Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Inclusive Excellence for a Technology driven business – Dean Ian Williamson " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Inclusive excellence for a Technology driven business – Dean Ian Williamson

Episode Summary: Businesses need leaders who are skilled not just in business but entrepreneurial and technology areas with a bent to bring economic as well as social well being. That is the mission of Dean Ian Williamson of Paul Merage School of Business at University of California at Irvine. Dean Williamson is passionate about building talent pipelines that are not just confined to organizations but that extend beyond into the community. With a bent on fostering inclusive excellence, Dean Williamson wants to ensure that minorities of all races, color, and ethinic groups are reached out and also are provided with an environment to thrive at school and beyond.

Topics discussed in this episode:

1:30: Two focus areas: (1) Business school built around Technology. Develop leaders to bring together business, entrepreneurial, and technology skills to bring economic and social well being to the society (2) Inclusive excellence – Greater representation of  individuals from all communities in business/entrepreneurial excellence. 

4:25: Talent pipeline – (1) Can’t get people to apply (2) Can’t develop the people (3) Can’t utilize them (4) Can’t retain them. Problems in any of those areas will affect the outcomes. Need to have resilient pipelines with no leaks. Biggest constraint in the talent pipeline in the community. 

6:00: Talent pipelines in the company are as a reflective of talent pipelines of the community. Be strategic and visionary about talent pipeline.

8:45: Struggling talent pipeline – example of New Zealand. Specific partnership with local community. FOcus on working professionals to access IT. 

12:04: Discrepancy of small & large; Resource rich / resource poor. Should not underestimate the nimbleness of small. There are other levers. Money is not the only lever. The enjoyment of social / intrinsic rewards of flexibility/variety/training etc.

17:00: 60% of the freshmen class is made up of first generation college students. Performance in any setting is also about how to behave and thrive in any environment. There is a lot of talent out there but focus needs to be on making them successful.

22:30: Digital transformation is central to Paul Merage school. Innovation is a 2 step process. One is invention & intellectual property. And the other element is harnessing the IP. Paul Merage will focus on harnessing the invention. 

24:43: One example is how to attract and retain the best talent. One way to use AI/ML use use it as a tool to harness to attract and retain talent.

26:00: One pathway to get jobs and advance career. And another pathway is to be an entrepreneur. Learn about financing a venture and launching a venture. Entrepreneurship is a behavior so people need to practice to be better at it.

30:00 (HEADLINER): Diversity in organization. Research has shown that women perform better in university. But they drop out at a much faster rate in organizations. What we found is that the environment is not supportive of them performing. Not just shape the pipeline but ecosystem.

Resources mentioned in this episode:

Podcast website: https://datatransformerspodcast.com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Analytics need to compel action from business leaders – Elif Tutuk " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Analytics need to compel action from business leaders – Elif Tutuk

Episode Summary: The holy grail of any data project is business insights driven by analytics. According to Elif Tutuk, VP of Innovation and Design at Qlik, analytics can’t be in isolation. It has to integrate with data, data governance, data management. And also process automation that goes with it. Data is messy. Whatever be the ease of use on the analytics side, data prep is necessary. One shift is data catalog to analytics catalog. Analytics catalog could be modeled after a netflix like model where people can upload models and consumers can rate the models. Humans trust humans so reviews will be important.

Topics discussed in this episode:

1:28: Elif Tutuk is the head of Innovation and Design. One part is focused on research and the other part is on the design. 

3:10: Current innovation is around augmented analytics. But analytics can’t be in isolation. It has to integrate with data, data governance, data management. And also process automation that goes with it.

6:00: Providing the right insight to the right people at the right time. That pushes analytics to the edge. From a consumption viewpoint. Having the right contextual information is important because data sources are plenty and it is not just for human consumption. 

10:13 (Headliner): Data is messy. Whatever be the ease of use on the analytics side, data prep is necessary. Technology is ready with integration. Tracking the changes is very important. One shift is data catalog to analytics catalog. Collective intelligence is important where data consumers and producers collaborate.

14:30: Analytics catalog could be modeled after a netflix like model where people can upload models and consumers can rate the models. Humans trust humans so reviews will be important.

18:20: Throwing terminology at the business user is no way to solve a problem. But data literacy and enabling the users to touch the data is important. 

21:30: Start with what problem needs to be solved. Then creating a user journey is the next step. Supporting data integration, catalog, analytics, and automation are key.

25:00: Being agile is important to meet the user needs. Having user designers along with product designers is very important to addressing the needs.

28:00 (Headliner): Process is very important for innovation. Business feasibility is important.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Continuous Data Quality Monitoring with Gangesh Ganesan " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode: Continuous Data Quality Monitoring with Gangesh Ganesan

Episode Summary: Is continuous data quality monitoring a myth? Not so fast. That is according to Gangesh Ganesan, Founder & CEO of PeerNova. Traditional data quality monitoring requires data to be in a repository and data quality platforms apply certain business rules to measure the data quality. And the exceptions are referred back to the data sources/owners to fix the exceptions. PeerNova’s Cuneiform solution, with its origins in data security & networking, applies a no-code approach to solving the measurement and monitoring problem. Additionally, Gangesh talked to us about measuring the business impact of the exceptions that are identified. With a technologist background, Gangesh got an opportunity to switch to the business side when the company he was working for decided to spinoff a division. After a couple of iterations, Gangesh sold his previous company to Qualcomm. And the he embarked on the data quality journey with special focus on financial institutions.

Topics discussed in this episode:

1:30: PeerNova is a SaaS company focused on data quality monitoring. Focused on quantitative measurement of quality of data. Data does not have to be in a central repository for PeerNova solution to do its job.

4:00: One of the earliest use cases is monitoring the data quality in stock settlements on the wall street. Settlement use cases. Data quality plays a big factor in settling trades and financial instruments.. This involves multiple parties and data quality is important to accurately settle the trades..

7:30: Monitoring data quality across the network i.e. within the enterprise as well as across the 3rd parties is very important. .

9:20: No-code approach is the best way to including business users in monitoring data quality. Otherwise it becomes an IT function’s problem.

13:00: Peernova journey has been going on for 7 years. With the observation that data quality problem is actually a data lineage problem across the network, a blockchain approach seemed relevant. Actually, financial institutions helped Peernova connect with other parties for much bigger impact.

18:20 (Headliner) Gangesh’s entrepreneurial journey. Started with Bosch. Had an opportunity to buy a division being sold off and sell it. Another similar opportunity and sold the next company to Qualcomm.

21:00: Where is the drive coming from? From the original goal of having an ‘exit’ and transitioning to ‘enjoy the journey’ liberated.  Flow state concept drives people to be 3x to 5x when they are in a flow.

24:00: Inflection point of transitioning from a technologist to an entrepreneur. Be curious. Trying to learn and grow constantly. When you are learning, you’ll know where the opportunities are and how to take advantage of.

26:00: Worked at Cypress semi. Went to a training session on first day. Learned to be a ‘precision questioning’. Answer just the question.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
The Future of Women in AI is bright according to The Data Leader of The year, Adita Karkera " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : The Future of Women in AI is bright according to The Data Leader of The year

Episode Summary: Even though women are 47% of the workforce, less than 28% of them are in tech and even less in senior data roles. Adita Karkera, CDO advisor and the data leader of the year with WIT, explains why there is that gap. With broad experience in state government and federal governments with data management, Adita discussed the nuances of leveraging data to impact public policy. With respect to AI, Adita believes that there is a tremendous opportunity to educate the professionals as well as the general public on AI and the benefits associated with AI. Adita also talked to us about her evolution from a small city in Allahabad, India to a data leader in the US.

Topics discussed in this episode:

01:40: Adita’s role at Deloitte as CDO Advisor on Data Strategy. Fellow at AI Institute of Government. AI institute is

04:00 (Headliner till 07:40): CDO role at Federal level vs. state level. Federal level, 79 CDOs at federal level after a statutory mandate in 2018.  Very focused on delivering the data strategy. Different states have different levels of data maturity. 25 states had identified CDO role.

08:20: Improving AI education in the public sector. There is a lot of opportunity for AI education. Organizations are still exploring the role of AI.

11:00: Reskilling & Upskilling the workforce for AI. First, there should be a recognition that upskilling is necessary. Also need to address the concerns and fears about AI as the very first question is about jobs.

15:30: Importance of data prep, data governance, and data management. Need to focus on the quality of data.

17:00: Adita’s progression into a data leader. From a small city of Allahabad, started education in Commerce and completed certifications in computer science. A certificate in Microsoft SQL DBA led to a career in data.

20:00: Career defining moment is when Adita was asked to act as a Deputy CDO in the State of Arkansas. Instead of one domain, looking at the entire state data changed her perspective.

23:00: How to address gender gap? Women represented 46% of the labor force but only 23% in data and tech. Reasons are: social/cultural influences, stereotypes well ahead of career; Lack of active mentors.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Five Types of Thinking for High Performing Data Scientists " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Five Types of thinking for high performing Data Scientists

Episode Summary: Artificial Intelligence is all the rage currently. But there was a time when AI has gone through ‘AI Winter’ when there was not much interest in AI. Dr. Anand Rao has gone through those AI Winters. To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk. In thos episode, Anand talks about five types of thinking that data scientists should focus on to be high performing data scientists. As the Global head of AI at PWC, Anand knows a lot about the customer uptake of AI and (un)surprisingly only 20% of the companies are actually deploying AI. Listen to the episode to find out which functions are adopting AI the most.

Topics discussed in this episode:

01:50: Global AI lead, Partner Global Analytics Insights lead, AI Innovation partner; Both on the client-facing side as well as product side.

03:00: Trends – Confusion about data (big Data, IOT Data,); Automation; Analytics; AI; All the technology is fine but where is the value?

05:00 (Headliner): Trends (1)  Convergence between Data, Models, and Software. (2) End to End lifecycle approach deployment of data, models, software (3) Governance, Risk and Controls to reap the benefit and minimize downside

08:40: To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk.

10:45: High Tech firms are primary promoters of AI and adopters; Financial services are next; Healthcare, Retail, Manufacturing/Energy follow the leaders. Agriculture is surprisingly also an adaptor of AI.

12:54 (Headliner): 30 to 40% of companies are in exploration. 50% are in experimentation stage; Only 20% are in deployment stage.

14:47: Leading use cases are in operations. AI use cases for strategy. Example is strategizing business models for a car share of autonomous vehicles. AI being used to present scenarios in strategy.

21:15:  5 types of thinking for high performing data scientists. (1) Models Thinking (2) Systems Thinking (3) Agent-based thinking (4) Behaviorl thinking (5) COmputational thinking

25:00 (Headliner): You don’t always need data to start with. But once you have a mental model, you can always work with Data later to work with. Need to always start with a problem that needs to be solved and the mental model. Then start thinking about data that is needed.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Data Analytics By Design with Dr. Kirk Borne " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Data Analytics By Design with Dr. Kirk Borne

Episode Summary: Managing data at the speed of business versus managing business at the speed of data. Data moves the fastest so business should be moving at the speed of data. Analytics is the main beneficiary of this data. Dr. Kirk Borne has always been a scientist with jobs in data science, Astrophysics, and data analytics. After a very successful stint with NASA, George Mason University, and Booz Allen, Kirk started a new chapter with a startup that matches job seekers with companies using ML algorithms to match. This fascinating episode traverses his journey across these organizations and functions

Topics discussed in this episode:

02:00: How Kirk came to be Chief Science officer at a startup. A different setup working with 15 people after working with 27,000 people at Booz Allen and 120K people at NASA.

04:00: The startup is like a dating service for candidates related to data and companies looking for data professionals. The platform matches companies with requirements with candidates skills.

08:58: The tension between overloaded job requirements and candidate skills. Big Data revolution started in 2012. Big data initiative by Whitehouse with $300M. Article by HBR about data scientist being the sexiest job. And McKinsey report about skills shortage.

12:00: Human in the middle helps validate the requirements versus the skills along with the data science platform.

15:00 (Headliner till 18:02): Managing data at the speed of business versus managing business at the speed of data. Data moves the fastest so business should be moving at the speed of data.

18:30 (Headliner): CIOs typically spend 3 years just preparing for ML. It could be seen as wasting 3 years. Kirk says ML journey should be like preparing for Olympics where you could be seen preparing for 3 years for the final Olympics. You can’t always be warming up but you need to run the race at some time. Also, you can’t warm up too late also. Need to build muscle memory.

26:30: ADAPT-C framework; Analytics opportunities, Data, Processes, Culture. Culture is key. Culture of data democratization and culture of experimentation are needed. Analytics By Design. What do we want to accomplish and how do we get there?

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Episode Title : Data Analytics By Design with Dr. Kirk Borne

Episode Summary: Managing data at the speed of business versus managing business at the speed of data. Data moves the fastest so business should be moving at the speed of data. Analytics is the main beneficiary of this data. Dr. Kirk Borne has always been a scientist with jobs in data science, Astrophysics, and data analytics. After a very successful stint with NASA, George Mason University, and Booz Allen, Kirk started a new chapter with a startup that matches job seekers with companies using ML algorithms to match. This fascinating episode traverses his journey across these organizations and functions

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Building a 24 hour Data Science Community with Danielle Oberdier of Dikayo Data " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Building a 24 hour data science community with Danielle Oberdier of Dikayo Data

Episode Summary: Data science community should be a representative community. Danielle has been facilitating that with Dikayo data, twitter communities, and Data Femme podcast.

Topics discussed in this episode:

02:00 Started Data Science community for data science professionals, collection of data science podcasts, and twitter community

04:00 DataFemme podcast with different season themes for data science professionals. Second season was focused on diversity in the data science community. Podcast gives exposure to marginalised sections of the society.

08:00: Discussion about women in data science. Danielle believes that there are an adequate number of women in data science but questions remain on the exposure/recognition for women.

12:00: Discussion about elevating voices. One of the ways to encourage minority representation is to actually reach out directly instead of just posting on LinkedIn for example.

15:30 Diversity of thought is also very important

24:00 Need to genuinely connect with folks. Being genuine is very important.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Cybersecurity and AI working together to make systems safe with Dr. Madiha Jafri " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title :

Cybersecurity and AI working together to make systems safe with Dr. Madiha Jafri

Episode Summary:

Artificial Intelligence and Cybersecurity are large and complex domains each by themselves. When you combine both of them, it could be overwhelming. Dr. Madiha Jafri, Associate Fellow at Lockheed Martin for AI & Cybersecurity navigates these domains to make systems safer. In this episode. Dr. Jafri articulates how AI can help speed up cybersecurity threat detection. She also talked about her journey starting from cryptography to nanotechnology to AI. The discussed addressed challenges such as data engineering, relevance of Ops, and navigating the plethora of technologies to find that needle in a stack.

Topics discussed in this episode:

02:00: Madiha role at Lockheed Martin as an Associate Fellow. 14 years at Lockheed Martin. Started in cybersecurity and worked in it for 10 years.Lockheed is the largest defense contractor in the US.

04:42 (Headliner till 6:25): What role does AI play in cybersecurity? Madiha’s starting focus was cryptography. AI as a tool is good at making things faster. AI can speed up detection of threats.

06:30 (Headliner till 09:30)): Cybersecurity and AI work together. Some hurdles are data engineering.. Second hurdle is the availability of technologies. Finding out which is real and efficient is a challenge.

11:00: Data literacy and data culture. They are both extremely important in any organization but each organization is different.

14:00:  Madiha’s passion is engineering and design. Her role recently requires strategy work. Strategy of how to get the required data and get it into the hands of people who need it in a secure way.

18:00: Role of Ops in AI in cybersecurity. Data Ops, MLOps. Madiha’s take is that we tend to overcomplicate things. Nothing unique about MLOps and Ops in general. These Ops need to happen.

21:00: Madiha career combining cryptography and nanotechnology. Original role involved cryptography. Another project came up in the nanotechnology space. Able to work together in many areas.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
To improve data quality, start at the source – Jacklyn Osborne " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : To improve data quality, start at the source – Jacklyn Osborne

Episode Summary:

Very often, organizations focus a lot on data cleansing after the data has been captured. But any incremental effort spent on focusing on data quality at the source will reap long term benefits. In this episode, Jacklyn Osborne, Data Quality Control executive at Bank of America, talks about the importance of data education to frontline employees so they can also be stakeholders of data quality. Jacklyn talked about the role of CDO, skills necessary for CDO, and how CDO can be empowered by where they sit in an organization. With deep expertise in the financial industry, Jacklyn talked about data monetization and treating data as an asset in financial industries.

Topics discussed in this episode:

03:36: Top 3 data challenges; (1) Expanding scope (2) Ill defined CDO role (3) Data democratization

04:49 : Necessary skills for a CDO. Needs to be inquisitive. Ask questions. Problem solving by nature.

06:30: Value of data in financial industry. Is it only because of compliance or is there a data monetization beyond compliance? Yes, there is a value.

09:05: Where should CDO sit in an organization? CDO should sit closest to the business.

11:30 (Headliner): First hand experience of a CDO role moving closer to CEO role. Accountability and ownership will increase and as a result, business believes data work is for them. And Data will be seen as an asset.

14:51 (Headliner) – Advice to financial institutions on data aspects. (1) Start small and scale (2) Align to a use case.

16:32 (Headliner): How do you know what is the right data quality? Starts with data production. Are we producing the right data to start with. And then you set controls in place as data moves along.

19:42 – If the data producers understand the value of the data, they are more inclined to improve the quality of the data at the source.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Data Ops should be part of everyone on the Data team – Christopher Bergh " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Data Ops should be part of everyone on the Data team – Christopher Bergh

Episode Summary:

Data Ops is about working with everyone who deals with Data to deploy data related projects together. It is not just one person’s job. Christopher Bergh, CEO of Data Kitchen has embarked on Data Ops journey much earlier than the industry was asking for it. Nowadays, everybody including Gartner is talking about Ops, Data Ops, Dev Ops, ML Ops, X-ops etc. But Ops should not be a single person’s job. It should be 10% of every team member’s job to think about Operations. Just like Deming prescribed in a manufacturing process, it should be part of the system and framework.

Topics discussed in this episode:

01:35: What is Data Ops? It is about making data related folks (engineers, scientists etc.) work together to deploy the projects

03:00: Difference between Dev Ops and Data ops. They are similar in concept with Dev ops focusing on applications and Data Ops focusing on analytics. The profound difference is in the scale of teams involved.

08:37: X-ops. Gartner is focusing on X-ops with Model Ops, Data Ops, Dev Ops etc. All these concepts are about working with the system as opposed to working on individual parts. Deming philosophy.

13:00: Companies put processes like checklist meetings, stage-gates so they don’t have major issues. Once issues are found, people work all hours to fix them. Instead , we should have a system in place to fix production issues in production. Also, we should have a system to see issues before customer sees them.

18:49: Need for building a system and framework is very important. Instead of just asking a lowly paid release engineer, all the team members should be responsible for Ops. 

20:59 (Headliner): Formed a Quality circle and entered each error in a spreadsheet. Love your errors. After 6 months, it got better. 

26:00: Data scientists are dissatisfied because they are unable to make a business impact. Looking for perfection is not the best strategy.  

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Chris Bergh: https://www.linkedin.com/in/chrisbergh/

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Combining the passions of Data and Teaching with Laura Ellis " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Combining the passions of Data and Teaching with Laura Ellis

Episode Summary:

Laura Ellis, IBM Cloud systems Architect, always wanted to be a teacher. A recognition here and an award there in Computer Science got Laura interested in computer science and later a job with IBM. Laura combined her passion for teaching with DB2 and toured the world training others in DB2. As the business intelligence started picking up in 2013, Laura completed a part time MS in Predictive analytics and switched in data science. Laura realized that the organization needed people with other skills in data engineering, data wrangling etc and adapted. Laura started Little Miss Data as a personal project to combine data science with her personal passions such as Peloton R and teaching kids about data science. Laura believes that the future trend is about data security and ethics.

Topics discussed in this episode:

1:40: Passion for data. Data found Laura. Luck and encouragement. Computer science award in college for a project. Little Miss Data is about teaching people about Data. Ended up getting an interview for a comp science job. Found a job teaching about DB2 / Data warehousing worldwide.

05:00: MS in Predictive data analytics. Why? BI was exploding around 2014 so decided to get a part time MS in predictive analytics at Northwestern.

09:00: Reasons for data analytics explosion around 2014? Availability of data. Availability of compute. People started looking for insights rather than just charts & reporting.

11:00 (Headliner around 13:00): Why predictive analytics? Wanted to learn about statistical modelling. Also, people were asking questions about predictions and differences in data.

14:30 (HEADLINER) : Why are good data scientists leaving the field? Misalignment on expectations. Data scientists don’t expect to do data wrangling and data engineering. Started working on making sure that good data was in good hands. Expectations may be that company needs data engineers, data communicators etc.

18:42 (Headliner): What skills are needed for data scientists? A good level of subject matter of expertise. Gotchas are not in how the models are. Should spend a lot of time understanding the business.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Ramesh consulting website: https://DigitalTransformationPro,COm

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Overview of 2021 MIT Sloan CIO Symposium with Allan Tate " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Overview of MIT CIO Symposium with Allan Tate

Episode Summary: Allan reviewed MIT CIO Symposium and how the organizing committee pivoted in 2020 and 2021 to make it a virtual community. Allan also went over his own professional journey and how he came to be the Exec Chair of the symposium.

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Data Diva talks data privacy – Conversation with Debbie Reynolds " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : The Data Diva talks about Data Privacy – Debbie Reynolds

Episode Summary:

Data privacy has come up on the trending topics recently because of Whatsapp policy changes by Facebook and news about Clubhouse app’s request for contacts list on the device. Debbie Reynolds explained the intricacies of data privacy, consent, and convenience. Debbie’s contention is that privacy can be used as a business advantage to acquire more customers. Debbie also talked about privacy risk index that she has come up with in association with Privacy & Cookies based in London. This episode ended with Debbie’s anecdotal story about her nick name Data Diva.

Topics discussed in this episode:

1:56: Privacy Risk index is a way to measure a website’s risk index based on how they manage the cookies. Debbie partnered with the privacy and cookies team of Lawrence and Robert to put together this index for Fortune 1000 companies.

05:01: Privacy is the right of an individual to be able to to control their information in some way. Debbie works with organizations to use privacy for business advantage.

05:41 (Headliner):  Facebook changed Whatsapp privacy terms and that caused lots of confusion. That also illuminated the fact that people are more aware of privacy. Customers will make choices going forward about privacy.

07:45 : People are not even aware that phone apps will be tracking even when they are not using the apps. Apple, starting with iOS14, is giving more options for users not to be tracked.

10:23 (Headliner): Privacy versus convenience. Companies make it convenient to make things less private with things like 80 page contract. Instead, it should be as convenient to keep things private as well.

12:55 (HEADLINER): Companies like Clubhouse give the app freely and in return ask for access to all the contacts for example. There are other apps where they download contacts without even asking.

16:47 (Headliner): Consent has some limits. The way clubhouse does is get contact list so they enable you to send invites.

19:32: The origins of Data Diva, the nick name. Debbie gave herself the nickname Data Diva at a networking event. Even though she was bashful at first, Debbie saw very positive reaction to it.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Iceberg strategy for Chief Digital/Data Officers " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
The pillars of a successful data strategy – Jennifer Agnes " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Storytelling ABOUT the data is as important as storytelling WITH the data " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Security, Privacy, Integrity, Transparency for AI Systems – Pamela Gupta " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Ethical considerations for companies in implementing AI " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Aligning data processes, management & tools in a CDO role " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Focus on business use cases first – Greg Coquillo " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Impact of COVID-19 on digital transformation of education and health " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Navigating a CDO role across geographies, organizations and cultures " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Data monetization starts with focus on Customer engagement & Customer Journey " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed
Collaboration and Data competency are key for Data Analytics Success " />



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

Using Data Analytics for human capital management and career management

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

Helping Businesses Responsibly Implement AI

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

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

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

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.

Topics discussed in this episode:

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

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

Is Video Artificial Intelligence Ready for Prime Time?

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title: Is Video AI Ready For Prime Time?

Episode Summary:

Video AI is a growing market with lots of innovation. The Video AI market encompasses Video surveillance, Automatic/self-drive vehicles, content moderation in video, automatic video editing. Convolution Neural Networks is the backbone of Video AI in many applications and the challenge lies in training the data as well as abstracting the outcomes for better outpost. The field is still emerging and the technology is still evolving in many of these areas. As an example, even though Youtube would like to have a general understanding of the video so they know when to insert relevant ads, the technology to do that is just emerging. Dale Hitt, who worked at many startups as well as large companies, goes over his experience with innovation at small and large companies.

Topics discussed in this episode:

(02:29) What is ADAS? Video intelligence in cars.

(03:10) Computer vision in sports

(04:43): Cambria technology – How it helps Vision AI

(05:58): Video intelligence in at-home fitness equipment

(08:11) Training data for video applications. Challenges with limited data and unrelated data

(10:20): Limitations of convolution neural networks and need for abstraction layers instead of more variables

(12:13): Various market segments for video AI applications

(14:37): Security surveillance market segment

(15:18): Video understanding in Youtube for monetization

(17:21): AV8.AI, a community for Video AI innovators for all market segments like content moderation, automatic video editing, self-driving vehicles

(19:29): Missile guidance systems to AI startups – Professional journey

(21:52): Startup journeys – From VC backed to bootstrapped – what is it like?

(23:19): Lessons from Tivo startup journey

(24:53): Challenges of innovating at large companies

(25:59): Transferable lessons from startups

Resources mentioned in this episode:

https://AV8.Ai Community

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

How to address the blind spots of data scientists

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : How to address the blind spots of data scientists

Episode Summary:

A good data scientist is not only good at coding, tweaking models but is also good at assessing the outputs of the models in the context of business decisions. As data scientists spend upto 80% of their time data munging, they will be better off spending some quality upfront time with the business leaders asking questions about the customer journey and how the data is collected along the way. Even though the data scientists are not expected to be business domain experts, they should think of their output in the context of business outcomes and communicate their results in business terms. The episode also emphasizes the need for all types and sizes of companies to get acquainted with analytics or fall behind.

Topics discussed in this episode:

B2C company Versus B2B company (01:00): Having come from a B2C company like Disney, Phil had to adapt to dealing with challenges of dealing with B2B company like Teradata. Each day could be different at Teradata as Phil would be talking one day to a video games company, next day a healthcare company, and a transportation company the next day. So Phil needed to learn to switch hats quickly.

Business focus first, Data focus next (04:00): Majority of a data scientist’s time (upto 80%) is spent on data wrangling and a maximum of 20% on actual model development & tweaking. So a data scientist’s time is well spent initially talking to the business units on 3 things: (1) What data is collected (2) What is the customer journey (3) When and how is the data collected along the journey? The more time spent in clarifying these questions, the better it is for the team.

Blind spots of data scientists (09:02): Majority of the data scientists do not focus on the business processes or attempt to understand the business domain and that is a mistake. Yes, it is true that the businesses know their domains better than anyone else but the data scientist should attempt to put the data in the context of the business domain and not look at it independently.

A solution that is appropriate for their business (11:06): Businesses should evaluate technology solutions which are relevant for their business based on their current state of business. Phil gives an example of hiring an expert from the casino industry while at NBC Universal and the solution proposed was ahead of its time and not suitable for NBC. And that was a mistake.

Analytics best practices (15:12): Every company should look at using analytics in their business or otherwise they’ll be left out. They can start with a small test data set but should start somewhere. Engage with an external company on a small project to get upto speed before building a data team. Then the business can start assessing if they want to continue relying on external teams or have a centralized/distributed teams internally.

What’s ahead (20:00): Data science and AI will continue to grow and become important. People wanting to come into the industry to become data scientists should keep learning code, models, and algorithms. They should also try to put their technical expertise in the context of what does it mean for the business? What can or should the business do with the outcomes of the models? And they should be able to communicate the technology outcomes in the content of business outcomes.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

Data Science teams need people with multiple skill sets

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Topics discussed in this episode:

Career transition from Engineer to Finance to Data Science (02:30): Phl Bangayan started as an engineer at JPL but soon realized that needed a business background so went and got an MBA. After joining in Finance at Disney, Phil got an opportunity to shift into Marketing which led to an Analytics opportunity at Universal studios. From there, Teradata opened an opportunity to get into Data Science.

Analytics courses (07:00): As Phil started making decisions based on Analytics, he went to get formally educated on Data Analytics. MOOCs are a great way to learn but only 4% of the people who start a course actually complete it. Phil’s advice is that what works for him may not work for others regarding courses. In general, University offered courses are a better bet.

Move into Data Science (14:00): With an Analytics background, Phil now felt confident to move into Data Science. That is when Teradata opportunity opened up.

If no one understands a model, no good (18:00): It doesn’t matter how sophisticated your model is if no one understands it. And one of my pet peeves is when you do about 99% of the work, and then the work goes to waste because someone doesn’t agree with you, or because you forget to do that final part, which a lot of time is communicating it. And so I don’t believe that every data scientist has to be the best communicator, but I do believe that there has to be someone on the team who can take some analytic analysis and, and distill it to a point where an executive, like a CMO or CFO or president can, who doesn’t understand it can say, okay, what does this mean for me?

Data science needs complementary skill sets (21:00): In Data Science teams, define who you are, and don’t go off and compete against people who are more qualified than you are. So, for example, when I was going from marketing to data science, I knew that I did not want to compete against the 28 year old with a PhD in statistics. That person can write better code than me and can do better proofs than me and not a good, not a good competition. However, I bring a lot of experiences that someone who’s in that situation might not.

Last mile problem – Closing the deal (23:00): So if someone brings it 90% of the way through, I can help with that 10% and close the deal, or I can go ahead and make that work actionable. So that’s, so it doesn’t go to waste and, and no one wants to see their hard work go to waste. In fact, is one of my leadership pillars. I always tell my team that everyone plays a part it’s very easy when you’re the data scientist, to be able to say, well, hold it, I’ve got the hardest here because no one can do what I do.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Episode Title : Data Science teams need people with multiple skill sets

Episode Summary: Data science career path doesn’t have to be purely technical. A data science team needs multiple skill sets. In this episode, Phil Bangayan, Principal Data Scientist at Teradata, talks about his career path from an electrical engineering background to MBA to Finance to Marketing and Data science. Phil talks about the need for the data science team to be able to communicate the outcomes of models in an understandable manner with CXOs. Phil also talks about the need for data science teams to have people with multiple skill sets.

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

What Does It Take To Be A Data Scientist?

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : What Does It Take To Be A Data Scientist?

Episode Summary: How does one get to be a Vice President of AI at a global semiconductor leader? What professional journey can take from a Ph. D. to that influential role? Patrick takes the audience on a journey from upbringing in Germany, Malaysia, Philippines to education in the UK to an initial job at Los Alamos lab to the CEO of a startup to VP at Samsung. Later on Patrick talks about the skills and experience needed to be a data scientist and the emphasis on communication and business acumen to be a successful data scientist. The episode also discusses where leaders can learn from and what they should be doing continuously.

Topics discussed in this episode:

Journey from Ph. D. to VP (01:30): Patrick grew up in Malaysia and Philippines and went to University in the UK. Luckily Patrick got a job in Los Alamos laboratory. The research work needed a mathematics background so switched to math and started digging deeper into AI. Realized that you can’t apply AI while being a researcher.

AI research Versus Applied AI (05:20): Business case for AI is crucial. Lots of work in research institutions may not be commercially relevant and may not be viable. Having started an AI company, it became imperative to convince businesses that AI can pay.

Skills & Experience to be a Data Scientist (07:40): For a data scientist, programming knowledge in a language like Python is a must. Experience with frameworks like Tensorflow, Pytorch, Keras. (3) Pre-process and data prep (4) Statistical testing & probability analysis. So some mathematical & statistical knowledge along with data wrangling skills. On top of that, there are skills that are less common and more valuable is communication. Should be able to translate from domain english to business english. Talk & Translate.

Importance of Communication(12:00): Data scientists can somewhat easily learn mathematical and programming skills. They find the business communication the hardest.

Where can you learn? (13:30): Learn a great deal from my own department. Learn from my management. Learn from partners and customers. Learn from my own thinking like the Covid testing mentioned earlier. Source of most info personally is LinkedIn.

Future AI engagements (16:20): Currently pushing AutoML. Which models and which parameters. Most of the time people do it by trial and error. We are trying to automate this using AI. The other thing is distributed training. Training takes a long time but using multiple computers we can reduce the training time.

Reality of AI (20:00): AI is a big hype topic. But unlike other hype topics, AI is here to stay. Whatever your role is, get upto speed on AI. If you are a business person, think of ways to use AI.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher

Apple Podcasts

Google Podcasts

Spotify

Stitcher
RSS Feed


Share






Link


Embed

Developing Practical AI Applications – Patrick Bangert

‘
/>



Apple Podcasts



Google Podcasts



Spotify



Stitcher

Episode Title : Developing Practical AI Applications – Patrick Bangert

Episode Summary: The episode focuses on developing practical applications using AI. Patrick Bangert, as the head of AI Engg and AI services at Samsung SDS, is in charge of including AI in almost all Samsung applications that are deployed on Samsung phones. If anyone is interested in learning the various phases of developing AI applications, this is the episode. Patrick discusses the avrious phases such as developing models, training the models, and deploying the models. The episode also goes over the proper characteristics of data for developing good ethical and explainable AI applications.

Topics discussed in this episode:

Types of AI practical applications (02:00) : Samsung SDS (Samsung Data Services) within Samsung. SDS is It services company; AI Engineering – Distributed Training, Auto ML, Algorithmic questions; AI Services –  Data science and AI projects for specific use cases. Natural language models for talking to your phone or Facial recognition for identifying. Textual recognition so you can answer that question.

Cost of training AI models (06:00) : Most of AI companies create AI products for other businesses and not for individuals. The models are not created for individuals as it is very expensive. Example is GPT3 model as the new state of the art language model from Google. The cost to train the model was $5 million. Only large companies can spend that amount of money.

AI Diagnostic models for Covid (10:00) (POTENTIAL FOR AUDIOGRAM). Ptarick’s team took the challenge of fast tracking Covid results using X-rays. You can take an X-ray of a chest to find accuracy of Covid tests. Hospital donated X-ray images (15,000 images); 6 doctors to assess X-rays; Lot of effort to label the data.

Relevant and Representative Data (14:50) – ANOTHER AUDIOGRAM – What problem are you trying to solve. What accuracy needs to be there? Raw dataset – 15,000 images. Data needs to be relevant. Data needs to be representative; Representation of the situation and Representation of the problem; (2) Label the data; Doctors may not be accurate all the time; May be accurate 80%. Labelling data is the domain expert’s job and not the scientist’s job

Ethical AI (21:00) – Patrick is on the board of AI Ethics journal; Some bad examples:  UK – AI was used to grade students; Facial recognition software was developed using caucasians but used on Africam people; Problems with Ethical AI are two-fold (1) Not a representative sample (2) Used on incorrect applications. Typically a data set problem

Explainable AI (23:30) – One of AI drawbacks is that it is a black box; The price for automation, reduction in costs is that it is not inherently explainable.

Why is it so difficult to explain AI? (26:20) – Neural network is about multiplying matrices with vectors.It is difficult to explain how this 1,000 coefficient equation came up with a prediction. Humans simplify assumptions. Neural networks will not simplify assumptions and compare details with details.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Data Transformers Podcast

Listen Now!

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.

Apple Podcasts



Google Podcasts



Spotify



Stitcher