle of it all. Within 6 months of accepting and defining the first ever Vice Chancellor & Chief Digital Officer of UC Irvine, Tom was thrust into shaping predictive analytics for identifying most likely patients for intensive care based on multiple sets of data and identifying patients who can be treated from home. Similarly, Tom’s team had to help educators who haven’t advanced much on remote learning to ensure that all the students are on par. Going forward, Tom is focusing on ensuring hybrid instruction is part of UCI’s DNA going forward. The episode discussed innovation and the strategic and tactical nature of innovation in great detail. Based on Phillips Health experience, Tom came to a conclusion that Data is a strategy and not just an asset and not just a facilitator and started using his experience at UCI. Given that data is interdisciplinary by nature, UCI team has started investing in Collaboratories where teams from multiple disciplines from UCI and the industry have come together to solve bigger problems.

02:00: Why was the role created? Effective and strategic use of data and information technology. Academic mission, Research mission, Healthcare mission. As per Chancellor, everything about the role should be different by 2025. And the university should be uniquely different by 2030.

02:57: Covid hit within 6 months. Very sick patients coming in. Lack of ability to see patients in emergency rooms. So Tom had to think very quickly to use technology to scale  the operations. Example: virtual visits.

04:10: Pulling data together on Covid patients and running predictive algorithms on likelihood of ICU admissions. Identifying patients suitable for infusion treatments. Identifying patients for home care with SPO2 monitors.

05:00: Hospital at Home initiative. Monitoring from home is connected to hospitals with algorithms to predict adverse events. Be ready to send medical professionals for any interventions. Changed healthcare for ever and what had to happen in 5 years happened in 12 months.

06:20: How did the role evolve or change because of the pandemic? Plans accelerated. Conversations were happening but implementation started early.

07:22: What did Covid do for the academic side? Covid has thrust UCI into the waters they have been dipping their toes in. Remote instruction. Students expect multiple modes. ‘Consumer’ choice impact

10:00 (Headliner): Hybrid instruction (Dual mode). Even non-instructional experience should be digital too. Why stand in line for financial aid if a student can set up an appointment and take care of it?

11:20 (Headliner): Big proponent of innovation. Built from multiple experiences from the past. Diverse perspectives give 2 ways of innovating. (1) Incremental innovation (2) Disruptive innovation. From Philips, learned to change the rules when you can’t win the game to disrupt the market. Innovation to get UCI the upper edge.

15:30 (Tom answer- Headliner): Focus on data and the contrasting opinions. . In Philips healthcare, the value was on the quality of MRI image and how fast the doctors can do it. So Data is a strategy and not just an asset and not just a facilitator.

17:30: Collaboratory. Interdisciplinary teams to solve inter-disciplinary problems. Bring experts from the pharma industry. Build an ecosystem. Health & Wellness is the first. Second one is around student success. Use Data and bring people together. The boundary does not end at UCI.

19:00: KPIs & Metrics. The metrics are not hard dollars and cents. They are more impact driven. Invited all 15 schools. 150 people showed up at the workshop. Second example is the # of partnerships that are built.

21:22: The beauty of bringing together data is the insights we can derive. UCI only sends out only transcripts, grades at graduation. What if UCI sends out competencies instead of just grades. What if a graduate in 2025 is able to show the competencies that the student has mastered. Second example is what an educational fitbit looks like? How do we engage the students to create an educational fitbit.

Resources mentioned in this episode:

Tom Andriola profile: https://odit.uci.edu/about/vc-profile.php

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 : Navigating a CDO role across geographies, organizations and cultures

Episode Summary: Althea Davis served as a Chief Data Officer (CDO) across at least 5 different organizations and multiple cultures. After shuttling between Canada and the United States, Althea studied in Germany on a Fulbright scholarship. From there, she settled down in the Netherlands for 30 years or so climbing up the ranks to become a CDO at multiple organizations. Interestingly, Althea served the CDO role not as an employee but as an external consultant. The episode covers Althea’s opinions on the role of CDO and her experience across multiple geographies and cultures.

04:20: In emerging countries, the governments have an outsized influence in certain regions with respect to digital transformation.

07:00: The top down approach in Gulf Council countries (GCC) does work. Almost all countries in GCC have bold and visionary plans and they do get things done by working top down.

09:40: Telehealth is an example of a digital transformation driven by local needs. Diabetes in the Gulf and in UAE is a real problem given local diets. So the investments in telehealth are expanding rapidly because of the need to address diabetes and other local social challenges.

11:19 (HEADLINER): The predictive models that are being built for telehealth need access to locally sourced metadata and catalogs. And the need to put the insights into knowledge graphs.

13:08 (HEADLINER): With large real estate projects in the middle east and an intent to make these ‘smart’ projects, there is a need for tremendous amounts of investments in data curation and transformation.

17:05: Abu Dhabi is one of the safest cities in the world and the police force use lot of digitization and AI with the use of data to keep it safe.

19:53: State of data literacy is not as mature as ambition. To be fair to the gulf region, the focus on data is relatively recent but they are catching up fast.

24:37: Althea is part of the International society of chief data officers and also a ‘data’ ambassador of the UAE. Yes, there is a lot of room for improvement in data literacy and ALthea thinks she can get there.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Althea Davis LinkedIn: https://www.linkedin.com/in/althea-davis-1005357/

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 : Data monetization starts with focus on Data usage and where value is created

Episode Summary: The key focus of data and analytics should be about data monetization. And Data monetization starts with the understanding of data usage and people who value data. Given that business drivers like inventory reduction and predictive maintenance improvement are key business metrics, data scientists and data engineers should start understanding the business drivers that attach value to data. Bill Schmarzo believes that customer engagement and operational improvement are teh key drivers for monetizing data. Bill also believes that employee learning and adaptation should also be key objectives of technology initiatives such as AI & ML. According to Bill, Data Monetization Officer role is more important than a Chief Data Officer role and the role should be cross functional directly under CEO/COO.

2:30: Bill promoting his recently released book which is his 4th book. Also updating his MBA videos while in between jobs.

4:40: How can you monetize data when there is no line item for Data on a balance sheet? That was the quandary for a research project at University of San Francisco where Bill was teaching a course. But quickly realized that that approach is wrong.

06:00: To create value, data scientists/engineers need to work with the business who can ‘value’ the data.

06:55 (Headliner): Economies of learning are more valuable than the economies of scale. The learning by use case by case in data and analytics can accelerate the value of data.

09:15 (Headliner): The business discussions should be about use cases like improving predictive maintenance and reducing inventory. The key phrase should be ‘value in use’ of analytics.

11:54: Instead of Chief Data Officer, we should have Chief Data Monetization Officer who sits across org boundaries reporting to a COO/CEO and who is a facilitator.

13:20 (Headliner): AI & ML will have the greatest impact at customer engagement and operationalization. Along with the models, we need to have the employees constantly learning and adapting.

17:30: Most successful companies in data and analytics will have a technology roadmap but also a human empowerment roadmap. And they are the ones who realize that the value creation comes from the customer engagement side initially.

21:00: Organizations need to create customer journey map. How the customers are interacting with their products at each stage. That is the best way to create and retain customers.

23:00: When an organization is only product focused, they don’t realize that the customers only value 20% of their product features. So why worry about the remaining 80% that customers don’t value?

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Connect with Bill Scmarzo https://www.linkedin.com/in/schmarzo/

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 : Collaboration and Data competency are key for Data Analytics Success

Episode Summary: Data analytics projects, as opposed to ERP or CRM projects, lack clear requirements. As the business owners are ultimately accountable for the outcomes of data analytics projects, they’ll be skeptical and possibly intimidated by the technologies used in analytics such as machine learning etc. The way to address this is with a collaborative platform that has workflows where multiple technology and business stakeholders can participate from the get go. The collaborative workflow based approach can also be used as a training and documentation platform for upskilling/reskilling employees as well as addressing regulatory/compliance requirements. The collaborative transparent workflow approach will also make the entire process more transparent with explainability built in.

02:01 Data analytics requires people in tech and business to work closely much more so than in ERP & CRM etc. Success in Volantsys is measured by how many people are successfully impacted in changing the org.

05:16 Unlike ERP & CRM, requirements in data analytics projects are not clearly laid out. So it’s very important for cross functional folks to work together and speak the same language.

07:00 An example is the cash flow management forecasting system that was built but the business leaders were not comfortable using it because they are not sure about how it was built.

10:00: Started with ‘Data coach’ as a consulting service which was a 2 week workshop using customer’s own data.

11:05: With Covid pandemic, changed the approach to a guided workflow platform to help tech and business folks to collaborate. The basis is the ‘Data Management’ platform and has a chatbot as a ‘data coach’.

13:08 The guided workflow documents the whole process which could be used for compliance, explainability, and help explaining ethical considerations as well. This could also be used for training purposes.

17:08 (Headliner) : Analytics problems by their very nature are driven by a business initiative. Given there are different ways to solve a problem like a more accurate way or a more robust way etc, different functions (finance, risk, legal etc.) have a stake in how the problem needs to be solved.

20:15 (Headliner): Instead of people punching holes in the final results, the approach will be smoother if multiple stakeholders are involved in the workflow process.

22:00: Interestingly data science people embrace the collaborative approach but business folks are still skeptical because they are accountable at the end of day and they are intimidated by the technology and the lack of transparency.

Resources mentioned in this episode:

Volantsys Analytics: https://www.volantsys.com

Gowri Selka LinkedIn: https://www.linkedin.com/in/gowriselka/

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.

Using Data Analytics for human capital management and career management

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01:38: Role of a chief data officer has evolved over the years. In early 2000, it was all about governance and the data world was not diverse.

03:30: CDOs now manage data in all of its aspects, quality, operations and even data science and business intelligence.

05:50: Human capital management has also evolved where analyzing the data helps with finding the right talent at the right talent and also finding patterns and trends. Geometric Results is at the forefront of this.

07:00: GRI, owned by Bain Capital, can predict the likelihood of labor spend by analyzing the data that has been collected over the years.

09:00: CDOs are looking at capabilities around Cloud so they can use hybrid model for faster data ingestion for example. With the explosion of unstructured data, it is more critical.

11:00 : By analyzing data, say for example job description and capabilities, we can figure out that great python developers evolve from java script developers. Similarly, we can identify that people with a specific skill are usually concentrated in a specific zip code.

14:33: We need to eliminate resumes completely and instead rely on ability to fit in an organization. Even after recruiting, ask them to put titles aside and be a problem solver.

18:50: Covid has impacted each one differently. Contingent workers were let go first. So have been working with them to place them in jobs. Also, some industries are growing very fast and they need new talent quickly.

20:00: For GRI and for Salema, the Covid provided an opportunity for them to be a true strategic partner and not just a vendor. Helping identify the trends, help find the talent with domain expertise etc.

Resources mentioned in this episode:

Salema Rice: LinkedIn: https://www.linkedin.com/in/sjrice/

Geometric Results: https://www.geometricresultsinc.com/us

Podcast website: https://DataTransformersPodcast.Com

Episode Title : Using Data Analytics for human capital management and career management

Episode Summary: Human capital management is not just about helping companies find the people with right talent at the right time. By using data analytics, companies can learn about patterns, trends, and predict labor spend. Salema Rice works as Chief Data & Analytics officer for Geometric Results Inc (GRI), one of the largest contingency talent providers, uses data extensively to be a strategic partner to the companies they work with. As a prior Chief Data Officer in many industries, Salema talked about the evolving role of a CDO over the years. It used to be just about data governance in the early 2000s. Now it is about all aspects of data even extending to data science and business intelligence. The discussion also focused on the impact of COVID pandemic on the recruiting process, placing contingency workforce, and training them.

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

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.

Helping Businesses Responsibly Implement AI

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Episode Title : Helping Businesses Responsibly Implement AI

Episode Summary:

Businesses need to evaluate AI strategy as a key element of corporate strategy and not as a separate strategy. Example, if diversity and inclusion is a core strategy, those values should be part of AI strategy too. Cortnie Abercrombie, CEO & Founder of AI Truth, worked with many businesses as part of IBM’s Digital Transformation team. Cortnie advocates that businesses need to start with a goal. Is this a goal we need to pursue AI for this goal? Could we pull the use case in a way that does more good? Example: Targeting heavy smoking people for additional sales? She also warns businesses that AI models are not just set and forgotten. They need to properly document so they can be explained and maintained.

2:20 – Evaluate AI strategy as a key element of corporate strategy and not as a separate strategy. Example, if diversity and inclusion is a core strategy, those values should be part of AI strategy too.

4:38: Cultural aspects of positions of data scientist and marketing. Social media analytics that marketing wants. But don’t have a clue on what is needed.

7:10: Data librarians – People who do Data labelling and data sourcing.

9:10: 90% of what goes wrong is data. Data sourcing, Data labelling. People who are labelling the data may not

11:20: Who is responsible for ethics in AI? Sometimes there are people who are accountable and sometimes there aren’t.

12:20: Looking at problems in the context of a business situation.

14:34 to 16:03: Starts with a goal. Is this a goal we need to pursue AI for this goal? Could we pull the use case in a way that does more good? Example: Targeting heavy smoking people for additional sales?

18:30: Fiduciary responsibility versus ethical responsibility.

22:30:  Chief legal officers, chief risk officers, chief compliance offers are more involved than chief data officers in data matters.

26:58 to 28:30: Algorithms are not just set it and forget it. Lack of documentation for AI models is a problem.

Resources mentioned in this episode:

AI Truth website: https://www.aitruth.org/

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.

How To Measure, Manage, and Monetize Information As An Asset

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Episode Title : How To Measure, Manage, and Monetize Information As An Asset

Episode Summary: It has become a cliche to say data is an asset. If an organization is not making an attempt to measure, manage, and monetize, information can’t be an asset. Doug Laney is one of the foremost thought leaders who has been espousing Infonomics and the need for organizations to monetize their data. Doug was also the leader who came up with 3 Vs to describe Big Data and the one who came up with 4 types of analytics namely Descriptive, Diagnostic, Predictive, and Prescriptive. Doug’s assertion is that leading organizations focus on reaping the benefits by implementing the last 3 types of analytics. To enable any type of analytics though, leaders in organizations have to ensure that data literacy and data culture are pervasive.

01:30: Origin of Infonomics. Coincided with the 2001 terror attack and how companies lost their data.

03:00: Insurance companies excluded data and accounting bodies didn’t allow data to be recorded as an asset.

05:00: Monetize, Managing, Measuring the data. Reason for re-ordering the book to start with Monetizing as an inspiration.

06:00: You can’t manage anything that you can’t measure. So organizations should start with measuring their data.

08:00: Monetization has been the big impact of the book. There has been concerted efforts by companies and government

09:00: Usually CDOs responsibility is to manage

10:30: Data initiatives failures. Probably not measuring the value of the project. If analytics are not being used, it is considered a failure

11:00: Data literacy and data culture are also key. Data science and analytics projects are research projects and often fail.

13:30: Relevance of 3Vs today.

15:45: Self-organizing data. Application of AI and ML to facilitate self-organizing data.

17:30: How organizations could move up higher levels of analytics.

18:00 to 19:55 (Headliner)  500+ ways organizations are high value; 95% of high-value analytics are diagnostic, predictive and prescriptive types of analytics.

20:15 : Looking at data holistically. Advocating separating managing data analytics from data itself.

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 : Defensive Versus Offensive Data Strategies

Episode Summary:

Data is never perfect. The key question for data practitioners should be ‘Is it good enough’ for the problem to be addressed’? Each analytics situation requires its own strategy with respect to the quality of data being fed and the time/cost it requires to incrementally improve the quality. Wendy Zhang had multiple hats at different companies as a data governance lead and data analytics lead. Wendy had the luxury of building a data governance team from scratch at Wells Fargo and now works with a consulting organization helping with data governance and analytics strategies. 

Wendy also believes that organizations may need defensive or offensive strategies depending on their situation. If a financial organization needs to comply with regulatory mandates, a defensive strategy may be best. On the other hand, a mature data-capable organization will be best served with offensive strategies.

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

02:00: Loan portfolio modeling includes credit modelling, data capability assessment and enterprise data governance. 

03:00: Even within one organization, especially if it is a large organization, there will be multiple ‘data’ organizations with their own policies and structures.

07:00: Examining data governance standards towards a successful implementation of data analytics. 

08:30 (Headliner): Data is never perfect. Key question is ‘Is it good enough’ for the particular challenge being addressed.

10:30: Wendy built a data governance team from scratch. Building a team groundup at Wellsfargo was a luxury but also a challenge of having to start from scratch. 

12:00: Key best known methods for any data projects are: Gap analysis, Assessment, Organizational buy-in. 

13:00: As an outsider coming in as a consultant, asking the right questions and providing a holistic and comprehensive assessment is valued by clients. 

14:30: Defensive Vs Offensive data governance. Defensive strategies are aimed at being compliant. Offensive strategies are best suited for more data mature organizations.

16:30: Defensive is only a short term strategy. Offensive data governance allows a much more aggressive implementation of data analytics. 
19:00 (Headliner): The talk about AI & ML. Every organization wants to do something about AI. The fundamental questions are ‘What do you want to do with AI’ and ‘Why? First assess the quality of data and skillsets and ask is ML needed?

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.