Data Transformers Podcast

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Amaresh Tripathi, Senior VP of Genpact manages about 10,000+ data scientists, data engineers, and technologists. H preaches ‘Making Tech work for business' with Data & Analytics. Amaresh talked about making the workforce ‘bilingual' i.e. business and technology. As a student of decision making, Amaresh believes that Analytics will be the front and center of all decisions going forward. Amaresh is also an adjunct professor at UNC Charlotte and chair of board for data science. With a passion for teaching and learning, Amaresh focuses on making technology real for employees as well as clients.

Data Transformers Podcast Inclusive Excellence for a Technology driven business – Dean Ian Williamson Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:35:12 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Analytics need to compel action from business leaders – Elif Tutuk Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:25:16 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

Is continuous data quality monitoring a myth? Not so fast. That is according to Ganesh Gangesan, 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.

Is continuous data quality monitoring a myth? Not so fast. That is according to Ganesh Gangesan, 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.

Data Transformers Podcast Continuous Data Quality Monitoring with Gangesh Ganesan Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:29:13 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast The Future of Women in AI is bright according to The Data Leader of The year, Adita Karkera Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:26:25 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Five Types of Thinking for High Performing Data Scientists Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:30:10 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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 Data Analytics By Design with Dr. Kirk Borne Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:26:37 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

Data Science community should be a representative community to drive meaningful impact according to Danielle Oberdier of Dikayo Data. She is facilitating that with a 24 hour data science community and DataFemme podcast and twitter communities.

Data Science community should be a representative community to drive meaningful impact according to Danielle Oberdier of Dikayo Data. She is facilitating that with a 24 hour data science community and DataFemme podcast and twitter communities.

Data Transformers Podcast Building a 24 hour Data Science Community with Danielle Oberdier of Dikayo Data Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:31:20 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Cybersecurity and AI working together to make systems safe with Dr. Madiha Jafri Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:25:39 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast To improve data quality, start at the source – Jacklyn Osborne Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:23:38 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Data Ops should be part of everyone on the Data team – Christopher Bergh Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:30:00 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Combining the passions of Data and Teaching with Laura Ellis Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:21:40 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

2021 The Big Reset - Digital Enterprises shift into High Gear. Much of the workforce has been forced into the digital space, a major shift, and leadership must adapt. The pandemic proved how vital a CIO is to an organization as they deploy technology to benefit employees and end users. More and more CIOs are becoming accountable for the digital performance of enterprises around the world.

2021 The Big Reset - Digital Enterprises shift into High Gear. Much of the workforce has been forced into the digital space, a major shift, and leadership must adapt. The pandemic proved how vital a CIO is to an organization as they deploy technology to benefit employees and end users. More and more CIOs are becoming accountable for the digital performance of enterprises around the world.

Data Transformers Podcast Overview of 2021 MIT Sloan CIO Symposium with Allan Tate Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:32:14 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Data Diva talks data privacy – Conversation with Debbie Reynolds Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:23:35 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Iceberg strategy for Chief Digital/Data Officers Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:27:12 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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 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 The pillars of a successful data strategy – Jennifer Agnes Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:30:12 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

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.

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.

Data Transformers Podcast Storytelling ABOUT the data is as important as storytelling WITH the data Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:21:51 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed

As AI and its subset Machine Learning systems continue to increase in breadth and depth around us from systems being used in courts around the country to assist in determining length of incarceration to connected systems to home based devices such as Alexa, Siri and Google home - one glaring gap and risk is that of security in the development of these systems. Traditional security SDLC is not going to be sufficient to identify security, privacy vulnerabilities in these systems. Artificial Intelligence systems require a different approach that includes the traditional security methods such as access control etc but more, a lot more - Pamela is proposing a model that aims to build 4 critical components as a part of the build process. Security, Privacy, Integrity and Transparency so we can ensure we have secure, resilient systems with outcomes that we can trust.

As AI and its subset Machine Learning systems continue to increase in breadth and depth around us from systems being used in courts around the country to assist in determining length of incarceration to connected systems to home based devices such as Alexa, Siri and Google home - one glaring gap and risk is that of security in the development of these systems. Traditional security SDLC is not going to be sufficient to identify security, privacy vulnerabilities in these systems. Artificial Intelligence systems require a different approach that includes the traditional security methods such as access control etc but more, a lot more - Pamela is proposing a model that aims to build 4 critical components as a part of the build process. Security, Privacy, Integrity and Transparency so we can ensure we have secure, resilient systems with outcomes that we can trust.

Data Transformers Podcast Security, Privacy, Integrity, Transparency for AI Systems – Pamela Gupta Play Episode Pause Episode Mute/Unmute Episode Rewind 10 Seconds 1x Fast Forward 30 seconds 00:00 / 00:24:13 Subscribe Share Apple Podcasts Google Podcasts Spotify Stitcher RSS Feed Share Link Embed