Today, we’re diving into one of the most transformative shifts happening in technology right now — the rise of platform engineering and its intersection with AI-powered DevOps.
For years, organizations have struggled with fragmented tooling, slow software delivery cycles, and growing infrastructure complexity. Cloud gave us scale. DevOps gave us velocity. But together, they also introduced a new challenge: how do we build systems that are fast, secure, reliable, and scalable without overwhelming engineering teams?
Enter platform engineering, a discipline focused on creating consistent, automated, self-service platforms that empower developers, reduce operational friction, and improve delivery speed.
With AI rapidly reshaping how we build and run software, platform engineering is taking center stage. Machine learning models are automating decision-making, copilots are supporting developers, and AI-driven observability is changing how we detect and resolve issues.
Which skills matter most, how AI and DevOps are converging, and why every modern organization needs to rethink how it builds and supports. The organizations that build strong internal platforms today will be the ones ready for AI-powered automation tomorrow.
In this episode, we sit down with a platform engineering expert to discuss how this role is evolving, what skills matter most, how AI and DevOps are converging, and why every modern organization — from startups to global enterprises — needs to rethink how they build and support engineering teams.
Whether you're a CIO, an engineering leader, a procurement professional evaluating technology partnerships, or simply curious about the future of enterprise technology, this conversation will give you a front-row seat to where the industry is heading — and why platform engineering is becoming a strategic advantage.
Let’s jump in.
This is a continuation of Part 1. This episode dives deeper into how the AI algorithms are built and how the risk is assessed. Other topics include how your data is collected and stored, as well as the risks you may not be aware of that are buried in the terms and conditions.
What does the future of UBI insurance look like 2 - 5 years out, and what potential concerns and issues should you be aware of? Can this data be used against you in (futurist) scenarios that may not be as far away as you once thought?
Suppose telematic data collection is combined with other data collection methods, such as in-cabin (inside the car or vehicle data) + connected vehicles to the infrastructure. How could insurance providers completely disrupt traditional auto insurance as we know it today?
Finally, what if other industries leveraged similiar telematics data as UBI for the insurance industry? How can that impact you? This and more, you are surely going to find that this episode will keep the technical and non-technical questioning the future of the auto insurance industry.
Music: Technology Update by Zydsoounds.
This episodes explore how AI adoption is accelerating inside organizations—from meeting summaries to marketing campaigns and gamified cost-saving challenges.
Our guest, a logistics director for a manufacturing plant in central France, shares how the organization has changed how employees interact with AI technologies in the past 12 months.
Leaders must embrace AI to remain competitive, but they must also ensure their people adapt and grow without losing the human touch.Melissa: "...if you're not learning it, that somehow will impact your ability to continue to be effective at your job?"
Response, "I was not thinking that last year. Okay, I'm changing my mind. I think if this year I'm not learning, next year I'll be behind."
Welcome back to another season! Auto insurance is undergoing one of its most significant transformations in decades — and it’s all powered by data. Usage-Based Insurance, or UBI, promises fairer pricing by tracking how, when, and where we drive.
For some, that means lower premiums and safer roads. But for others, it raises serious questions: How much of our personal data should insurers collect? Who really owns that data? And what happens when policies end — does the data disappear, or does it live on in corporate servers?
In Part 1 of 2, we explore the promises and pitfalls of UBI. We start with the definition of UBI, how it works, and a list of the eight US Companies that support UBI, and the pros and cons.
By the end of this episode, you will know what questions should be asked with the goal of understanding if UBI is right for you.
We talk a lot about AI, dashboards, and savings targets, but here's the truth: none work without clean, consistent, and trusted data.
In this episode, we dig into:
✔️ How data governance powers accurate spend visibility
✔️ Why supplier duplication and inconsistent categorization kill strategic sourcing
✔️ What procurement leaders must do before launching analytics tools
✔️ The role of data ownership, master data, and integration in unlocking savings
💬 "If you can’t trust your data, you can’t trust your decisions."
Whether you're just getting started with spend analytics or trying to scale its impact across global teams, this conversation is your foundation.
🎧 Listen in and discover how to make your data your most strategic procurement asset.
In this episode, we explore the growing phenomenon of technology-based anxiety — the fear, stress, and uncertainty people experience in response to the rapid advancements in technology, particularly artificial intelligence.
We dive into how the speed of innovation is fueling anxiety. Joined by a mental health expert and real-world stories, we take our time to understand anxiety, potential triggers, demographics, and the social and community impacts.
Whether you're a tech enthusiast, a skeptic, or somewhere in between, this episode offers insights into navigating the emotional side of our increasingly digital lives.
The music featured in this episode is "Future Lovers" by MothaCode.
Discover how AI revolutionizes procurement in our latest video, "Top 10 AI Use Cases on Procurement: Transforming the Future!" 🚀 We dive into AI's transformative power, showcasing its incredible impact. Learn how AI enhances efficiency, mitigates risks, and drives sustainability, ensuring your procurement processes are future-ready. Spend Analytics & Opportunity IdentificationSupplier Discovery & Market IntelligenceStrategic Sourcing & Negotiation SupportContract Lifecycle Management (CLM)Supplier Performance & Risk ManagementDemand Forecasting & Inventory OptimizationPurchase Order Automation & Processing EfficiencySustainability & ESG IntegrationFraud Detection & Compliance AssuranceEnhanced User Experience & Stakeholder Interaction
A quick overview of AI Agents, what they are, examples of where we interact with them today, and how they work.
This content was created with my two daughters in 9th grade. They are experiencing AI from their local county school board, who are developing policies on using AI in school and creating 1 page flyers with an overview of AI.
In summary, my daughters may not understand all there is to know about AI, but they are asking the right questions and evaluating their level of trust in the information they receive. For example, who are the people developing these AI policies and documents, and do they have a background or knowledge on this topic to be a reliable source?
AI technologies impact all of us, no matter where we are in our life journey. From high school students to full-time retirement on the beach, we each have a different perspective. How we navigate forward depends on our own experiences with AI technologies.Meet Devadharshinin in her first interview ever!! She received her bachelor's degree in Data Science and started the M.B.A. program in Business (Artificial) Intelligence in the Summer of 2024. At the same time, she works full-time at Mavdero Techservices Pvt Ltd as a software (python) developer. This episode is hearing the perspective of someone continuing her (AI) educational journey and, at the same time, just starting her professional journey. Topics covered are: - Developer vs Data Science- MBA in AI Business Strategy- Value of automation- The software she uses in her job - Learning on the job- Her pick of the most exciting feature she is working on- What is an AI Agent?Mavdero is a technology provider for intelligent automation services using artificial intelligence, RPA, and machine learning. The Devadharshinin is working on contract management automation.
'AI technologies' is a generic phrase that combines 7 different types of AI technologies in a broader group. If you are new to AI or interested in a refresher, this is a good use of 15 minutes. This episode is a debrief and definition of key terms: - AI compared to AI Model - Narrow AI compared to General Intelligent and Super intelligent AIComponents of AI Technologies:- Machine Learning- Deep Learning- Natural Language Processing (NLP)- Neural Networks- Robotics Processing Automation (RPA)- Computer Vision- Generative AI
This episode discusses an often overlooked area of data cleansing and its enduring value, even in the age of AI technologies. In our data-driven world, companies are racing to adopt AI to automate processes at the expense of pausing longer to evaluate potential adverse side effects. This is why the human element remains a critical component.
This conversation aims to dive deeper into the benefits of data cleansing with human expertise, challenging the premise that AI is the only solution. With our guest this week, Susan Walsh, we explore how human intervention continues to add value.
While AI can offer speed and efficiency, it cannot fully understand the contextual use of the same data set across industries. Leveraging human intelligence in conjunction with automation tools strengthens the outcomes.
Sprinkled with real-world stories, the conversation will uncover the 'behind the scenes' human-driven data cleansing methodologies. This episode promises to offer a different perspective on AI and explain why the human element will be around for a while.
Julienne B. Ryan shares valuable insights on the importance of effective communication in a digital world post-COVID. She highlights the impact of AI technologies on communication, including the limitations and potential inaccuracies of AI language models like ChatGPT. Ms. Ryan emphasizes the need to balance technology's benefits with the importance of the human experience. In her discussion, Ms. Ryan stresses the significance of critical thinking, trust, and authenticity in using technology for communication. She also explores creativity, experimentation, and failure in the context of technology and communication. Furthermore, Ms. Ryan highlights the importance of empathy and nuance in effective communication. Drawing from her consultant practice, Ms. Ryan encourages the audience to approach technology-assisted communication with a critical mindset while trusting their instincts and experiences. Her insights are more relevant than ever as we navigate a virtual and digital world.
Adita Karkera, "..motivated and inspired in how the public sector can cater to the needs of our citizens. My heart lies in the public sector." Data.gov was launched in 2009 and is the United States government's open data website. It provides access to datasets published by agencies across the federal government. It wasn't until 2018 that the federal government's mandate became a clear sign acknowledging the value and effectiveness of data. The 2018 Foundations for Evidence-Based Policymaking Act is a United States law that requires the federal government to modernize its data management practices. Its goal was to change the way data is used in the government to create policy. The law aims to improve access to federal data so agencies and policymakers can craft better, more effective policies and programs and deliver on services promised to the country. In 2019, as a follow-up to the prior 2018 Foundations Act, the head of each agency was tasked to appoint or designate a qualified #chiefdataofficer (CDO) without regard to political affiliation. Since then, a Federal Data Council has been established, bringing # CDOs across all federal government agencies. This interagency council has a charter establishing best practices to leverage data as a strategic asset. As of this recording, around 80+ individuals make up that council. Some of the other topics explored with Adita: - Are there challenges for the CDO in the public and private sectors? - Skills for a data-literate workforce - Understanding the 360-degree view of the citizen - Perception the public sector moves slowly may be an advantage rather than a hindrance - Potential risks in leveraging data can not be ignored, no matter how excited we are about emerging technologies such AI We need more women in the public sector to help shape our future. I am interested in serving the public after listening to Adita speak so passionately. Reach out directly to discuss the role of Women in Data in the Public Sector. / aditakarkera
The strategy and executive of marketing is not commonly considered in the early part of the product development and lifecycle. The most common reasons include, but not limited to: 1. to ensure the product and service works before taking it commercial (internally or external) and 2. the product or service can evolve during the product development cycle. Our guest, Isabella has an expertise is working with new and emerging products after the fact. Often 1-4 years after the product or service has been created. "...a lot of times we (the marketer) miss the actual problem the product or services was created to solve, " Isabella mentions, "it is common to have a disconnect between engineering and marketing but also sales and marketing," During this episode we discuss a marketing methodology: 1. Business Challenge - What was this product created for? 2. Refining the buyer persona - who are we targeting ? Who is going to use this product? 3. Validate with Customers - Schedule time with potential buyers for feedback. 4. Translate into what will resonate - Breaking through the noise. 5. Educating value - What is the value proposition? It is more than features & capabilities 6. Establish the story, but stay consistent. Other topics discussed were: - Educating new emerging technologies - Working around disadvantages: - What about good and negative publicity. Is it all good? - Startups. Are they spending their money wisely with lack of resources in marketing? - What do we mean when we talk about 'the story' - What approaches do we want to avoid? - Customer attention spans - Prioritize marketing efforts - Supplementing expertise with external content - Generation Z perspectives - Women on board of advisors at the university level, supporting the next generation - Lessons learned - women having a voice. You are there for a reason. You are where you are supposed to be. It is out Feel free to DM Isabella: https://www.linkedin.com/in/isabellarichard/
From housing data on a single server to global, distributed storage with zettabytes of available data. Over the past 20 years we have continued to modify the underlying architecture to match our desire to store more data. We use this data in consumer hyper personalization, detect vulnerabilities and data breaches, and to have a higher level of confidence in the answers to our questions. As we collect more data to address the questions from today, consumer expectations shift again and have more complicated questions tomorrow. Just when we think we have enough data, the line in sand moves again. In fact, the line is moving so much these days, one could say it doesn't really exist anymore. My guest today is Laura Ellis, who originally thought she was destined to become a teacher. Today she is a data engineering & platform analytics executive who started her professional career in technology and recently received CDO executive certificate. She is also the founder of the Blog 'Little Miss Data', focusing on living out loud and talking about all things data. In her spare time, Laura produces and hosts the annual event (for the past 3 years) 'Data Mishaps'. This event is a virtual, safe space where data mistakes are shared to learn from each other. With ~400 participating in the last event it is becoming a much anticipated event each year. Topics discussed today are: - What is cybersecurity - the simple definition - Is the role of data engineering different than a data scientist - How has the interpretation of data changed over the years - Is there a business case for dark data - Pivoting into a data profession - The value of returning to school after 15+ years
We hear and understand that step 1 in developing a #dataorganization is to clean up the data. This often includes data harmonization from multiples sources, classification, mapping, labeling, etc. Most often it usually stops there. We cleaned it... we are done, right? No, not really. In my conversations with Himali Kumar an IT and Data Executive, new data is constantly be added into the enterprise data set. From information provided by new customers, inclusion of new data sources, changes in data sources such as POS system, customer preferences, etc. Question: How do you gather and ensure value in enterprise data if the data is constantly changing? If you are not leveraging a #datagovernance program with resources dedicated to continue to re-evaluate the data, then the organization is already falling behind or creating a negative perception between the company and the customer. Examples of challenges that can occur after the initial #datacleansing include: - duplicate customer data leading to sending the same message multiple times not realizing it was the same person (from a data perspective) - data schema issues leading to many man hours of rework - #dataaccuracy issues causing outages to internal and external stakeholders A decision-based organization is only as good as its data. If the data is constantly changing, then it is critical to continue to evaluate (or audit) the data to minimize gaps and mitigate data strategy challenges. My conversation with Himali, also highlighted: - Impacts of not performing regular reviews (audit) of the data - Three (3) areas critical to developing a data organization - the minimum to get started with - Establishing tolerances / risk thresholds to enterprise data - Who is really the end user? - Achievability of #dataquality
Lloyd Skinner , CEO Greyfly.ai interviews Melissa to discuss how AI technologies are impacting project management. Specifically providing 'project intelligence' to the organization and reducing project risks before the project even starts.
Bhuva Shaki is the Chief Sustainable Innovation Officer of Bhuva's Impact Global and Global Chief Ethics & Culture Officer (CECO) | Director of the Americas (Not-for-Profit), Women in AI. She embraces the concept of continuous learning and opportunities for personal growth: sustainability, entrepreneurships, private equity, venture capital. All of which intertwine to support her advocacy of others, helping startups expand sustainable innovation and championing women's financial inclusion with technology with a goal to eliminate exclusion. With such purposeful engagement in her continuous learning, I had to ask, which is first the education or the experience? Bhuva, replied experience first, then focus on specific areas of education that empahsizes your interest. This lead to the bulk of our conversation today, covered across the following four (4) questions: 1. How can we eliminate bias and integrate ethics into financial services industry? 2. 21st Century Skills are suppose to outline the grouping of skill sets our next generation needs as they enter the workforce. The phrase literacy tends to emphasis media, technology, and information literacy. There are so many other types of literacy the next generation should also focus on. How do we ensure the balance from those literacy skills while adding in others such as data literacy, data bias, financial literacy? 3. Do governments need to play a role in order to improve of gender equity? More specficially, a recent article was referenced that real gains in gender equity will require technical equity as well and the role of government agencies is another variable required in the larger equation. 4. Do you have recommendations to close that gap in the middle management of the workplace? More specifically, when we look at where women are in leadership roles today, we have improved at the level of VP and higher. We also see a lot of women leaving college and entering technical roles. There is a clear gap in recent studies in the middle section. If we don't have women throughout the oerganization do we risk our progression falling backward? 5. Generative AI - from a textual perspective (not generating art). Is this going to help or hinder the financial customer, specifically from the lens of women-owed startups and financial inclusion. Finally, two (2) key takeaways for women-owned startups. What can they start doing after listening to this podcast to really change their financial equity? https://www.linkedin.com/in/bhuvashakti/
https://www.linkedin.com/company/bhuvas-impact-global
/https://www.bhuvas-impact.global/
Data is a rich asset. As we push the boundaries of available data beyond Zettabytes and into Bronobytes and Geobytes, how will we achieve accurate data intelligence? According to Forbes, 90% of the world's data was generated in the last two years with 2.5 quintillion bytes data being created each day. Is all data equal, and how much data do we need to address our questions? In talking with Kamal Distell, from Toyota Motor Corporation, "there are similarities in how we think about data governance, collection, labeling, architecture, etc., across all industries." These foundational elements remain static, regardless of which industry or company you work for. "It is the context that is different across each industry. Yet, the context of the data can be learned." Kamal adds, "As we increase how we gather more information, there will be a hypothesis, and the data will either refute or accept." In the 1990s, the term Data Mining became more mainstream in the database communities. It was one of the foundational concepts of my Master's degree in Management Information Systems, focusing on Data Management. I was more vocal about the declining expectations as data increased in ways we could no longer imagine; i.e. how could we store and report on meaningful data. As we continue to collect and store beyond terabytes of data (this was in 1996), the relational architecture would no longer be sufficient. What was working, in theory, would not work in the real world. Moving forward to 2022, there are many more levels above a Terabyte just so we can quantify the amount of data accessible today. We are having the same conversations we started in 1996. How do we wrap our arms around the right balance of relevant data to support the business challenges, to create an effective decision at a time when that decision has the most impact? Kamall and I discuss the concept of minimal viable data, which will be a critical component to the future success of data intelligence. Additional topics in under 30 minutes include: - Value of working with data across industries - Are we able to solve any business challenge with data alone? - Future of Data Trends - Minimal Viable Data - 80/20 rule from the past. Is this still relevant today? - Using data differently - Lessons learned
We talk about challenges in moving AI solutions from testing into production or commercialising AI solution for all users internall / externally. We haven't really focused a lot of attention on the other extreme: the data collection and developing the proof of concept.
Where do you start ? Ask yourself, what is the outcome you want to achieve? What is the business problem you are trying to solve? Not understanding this is the #1 blocker to success.
Prerequisities: There are a lot of moving parts just to get started, some examples, but not limited to: 1. what data is needed & where are you going to get it 2. availabilty of data; do you have access to this data in a repeatable way 3. what about data privacy, white room (data can not leave the site) 3. how will the data be organized 4. who is going to own the data goverance 5. do the data scienists have the right technologies to do their job 6. is the organization ready for this to start Additional topics discussed: - How to priortize where to start - 80% of the work by data scientist is data rangling - Example use cases in the insurance sector - Example use cases in asset management companies - Proof of concept validates if the outcomes we want to or expect to see, can really happen - Establishing the architecture in a way we can scale if the POC is sucessful - Technologies data scientist should have access to - Monitoring data drift effectively - Different languages across the roles. Takes a lot of change management to get this right.
Ultlimately is the organization 'data ready'?
