// Higher Education · Institutional Intelligence

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    <div data-screen-label="09a Higher Ed · Institutional Intelligence">
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          <div className="q-eyebrow q-eyebrow--dark">Higher Education · Institutional Intelligence</div>
          <h1>Your institution generates the data. <em>We help you see it.</em></h1>
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            Most colleges and universities are sitting on data that could transform how they operate — donor records, enrollment patterns, research outputs, financial flows. That data exists across a dozen systems. The infrastructure to make it useful, and the intelligence to act on it, usually doesn't. We build both.
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            <div><dt>Entry point</dt><dd>Data and Intelligence Assessment</dd></div>
            <div><dt>Typical scope</dt><dd>Weeks to months, depending on infrastructure complexity</dd></div>
            <div><dt>Who leads</dt><dd>Senior strategist and technical director, together from the start</dd></div>
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              <div className="q-eyebrow">The situation</div>
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              <p><strong>Advancement teams know that donor engagement is uneven, but they can't easily see which relationships are slipping, which are ready to deepen, or how to prioritize staff time against a portfolio of thousands.</strong> The data to answer those questions exists — in giving history, in event attendance, in communication records. But it isn't connected in ways that support real decisions.</p>
              <p><strong>The same problem appears across the institution.</strong> Enrollment data in one system. Financial data in another. Research productivity and grant performance in a third. Leadership is making strategic decisions without a clear picture of what the institution actually looks like from the inside.</p>
              <p><strong>AI can close this gap — but not without a data foundation worth building on.</strong> The institutions getting real value from AI analytics aren't starting with the AI. They're starting with infrastructure: warehousing the right data, cleaning it, building the pipelines that let a model do meaningful work. We help institutions build the foundation first, and the intelligence on top of it.</p>
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            <div className="q-eyebrow">Three ways in</div>
            <h2>The data problem looks different <em>depending on where you're standing.</em></h2>
            <p>We scope each engagement around the actual question. Most of the institutional intelligence work we do fits one of these shapes.</p>
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              <div className="q-entry-num">01</div>
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                <span className="label">Advancement and donor analytics</span>
                Your team is working harder than your data is.
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                <p>Advancement offices are often producing reports that describe the past rather than tools that support the future. Which major gift prospects are showing early signals? Which mid-level donors are at risk of lapsing? Which event attendees have never been asked? The data to answer these questions is in the system — it just isn't organized to produce answers.</p>
                <p>We build the data infrastructure and the intelligence layer that turns advancement data into operational insight. Our analytics work with the New England Conservatory — recognized with a 2025 CASE Circle of Excellence Award — demonstrates what this looks like in practice. The result is a team that spends less time pulling reports and more time acting on what the reports say.</p>
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              <div className="q-entry-num">02</div>
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                <span className="label">Unified institutional data</span>
                Leadership is making decisions with an incomplete picture.
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                <p>Presidents, provosts, and cabinet-level leaders are often working from dashboards built by different departments, on different timelines, using different definitions of the same numbers. The consolidated institutional picture — the one that would support a board presentation, a strategic planning process, or a budget decision — doesn't exist in one place.</p>
                <p>We design and build the data warehouse and reporting architecture that gives institutional leadership a coherent view. Not a new dashboard layered on top of broken data — a foundation that actually works.</p>
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              <div className="q-entry-num">03</div>
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                <span className="label">AI readiness and data infrastructure</span>
                You have an AI strategy but no data foundation to run it on.
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                <p>Many institutions have made commitments to AI adoption — tools purchased, pilots launched, task forces convened — without addressing the underlying question of whether the data infrastructure is ready to support it. AI models are only as useful as the data they're trained on and connected to.</p>
                <p>We assess data readiness honestly, design the infrastructure that needs to be in place, and build it. We don't sell AI tools. We build the foundation that makes AI tools worth having.</p>
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            <div className="q-eyebrow q-eyebrow--dark">How the work flows</div>
            <h2>Assess. Architect. Build. Activate.</h2>
            <p>The sequence is consistent. The depth of each phase depends on the complexity of your data environment and what the engagement calls for.</p>
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              <div className="step">Phase 01</div>
              <h4>Assess</h4>
              <p>We audit existing systems, data sources, and workflows. We identify what data exists, where it lives, what's missing, and what AI could realistically support given what's there.</p>
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              <div className="step">Phase 02</div>
              <h4>Architect</h4>
              <p>We design the data warehouse and pipeline infrastructure. We make explicit decisions about what gets unified, what gets cleaned, and what gets left out — and we explain the reasoning.</p>
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              <div className="step">Phase 03</div>
              <h4>Build</h4>
              <p>We build the infrastructure. The same team that did the assessment and the architecture — no handoff, no version of the plan lost in translation.</p>
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              <div className="step">Phase 04</div>
              <h4>Activate</h4>
              <p>We layer the intelligence — dashboards, models, reporting — and train the people who will use it. The deliverable is institutional capability, not a system that requires us to operate it.</p>
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            <div className="q-eyebrow">The entry offer</div>
            <h2>Start with a <em>Data and Intelligence Assessment.</em></h2>
            <p>A contained first engagement. We audit your existing data infrastructure, assess what AI could realistically support, and produce a written plan — specific, prioritized, and honest about what needs to happen in what order.</p>
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              <div className="what">Data and Intelligence Assessment</div>
              <h3>A clear picture of what you have, what's possible, and what to build first.</h3>
              <p>Two to four weeks. A senior strategist and a technical director working together. The deliverable is a written assessment and architecture plan — not a vendor recommendation, not a product pitch.</p>
              <p>If we are the right partner for the build that follows, we'll say so. If you are better served with a different approach, we'll say that too.</p>
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                <div><dt>Duration</dt><dd>2–4 weeks</dd></div>
                <div><dt>Team</dt><dd>Senior strategist + technical director</dd></div>
                <div><dt>Format</dt><dd>Systems audit, stakeholder interviews, data review</dd></div>
                <div><dt>Output</dt><dd>Written assessment and architecture plan</dd></div>
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                <li>Audit of existing systems, data sources, and integration points</li>
                <li>Honest assessment of data quality and readiness</li>
                <li>Identification of the two or three highest-leverage opportunities</li>
                <li>Architecture plan for the infrastructure that needs to be built</li>
                <li>Written deliverable suitable for CIO, cabinet, or board presentation</li>
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            <div className="q-eyebrow">What clients usually ask</div>
            <h2>Common questions.</h2>
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            {[
              { q: 'How is this different from buying an analytics platform?', a: 'Platforms assume the data underneath them is clean and connected. It usually isn\'t. We build the foundation the platforms depend on — and then, where it makes sense, we help you select and configure the right platform on top of it.' },
              { q: 'Do you work with our existing systems, or do you replace them?', a: 'We start with what you have. The goal is to build a data layer that unifies existing systems, not to replace them. Replacement sometimes becomes the right call, but it\'s never the starting assumption.' },
              { q: 'What\'s the relationship between this work and AI adoption more broadly?', a: 'It\'s upstream. An institution that wants to use AI well needs data infrastructure that supports it. We often start here and then help institutions layer AI capability on top of a foundation that\'s actually ready for it.' },
              { q: 'How long does implementation actually take?', a: 'Assessment is two to four weeks. Implementation depends on the complexity of the data environment — typically months, not years. We scope it specifically before you commit.' },
              { q: 'Can you work within our existing procurement and IT governance processes?', a: 'Yes. We are familiar with institutional procurement, RFP processes, and IT governance structures in higher education. Tell us what your process looks like and we\'ll tell you how to navigate it.' },
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              <div className="q-eyebrow q-eyebrow--dark">Next step</div>
              <h2>Tell us what you are <em>trying to figure out.</em></h2>
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              <p>A first conversation is twenty minutes, specific, and free. If a Data and Intelligence Assessment is the right next step, we scope it in writing before you commit to anything.</p>
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