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Data Transformers Podcast
Data Transformers Podcast episodes
- SMARTER framework to drive decisions using dataData 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 thinking. All of this is only possible by increasing data literacy and data culture in organizations.24 min
- Data to Insights & Decisions to Actions with Lori SilvermanThe 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 decisions made. Data should be secondary thought compared to the business decisions and the inputs to make those decisions.24 min
- SMARTER framework to drive decisions using dataData Transformers PodcastSMARTER framework to drive decisions using dataPlay EpisodePause EpisodeMute/Unmute EpisodeRewind 10 Seconds1xFast Forward 30 seconds00:00/00:23:39SubscribeShareApple PodcastsGoogle PodcastsSpotifyStitcherRSS FeedShareLinkEmbed
SMARTER framework to drive decisions using data
‘
/>Apple PodcastsGoogle PodcastsSpotifyStitcherEpisode Title : SMARTER framework to drive decisions using data
Episode Summary: 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 thinking. All of this is only possible by increasing data literacy and data culture in organizations.
Topics discussed in this episode:What’s the one key takeaway (03:26): Data visualizations and dashboards are great but they don’t give context to what’s the business question it is trying to address. The executives who are not with the data 24x7want to know what’s that one key takeaway? As a storyteller or a data scientist, always think about that one key takeaway from your visualizations or dashboards.
SMARTER decision framework (05:00): Lori has been able to summarize her methodology into a SMARTER framework. S: Set context; M: Managed Data; A: Assurance/Confidence; R: Reveal insights; T: Take a stand; E: Execute; R: Relay results;
Strategic thinking is integral to analytical thinking (13:16): Most of the strategic thinking taught to senior executives does not include analytical thinking. For example Paul Schumacher from MIT wrote in a paper that intelligent organizations can’t be created without human reasoning. Human reasoning is the combination of structured and unstructured thinking.
Replace the word data with decisions (16:11): The main purpose of data is to drive decisions. Given that, why shouldn’t we replace the word data with decisions? Chief Decision Officer. Decision governance etc.
Data literacy & Data culture (20:07): Lot of it comes down to increasing the level of data literacy and inculcating data culture in organizations to help make decisions. Data literacy is talked about in the context of individual competence but it should be enterprise core competence.
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 PodcastsGoogle PodcastsSpotifyStitcher24 min - Data to Insights & Decisions to Actions with Lori SilvermanData Transformers PodcastData to Insights & Decisions to Actions with Lori SilvermanPlay EpisodePause EpisodeMute/Unmute EpisodeRewind 10 Seconds1xFast Forward 30 seconds00:00/00:23:32SubscribeShareApple PodcastsGoogle PodcastsSpotifyStitcherRSS FeedShareLinkEmbed
Data to Insights & Decisions to Actions with Lori Silverman
‘
/>Apple PodcastsGoogle PodcastsSpotifyStitcherEpisode Title : Data to Insights & Decisions to Actions with Lori Silverman
Episode Summary: 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 decisions made. Data should be secondary thought compared to the business decisions and the inputs to make those decisions.
Topics discussed in this episode:Total Organizational Management Versus TQM (03:04): The reason many data-led initiatives fail, actually 85% failure rate, is because the focus of these initiatives is narrow. They don’t focus on organizational management but a narrow technology initiative. Instead of focusing on the quality of one narrow as pect, Total Organizational Management should be the approach from day one.
Art of Storytelling to influence actions (05:12): The purpose of stories in data science are meant to move data to insight and decisions to action. And once it is done, we need to process around it to make sure that the cycle gets followed over and over again.
Inputs to making decisions (09:42): CEOs need inputs to make decisions. And they are caught between 2 things about decisions: (1) Cost of making a wrong decision (2) Cost of making the right decision and not following through. Cost of making the wrong decision is that innovation will stall/prolong as the org has to make the right decision again later.
Data Literacy 2.0 (14:44): Wanting to make decisions based on data is nothing new. But not many organizations start with the business strategy end goals in mind which is wrong. The end goal will help decide the approach and also help how to frame collecting data which will result in actionable insights. To make this possible, organizations need to facilitate data literacy in their organizations. They need to be more data aware.
Asking the right questions (20:13): Many times executives start digging into data but the very first thing to understand is the context in which that data was collected. What were the questions that were asked. Was there enough prep in identifying the questions that were asked. A wrong set of questions without the end in mind will generate wrong sets of data for a different problem.
Resources mentioned in this episode:
Partners for Progresshttp://www.partnersforprogress.com/
Lori’s speaking engagements:https://bit.ly/3jPuUJz
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 PodcastsGoogle PodcastsSpotifyStitcher24 min - Data Strategy for FinTech use cases such as Fraud DetectionArtificial 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, organizations need to think through the entire process of AIML instrumentation to adopt to changing use cases and not just data and models. Lastly, COVID focused businesses to compress technology adoption to a few months and this has been good for businesses.33 min
- Artificial Intelligence & Machine Learning in Financial Sector – Shailendra MalikFinancial 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 technologies. Additionally, Shailendra talks about his pwn journey that took him across many countries and many domains. Lastly, he talked about a professional blogging platform that he was able to successfully build on the side.30 min
- Data Strategy for FinTech use cases such as Fraud DetectionData Transformers PodcastData Strategy for FinTech use cases such as Fraud Detection1x00:00/00:32:24SubscribeShareApple PodcastsGoogle PodcastsSpotifyStitcherTuneInRSS FeedShareLinkEmbedData Strategy for FinTech use cases such as Fraud Detection'/>Apple PodcastsGoogle PodcastsSpotifyStitcherhttps://youtu.be/nYJ13ACPFbkEpisode Title : Data Strategy for FinTech use cases such as Fraud DetectionEpisode Summary: 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, organizations need to think through the entire process of AIML instrumentation to adopt to changing use cases and not just data and models. Lastly, COVID focused businesses to compress technology adoption to a few months and this has been good for businesses.Topics discussed in this episode:Collaboration on ‘AI - The Book’ (04:33): This team in UK came up with the concept of a crowdsourced book about AI. They requested initial drafts from experts in FinTech world and they selected the final authors. Each author wrote one section of the book for a completely crowdsourced book. Both Peggy Tsai and Shailendra Malik contributed to the book and are co-authors along with others.Fraud detection using AIML (09:48): Prior to AIML, banks could only select a sample of transactions to detect fraud and that too at the end of the day or so. With AIML, banks are now able to detect for anomalies in real-time at least for known fraud schemes. For newer types of frauds where the models are not trained yet, the models still need to get trained after the fact. Still, we are way ahead from where we were few years ago.Data, modeling, and instrumentation (15:30): Typically companies identify a use case, build models, train the data, deploy in production and consider done. What if the use case changes? How can you re-instrument the entire model and training data etc? To avoid this ongoing technical debt, there are newer frameworks where the entire process/model can be containerized into a framework.Data wrangling, data preparation for AIML (24:07): Putting together an enterprise data platform is absolutely essential for any AIML work. DBS was proactive in establishing this model. Without those guidelines that are somewhat customizable for each of the departments, implementation of modern technologies will not take off. A platform approach for consumer banking, investment banking etc. will be very helpful.COVID impact on FinTech (27:07): COVID has compressed the technology timeframe from multiple years to a few months. This has been very beneficial for many companies. Because of this the financial industry will go through a quantum leap in the next few years.Resources mentioned in this episode:DBS Bank https://www.dbs.comAgility Exchange blogging Platform: https://agilityexchange.com/Data Transformers PodcastListen 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 PodcastsGoogle PodcastsSpotifyStitcher33 min
- Artificial Intelligence & Machine Learning in Financial Sector - Shailendra MalikData Transformers PodcastArtificial Intelligence & Machine Learning in Financial Sector - Shailendra Malik1x00:00/00:29:51SubscribeShareApple PodcastsGoogle PodcastsSpotifyStitcherTuneInRSS FeedShareLinkEmbedArtificial Intelligence & Machine Learning in Financial Sector – Shailendra Malik'/>Apple PodcastsGoogle PodcastsSpotifyStitcherhttps://youtu.be/obJTWpTGci0Episode Title : Artificial Intelligence & Machine Learning in Financial Sector - Shailendra MalikEpisode Summary: 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 technologies. Additionally, Shailendra talks about his pwn journey that took him across many countries and many domains. Lastly, he talked about a professional blogging platform that he was able to successfully build on the side.Topics discussed in this episode:Machine Learning in Financial sector (02:57): Pretty much every bank in the world is playing with AI and Machine learning nowadays. But many times companies keep going from one use case to another use case before completely optimizing the furst use case. This presents challenges for operations teams.Machine learning in internal audit & compliance (05:39): Primary goal of internal audit is to ensure compliance and assurance of what each department is doing what they say are doing. Given that a small internal audit team can’t work with all different systems & data, machine learning based automation is inevitable.Ensuring data quality for AIML models in FinTech (09:58): DBS bank has an enterprise data platform that sets the data strategy and data governance framework. That team has been very proactive in embracing the modern technologies and ensuring the data strategy is ready for it. Unfortunately many financial companies have a lot of technical debt with legacy systems etc. that they have to tread cautiously to ensure that financial business risks are managed.Professional journey across many domains (14:02): Shailendra came from Telecom background and started in FinTech by managing a risk management platform for investment banking and treasury. From there, Shailendra transitioned into audit as there is a lot of overlap with risk management and internal audit.Personal journey with Agility Enterprise blog (20:26): Initially started with personally blogging, Shailendra transitioned into demystifying and decluttering complexities in technology that at times seemed to be deliberately made complex. While trying to learn banking products, Shailendra was able to blog about his learnings about banking, interest rates and swaps and this seemed to attract a following.Individual blog to a blogging platform (27:37): That individual blog attracted enough followers and also creators that it now has more authors maintaining different topics such as project management, investment banking, agile framework etc.Resources mentioned in this episode:DBS Bank https://www.dbs.comAgility Exchange blogging Platform: https://agilityexchange.com/Data Transformers PodcastListen 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 PodcastsGoogle PodcastsSpotifyStitcher30 min
- AI requires interdisciplinary teams, Quality Data & ExplainabilityArtificial 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. Lastly, Explainability of models and data is becoming important and as such explainability needs to be baked in.25 min
- AI requires interdisciplinary teams, Quality Data & ExplainabilityData Transformers PodcastAI requires interdisciplinary teams, Quality Data & Explainability1x00:00/00:24:34SubscribeShareApple PodcastsGoogle PodcastsSpotifyStitcherTuneInRSS FeedShareLinkEmbedAI requires interdisciplinary teams, Quality Data & Explainability'/>Apple PodcastsGoogle PodcastsSpotifyStitcherhttps://youtu.be/yCxofWqPyyUEpisode Title : AI requires interdisciplinary teams, Quality Data & ExplainabilityEpisode Summary: 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. Lastly, Explainability of models and data is becoming important and as such explainability needs to be baked in.Topics discussed in this episode:Leveraging past experience in current job (01:47): Fiona’s past experience in SW engineering, Ph.D., and SW development has been very helpful in the current job as the head of AI. The rigor associated with SW Engg, the discipline of research methodology is extremely helpful in the multidisciplinary area of data science and AI.Deploying models (05:15): Deploying machine learning models requires dev ops, infra skillset, testing strategies, SW versioning of not only code but also data etc. So the data science teams need inter-disciplinary skills like dev ops, data quality, data governance, sw engineering etc. in addition to hard code coding.AI for improving data quality (07:07): AI requires quality data. But AI can also be used for improving data quality. As the data is fed in, AI models can be used to identify data anomalies and outliers and that information can be used to prescribe data quality tasks. While matching and de-duplicating, the models can use deterministic methods to identify anomalies and then use human in the middle to resolve the low confidence areas.Explainability AI ground-up (14:13): Explainability and transparency in machine learning models has gained a lot of traction. In real-life cases, an example of a doctor prescribing a treatment predicted by a model but unable to explain the reasoning would not work. There should be transparency around representative data and the models used. So it is important to build that transparency and explainability from ground up.Resources mentioned in this episode:https://Datactics.ComCall To Action:Connect with Fiona Browne on LinkedIn: https://www.linkedin.com/in/flbrowne/Data Transformers PodcastListen 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 PodcastsGoogle PodcastsSpotifyStitcher25 min
About Data Transformers Podcast
From the publisher's feed
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 interviewing the transformers in the industry who are leading the way in digital transformation. We also would like to bring our perspectives, latest trends and most valuable resources to you so you could be a data transformer in your organization.