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.
Data Strategy for FinTech use cases such as Fraud Detection
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.
Artificial Intelligence & Machine Learning in Financial Sector – Shailendra Malik
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.
AI requires interdisciplinary teams, Quality Data & Explainability
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.
A fascinating journey as the head of Artificial Intelligence with Fiona Browne
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 datasets both in protein development Ph.D. projects and later in virtual telescopy was ideal for data science. As head of AI at Datactics, Fiona focuses on financial/banking/government sectors in dealing with data profiling/matching and self-service analytics.
How to build and scale a data advisory business with Jay Zaidi
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.
Deep Dive on Data strategy, Data management, Data Governance with Jay Zaidi
Any digital transformation requires a strong focus on data strategy and data management. Jay lays out a blueprint on how to engage in a data management initiative starting with a data maturity assessment. The episode covers key success factors for a data management initiative and goes over why most data initiatives fail.
Customer ReviewsSee All
Great guest lineup
I saw the guest list on LinkedIn and was intrigued. I didn’t listen to the entire episodes but what the little I heard was good to get me subscribed.
Nice chemistry between the hosts. Intro episode seemed little hesitant but the second episode was better. Seemed like good chemistry between the hosts. The topics they mentioned are also very well balanced.
Wow, what a great intro episodes. Can the hosts really live upto the expectations they have set? I have seen so many other podcasts come and go after setting up lofty expectations. I am skeptic but wish them the best.