Part II of The Art of Data War — Knowing Your Position, PRAISE Framework. Previously:
“The good fighters of old first put themselves beyond the possibility of defeat, and then waited for an opportunity of defeating the enemy.” ~ The Art of War, by Sun Tzu.
Every firm will meet a shock it did not choose. How it comes through was settled long before, in calm years, by what it built while nobody was asking for it. A firm built for change absorbs the shock, and gains the ground its competitors lose while they rebuild.
Adaptation & Resilience, the Evolve dimension of PRAISE, addresses whether your organization and data infrastructure can withstand external shocks and adapt to forces beyond your control. You’ve prioritized data strategically, resourced it with talent, accounted for what you have, integrated it into business operations, and stabilized systems to run reliably. All that internal discipline still has to evolve when technological disruption obsoletes your competitive advantages, when regulatory shifts demand a new architecture, or when market dynamics put pressure on your viable economics.
BlackRock — Aladdin built in calm and for advantage
BlackRock’s founders bet that complexity would overwhelm fragmented systems. The 1987 crash exposed Wall Street’s inability to coordinate during rapid market moves: portfolio insurance and program trading created cascading failures when disconnected tools couldn’t keep pace. BlackRock saw the integration tax: firms stitching together best-of-breed vendors spent enormous effort on manual reconciliation. They also bet that platform economics would favor unified infrastructure. Starting 1988, they built Aladdin internally, a contrarian choice when vendor solutions seemed safer. The 2008 crisis tested the bet. The New York Fed hired BlackRock to manage the assets it took on from Bear Stearns in March, and then the AIG portfolios, which Fed-funded vehicles bought at prices based on BlackRock’s own valuations. By 2025 BlackRock was earning $2.0 billion a year from technology services and subscriptions, most of it from Aladdin.
BlackRock built Aladdin from modular systems and common data semantics that could expand across asset classes without expensive rewrites. The platform started with fixed income in 1988, then absorbed equities, derivatives, real estate, and alternatives as markets evolved, adding some of them through software it bought and folded in, such as an equity system in 2008. When regulatory shifts arrived, from Basel requirements to Dodd-Frank reporting mandates and MiFID II transaction reporting, the modular design was built to absorb new compliance obligations through configuration rather than reconstruction.
Building in-house kept vendor dependencies few. While competitors relied on third-party risk systems that could disappear through acquisition or obsolescence, BlackRock had the scale and the focus to own its own destiny, which few firms have. Aladdin still draws its data from many sources, exchanges, brokers and custodians among them, and BlackRock’s own annual report lists its dependence on third-party data and cloud providers as a risk to Aladdin’s resilience. By 2013, about $15 trillion of assets ran on Aladdin, among them those of direct competitors who pay BlackRock for access. The platform maintains 98% client retention rates precisely because switching costs are prohibitive.
The Signals to Look For
Read the case back and it shows three things. BlackRock saw the change coming, which bought it time to build. It built so that new asset classes and new rules arrived without major infrastructure resets. And it kept its dependencies few, the narrow case of adapting, where the change is a loss. The signals below look for each in your firm.
1. Are technological and competitive threats seen coming, before they obsolete an advantage?
What to look for: Does your firm watch the outside world on purpose, through research firms, brokers, venture capitalists, startups and candidates? Does what it learns reach someone with the budget and the mandate to act on it? Is what it builds today made to be monitored, measured and replaced?
Are you watching the horizon, or staring at your feet? Sometimes the status quo seems stable for years, but somewhere, someone is working on the next major thing that will reshuffle the market, dethroning established players or creating new winners. Disruption emerges quietly in research labs, competitor roadmaps, or adjacent industries until suddenly it’s unavoidable. The test came when ChatGPT launched in November 2022: some organizations went into panic mode, scrambling to understand what it meant, while others were already preparing product launches and offering AI consulting to clients. Morgan Stanley was one of them: its wealth management arm had been building on OpenAI’s models since 2022 and announced an assistant for its advisors on GPT-4’s launch day in March 2023. The work had started while nobody was asking for it.
Disruption comes from multiple directions. Technology shifts fundamentally alter what’s possible. Quantum Machine Learning holds immense disruptive potential for data and prediction: it promises to automate the discovery of complex data relationships, shifting the focus away from manual feature engineering as a legacy methodology. Vendor changes create sudden advantages. By 2017, satellite providers were estimating oil inventories from space, and funds using the data had each week’s estimate hours before the government published its own figure. Competitive moves force response. Market makers continuously enhance predictive models to quote microseconds faster, dominating order flow and forcing rivals to match their infrastructure investments or cede ground. Each advancement resets the baseline—yesterday’s cutting edge becomes tomorrow’s table stakes.
A firm stays ahead by building deliberate organizational antennae. Research firms like Gartner and Forrester track emerging technology trends and investment patterns. Brokers and market facilitators bring competitive intelligence to their clients. Interviews and meetings with venture capitalists and startups reveal what’s being funded and built and which cross-sector trends are applicable. Interviews with candidates are a source as well. Their achievements show what is being built elsewhere, their frustrations show where the competition stands, and their skills show which tools are being adopted.
With this information in hand, a firm needs the internal capacity to translate it into strategies, which Prioritize covered. Integrate described the need for dedicated innovation budgets: reserved personnel experimenting with emerging technologies, time allocated for exploration, partnerships with universities and research labs testing new approaches, or even strategic stakes in promising companies to learn from their innovation while capturing potential upside. These investments require clear, measurable KPIs with different lifecycles: some for short-term operational improvements or competitive adjustments, others for long-term planning for your organization’s evolution.
The pace is now set by the AI frontier labs that build the models, and anything built on today’s model can be overtaken by the next. A firm builds with confidence by building to monitor, measure and swap, like a well-run factory where any station can be refitted. Signal 2 asks whether the infrastructure is built to support this goal.
2. Can the data infrastructure adapt to regulatory shifts, technological disruption, or new data products?
What to look for: Do your technical architecture and organizational structure absorb change? When regulatory enforcement shifts, technological disruptions emerge, or new data products appear, do platform modularity and operational abstractions enable rapid adaptation without requiring heroic effort or a complete re-platformization?
A new rule shows quickly whether a firm can adapt. When Dodd-Frank passed in July 2010, financial institutions faced 243 new rulemakings, by Davis Polk’s count, with implementation timelines measured in months. Meeting them was expensive and slow for every bank, and slowest for the ones whose data lived in silos. Goldman Sachs had run SecDB since the 1990s, aggregating risk across the whole firm on one platform. A firm with that kind of unified semantic layer could turn the new rules into manageable reconfiguration. Firms trapped in fragmented silos met them by hand. Without standardized data schemas, matching positions required an army of manual reconciliations every quarter. The consequences were public: major financial institutions failed stress tests by falling below capital minimums, while others received qualitative objections for deficient risk management processes.
Technology shifts test the same thing. Over the decade after 2008, cloud migration shifted from experiment to competitive necessity. Firms with decoupled modules and microservices migrated components incrementally, phasing storage, compute, and data pipelines into elastic environments without halting operations. They gained autoscaling, eliminated hardware management overhead, and tapped into new cloud-native services while competitors were deterred by misunderstood risks and unclear ROI. Monolithic applications faced a rigid “all-or-nothing” migration path. Analysis paralysis over deeply coupled dependencies often didn’t find motivated owners. The executives who would have owned the move were intimidated by the load of anti-migration complaints and by the political and technical work, and the paralysis ultimately justified staying put, a choice that would haunt those firms a decade later. Organizations that delayed migration now face a compound choice of rebuilding their legacy applications while simultaneously adopting AI capabilities, and the gap to the firms that moved widens every year.
A new kind of data tests it a third way. When healthcare alternative data reached the investment market, HIPAA and GDPR made on-premises storage a significant compliance bottleneck. Firms with modern architectures bypassed this through data clean rooms on outside platforms like Snowflake and AWS. They analyzed sensitive datasets inside the clean room, a compliant third-party environment, without ever “taking possession” of the raw files, which eliminated the need for local egress and complex de-identification pipelines. Many firms, unable to build the necessary guardrails in time, simply abandoned data that could have made them money, ceding alpha to competitors.
3. Are there contingency plans for the vendors, data sources, and platforms the firm depends on?
What to look for: Do you have single-source dependencies on critical external data, undocumented assumptions in models, release-timing fragility, or teams unable to operate without specific feeds? Is there evidence of redundancy: alternative providers, proxy signals, scenario modeling, and processes designed to continue decision-making when primary data disappears?
LSEG completed its purchase of Refinitiv in January 2021, in an all-share deal that valued Refinitiv, debt included, at about $27 billion. Clients on the front line witnessed aggressive commercial escalation. Renewal negotiations turned combative, with unprecedented price increases justified by original deep discounting strategies that now felt like traps. There were early signs of the trouble ahead, visible to any client before the deal: legacy products like IBES, StarMine, and Lipper, acquired by Reuters and Thomson over the years, were never properly “digested” and operated in expensive silos. Tick History, the tick data service, stopped innovating, losing ground in a commoditized space to more nimble operators. Recurring “strategic” API migration mandates had already created client fatigue and frustration. In response, hedge funds with dual sourcing, pulling tick data directly from exchanges or alternative aggregators, maintained leverage and could walk away. Similarly, firms using abstraction layers were able to replace LSEG without disrupting downstream users. Firms that depended on LSEG’s proprietary datasets and had no contingency plan were left to choose between absorbing unsustainable costs and decommissioning strategies entirely.
Cloud adoption brings lightweight operation, ROI efficiency, and speed of innovation. But cloud engineering brings its own risks, and availability is the one most firms underestimate. While cloud services advertise 99.5% to 99.99% uptime, the remainder can arrive at the worst possible moment. Multi-region and hybrid architectures, data replication strategies, and chaos engineering often sound like ambitious over-engineering during calm periods. Then a crisis strikes. On December 7, 2021, a networking impairment in AWS US-EAST-1 took down the controls customers use to launch servers and change their DNS, during US market hours, and investment firms whose operations or failover needed those controls lost trading capacity. Market positions went stale, alpha opportunities evaporated, and service credits couldn’t recover lost revenue. The organizational dynamics followed a predictable pattern: business leadership escalated to technology, demanding explanations for the outage. Some technology leadership escalated to cloud vendor management, demanding accountability—some of it deserved. But others turned the lens inward: the shelved availability engineering projects became top priorities.
Macroeconomic visibility across industries rests on a surprisingly small set of government statistical pipelines. In the United States, indicators such as employment, inflation, retail sales, and GDP originate from agencies like the U.S. Bureau of Labor Statistics, the Census Bureau, and the Bureau of Economic Analysis. During the federal government shutdown of October 2013, these pipelines stalled. Surveys were suspended, collection stopped for sixteen days, and releases were delayed. Economists, corporate planners, and policy analysts navigated the economy with partial instruments: models calibrated on indicators that were no longer updating, on the assumption that the government’s numbers would always arrive. The disruption exposed a divide between organizations treating macro data as a single authoritative feed and those that built redundancy into their economic intelligence. Institutions with alternative indicators, such as payroll processor data, rail and freight activity, and regional Federal Reserve business surveys, continued producing internal nowcasts. Others waited for Washington to reopen, and lost the informational edge to competitors still measuring the economy in real time.
Now read your own organization. Who saw the last big shift coming, and through which channel? Did the last new rule, technology or kind of data land without a major infrastructure reset? Which data source, vendor or platform could you lose tomorrow, and what would you run on instead?