Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Mark Cooper
Mind the Gap: Accountability – Connections Across the Enterprise
Mind the Gap is a DATAVERSITY column published on the first Thursday of every month. An executive is in a meeting with the CEO. The executive shares a report. The CEO asks, “Do you trust this report?” The executive replies, “Yes.” To which the CEO responds, “Why?” How often is that question asked, and when it’s asked, how often is it answered confidently or honestly? Think about the hardest question your boss ever asked you.
Mind the Gap: Accountability – What AI Will Never Do
Our profession needs to reorient its thinking. We are a stubborn bunch. We’ve been banging our heads against the proverbial wall for decades. Maybe if we just package information management and data governance in a veneer of data stewardship or data literacy, leadership will “get it.” Maybe our development and business partners will start caring as much about the data as they do about the applications. A new training program. A new infographic. A new slogan. Lots of effort. Little has changed.
Mind the Gap: Data Rabbits
Show of hands: How many of you have ever heard corporate leadership say something like, “We are moving everything into the cloud and closing down our data center so we can scale faster and save money”? Maybe you heard the slightly constrained version, “We are moving all of our analytics into the cloud and getting rid of our on-premise data warehouse so we can scale faster and save money.” How’d that work out? Let’s focus right now on data and analytics, but the same ideas apply to other areas.
Mind the Gap: Deploying Data Products … Finally
For the past several months we’ve been working our way through the process for delivering data products: definition of done for data products, then ensuring that the enterprise is ready to begin development, and finally data product design and specification. At this point you are, no doubt, ready to get this thing started already and burst out of the gate coding. You know you have already started the project, but I get it. Just one more minute.
Mind the Gap: Deploying Data Products … Almost
In the past couple of months I’ve covered the Definition of Done for Data Products as well as ways to ensure that the enterprise is ready to begin development, taking you through phase negative one and phase zero in your data products journey. Congratulations, you’ve reached the starting line. Ready to implement something? Me too, but not quite yet. Let’s briefly recap. Phase Negative One: Preparation You have convinced the enterprise, and especially upper management, to pursue data products.
Mind the Gap: Data Product Deliverables
The “product” metaphor has been useful when making the point that there is more to delivering a table, file, report, dashboard, or summary than just the data itself. At this point, the definition of data product is pretty clearly and consistently understood. You can read more about that here and in other data product blog posts. And, as always: The “product” in data product is reliability. OK. That’s great. But I can’t make “reliability” a user story or put it on the deliverables list.
Mind the Gap: Are You Even Ready to Be Thinking About Deploying Data Products? - Dataversity
Over the next several months I’m going to dive back into data products. If you’re not already familiar with the concept, a refresher on the foundations of data products may be useful. Take a minute and check out some of my articles on the subject from the past couple of years. Just click on the data products category. I’ll wait. … OK. Let’s continue.
Mind the Gap: Agentic AI and the Risks of Autonomy - DATAVERSITY
The ink is barely dry on generative AI and AI agents, and now we have a new next big thing:agentic AI. Sounds impressive. By the time this article comes out, there’s a good chance that agentic AI will be in the rear-view mirror and we’ll all be chasing after the next new big thing. Anyone for autonomous generative agentic AI agent bots?
Mind the Gap: Agentic AI and the Risks of Autonomy - DATAVERSITY
The ink is barely dry on generative AI and AI agents, and now we have a new next big thing:agentic AI. Sounds impressive. By the time this article comes out, there’s a good chance that agentic AI will be in the rear-view mirror and we’ll all be chasing after the next new big thing. Anyone for autonomous generative agentic AI agent bots?
Mind the Gap: The Many Faces of ChatGPT in the Mirror
One of the most significant areas of data ethics concern is bias in generative AI models. Concerns about algorithmic bias are not new. Associative model (i.e., neural network) bias is not new. Even the ceding of consequential decisions to these models is not new. Just a few examples: AI is used to screen resumes and evaluate candidates. It is used in loan origination, underwriting, and risk assessment. It is used in health care diagnoses. Facial recognition is used by law enforcement.
Mind the Gap: AI-Driven Data and Analytics Disruption
We are at the threshold of the most significant changes in information management, data governance, and analytics since the inventions of the relational database and SQL. Most advances over the past 30 years have been the result of Moore’s Law: faster processing, denser storage, and greater bandwidth. At the core, though, little has changed. remains the same as it was in 1992.
Mind the Gap: Assessing Data Quality Readiness
This is the second in a two-part series exploring data quality and the ISO 25000 standard. OK. You’re with the program. You recognize that having quality data is important for accurate AI models. You would even concede that it’s important for other stuff too, but whatever, because AI is getting all the attention right now. You want to start publishing data quality metrics. All good. You’ve done some research, read some articles, and found a data quality framework.
Mind the Gap: Did You Know About the ISO 25000 Series Data Quality Standards? Me Neither
This is the first in a two-part series exploring Data Quality and the ISO 25000 standard. In the 1964 dark comedy Dr. Strangelove, or How I Stopped Worrying and Learned to Love the Bomb, General Jack D. Ripper orders a nuclear strike on the USSR. Despite efforts to recall the bombers, one plane successfully drops a bomb on a Soviet target, triggering the Doomsday Device. Once activated it cannot be disarmed and the result is global annihilation.
Mind the Gap: R.I.P. Data Governance
Happy New Year, and welcome back to Mind the Gap. OK, enough pleasantries. Recently, I’ve been thinking about the following observation: Many, if not most, organizations have been struggling with the same information management and data governance challenges for decades. Certainly, we’ve seen traction in some places, and those organizations are reaping the benefits. But show of hands: How many of us would say that data governance has been broadly successful? Probably not many.
Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database - DATAVERSITY
You never know what’s going to happen when you click on a LinkedIn job posting button. I’m always on the lookout for interesting and impactful projects, and one in particular caught my attention: “Far North Enterprises, a global fabrication and distribution establishment, is looking to modernize a very old data environment.” I clicked the button and almost immediately received notification that a new email from clauss@farnorthenterpises.com had arrived. “That was fast,” I thought.
Mind the Gap: Migration Projects – Gaining Traction or Spinning Your Wheels - DATAVERSITY
Hardware and software migration projects are like crack for IT executives. Especially when the number on the new invoice is smaller than the number on the old invoice. Even better when the products can be folded into a previously negotiated enterprise agreement and made to appear free.
Mind the Gap: Ask for Business Actions, Not Business Value
I’m not sure I know anyone in the data and analytics field whose platform doesn’t face budget scrutiny. Analytics is expensive. And when cost-cutting is the order of the day, analytics is a big target. Up shields. Time for another business value inventory. I’ve spoken with consultants who have completed analytics business value inventories at several different companies, and I’ve done a few myself. Each time we all hope that when we quantify the value we’re generating, the questions will stop.
Mind the Gap: Start Modernizing Analytics by Reorienting Your Enterprise Analytics Team
… and your data warehouse / data lake / data lakehouse. A few months ago, I talked about how nearly all of our analytics architectures are stuck in the 1990s. Maybe an executive at your company read that article, and now you have a mandate to “modernize analytics.” Let’s say that they even understand that just putting everything into the cloud or using cloud tools doesn’t count as modernization. They want to take advantage of that new data cloth or data fabric or mesh or something. That’s OK.
Mind the Gap: The Product in Data Product Is Reliability
Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored analytics architecture stuck in the 1990s. This month, we’ll look at the rise of the data product. It wasn’t so long ago that data products were the next big thing, and many of us were very hopeful. After all, data products are more than just the data.
Mind the Gap: Analytics Architecture Stuck in the 1990s
Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the data chasm. This month, we’ll look at analytics architecture. From day one, data warehouses and their offspring – data marts, operational data stores, data lakes, data lakehouses, and the like – have been technological workarounds.
Nav Canada CEO: How one sky can transform global aviation
The following is an opinion column by Mark Cooper, president and CEO of Nav Canada. If there’s one thing the aviation industry has taught me over the past few years, it’s this: we’ve built an incredibly interconnected global system, yet we’re still trying to run it in silos. The stakes couldn’t be higher. Millions of travellers, shippers and businesses move through the global aviation network every single day.
Smart city wireless networks - do open standards matter? (Reader Forum)
Home›Buildings›Smart city wireless networks – do open standards matter? (Reader Forum) BuildingsCarriersConnectivityInfrastructureInternet of Things (IoT)NB-IoTOpinionReader ForumSensorsSmart Cities • May 12, 2022 • 6 The initial hype around smart cities has now passed, with applications moving beyond early pilots and proof-of-concepts into commercially deployed systems.
Colocation and high-frequency trading
By Mark Cooper, Business Development Manager at Exponential-e In the rush for speed in trading, ex¬changes are building huge data centres where members, non-members and traders place computers containing their trading algorithms next to an exchange’s matching engine, which matches ‘buy’ and ‘sell’ orders. Otherwise known as colocation, the practice shaves crucial milliseconds from the time it takes to complete a trade.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Mark
Contact Mark, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack