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Data Transformers Podcast

Data Transformers Podcast

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The primary goal of Data Transformers podcast is to accelerate digital transformation by bridging the gap between business goals and technology initiatives using Data as glue. Visit https://datatransformerspodcast.com for more details. With the rapid advancement of technologies such as AI, ML, IOT, Cloud computing et al and the explosion of data that these technologies rely on, it is absolutely important to manage the data in intelligent and efficient ways. We’d like to enable that by interv ...
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show series
 
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 …
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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 n…
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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 fi…
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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 …
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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 epi…
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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 NAS…
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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.بقلم Data Transformers Podcast
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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…
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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…
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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 …
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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, L…
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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 per…
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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 c…
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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 …
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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 strate…
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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 dat…
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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 th…
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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 mod…
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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-en…
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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 scien…
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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 …
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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 organizati…
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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 bu…
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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…
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Business executives using data to drive decisions has only gone from 10% to 13% over a period of 20 years. The reason for such a small shift is that the key business people are still not brought up to speed on how focusing on data could improve EBITDA and other business metrics significantly. One of the ways that can be done is by creating a role o…
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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 dat…
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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 advoc…
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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 desc…
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Data is never perfect. The key question for data practitioners should be ‘Is it good enough’ for the problem to be addressed’? Each analytics situation requires its own strategy with respect to the quality of data being fed and the time/cost it requires to incrementally improve the quality. Wendy Zhang had multiple hats at different companies as a …
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Video AI is evolving and evolving rapidly in many segments such as healthcare for diagnostic purposes, MarTech for analyzing videos for brand recognition and Ad placement for example. Video AI usage in elderly care and child care are ripe for huge benefits as they require significant human participation. Video AI can address both costs as well as s…
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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 fo…
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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…
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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 …
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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. Late…
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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. Pat…
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AI governance is about AI being explainable, transparent, and ethical. However, those three words mean different things to different organizations or functions within organizations, which results in slightly different definitions or descriptions of what AI governance is. David Van Bruwaene goes over his own professional journey which started with a…
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Can Artificial Intelligence help society as much as it helps business? Is this the golden age for AI but only for certain sections of the society and not for all? We need to establish ethical standards in dealing with artificial intelligence - and to answer the question: What still makes us as human beings unique? Mankind is still decades away from…
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A data strategy and data policies resulting from the strategy should be a collaborative approach. Diane explains the process in which London Stock Exchange Group (LESG) went through the survey process to collaborate with stakeholders to get their feedback and in the process elevate the level of data literacy. The episode also covers how Diane start…
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This episode with Diane Schmidt is an inspiring story of how to grow in a data career. Diane started her journey as a data modeler and gradually grew to become the chief data officer of London Stock Exchange. Diane is a student of Data and the episode covers all aspects of data analytics, data governance, and data strategy.…
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The episode covers the professional journey of Jill Dyche from a liberal arts background to data strategy consulting founder to being an author of 4 books on data and analytics. The theme of her work as well as the books has been to extract the business value of data and technology. Jill has been able to combine both her passions of shelter dogs an…
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Data mature companies tie their corporate objectives to data and analytics initiatives. It is no longer sufficient to focus on revenues and costs but leaders are looking at enhancing brand value with analytics. Given the higher purpose of data and analytics, strategy and data culture are critical in organizations. With the advent of AI, organizatio…
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The episode starts with a focus on the 3 legs of information management which are data quality, data sensitivity, and master data management. Later, the discussion focuses on advice to aspiring data management and data governance professionals about most resourceful web sites, conferences and certifications. The episode concludes with a discussion …
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Organizations used to ask what and how when it came to data governance and now they have progressed to asking why. To a large extent this is driven by an intent to monetize data. Gradually we are also seeing data monetization officers in organizations. In order to be really successful with data governance, organizations need to look at capabilities…
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Data is meant to drive decisions. So start replacing the word data with decisions. For example, Chief Decision Officer instead of Chief Data Officer. Lori Silverman has summarized her years of experience into a framework called SMARTER to enable executives to focus on decisions using data. The framework will add analytical thinking to strategic thi…
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The primary focus of a business driven organization should be to derive actionable insights based on data. So the focus should not be on data but on what insights help make decisions that they can implement. The episode discussion focuses on the challenges that CEOs are faced with respect to making decisions (right or wrong) and acting on the decis…
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Artificial Intelligence and Machine Learning (AIML) has been extremely beneficial for some use cases such as fraud detection in FinTech sector. AIML enabled companies to do real-time fraud detection from what used to be a batch-oriented fraud detection. But to be able to do that, companies need to have an enterprise wide data platform. Additionally…
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Financial institutions have been both leaders and laggards in adopting Artificial Intelligence and Machine Learning. Shailendra Malik is the Tech delivery lead for DBS bank’s internal audit, a major financial institution in Asia based in SIngapore. Shaliendra walks us through the areas where banks are leading and also lagging in adopting modern tec…
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Artificial Intelligence and Machine Learning projects require interdisciplinary skills in devops, SW engineering in addition to hard core data science coding skills. Additionally, lot of rigor needs to be put into cleaning up the data that is fed into the models. On an interesting note, AI models can also be used for improving data quality as well.…
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Fiona Browne is the head of Artificial Intelligence at Datactics. Her journey from an all-female school into computer science, a Ph. D. in BioInformatics, lecturer, and corporate experience in software engineering / development prepared her for her current position. The rigor/discipline in Ph.D., experience of dealing with large and incomplete data…
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Key topics covered in this episode are (1) how to build and scale a data advisory business (2) Key influences for data management & data strategy (3) Key trends in the data management area. The episode goes into significant details on the external and internal drivers for data governance.بقلم Data Transformers Podcast
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