Enterprise AI starts with better data.
Don't Panic! It's Just Data examines the practical data work behind enterprise AI, from quality controls to data products. Its conversations favor architecture, governance, procurement, analytics, and reporting realities over loose futurism.
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Don't Panic! It's Just Data covers the enterprise data work that has become impossible to ignore in the AI cycle. It is not a broad tech chat built around novelty. The show lives in the operational middle: dashboards, data products, supplier records, reporting processes, governance programs, and the confidence organizations place in AI outputs. Episodes repeatedly ask why AI projects stall after promising pilots. The answer is rarely the model alone. Data quality, completeness, lineage, security, and shared definitions sit at the center of the conversation. One episode frames the shift from BI to AI around reusable data products, arguing that business teams can trust dashboards and agents only when both draw from the same governed source. Another looks at procurement and supply chain, where fragmented supplier data can limit what AI agents can safely recommend. The show also spends time on revenue impact. It treats data contracts, pipeline maturity, and incremental governance improvements as practical ways to create business results while larger modernization work continues. Responsible AI gets similar treatment. Trust is presented as something earned through preparation, controls, feedback, and clear accountability, not something created by branding an application as intelligent. The guests tend to be product leaders, analysts, data executives, and technology practitioners from companies working directly on enterprise data systems. The hosts keep the frame accessible, but the vocabulary assumes interest in real data operations. That balance gives the show a useful range. A CIO can hear the strategic implications, while a data leader can recognize the architecture and process tradeoffs underneath. The pacing is orderly and professional. There is some vendor context, but the better moments come when guests explain the actual failure modes: inconsistent BI transformations, stitched-together acquired platforms, unstructured documents, spreadsheet dependence, and AI agents lacking enough reliable context. Don't Panic! It's Just Data suits listeners who want less AI theater and more attention to readiness. It treats modern analytics as a trust problem, a governance problem, and an engineering problem all at once.
Made for: Designed for CIOs, CDOs, data leaders, analytics teams, procurement and supply chain stakeholders, and B2B technology professionals. It suits listeners who need practical language for AI readiness, governance, and measurable business outcomes.
What sets it apart: The show keeps AI grounded in enterprise data mechanics: quality, contracts, unified models, BI consistency, and governance. Instead of chasing model hype, it follows the infrastructure and organizational choices that decide whether AI can be trusted.
Not only do many businesses have more data than they know what to do with, but they also often struggle to gain insights from some of the most valuable data in their possession, leading to many of their crucial data assets going unused. Whether it's issues with data quality, visualization, or management, getting lost in the sea of enterprise data at your possession can make it impossible to make smart, data-driven decisions that improve your business. The "Don't Panic! It's Just Data" podcast delves deep into the power of enterprise data. From groundbreaking vendor solutions to expert-backed best practices for making the most of your data assets, join us as we gather insights from leading te…
AI ambition meets the awkward question: can you trust the data underneath it?
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Short reviews from the PlayNext desk, based on the episodes we processed.
A grounded review of how shared data products could link AI's speed with BI's hard-won trust.
The episode frames AI replacing BI as a bold but practical argument, rooted in the everyday frustration of mismatched numbers, repeated transformations, and teams losing faith when reports fail to agree.
A focused case for treating unified procurement data as the foundation of useful AI.
AI ambition in procurement and supply chain meets a less showy constraint: whether an organization can give its models consistent, complete context.
A grounded argument for pairing quick data wins with the governance AI ambitions eventually need.
This episode frames the distance between big AI plans and measurable revenue as a practical operating challenge. Trisha Pillay leads Paul Brunel and Sergio Morales of Growth Acceleration Partners through the machinery that often decides whether ambition turns into results:…
A survey-led look at why AI trust depends on data quality, context, and governance.
AI adoption runs into a familiar human tension: employees can trust AI outputs too readily, while their expectations may not line up with those of CDOs and CISOs.
A clear reminder that better analytics starts with better questions, not just more data.
Data abundance can make analytics feel like a race to gather everything, but Don't Panic, It's Just Data keeps returning to the better starting point: what work is the data meant to answer?
Enterprise IT after the sales deck.
Federal tech, grounded in execution.
Candid enterprise data talk, minus the gloss.
Shabhangi Dua and Adrian Estala frame Don't Panic!
["Don't Panic!
["Trisha Pillay opens \"Don't Panic, It's Just Data\" with Paul Brunel and Sergio Morales from Growth Acceleration Partners to explain why AI projects still miss revenue impact.", "Paul says many enterprises chase AI as a \"shiny penny\" without a data hypothesis, while poor data quality, missing data location knowledge, and weak governance block cost reduction or revenue lift.", "Sergio says AI delivery has the same engineering burden as cloud architectures, microservices, and NoSQL migrations, so teams need business requirements and a North Star before building.", "The episode lands on data contracts, where Paul and Sergio describe schema enforcement, master data management, and a manufacturing example with ERP and inventory systems that need reliable data before decisions can scale."]
["Kevin Petrie and Nathan Taraski focus on Informatica's 2026 CDO Insights Report and the trust paradox in AI adoption, where 65% of data leaders say employees trust AI data and 76% say governance has not kept pace.", "Nathan says 75% of leaders think the workforce needs data-literacy upskilling and 74% say AI literacy is necessary, while 86% of organizations are increasing data-management investment and 39% are prioritizing upskilling.", "The discussion ties AI readiness to trusted context, metadata, data quality, and unstructured sources like PDFs and email, while also noting that leaders expect about seven data-management vendors, eight AI vendors, and nine for agentic AI.", "The episode closes on use cases such as customer loyalty at 29%, BI and decision-making at 28%, post-sales support at 25%, and regulatory or ESG requirements at 27%."]
Shabhangi Dua of EM360 Tech interviews Herb Plecker, Research Director, Data and Analytics at Enterprise Management Associates, on "Don't Panic!
Hex CEO Barry McCardell argues that trustworthy AI analytics requires distinguishing questions such as total fraud dollars versus fraud rates, beyond generating accurate SQL.
Jorgen Oberman and Piotr Kobziakowski of Vespa AI describe 90-to-180-day engineering waits and fragmented commerce systems as barriers to AI search.
Holland and Barrett CFO Vinita Bajaj says the CFO should act as product owner for finance data and intelligence.
Kevin Petrie and Amy Horowitz discuss the critical role of trusted data inputs in building responsible AI systems, highlighting new research showing data quality has become the top obstacle to AI success.
Kevin Petrie interviews Megan Carrier and Nate Cook from Insight Software about how finance teams can overcome reporting inefficiencies and turn spreadsheets into real-time reporting powerhouses.
Tricia Pillay interviews Andy Steens and Roisin McCarthy about the evolution of Big Data London and their partnership promoting diversity and innovation in data.
Kevin Petrie interviews Nidhi Ram about her background and role in data modernization and operational excellence at Precisely.