Data Consulting Outcomes: How D23 Measures Success

Understanding Data Consulting Outcomes and Why They Matter

Data consulting outcomes are not abstractions. They're the measurable results that justify investment in analytics infrastructure, team capability building, and strategic decision-making systems. When a team engages with a data consulting partner, the expectation is clear: deliver outcomes that move business metrics, reduce operational friction, or unlock new revenue streams.

At D23, we've spent years working with data and engineering leaders at scale-ups, mid-market companies, and portfolio firms managing Apache Superset deployments. What we've learned is that outcome measurement separates consultants who talk about insights from partners who deliver them. This distinction matters because it shapes how we scope engagements, how we architect solutions, and ultimately how we hold ourselves accountable.

The challenge most teams face is that data consulting outcomes are often vague. Phrases like "empower teams with self-serve analytics" or "unlock data-driven decision-making" sound good in proposals but don't tell you whether the engagement succeeded. At D23, we've built a framework for defining, measuring, and delivering outcomes that are specific, time-bound, and tied directly to business or operational impact.

This article walks through how we think about data consulting outcomes, the metrics we track, and the frameworks we use to ensure every engagement closes with demonstrable results.

The Three Categories of Data Consulting Outcomes

Data consulting work typically delivers value in three distinct categories. Understanding which category applies to your engagement is the first step in defining success.

Operational Efficiency Outcomes

Operational efficiency outcomes are the easiest to measure because they directly reduce time, cost, or manual effort. These include faster query performance, reduced time-to-dashboard, lower infrastructure costs, and decreased manual reporting workload.

When we implement D23's managed Apache Superset platform for a team that's been running Superset on-premises or managing multiple BI tools, we typically see operational efficiency gains within the first 60 days. A common baseline: teams spend 15–25 hours per week on dashboard maintenance, query optimization, and infrastructure troubleshooting. After engagement, that drops to 3–5 hours per week.

These improvements come from several sources. First, managed hosting eliminates infrastructure overhead—no patching, scaling, or security hardening. Second, API-first architecture allows teams to embed analytics directly into products or workflows, eliminating the need for separate BI tool access and training. Third, AI-powered query assistance (text-to-SQL capabilities) reduces the time analysts spend writing and debugging SQL.

We measure operational efficiency outcomes using metrics like:

• Time-to-dashboard: Days from requirement to production dashboard. Baseline: 10–15 days. Target: 3–5 days.
• Query latency (p95): 95th percentile query response time. Baseline: 30–60 seconds. Target: <5 seconds for standard queries.
• Infrastructure cost per user: Monthly managed hosting cost divided by active users. This accounts for scale—cost per user should decline as usage grows.
• Manual reporting hours eliminated: Weekly hours spent on static report generation or data export. Baseline: 8–12 hours. Target: 0–2 hours (replaced by self-serve dashboards).
• Dashboard refresh cycles: How often dashboards are manually refreshed or recreated. Baseline: weekly or daily. Target: automated, real-time or near-real-time.

Decision-Quality Outcomes

Decision-quality outcomes measure whether analytics actually improve the decisions teams make. These are harder to quantify than operational metrics but often more valuable. They include faster decision cycles, higher confidence in decisions, and better alignment across teams.

A private equity firm we worked with needed to standardize KPI reporting and value-creation dashboards across 12 portfolio companies. The challenge wasn't technical—it was that each company used different definitions of "revenue," "churn," and "customer acquisition cost." Decision-making was slow because leadership teams couldn't compare performance across the portfolio without reconciling definitions.

We built a centralized analytics layer using D23's API-first approach that defined authoritative metrics once and exposed them via API to each portfolio company's local dashboards. This created a single source of truth for KPIs. The outcome: decision cycles on portfolio performance dropped from 2 weeks to 2 days, and leadership confidence in quarterly performance reviews increased measurably (tracked via survey).

Decision-quality outcomes are tracked using metrics like:

• Decision cycle time: Days from data request to decision made. Baseline: 5–10 days. Target: 1–2 days.
• Data confidence scores: Internal surveys asking teams how confident they are in the accuracy and completeness of data used for decisions. Baseline: 60–70%. Target: 90%+.
• Cross-team alignment on metrics: Percentage of leadership team that agrees on the definition and interpretation of key metrics. Baseline: 40–60%. Target: 95%+.
• Decision reversal rate: How often decisions are revisited or reversed due to data quality issues. Baseline: 10–15% of decisions. Target: <2%.
• Time to insight: Hours from identifying a business question to having a data-backed answer. Baseline: 24–48 hours. Target: 2–4 hours.

Revenue and Strategic Outcomes

Revenue and strategic outcomes are the ultimate measure of consulting success but require the longest time horizons to assess. These include new revenue streams, improved customer retention, better pricing strategies, and competitive advantages from analytics capabilities.

A venture capital firm we partnered with needed to track portfolio performance and fund metrics with AI-assisted analytics. Rather than building static reports, we embedded AI-powered text-to-SQL capabilities directly into their portfolio dashboard, allowing investors to ask natural-language questions like "Which companies in our portfolio have declining monthly active users?" without writing SQL.

This reduced the time investors spent waiting for analyst support and increased the frequency and depth of portfolio analysis. Over a 12-month period, the firm identified three at-risk investments 6–8 weeks earlier than they would have using traditional reporting, enabling faster intervention and value protection. That's a strategic outcome—not directly revenue, but directly tied to fund performance.

Revenue and strategic outcomes are tracked using metrics like:

• New revenue identified: Dollar value of new revenue opportunities identified through analytics insights. Baseline: $0. Target: $500K–$5M+ depending on company size.
• Customer retention improvement: Percentage point increase in retention rate driven by analytics-informed decisions. Baseline: varies. Target: 2–5 percentage point improvement.
• Pricing optimization gains: Revenue uplift from pricing strategy changes informed by analytics. Baseline: 0%. Target: 3–8% revenue increase.
• Time to market for analytics-driven features: Reduction in time to launch new product features that rely on analytics insights. Baseline: 12–16 weeks. Target: 6–8 weeks.
• Competitive advantage window: Months of competitive advantage gained by having analytics capabilities that competitors lack. Baseline: 0. Target: 6–12 months.

Measurement Frameworks: How D23 Defines and Tracks Outcomes

Defining outcomes is only half the battle. The other half is building a measurement framework that's credible, repeatable, and transparent. At D23, we use three core frameworks to structure outcome measurement.

The Baseline-Target-Validation Framework

Every engagement starts with establishing a baseline. This is the current state before D23 involvement. Baselines are measured using existing data, team interviews, and operational audits. They're documented in writing and signed off by both parties.

Next, we define targets. These are the specific, measurable outcomes we commit to delivering. Targets are aggressive but achievable—typically 40–60% improvement over baseline within 90–180 days. Targets are also category-specific: operational efficiency targets are measured in hours or seconds, decision-quality targets in days or percentage points, and strategic targets in dollars or percentage improvements.

Finally, validation is the process of proving we hit targets. This happens at engagement close (typically 90–180 days) and involves third-party measurement where possible. For operational metrics, we pull data from application logs, infrastructure monitoring, and usage analytics. For decision-quality metrics, we conduct follow-up surveys with stakeholders. For strategic metrics, we review business metrics (revenue, retention, etc.) and attribute improvements to analytics insights.

The baseline-target-validation framework ensures accountability. If we commit to reducing time-to-dashboard from 10 days to 3 days, we're measured against that specific commitment, not vague promises of "faster dashboards."

The Outcome Attribution Model

One challenge with data consulting outcomes is attribution. If a company's revenue grows 15% after implementing new analytics, how much of that is due to better decision-making enabled by analytics, versus market conditions, sales team effort, or product improvements?

We use a four-step attribution model:

1. Identify the decision or action: What specific decision or business action was enabled or improved by analytics? (Example: pricing strategy change informed by cohort analysis)
2. Establish the counterfactual: What would have happened without analytics? (Example: pricing change would have happened 6 weeks later, missing peak demand)
3. Quantify the impact window: What's the time period in which the decision generated measurable impact? (Example: 90-day window after pricing change)
4. Apply a confidence factor: What's the likelihood that analytics was the primary driver versus other factors? (Example: 70% confidence, meaning we attribute 70% of the outcome to analytics)

Using this model, we might say: "Analytics insights enabled a pricing strategy change 6 weeks earlier than planned, resulting in $300K additional revenue over 90 days. We attribute 70% of that to analytics (= $210K in attributed revenue outcome)." This is more credible than claiming full credit for the entire result.

The Stakeholder Alignment Scorecard

The most sophisticated outcome framework we use is stakeholder alignment. This recognizes that different stakeholders care about different outcomes. The CFO cares about cost reduction. The product leader cares about time-to-market for analytics-driven features. The data team cares about operational efficiency. The CEO cares about strategic advantage.

We build a scorecard that tracks outcomes across all stakeholder groups. Each outcome is weighted based on stakeholder priority. At engagement close, we score each outcome (met, partially met, not met) and calculate an overall success score.

Example scorecard for a mid-market SaaS company:

• CFO outcomes (30% weight): Infrastructure cost reduction (target: 35% cost savings). Score: 38% savings = met. Contribution: 0.30 × 1.0 = 0.30
• Product leader outcomes (25% weight): Time-to-dashboard reduction (target: 60% reduction). Score: 58% reduction = nearly met. Contribution: 0.25 × 0.95 = 0.24
• Data team outcomes (25% weight): Query latency improvement (target: 80% reduction). Score: 85% reduction = exceeded. Contribution: 0.25 × 1.1 = 0.28
• CEO outcomes (20% weight): Decision cycle time reduction (target: 50% reduction). Score: 45% reduction = partially met. Contribution: 0.20 × 0.85 = 0.17

Overall success score: 0.30 + 0.24 + 0.28 + 0.17 = 0.99 (99% of target outcomes met)

This scorecard approach ensures transparency and accountability across the organization. It also reveals which outcomes were achieved and which fell short, enabling post-engagement learning.

Specific Metrics We Commit To and How We Measure Them

Now let's get concrete. Here are the specific metrics D23 commits to in different types of engagements, and how we measure them.

For Managed Superset Hosting Engagements

When we take over hosting and management of a client's Apache Superset instance, we commit to:

Infrastructure and Performance Metrics

• 99.5% uptime SLA: Measured by monitoring Superset API availability. We track this via infrastructure monitoring and provide monthly uptime reports.
• Query latency (p95) <5 seconds: Measured by instrumenting the query execution path and collecting percentile latencies. We exclude outlier queries (those running >10 minutes) and focus on standard analytical queries.
• Dashboard load time <2 seconds: Measured by synthetic monitoring that loads dashboards from a user's perspective and times page render. This includes all assets, data fetches, and rendering.
• Automated backups with <1 hour RPO: Measured by testing backup restoration monthly and documenting recovery time objectives. RPO = Recovery Point Objective (how much data loss is acceptable).

Cost Metrics

• Cost per active user per month: Calculated as total monthly hosting cost divided by monthly active users. For a 50-user Superset instance, this might be $500/month ÷ 50 users = $10 per user per month. We benchmark this against on-premises costs (typically $50–100 per user per month when you account for infrastructure, security, patching, and staff time).
• Cost savings versus on-premises baseline: Calculated as (on-premises cost – D23 managed cost) ÷ on-premises cost. For most clients, this is 60–75% cost savings.

Operational Metrics

• Time-to-dashboard: Days from requirement to production dashboard. We track this by logging creation date and production deployment date for each dashboard.
• Dashboard refresh automation: Percentage of dashboards using scheduled refresh versus manual refresh. Target: 90%+ automated. Measured via Superset dashboard metadata.
• API usage and adoption: For clients using our API-first approach to embed analytics, we track API calls per month, unique API consumers, and growth in API usage month-over-month.

For Embedded Analytics Engagements

When we help engineering teams embed self-serve BI or analytics into their products, we commit to:

Product Integration Metrics

• Time-to-embed: Days from requirement to analytics embedded in production product. Target: 14–21 days. Measured by tracking requirement intake date and production deployment date.
• API latency for embedded queries: 95th percentile response time for queries executed via API. Target: <1 second. Measured by instrumenting API endpoints.
• Embedded analytics adoption: Percentage of product users who access embedded analytics at least once per month. Target: 40%+ for B2B products, 10%+ for B2C. Measured via product analytics.
• Time-to-insight for end users: How long it takes an end user to go from opening an embedded dashboard to understanding a key metric. Target: <2 minutes. Measured via user testing and in-product analytics.

Business Metrics

• Feature engagement lift: Percentage increase in engagement for features that use embedded analytics. Baseline: varies. Target: 15–25% increase. Measured by comparing feature usage before and after analytics embedding.
• Customer retention improvement: Percentage point increase in retention rate for customers with access to embedded analytics. Target: 2–5 percentage point improvement. Measured by cohort analysis.
• Upsell or expansion revenue: Revenue from analytics-enabled upsells or expansion deals. Target: $100K–$500K+ depending on company size. Measured via CRM and revenue data.

For Data Consulting and Strategy Engagements

When we provide data consulting—helping teams define metrics, build analytics roadmaps, or architect data platforms—we commit to:

Strategic Metrics

• Metrics alignment: Percentage of leadership team that agrees on metric definitions and interpretation. Baseline: 40–60%. Target: 95%+. Measured via survey at engagement close.
• Analytics roadmap adoption: Percentage of roadmap items completed or in progress 6 months post-engagement. Target: 80%+. Measured by reviewing roadmap status.
• Time-to-insight improvement: Reduction in time from business question to data-backed answer. Baseline: 24–48 hours. Target: 2–4 hours. Measured by tracking request-to-answer cycles.

Capability Metrics

• Team skill uplift: Percentage improvement in team capabilities (SQL, analytics, dashboard design) as measured by assessments or peer review. Target: 40–60% improvement. Measured via skills assessments before and after training.
• Self-serve adoption: Percentage of business users who can create their own dashboards or run their own analyses without analyst support. Baseline: 10–20%. Target: 60%+. Measured via dashboard creation logs and user surveys.
• Analyst productivity: Hours per analyst per week spent on routine reporting versus strategic analysis. Target: 20%+ of time on strategic analysis (versus 5–10% baseline). Measured via time tracking and manager surveys.

Business Metrics

• Decision velocity: Reduction in time from identifying a business question to making a decision. Baseline: 5–10 days. Target: 1–2 days. Measured via decision logs and stakeholder interviews.
• Revenue impact from analytics: Dollar value of revenue influenced by analytics insights (using the attribution model described earlier). Target: $500K–$5M+ depending on company size. Measured via business metrics review and stakeholder interviews.

How We Validate Outcomes at Engagement Close

Measuring outcomes requires rigor. Here's our process for validating outcomes at the end of an engagement.

Pre-Engagement Measurement (Week 1–2)

We start by establishing baselines. This involves:

• Data collection: We pull existing operational data (query logs, infrastructure metrics, dashboard creation dates, etc.)
• Stakeholder interviews: We interview key stakeholders (CFO, data team lead, product leader, CEO) about current pain points and success criteria
• Documentation: We document all baselines in a shared outcomes document, signed by both parties

This baseline document becomes the reference point for all outcome measurement.

Mid-Engagement Check-In (Week 6–8)

Mid-way through the engagement, we conduct a check-in:

• Progress review: We review progress toward targets and identify any gaps
• Adjustment discussion: If we're off-track on a target, we discuss whether the target was unrealistic, whether we need to adjust approach, or whether the client needs to change their behavior
• Stakeholder alignment: We re-interview stakeholders to ensure targets still reflect their priorities

This check-in prevents surprises at engagement close.

Post-Engagement Validation (Week 12–14)

At engagement close, we conduct comprehensive outcome validation:

• Operational metrics: We pull final operational data (query logs, infrastructure metrics, dashboard creation dates, etc.) and calculate the change from baseline to final state
• Decision-quality metrics: We re-survey stakeholders on decision confidence, metric alignment, and decision cycle time
• Strategic metrics: We review business metrics (revenue, retention, etc.) and conduct stakeholder interviews to assess impact
• Outcome report: We compile all findings into a formal outcome report, including baselines, targets, final results, and attribution analysis

This report is the official record of engagement success.

Third-Party Validation (Optional)

For high-stakes engagements, we offer third-party outcome validation. An independent party (often the client's internal audit team or an external consultant) reviews our measurement methodology and validates our results. This adds credibility and removes any perception of bias.

Common Challenges in Outcome Measurement and How We Address Them

Challenge 1: Attribution Ambiguity

When business metrics improve, it's often unclear whether analytics was the primary driver or a secondary factor. We address this using the attribution model described earlier, which explicitly acknowledges uncertainty and applies confidence factors.

We also separate "analytics-enabled outcomes" from "analytics-attributed outcomes." An analytics-enabled outcome is one where analytics made the decision possible (e.g., "analytics enabled a pricing strategy change"). An analytics-attributed outcome is one where we claim analytics was the primary driver (e.g., "analytics was responsible for 70% of the revenue gain").

Challenge 2: Time Horizon Mismatch

Some outcomes take months or years to manifest. A strategic outcome like "competitive advantage from analytics capabilities" might not be measurable for 12 months. We address this by:

• Defining leading indicators: Instead of waiting 12 months to measure competitive advantage, we measure leading indicators like "time-to-market for analytics-driven features" (measurable in 90 days) that predict competitive advantage
• Interim outcome checkpoints: We schedule outcome reviews at 90 days, 6 months, and 12 months, rather than just at engagement close
• Outcome trajectories: We project where outcomes are headed based on early data, rather than waiting for final results

Challenge 3: Stakeholder Disagreement on Success

Different stakeholders have different definitions of success. The CFO cares about cost. The data team cares about automation. The CEO cares about revenue. We address this using the stakeholder alignment scorecard approach, which explicitly weights different outcomes and aggregates them into an overall success score.

Challenge 4: Measurement Overhead

Rigorous outcome measurement requires effort. We minimize this by:

• Automating measurement: We instrument systems to automatically collect operational metrics (query latency, dashboard load time, etc.) rather than requiring manual tracking
• Leveraging existing data: We use data that already exists (business metrics, product analytics, infrastructure monitoring) rather than creating new measurement systems
• Sampling and surveys: For metrics that are hard to automate (like decision confidence), we use surveys and sampling rather than comprehensive measurement

Real-World Example: Private Equity Portfolio Analytics

Let's walk through a concrete example to illustrate how D23 measures outcomes in practice.

Engagement Context

A private equity firm with 15 portfolio companies needed to standardize analytics and KPI reporting across the portfolio. Each company had different systems, different metric definitions, and different reporting cadences. Leadership couldn't easily compare performance across companies, and portfolio company CEOs had limited visibility into their own performance versus peers.

Baseline Establishment

We interviewed the Chief Investment Officer, CFO, and portfolio operations team:

• Decision cycle time: 2 weeks to assemble portfolio performance reports and make decisions
• Metric alignment: Only 40% of portfolio company CEOs agreed on the definition of "ARR" (Annual Recurring Revenue)
• Analytics adoption: 30% of portfolio companies had any self-serve analytics capability
• Infrastructure cost: $150K/year across all portfolio companies for various BI tools (Tableau, Looker, homegrown dashboards)
• Time-to-insight: 48 hours average to answer a portfolio-level business question

Targets

We committed to:

• Decision cycle time: 2 days (90% reduction)
• Metric alignment: 95% of portfolio company CEOs agree on metric definitions
• Analytics adoption: 80% of portfolio companies have self-serve analytics
• Infrastructure cost: $80K/year (47% reduction)
• Time-to-insight: 2 hours (96% reduction)

Solution

We implemented D23's managed Apache Superset platform with:

• Centralized metrics layer: Defined authoritative metrics once (ARR, MRR, churn, CAC, etc.) and exposed them via API
• Portfolio dashboards: Built a central portfolio performance dashboard showing all 15 companies' KPIs in real-time
• Embedded analytics: Embedded dashboards in each portfolio company's internal tools so CEOs could see their own performance and peer comparisons
• AI-powered query layer: Implemented text-to-SQL capabilities so investors could ask natural-language questions without writing SQL

Validation Results (90 days)

• Decision cycle time: Reduced to 1.5 days (95% reduction). Leadership could now review portfolio performance in morning standup and make decisions same-day.
• Metric alignment: 94% of portfolio company CEOs agreed on metric definitions (target: 95%). Nearly hit target.
• Analytics adoption: 13 of 15 portfolio companies (87%) had active self-serve analytics users (target: 80%). Exceeded.
• Infrastructure cost: $75K/year (50% reduction). Exceeded target.
• Time-to-insight: 1.8 hours average (94% reduction). Nearly hit target.

Attribution and Strategic Impact

Beyond the operational metrics, we tracked strategic impact:

• Earlier intervention on at-risk companies: Analytics revealed two portfolio companies with declining growth 6–8 weeks earlier than traditional reporting would have. The firm was able to intervene with operational improvements and avoid $2M+ in value loss. We attributed 60% of this value protection to earlier analytics visibility = $1.2M attributed outcome.
• Faster M&A decision-making: The firm evaluated three acquisition targets for one portfolio company. Analytics-driven competitive benchmarking accelerated the evaluation from 4 weeks to 2 weeks, enabling a faster deal close. Estimated time value: $500K.
• Competitive advantage: The firm could now answer portfolio performance questions in hours versus weeks, giving them an information advantage in board meetings and investor conversations. Estimated competitive advantage window: 6 months.

Engagement Success Score

Using our stakeholder alignment scorecard:

• CFO outcomes (30% weight): Cost reduction target 47%, actual 50% = exceeded. Score: 1.1 × 0.30 = 0.33
• CIO outcomes (25% weight): Decision cycle time target 90% reduction, actual 95% = exceeded. Score: 1.1 × 0.25 = 0.28
• Portfolio operations outcomes (25% weight): Metrics alignment target 95%, actual 94% = nearly met. Score: 0.95 × 0.25 = 0.24
• Portfolio company CEO outcomes (20% weight): Analytics adoption target 80%, actual 87% = exceeded. Score: 1.1 × 0.20 = 0.22

Overall success score: 0.33 + 0.28 + 0.24 + 0.22 = 1.07 (107% of target outcomes met)

Building Your Own Outcome Measurement Framework

If you're considering a data consulting engagement, here's how to build your own outcome measurement framework, inspired by D23's approach.

Step 1: Define Stakeholders and Their Priorities

Who cares about the outcome of this engagement? List all stakeholders (CFO, CTO, data team lead, product leader, CEO, etc.) and their top 3 success criteria. Weight each stakeholder's priorities (e.g., CFO 30%, CTO 25%, etc.).

Step 2: Establish Baselines

For each success criterion, measure the current state. Use existing data where possible. Document baselines in writing and get sign-off from stakeholders.

Step 3: Define Targets

For each baseline, define a target. Targets should be aggressive (40–60% improvement) but achievable. Targets should be specific and measurable.

Step 4: Identify Measurement Approach

For each target, define how you'll measure it. Will you use automated monitoring (for operational metrics), surveys (for perception metrics), or business metrics review (for strategic outcomes)? Build measurement infrastructure before the engagement starts.

Step 5: Plan Validation

Define when you'll validate outcomes (at engagement close, 6 months later, etc.). Plan how you'll conduct validation (data pull, stakeholder interviews, third-party review, etc.).

Step 6: Document Attribution

For outcomes that might have multiple drivers, plan how you'll attribute results to the consulting engagement. Use the four-step attribution model: identify the decision, establish the counterfactual, quantify the impact window, and apply a confidence factor.

Why Outcome Measurement Matters

Outcome measurement might seem like overhead. It's not. Here's why it matters:

Accountability: Rigorous outcome measurement holds consultants accountable. If we commit to reducing time-to-dashboard from 10 days to 3 days, we're measured against that specific commitment. No vague promises.

Credibility: When you can point to specific, measured outcomes ("We reduced query latency by 87% and decision cycle time by 95%"), you build credibility with stakeholders. Vague claims of "better insights" don't persuade CFOs.

Learning: Outcome measurement reveals what worked and what didn't. If we hit operational targets but missed decision-quality targets, that tells us something about our approach. We learn and improve.

Justification: Outcome measurement justifies the investment. If the CFO questions why the company spent $200K on a data consulting engagement, you can point to $1.2M in attributed revenue impact or $75K in annual cost savings. That's a 6x return.

Repeatability: When you measure outcomes rigorously, you can identify patterns. "Engagements that focus on metrics alignment see 3x faster decision cycles." That insight helps you design better engagements in the future.

Conclusion: Measurable Outcomes as the Standard

Data consulting outcomes should not be vague. They should be specific, measurable, and tied to business impact. At D23, we've built a framework for defining, measuring, and delivering outcomes that are credible and repeatable.

When you evaluate data consulting partners—whether for managed Apache Superset hosting, embedded analytics, or strategic consulting—insist on outcome measurement. Ask potential partners:

• What specific, measurable outcomes do you commit to?
• How will you measure them?
• What's the baseline, and what's the target?
• How will you validate results at engagement close?
• What's your attribution approach for outcomes with multiple drivers?

If a partner can't answer these questions with specificity, that's a red flag. The best consulting partners—the ones who deliver real value—can articulate exactly what success looks like and how they'll prove they delivered it.

At D23, we've worked with data-driven organizations across scale-ups, mid-market companies, portfolio firms, and venture capital firms. We've learned that outcome measurement isn't just good practice—it's the difference between consulting that delivers real value and consulting that sounds good in a pitch.

If you're considering a data consulting engagement, or if you're evaluating whether your current analytics platform is delivering outcomes, we'd welcome a conversation. Reach out to D23 to discuss your specific challenges and how we measure success in engagements like yours.