--- title: "Decision-Making" description: "7 posts about Decision-Making, summarised, each linking to the original." --- # Decision-Making > 7 posts about Decision-Making, summarised, each linking to the original. ## Articles ### [Enterprise Data Strategy Roadmap for Business Outcomes](https://yomu.fyi/post/enterprise-data-strategy-roadmap-for-business-outcomes.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: Jun 8, 2026 An enterprise data strategy connects organizational data assets to measurable business outcomes, while fragmented architectures can leave data investments uncoordinated and limit real-time analysis and action. The roadmap starts with purpose, scope, executive sponsorship, measurable objectives, KPI mapping, and use-case prioritization based on business impact, feasibility, time to value, and organizational readiness. It then organizes governance, lifecycle management, data quality, target-state architecture, integration, analytics, team structure, compliance, and measurement as interdependent capabilities, emphasizing owners, stewards, decision rights, executable quality rules, and automated cleansing. Implementation proceeds through a time-boxed cross-functional pilot, documented learnings, and incremental scaling, with steering-committee oversight and governance that evolves through feedback. The text gives indicative timelines of 60 to 90 days for a focused pilot, 12 to 18 months for a foundational platform across multiple business units, and multiple years for a mature data-driven culture. ### [From emissions reporting to decarbonization decisions](https://yomu.fyi/post/from-emissions-reporting-to-decarbonization-decisions.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Caitlin Gordon - Published: May 21, 2026 Energy companies have built sophisticated infrastructure for Scope 1, 2, and 3 emissions reporting across complex asset portfolios, but that infrastructure is often oriented toward historical disclosure rather than forward-looking action. Sustainability leaders still need timely answers about which assets warrant intervention, what operational choices drive carbon intensity, and whether current performance is tracking toward commitments. Databricks Genie addresses this gap by letting users query emissions, operational, and financial data in natural language, linking results to dispatch decisions, fuel purchases, and asset utilization. It supports multi-scope analysis, traceability to source data, and scenario questions such as retiring an asset or increasing renewable PPA volume. The stated outcome is a shift from compliance reporting toward data-driven decarbonization decisions, with Genie available today for energy-sector use. ### [Operating room utilization is hiding in your scheduling data](https://yomu.fyi/post/operating-room-utilization-is-hiding-in-your-scheduling-data.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Adam Crown - Published: May 8, 2026 Operating room utilization measures in-room surgical minutes against allocated block-time minutes, yet most US health systems reportedly run at 65–75% versus an 80% industry target. Daily performance reports arrive the next morning, after schedules are set and opportunities to release unused blocks, redeploy staff, or backfill add-on cases may have passed. The post presents Databricks Genie as a natural-language interface for querying scheduling, utilization, and outcomes data without a data analyst request. Its proposed analytical environment combines scheduling data, actual case logs, block-release records, contribution margin, and staffing costs, with breakdowns by surgeon, service line, facility, and day of week. Genie surfaces specific intervention targets, although the post says it provides data access rather than automating OR management. ### [When predicting the next hit requires more than intuition](https://yomu.fyi/post/when-predicting-the-next-hit-requires-more-than-intuition.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Elena Tesser - Published: Apr 30, 2026 Content investment decisions in entertainment commit substantial funds despite limited information, because leaders often combine executive instinct, competitive benchmarks, and historical performance data. Although companies hold viewing and subscriber signals such as episode completion, skip patterns, genre performance by demographic, acquisition, and retention, those insights are frequently buried in dashboards and spreadsheets. The post presents Databricks Genie as a natural-language interface that lets content leaders query governed performance data directly, including comparisons linking content types with 90-day retention, subscriber demographics, lifetime value, acquisition source, and historical greenlights. It argues that current, conversational access can complement creative judgment by reducing dependence on delayed analyst recommendations, while external market data can add competitive context. ### [How conversational analytics removes the BI bottleneck](https://yomu.fyi/post/how-conversational-analytics-removes-the-bi-bottleneck.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Catherine Brown - Published: Apr 22, 2026 The interview presents conversational analytics as a way to move beyond fixed dashboards and turn governed data into decisions and workflows. Databricks’ Genie lets nontechnical users ask open-ended questions in plain language across Lakebase, Lakehouse, and federated sources, while Unity Catalog applies shared definitions and access controls. Lakebase provides transactional storage, instant provisioning, environment forking without copying underlying data, and scale described as billions or trillions of records; examples include real-time matchmaking, routing, and purchase decisions. The discussion stresses that human oversight, refreshed data, business context, and controls against hallucination remain necessary, citing Fox Sports’ chatbot deployment as an example. It concludes that organizations combining trusted semantics with operational data systems can make faster decisions and automate work, while the competitive gap may compound for slower adopters. ### [Make Better Decisions by Embracing Uncertainty](https://yomu.fyi/post/make-better-decisions-by-embracing-uncertainty.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: Ryne Carbone - Published: Jun 11, 2024 Ramp uses multi-armed bandits to choose among third-party bank-linking providers when provider performance varies by financial institution and available data is noisy, incomplete, or stale. Instead of maintaining separate, static A/B tests, it applies Thompson Sampling, modeling each provider’s success probability with a Beta distribution and repeatedly sampling, routing traffic to the highest sampled value, then updating the model from the result. Simulations examined changing success-rate trends, noise, volume, rolling-window size, trend type, and initial conditions; the algorithm generally exploited clearly better providers, split traffic more evenly when rates were close, and recovered eventually from incorrect starting conditions. In a sequential quasi-experiment, Thompson Sampling increased financial-institution linking success by 10% and reduced customers with manually uploaded bank statements by 25%. ### [Make Great Decisions Quickly with TOMASP](https://yomu.fyi/post/make-great-decisions-quickly-with-tomasp.md) - Company: [Shopify](https://yomu.fyi/company/shopify.md) - Author: 2023-10-18 - Published: Oct 22, 2019 Technical leaders and managers face high-impact decisions about hiring, technology, architecture, and priorities, but cognitive biases, short-term emotion, and overconfidence can distort judgment. TOMASP is a framework for making decisions timely, considering alternatives, defining decision criteria, examining uncertainty, and preparing to be wrong. It recommends timeboxing the decision, generating at least three options, making a meta-decision about optimization criteria, analyzing options, stepping back from emotion, and preparing to be wrong through prototypes and tripwires. In the Flutter rewrite example, Michelle compares alternatives against cross-platform consistency, performance, and development speed, then sets a two-week prototype and blind test. The framework treats decision quality as a function of how a choice is made and implemented, not simply its eventual outcomes.