Part II of The Art of Data War — Knowing Your Position, PRAISE Framework. Previously:
“The clever combatant looks to the effect of combined energy, and does not require too much from individuals.” ~ The Art of War, by Sun Tzu.
An organization’s data capability is only as durable as the structure that holds it. Hire strong people and attach them to tasks, and the work walks out with them. Build teams around focus areas, and their innovation and built capabilities compound through turnover. The same holds for leadership: each new data leader brings a philosophy worth learning from; strategy doesn’t shift with every hire. Management that is aware and involved commits with a clear view of impact, risks, costs and upside — and a strategy held that way outlives the leader who brought it.
Skills & Continuity, the Resource dimension of PRAISE, reads how durable that structure really is: whether the organization can attract and keep data talent, whether it holds beyond individual experts, and whether capability is spread wide enough that the whole business can act on it. The three signals run in order; each builds on the one before. Few companies show what durable resourcing looks like at scale better than Intuit.
Intuit — Resourcing durable data capability at scale
Intuit offers a strong example of how organizations can resource data capabilities in a way that survives growth, turnover, and changing business priorities. Beginning in 2007, under founder Scott Cook, Intuit launched Design for Delight (D4D), an operating model that embedded experimentation, customer data, and rapid learning into how products were built. Intuit treated it as a business discipline from the start. The company invested heavily in hiring and upskilling data scientists and analysts, made data literacy a baseline expectation across teams, and has since trained more than 1,500 employees as Innovation Catalysts: internal coaches who spread experimentation and data-driven practices throughout the organization.
Intuit also designed its organization to avoid reliance on individual experts. Experimentation platforms, standardized metrics, and shared analytical tooling let teams generate insights without depending on a small set of specialized analysts. By 2012 the company was running over 1,300 experiments a year, across every function from product to legal, and maintained the pace. The program outlasted two changes of chief executive, in 2008 and 2019, and is still active today. Knowledge lived in systems, processes, and teams, not in personal spreadsheets or tribal memory.
Data teams sat close to business execution: analysts and data scientists worked product reviews, growth discussions, and pricing decisions directly, while business teams were trained to explore data themselves, a structure Intuit still hires for today. When Intuit’s finance team discovered that 25% of QuickBooks customers weren’t updating payment information on time, contextual research revealed emails weren’t reaching the right people. Through rapid experimentation, they fixed the communication gap and recovered approximately $8 million annually. Between 2010 and 2020, Intuit more than doubled revenue while expanding operating margins, growth that leadership repeatedly attributed to faster learning cycles and better customer insight. Other forces worked in the same window: the shift to cloud subscriptions, a tax-preparation market that kept growing on its own, and the acquisition and integration of new businesses, from Mint in 2009 to Credit Karma in 2020. Scott Cook has also stood behind D4D for its entire life; continuity that has never lost its founding sponsor is a postponed test, not a passed one.
The Signals to Look For
Read the case back and three structures carried it. Intuit gave its data talent work worth staying for, and kept training more of it. It moved knowledge out of individual experts into platforms and standards its teams owned together. And it spread capability across the business, from product reviews to pricing decisions. The three signals of the Resource dimension read for exactly these, one at a time.
1. Is the organization able to attract, upskill and retain data talent?
What to look for: Does the organization invest in senior data leadership with real authority? Is the firm able to attract top talent in the data space (people with strong track records) and position them for success? Are there visible programs that help people grow their skills and take intelligent risks? And critically, do talented data professionals stay and grow with the company, or does turnover tell you the opportunity isn’t delivering?
Attracting strong data talent starts with credible leadership at the top. Organizations serious about data and AI invest in a Chief Data or Chief AI Officer who can navigate the complexity of technology, business strategy, and organizational change. The investment itself announces, to the market and to internal teams, that data is treated as strategic. Even more importantly, when that leader articulates a compelling vision, the vision becomes a formidable recruitment tool. While compensation and titles play an important role, top talent also seeks opportunities where they can grow and have high chances of success. They join because they believe the problems are worth solving and the organization is committed to solving them well.
Retention was one of the hardest problems I watched hedge funds fight, and it concentrated in the central data engineering and data science teams. For a new hire it is the most exciting job in the world: they arrive expecting to hunt alpha signals and build trading models. What they find is custom data pipelines to build, and to maintain on weekdays and weekends. Some of these hires are engineers at heart, drawn to design patterns and pure engineering problems; others came for the data, the analysis, and the sector background. Pooled into one team and attached to whatever needs doing, both kinds leave, and no compensation review fixes it, because the job was never the one they came for. Structured around focus areas, the same people flip: teams own their ground and innovate on it, and upskilling happens naturally as people take on harder problems and stretch into new capabilities. That ownership generates tremendous business value, from central data platforms and well-maintained knowledge bases all the way to the MLOps and guardrails for spinning off new predictive models.
The restructuring I saw work drew exactly that line. The central organization was rebuilt into focus areas: a platform team building the central data management platform; data engineering teams using it to onboard datasets, learn them, and work use cases with the portfolio managers; data scientists forming hypotheses and answering investment questions with the data and tooling made available to them. After the change, retention issues all but disappeared. Retention follows naturally when people are growing: taking on bigger problems, seeing their work drive business decisions, and building capabilities that compound over time. The company scales, new challenges emerge, and people grow into larger roles without needing to leave. When attrition becomes a pattern, especially among high performers, it tells you something has broken: the work isn’t producing meaningful results, leadership credibility has eroded, or the organization stopped creating opportunities for growth. The reasons to stay are built, not offered.
2. Is the organizational structure resilient beyond individual experts?
What to look for: Are critical data and business capabilities dependent on a few key individuals, or can the organization continue functioning when those people are unavailable? Look for shared tooling, accessible knowledge bases, standardized processes, and governance protocols that create transparency and eliminate dependency on specific individuals.
Key-person dependency, commonly referenced as the “no heroes” dilemma, is one of the most common failure modes in data organizations. When critical work, a revenue forecasting model, a customer segmentation pipeline, the knowledge of how legacy systems connect, depends entirely on one or two individuals, the organization becomes fragile. When that person goes on vacation, progress halts. When they leave, it creates panic and forces non-optimal knowledge transfer protocols. Projects stall, deadlines slip, and institutional knowledge evaporates.
The issue isn’t that experts exist; every organization needs deep expertise and specialized skills. The problem is when that expertise isn’t integrated into the broader system. A strong data organization takes what experts build and makes it part of the larger intelligence. This means decomposing complex work into manageable components with independent lifecycles. Consider a predictive analytics initiative: it might involve scraping external data, building ingestion pipelines, enriching the dataset with a semantic layer, developing a predictive model, and inferring results in product. Each of these components has its own development, deployment, and operational requirements. When structured well, dedicated teams can focus on managing those parts correctly, ensuring automation, quality, and reliability.
This structure serves the expert as much as the organization. Experts spend their time on the novel work they came for, the analysis and the strategic insight, instead of rebuilding foundational components every time. They work on platforms with reliability built in, freed from operational burdens. As they become more productive and deliver greater impact, they’re better positioned for recognition and advancement. They’re not stuck maintaining what they built; they’re solving the next problem.
For the organization, this approach ensures continuity when people move on, accelerates innovation by avoiding redundant work, and reduces cost by building shared capabilities once rather than repeatedly. It eliminates hero dependency and makes the organization far more attractive to new talent, who can see they’ll be set up for success rather than starting from scratch. Knowledge compounds, systems improve over time, and the organization grows stronger with each hire rather than more fragile.
3. Are data capabilities democratized across the organization to enable cross-functional execution?
What to look for: Can business teams explore data and answer their own questions without waiting for a central data team? Do data professionals participate directly in business strategy discussions, embedded where decisions are made and analytical capabilities are needed most? Does this structure enable faster innovation across the organization?
Whether business and mid-office teams can explore data and answer their own questions is a resourcing decision, and it only works with the right foundation. Self-service needs an open, capable platform with integrated tools that make data accessible without demanding specialized expertise for every operation. It also needs enabled employees, through training and accessible knowledge systems, or through hiring data-literate professionals from the start. Quantitative investment firms hire this way across all roles, expecting everyone to work with data directly. When this capability is missing, every question becomes a ticket, every experiment requires a specialist, and innovation slows to a crawl.
Equally important is ensuring data professionals are embedded directly in business execution, not isolated as infrastructure support. Data teams need access to strategy discussions, business debates, and the problems being solved in real time. This doesn’t mean abandoning centralized platforms; in fact, strong central platforms enable embedded teams to move faster. But proximity to the business is what allows data expertise to shape outcomes rather than just measure them. The quiet failure mode is the reserved task: analysis one team holds against another, capability treated as territory. Held that way, its strategic value diminishes; resourced as shared muscle, it compounds.
When both elements work, business teams empowered to explore data and models, and data professionals embedded where decisions are made, innovation accelerates across departments. Teams ideate and experiment faster, insights surface earlier, and execution becomes cross-functional rather than siloed. Organizations that structure around this principle create compounding advantages. Those that don’t find themselves perpetually behind, waiting for insights that arrive too late to matter.
AI has collapsed this gap from both directions at once. Business teams can now run analysis that once required a specialist: the question that used to sit in a ticket queue gets explored the same afternoon, against governed data, with guardrails the platform enforces. Engineering teams gain the other half: through AI and agents they carry a working knowledge of the business context they build for, and they innovate inside it instead of waiting to be briefed. Central engineering still has to be thoughtful about how content is organized and cataloged; agents are only as good as what they can find. None of this replaces deep expertise. It deepens what specialization earns: when every team can reach the data and the context, the experts’ work travels further, and the gains compound where the two meet.
Now read your own organization. Does your data leadership inspire the roadmaps and the hires, and does the work people came for match the work they do? Does anything critical stop when one person is unreachable? Does a question from the business become an exploration, or a ticket? The three answers are a position on the Resource dimension, and unlike the people who carry your capability today, a position holds only as long as you maintain it. Structure is the part of the resourcing and talent strategy that nobody resigns from.