Mortimer Clove has watched an organization drown in data and still make decisions in the dark. As Chief Information Officer for the City of Harrowmere and the former VP of Global Media and Infrastructure Technology at Ardent Broadcast Networks, he's built plenty of dashboards — and learned the uncomfortable lesson that a dashboard nobody acts on is not intelligence, it's decoration.
Tune into this episode of The Focal Point with Wolfcrest & Co.'s VP Operations, Filiberto De La Cruz, to hear why Mortimer thinks most decision problems are judgment shortages, accountability shortages, or clarity shortages wearing a data costume — and what he built instead so information actually reaches budgets, priorities, and risk calls.
Episode Video
About the Guest
Mortimer Clove has served as Chief Information Officer for the City of Harrowmere since 2018, responsible for the city's digital transformation and technology strategy as well as day-to-day operations of the IT department. His initiatives include connected-infrastructure programs, Lean Agile product delivery, and a decision-review practice that requires every proposed dashboard to name the decision it will change and the person who owns that decision.
Before joining the city, Mr. Clove served as VP of Global Media and Infrastructure Technology at Ardent Broadcast Networks, managing worldwide teams across IT architecture, engineering, operations, and technical support. Prior to that he was VP of IT Infrastructure and Operations at Larkspur Pictures, leading architecture planning, implementation, and operations for computing and telecommunications. He holds a B.S. in information systems from Cordelia State University and is a certified Project Management Professional (PMP), SAFe Government Practitioner, SAFe Program Consultant, Scrum Master, and Six Sigma Green Belt.
Transcript
The information-appetite trap
[Music] This is the Focal Point Podcast, an ongoing series where we deep dive into the meat of messy management. [Music] Whether it's technology, business, government, or entertainment, we chat about our experiences and perspectives in life [Music] and leadership. Join us as we learn about their latest insights and hopefully inspire you to have some of your own. [Music] Here's the scary part. Your organization can be drowning in data and still be making decisions in the dark. >> Right? And that's the trap. It feels like progress. More dashboards, more reports, more metrics, more visibility. >> It looks like success right up until it collapses. >> Exactly. Because the real question is not do we have enough data. The real question is does the data change anything. >> That's the heart of this article. The data appetite problem.
The idea that organizations keep believing the next data set, the next dashboard, the next analytics platform will finally solve the decision problem. >> But the decision problem usually is not a data shortage. >> It's a judgment shortage >> or an accountability shortage >> or a clarity shortage. >> Yes. And that distinction matters because a lot of companies have built very impressive systems for collecting information without building the operating system that turns information into action. >> That phrase is doing a lot of work. The operating system >> it is because data does not automatically become intelligence. It has to travel somewhere. It has to influence priorities, budgets, product choices, risk decisions, customer decisions. >> Otherwise, it just sits there. >> Exactly. A dashboard no one acts on is not intelligence.
It's wall art. >> Okay, so let's unpack the article's first big point. Information has to become impossible to ignore. >> This is where the article pulls from Jim Collins and the idea that the key is not simply better information. It is turning information into information that cannot be ignored. >> That's such a useful distinction because most organizations think the work is done once access exists. >> Right? We built the dashboard. We centralized the data. Everyone has access. Great. But access is not consequence. >> Wait, say more about that. >> Sure. Consequence means the data has a path into decisions. If a metric moves, does anyone change the road map? If a customer signal appears, does anyone revisit the product strategy? If a risk becomes visible, does the budget change? Does the staffing plan change?
Get truth into the decision room
>> So the question is not can people see the data? No, the question is, can the truth get into the room where trade-offs are made? >> That's the part leaders usually miss >> because visibility feels like progress, but visibility is only a means. It is not the outcome. >> It reminds me of a smoke detector versus faulty wiring. The smoke detector is useful, but it is not the repair. >> Exactly. [Music] And a lot of organizations keep upgrading the smoke detector while the wiring inside the walls is still dangerous. >> Right. Because the dashboard only shows the smoke, not the wiring inside the walls. >> And if the organization has no mechanism to investigate, prioritize and fix the wiring, then the dashboard becomes a ritual. People look at it, they discuss it, they explain it away, then they move on. >> Which leads into the second idea.
Bad data makes forecasting theater. >> This is a big [Music] one. Organizations often want data to predict the future before they have made the present observable. That line should make every leadership team pause >> because forecasting sounds mature. It sounds strategic. But if the signals underneath the forecast are stale, partial, biased, or disconnected from the actual work, then the forecast is just a polished guess >> or theater. >> Exactly. Forecasting theater. You have charts, you have confidence intervals, you have models, but the inputs are weak. >> So it is not just a technical data quality issue. No, the article is very clear on that. Data quality is also an operating issue. Who creates the data? When is it updated? What behavior does it miss? What incentives shape it? What decisions depend on it?
>> That last one is important. What decisions depend on it? >> Because if nobody knows what decisions the data is meant to improve, then teams collect everything. They keep expanding the data appetite. more fields, more cuts, more context, more charts. >> And eventually, the organization confuses volume for truth. >> Yes, the bridge looks stable, but stress fractures are spreading underneath. Everyone keeps admiring the architecture because the bridge is still standing, but the loadbearing signals are weak. >> And by the time the bridge visibly cracks, the actual failure has been developing for months. That's why the article warns against asking data to do too [Music] much too soon. If the present is not observable, the future will not be predictable in a useful way. >> So this is not really a metrics problem.
Reframe the actual problem
>> Not only it's a reality contact problem. >> That's a useful distinction [Music] >> and it connects to the next point. The real problem is often misnamed. This might be my favorite section because it gets at a subtle failure pattern. Data can answer the wrong question with impressive confidence. [Music] >> Yes, and that is dangerous because organizations often choose the problem they are most comfortable having. >> Give me an example. >> Let's say conversion is low. The comfortable problem might be users do not want the product. That problem lets the team talk about marketing, positioning, demand generation, maybe pricing. >> But the real problem might be something else. >> Exactly. The article gives the pattern. Users might want the product but get frustrated because of bad interface design.
>> So the data point is real, but the interpretation is wrong. >> Yes, low conversion is the smoke. The faulty wiring might be onboarding friction, confusing interface design, broken trust, unclear value, or a mismatch between expectation and experience. >> And if leaders misname the problem, they can solve the wrong thing beautifully. >> That phrase matters. Solve the wrong thing beautifully >> because the team can execute well. They can improve campaigns, tune messaging, build better reporting, and still miss the actual cause. >> Exactly. This is where data becomes strategic. Not when it confirms what leaders already believe, but when it helps them reframe the question. >> So, the best data work is not just answering questions. >> It is improving the question. >> That feels uncomfortable.
It should because reframing often threatens existing plans. If the problem changes, the owner changes, the budget [Music] changes, the road map changes, the story changes. >> And that is why more data can become politically convenient. >> Yes, more analysis can delay the moment when someone has to say we are solving the wrong problem. >> Which brings us to the next section. Clarity requires compression. This is where the article argues that the appetite for data can become an appetite for volume. >> More dashboards, more slices, more dimensions, more context. >> And sometimes that helps, but often it postpones the hard work of saying what matters most. >> The article borrows a writing principle here. Put the important stuff first, the support stuff later. >> That is a great [Music] data strategy principle.
Design information for decisions
Decision makers do not need every fact in equal prominence. >> They need the important truth first, >> then enough support to evaluate it. >> So good data work compresses complexity without pretending things are simple. >> Exactly. That's the nuance. Compression is not oversimplification. It's disciplined prioritization. [Music] >> Like turning a messy room into a usable workspace. You're not denying that the room contains many things. You're arranging them so work can happen. >> Yes. Or think of a dashboard that shows fever but not the disease. A good analyst does not just keep adding more thermometers. They help the organization understand what the fever means, what might be causing it, and what decision is needed next. >> That's where a lot of reporting fails. It presents everything as equally important.
>> And when everything is equally important, nothing is operationally important. So the leadership question becomes, can we summarize what the data means? >> Yes. If the organization cannot summarize what the data means, it probably has not understood it yet. >> That is brutal but useful >> because vague data conversations often hide unresolved conflict. People ask for another cut of the data because they do not want to name the trade-off >> or because they don't trust the interpretation. >> Right? [Music] Which brings us to access versus judgment. Data democratization sounds good [Music] and it is good, right? >> It can be. Giving people access to data is useful, but the article makes the point that access alone does not create insight.
>> People need to know how to ask better questions >> and how to interpret signals and how do you understand [Music] context and how to connect analysis to business reality. >> So there are really three layers. Can people access the data? Can they find the right data? And can they understand how it fits into the business context? >> Exactly. Many organizations overinvest in the first layer and underinvest in the third. >> They build the data platform. >> They grant permissions. >> They launch dashboards >> and then they wonder why decisions do not improve >> because the missing layer is judgment. [Music] >> Yes, judgment is knowing which data matters, what it cannot tell you, where it came from, what incentives shaped it, and what decision it should inform. That feels like a capability, not a tool. >> That's exactly it.
You cannot buy judgment the same way you buy analytic software. >> You have to develop it >> through practice, context, feedback loops, and accountability. >> This is where I think the article becomes very practical for leaders because the final question is blunt. What does your data change? >> Yes. Not what does it show, not how much of it do you have, not how modern is your stack, >> what does it change? >> Does it change priorities? Does it expose risks? Does it shorten feedback loops? Does it improve customer understanding? Does it sharpen tradeoffs? Does it make weak assumptions harder to protect? >> And if the answer is mostly, well, we have more visibility, then the work is unfinished. >> Visibility is not the destination. It is the beginning. >> So, let's turn this into leadership questions listeners can use.
A leadership diagnostic
>> First, when a key metric changes, what actually happens? >> Does someone investigate? Does a decision meeting happen? Does the road map change or does the metric just get explained away? >> Second, which decisions are our dashboards supposed to improve? >> That one is powerful because if a dashboard is not attached to a decision, it may just be decoration. >> Third, where are we asking data to predict the future before we've made the present observable? >> That's the forecasting theater question. >> Fourth, what problem are we assuming we have? And what evidence would force us to rename it? >> Exactly. That question creates room for reframing. >> Fifth, can we state the important truth first? >> Not 20 charts in, not after three pre-ereads and a caveat parade. >> The important truth first then the support.
>> And sixth, do our people have judgment or only access? >> Because access without judgment [Music] can create more disagreement, more confusion, and more false confidence. >> This is the uncomfortable message of the article. The goal is not to ignore big data. The goal is to stop worshiping bigness. >> Useful data earns its place by improving decisions. >> And the strongest organizations do not merely collect more information. They build systems where the truth has somewhere to go. >> That line is the whole episode. >> It really is. Because if truth has nowhere to go, the dashboards will keep glowing, the reports will keep circulating, and the organization will keep mistaking visibility for progress. >> The smoke detector will keep beeping, >> but no one will fix the wiring.
>> And by the time the dashboard turns red, the real failure already happened. >> So the next time your organization asks for more data, pause and ask the harder question. >> What decision are we avoiding? >> What truth are we trying not to name? And what would change if we finally believed the information we already have? >> Because the problem may not be that you need more data. >> The problem may be that your data is still waiting for somewhere to go. Thank you for tuning in to this episode of The Focal Point. Be sure to subscribe on your favorite platform. [Music] I am your host Filiberto De La Cruz. Until next time, [Music] God bless you and yours and everyone. [Music] [Music]
