Network Intelligence: Transforming Fragmented Data into an Ecosystem for Action and Decision-Making
They know thousands of people and organizations. They sponsor programs, fund initiatives, run pilots, and collaborate across teams and regions.
Yet when it is time to answer a simple question like “Who do we already know who has done this before, in this context, with these constraints?”, the answers sound more like: “I usually ask the same colleague, but they do not really know that domain,” or “We made a spreadsheet once, but it is out of date,” or, if you are lucky, “We have a database or CRM, but the search is so limited that we still end up asking around.”
This is the gap that network intelligence is designed to close.
The problem: networks are underused, data is fragmented and quickly outdated
Most organizations hold only the thinnest slice of information about the people and organizations they rely on. At best, this is usually a name, a title, a company, maybe a CRM record.
The real context lives elsewhere, in emails, PDFs, call notes, proposals, websites, and in people’s heads. Even when that context is captured, nothing turns these fragments into a unified network that stays accurate over time.
Meanwhile, enterprise data is moving in the opposite direction:
- Analysts estimate that 80-90% of new enterprise data is unstructured (e.g. documents, messages, media, logs) and it is growing three times faster than structured data.1
- Industry leaders in data, such as IBM, report that unstructured data now makes up roughly 90% of all enterprise-generated data.2
- There are several studies that show around two-thirds of the data available to enterprises goes completely unused despite the investment required to collect and store it.3
Much of this information is labeled “dark data,” which is a term to describe the material an organization has already collected, but does not actively use for decision-making, relationship management, learning, research, or hiring.4
At the same time, the small slice of data that does make it into systems like CRMs and talent databases gets outdated quickly, and used rarely. Gartner-cited research shows that annual B2B contact decay is as high as 70%, while other analyses place average decay around 30% per year. This means that almost half of a database can become unusable within two years.
Here is the resulting paradox:
Organizations have more connections and richer context about their networks than ever before.
Yet the view they actually work with is thinner, more fragmented, and more short-lived than the reality, which makes it harder to find the right people when it matters.
The hidden human cost is the time lost searching instead of tackling actual work
This fragmentation shows up directly in how people work.
Multiple studies based on McKinsey research report that knowledge workers spend around 1.8 hours every day, roughly a full working day each week, searching for and gathering information.
That time is not spent designing better programs, building partnerships, or supporting teams. It is spent hunting for the latest version of a list, the right slide, the contact someone mentioned in a meeting last year.
Research has also shown that organizations that fully adopt social and collaborative technologies can bolster the impact and productivity of workers by 20-25% by improving knowledge sharing and visibility. But they note that most of this potential remains untapped.
That finding tells us something important: connection and visibility clearly create value, yet most organizations still lack systems that make that level of connection and visibility part of everyday work and of how their networks are stored and searched.
Networks are the social capital, but most systems see only rows and columns
Social scientists and institutions like the World Bank, OECD, and others have long described social capital as the value embedded in relationships, trust, and collaboration.
Research on organizations and social capital shows that:
- Networks carry trusted information about who is credible, who has delivered in difficult conditions, who works well together.
- This social capital enables creativity, problem solving, and efficiency when it is actually mobilized.
Yet most enterprise systems treat networks as static contact lists or graphs. They know that two entities are “connected,” but they lack the context that actually matters:
- In what capacity did they work together?
- On which project, in which domain, with what results?
- Under which constraints; budget, geography, regulation, urgency?
- Who would trust this recommendation enough to act on it?
When people leave an organization or move roles, a large part of this contextual network knowledge leaves with them. The social capital remains in the world, but the shared organizational memory of it disappears.
From dark data to network intelligence
Across industries, there is a growing recognition that dark, unstructured, and underused data holds significant strategic value, but only if organizations find ways to make it intelligible and actionable.
Researchers describe “dark analytics” as the use of advanced analytics to mine unstructured and dark data for nuanced business, customer, and operational insight that traditional structured datasets cannot provide.
Recent work on AI-driven data platforms highlights similar themes: nearly 90% of enterprise data is now unstructured, and the opportunity lies in converting that mass into operational knowledge through techniques such as AI-powered knowledge graphs and agents capable of contextualizing and acting on insights.5
At Neol, we see network intelligence as a specific, practical application of this shift.
Network intelligence is the information layer that turns scattered data about your relationships into an actionable map of your ecosystem, so you can finally see the networks you already have, along with the additional peripheral networks you can tap into, and use them to take focused, strategic action.
- People, organizations, and teams
- The projects, roles, and initiatives that connect them
- The contexts in which they have worked together
- The skills, patterns, and trust signals that emerge over time
This goes beyond static charts or one-off data integrations. A true network intelligence layer is:
- Context-aware: It understands not only who is connected to whom, but how and why those connections matter.
- Continuously refreshed: It pulls from live systems and human input, so data decays more slowly and becomes easier to correct.
- Interact with the data with natural language: It allows teams to ask real questions like “Who has led a climate-focused program with local governments in the last five years?” instead of “Do we have a list of people who did something like this?”
- Shared but governed: It respects data sovereignty, privacy, consent, and different levels of visibility while still giving leaders and teams a coherent view of their ecosystem.
What changes when your network becomes “intelligible?
When organizations build this kind of network intelligence layer, several shifts become possible.
Staffing and matching move from reactive searches to proactive fits
Instead of starting a search from scratch manually each time, teams can start from a living map of people and organizations where skills, experiences, and relationship context is available. Matching people to work becomes faster and more precise.
The type of questions you can ask "your network" that wasn't possible before look like:
- Who has delivered relevant work in a context similar to this one?
- Who comes recommended and has worked with our organization or partners before?
- If we need a shortlist tomorrow, which 10 people combine the right skills, languages, and regional experience for this brief?
Ecosystem-wide collaboration becomes possible, siloes break
When projects, partners, and teams are part of the same network view, it is clear to see where efforts overlap, where knowledge already exists, and where existing connections can provide insight and prevent extra work.
Questions you can ask "your network" look like
- Which people or teams across our ecosystem are working on similar challenges right now?
- Who in our wider network has led a similar initiative in [country or sector] in the last three years?
- Where do we already have relationships that could unlock this initiative faster?
Social capital becomes a shared asset, not an individual memory
Informal networks do not disappear when individuals move on; what disappears is the organization’s ability to see and use them. Network intelligence creates an institutional memory of how people have worked together, so trust and experience can compound instead of resetting.
Example questions your team can ask:
- Who did our teams collaborate with in the past year on (topic/region), and what came out of those efforts?
- Which alumni from our leadership, fellowship, or accelerator programmes are now working on this topic, and who has collaborated with them before?
Leadership gains a better sense of the ecosystem, and can act strategically
Rather than relying solely on static lists, organizational charts, or CRM rows, leaders can see a real-time view of their ecosystem and act on it: areas of expertise, bridging individuals, under-connected communities, and emerging fields of practice.
- Which Fortune 500 CEOs do we have a connection path with through our executives?
- Across all our hubs, funds, and programmes, where do we see momentum building around priorities such as climate, AI, or youth employment?
- If we mapped our top 100 strategic relationships today, who would appear, which teams are closest to them, and where are the gaps?
Principles for building network intelligence
In this transformation, every organization’s path will look different, but we see a few common principles:
- Start from real work, not abstract profiles: Profiles gain meaning when they are connected to real projects, roles, and collaborations. Begin by mapping who did what, with whom, in which context.
- Connect structured and unstructured data: Do not force everything into rows and columns. Use AI to extract relevant signals from unstructured sources such as PDFs, call notes, proposals, emails, and connect them to your existing records.
- Design for ongoing refresh and enrichment, not one-off clean-ups: Data decay is a constant. Build lightweight ways for people to confirm, correct, and enrich information as they work, so the network stays alive.
- Make it possible to take action by working with AI: Thanks to the advancement of LLMs and GPTs, people do not need to learn a complex query language to benefit from network intelligence. AI makes it possible to make complex searches with natural-language.
At Neol, we are building this networked future with future-thinking, mission-driven organizations
Neol was built to help organizations move from static, fragmented records to a network intelligence layer that reflects how their world actually works.
We connect existing data, make hidden context available, and make it easier for teams to search, explore, and act on the internal and external networks they already have across teams, partners, programs, and ecosystems.
For governments, public institutions, and any large organization that operates with a clear mandate or long-term agenda (employment, climate, innovation, economic diversification, etc.) that benefit most from a deeper, more coherent view of their relationships, this intelligence becomes a useful strategic tool to utilize their networks.
If you are exploring how to make your organization’s networks more visible, more searchable, and more alive over time, network intelligence is a natural next step. Our work is to help you build that layer in a way that fits your systems, your governance, and your ambitions.
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References
- https://researchworld.com/articles/possibilities-and-limitations-of-unstructured-data
- https://www.ibm.com/think/topics/structured-vs-unstructured-data
- https://www.secoda.co/learn/unleashing-the-power-of-data-study-shows-2-3-of-company-data-goes-unused
- https://www.ibm.com/think/topics/dark-data
- https://www.techradar.com/pro/transforming-dark-data-into-ai-driven-business-value
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