Part II of The Art of Data War — Knowing Your Position, PRAISE Framework. Previously:
“The control of a large force is the same principle as the control of a few men: it is merely a question of dividing up their numbers.” ~ The Art of War, by Sun Tzu.
A firm can have data teams, dashboards, a data budget and a seat for its CDO, and still be running around its data rather than on it. The decisions that matter get made, and the analysis arrives afterward to support them. The data bill is paid centrally, so nobody asks what any dataset returns against what it costs. Each new team builds the stack it needs, and the firm carries the cost of building the same thing again. his dimension asks three questions: are business decisions data-led, are data assets managed for their value, and do platform returns compound across the business?
Business Enablement, the Integrate dimension of PRAISE, assesses whether data is woven into the organization’s DNA: embedded across the business in how decisions are made, strategies are formed, and value is created. Organizations with strong integration generate strategies from data, not just validate them. Data teams participate where questions are formed, not where they’re answered. Business units connect their data investment to business outcomes and hold their data assets accountable for returns, not just delivery. Without shared standards and unified capabilities, effort gets duplicated across departments rather than compounding into platform advantage that scales across the organization.
Netflix — Running on data from the start
Marc Randolph and Reed Hastings founded Netflix in 1997 and put it online the following year, and from the first rental the company tracked every search, rating and return. Blockbuster, renting the same films from nine thousand stores, could not see any of it. That advantage guided the 2007 streaming launch: while competitors dismissed internet video as premature, Netflix’s behavioral data showed customers would abandon DVDs for convenience. By 2012, recommendations drove roughly three-quarters of what subscribers watched, turning user behavior into decisions the business made at scale.
Netflix held its data organization accountable for measurable returns. The recommendation engine delivered over $1 billion a year in retention value, keeping subscribers engaged and churn among the lowest in the industry. But Netflix tracked costs just as rigorously. Data showed what streaming cost per view and where it could fall, so the company built Open Connect, its own delivery network, placing caching servers inside internet providers’ own networks. It completed a seven-year migration to the cloud in 2016, and its engineers reported cloud costs per streaming start “a fraction of those in the data center.” Efficiency dashboards allocated every dollar of infrastructure spending to the teams and services that incurred it, and the cleanup that followed retired tens of petabytes nobody was using.
The platform Netflix built for streaming carried it into original content. The talent arrived packaged from an outside studio. What Netflix supplied was the audience: its subscribers had already shown they watched political dramas, and they watched Fincher and Spacey. It committed $100 million to House of Cards in 2011, before a frame was shot. The show arrived in 2013 and the slate scaled behind it, each new title inheriting the infrastructure rather than rebuilding it.
A disciplined buyer is taken seriously, even with the lower offer. When Warner Bros Discovery weighed its sale, Netflix’s $83 billion offer was the lowest headline bid on the table, and the board chose it first, on value and certainty. And when Paramount’s bid climbed past what the numbers could carry, Netflix walked: the same discipline that priced a $100 million show priced an $83 billion one. Netflix had one advantage most firms did not: it started on data and never had to change course to get there.
The Signals to Look For
Read the case back and three things carried it. Netflix decided from data before the market agreed, on the launch that remade the company. It held what its data cost against what its data returned, and it knew both numbers. And it built one platform, so every new business it entered ran on what was already there. The three signals of the Integrate dimension read for exactly these, one at a time.
1. Are business decisions data-led?
What to look for: Do your decisions originate from data insights, not gut feelings justified afterward? Are data capabilities built into your regular operating rhythms: reviews, planning cycles, resource decisions? Do business units have the governance and independence to challenge leadership’s assumptions with data? Can teams get their own insights without gatekeepers, and test and iterate fast?
In investment management, sell-side analysts maintain privileged access to companies they cover, conducting management meetings and site visits that shape their research narratives. But this traditional insight channel is now complemented—and sometimes challenged—by alternative data sources that reveal what companies won’t disclose: satellite imagery tracking construction completion rates, sentiment analysis detecting shifts in earnings call tone, social media signals predicting consumer behavior. Advanced analytical tools democratized what used to require exclusive relationships. Research teams that built alternative data into their own research set themselves apart: their analysts went into management meetings already holding a view, and their clients got sharper insight. The firms gaining competitive advantage are those embedding analytical capabilities directly into investment workflows—using data to originate investment decisions, not simply validate them after the fact.
In data-driven organizations, data capabilities are embedded in regular operational rhythms: automated decisioning for routine choices, real-time dashboards powering recurring reviews, analytics frameworks guiding planning cycles. In these organizations, business units are empowered to make decisions independently using defined data governance, challenging assumptions from leadership with evidence. Beyond supporting decisions, embedded data capabilities systematically trace performance and generate new opportunities: identifying underserved customer segments, detecting operational inefficiencies, surfacing market shifts before competitors notice. Teams self-serve insights, enabling rapid testing and iteration, the democratized capability Resource describes.
When data comes as an afterthought, commitments are made first and data teams are asked to validate decisions already made. Numbers get cherry-picked to support predetermined narratives. When a marketing campaign underperforms, leadership moves to the next initiative without understanding what failed. Data professionals become order-takers—producing reports nobody questions or acts upon. Analysis arrives too late to influence strategy, serving only to document choices already in motion. Business units lack self-service access, waiting days or weeks for centralized teams to produce insights. By the time analysis arrives, the opportunity has passed or the mistake has compounded.
2. Are data assets managed for their value?
What to look for: Do your data investments show clear business returns? Can you see what each data asset costs and what it earns? Do chargeback or P&L models make those costs visible, so high-value assets get priority and low-value ones get retired? Do you track vendor ROI well enough to renegotiate or walk away from underperforming products? Do R&D budgets keep exploratory work apart from production, with the business involved so each initiative graduates to operations, gets sunset, or delivers strategic learning?
The Account dimension covered seeing what data assets cost and what they are worth. But tracking alone doesn’t drive discipline. Mature firms embed ROI discipline directly into business decisions: which data assets to license or build, which platforms to develop and manage, which business lines to continue or sunset, and where to invest next. Many choose to treat data services as P&L centers, where business units “pay” for what they consume. Both chargeback and direct pricing models make storage, compute and data asset costs visible, forcing conscious trade-offs. How those models are built is the Value Framework’s subject, later in the book. Business units carry the cost, which keeps them lean and competitive, and the data organization is free to put its time and investment into the capabilities that matter. Data stops being hidden corporate overhead and becomes part of the true cost of doing business.
ROI visibility gives negotiation leverage with data vendors, who price opportunistically when they don’t know the value their data creates. Global financial market data spending crossed $30 billion for the first time in 2018 and reached $49 billion by 2025, a surge of over 60% as the uses for market data multiplied. Without clear ROI tracking, firms couldn’t distinguish high-value feeds from noise, and bloated budgets funded vendor growth rather than selective buying. As the Account dimension showed, firms that track returns can renegotiate contracts, walk away from underperforming feeds, or reallocate budgets to higher-return investments.
Some data investments are bets whose return comes later. Firms allocate dedicated budgets for data R&D: exploring new tools and modeling approaches to optimize operations, enable new business lines, or simply stay ahead of disruption. The wave of enterprise AI investments in 2022–2023 was a good example: many firms invested defensively, needing to understand the potential impact before competitors did, without a clear immediate return. Some R&D initiatives carry defined success criteria: fail fast or graduate to production. Others run on longer, more strategic or educational horizons, the balance of bets Prioritize describes. Either way, the business has to be involved: ensuring exploration stays grounded in real problems, learnings get translated into opportunities, and promising capabilities find their way into operations rather than remaining academic exercises.
3. Do platform returns compound across the business?
What to look for: Does the whole business run on one central platform, so every improvement lands in every business at once? Do business units share common semantic layers and platforms, not separate stacks per division? Do new teams inherit existing infrastructure without the need to build their own from scratch? Can talent move freely between units without retraining on different systems?
Successful firms make deliberate foundational investments in data and platforms aligned with their business strategy. When business units lack those investments, they develop tactical solutions to make things work, creating silos that harden over time. Management loses direct access to key metrics, and getting one view of the business becomes a manual exercise of stitching together incompatible systems. Dismantling silos later proves extraordinarily difficult—disrupting operational routines, invalidating institutional knowledge, and meeting fierce resistance from teams defending their investments. The alternative requires a common semantic layer, one set of definitions for the customers, products and positions every unit works with, plus centralized platform capabilities that units inherit rather than rebuild, avoiding duplicate spending. That layer matters more now, when firms are looking to extend their capabilities through AI. An agent put to work on the business is only as good as the shared meaning it can reason over. With that layer in place, AI brings more digested context into decisions than any analyst team could supply, and the decisions stay with the people who make them.
Beyond centralized capabilities, successful firms foster institutional transparency around existing and planned initiatives, so every unit knows what the others have built. Independent business units see what’s available and what’s coming, aligning themselves to benefit while maintaining their perspective as internal clients, judging whether capabilities serve their needs. Platform investments compound naturally: building recommendation capabilities benefits content, commerce, and customer service simultaneously rather than each funding redundant solutions. Cross-business insights emerge as teams share learnings, discovering that operational improvements in one market apply to others.
Two multi-strategy managers show what that compounding is worth. At the first, every new pod built its own stack, up to a year of work before it could trade. At the second, one platform was already there: new pods traded within months of being hired, costs were lower, and portfolio managers spent their time finding alpha. The business side ran at better odds, and spent its own time finding the next pod. The same holds true outside hedge funds. Platforms like Focus Financial let boutique wealth management firms inherit institutional scale the day they join: vendor rates a five-person firm could never negotiate alone, compliance infrastructure that takes operational overhead off the advisors so they can spend their time with clients, and investment platforms with institutional-grade capabilities. Each firm that joins adds to the buying power and the shared infrastructure every other firm draws on, which is the compounding at work.
Now read your own organization. Did the last significant decision start from the data? Can you say, for any data asset that you pay for from your P&L, what is its contribution to your top line? Does every new product, team or business line launch driven from insights and operational capabilities the firm provides? Three yeses mean the business runs on its data. Most firms will find at least one no, because unlike Netflix they have to change course to get there, and each no marks where to start. The gap to the firms already running on data compounds for as long as it stays a no.