Competitive Intelligence & Technology Watch Glossary

Signal

In a sentence: A signal is evidence that points to an event or change in the external environment and is relevant to the organisation.

Signals can be identified and categorised by analysts, by automated systems, or in a hybrid way. They are created by applying intelligence hypotheses, critical monitoring factors or predefined filters to external or internal information, to separate what matters from noise and classify it by topics, impacts or business units.

Signals can incorporate metadata that enriches the associated information and facilitates analysis, such as the date, source identification, the reasoning that justifies its relevance, or audit data on who has carried out an analysis, among others.

Noise

In a sentence: Noise is the set of signals that seem relevant because of how they are captured/filtered, but do not add value to an intelligence hypothesis or to a decision.

In practice, noise appears when capture and filtering confuse the presence of terms with meaning or relevance, and “let in” items that do not match the monitoring focus. It usually comes from four typical sources:

• Content extraction noise: text from menus, headers, listings and repeated blocks from a portal that get mixed with the main content. Antara addresses this with heuristic extraction of the main text to avoid false positives.

• Semantic noise (polysemy / context): a word or concept appears, but in an unintended sense; it is reduced with context exclusions (automatically discarding undesired contexts).

• Noise from over-delivery: receiving “everything that matches” instead of “what matters” (massive alerts that hide what is relevant). 

• Noise from poor focus configuration: hypotheses that are too broad, vocabulary that is not discriminating enough, or a lack of prioritisation criteria.

Example: If you monitor “battery technology for kitchen appliances” and you receive alerts about an “assault and battery” incident in a kitchen, that is noise: there is a keyword match, but battery refers to a legal offence, not an electrical battery.

Semantics

In a sentence: Semantics is the meaning of words and expressions in context, and it allows you to move from “text matches” to relevance for an intelligence focus.

In competitive intelligence and technology watch, working with semantics means interpreting what a document is really about: which concepts it expresses, in what sense it uses them, and how they relate to each other. This is key because many false positives come from linguistic ambiguity (polysemy) or from terms that appear in irrelevant contexts. That is why a semantic approach is not limited to searching for keywords: it uses concepts, synonyms, hierarchies (for example, a thesaurus) and context rules to classify and filter information more accurately.

Applied to a monitoring system, semantics makes it possible to:

• Reduce noise (distinguish “battery” as a technology from “battery” as an instrument or in a cooking-related sense).

• Expand coverage without losing precision (capture variants, synonyms and related terms).

• Prioritise by intent/impact (e.g., whether the text discusses a commercial launch, a regulatory change, or a technical advance).

Example: A semantic filter can identify that a news item about “masterbatch” belongs to materials for packaging even if it does not explicitly mention “packaging”, while excluding content where “packaging” is used to mean logistics packing unrelated to materials.

Thesaurus

In a sentence: A thesaurus is a controlled vocabulary of concepts and relationships (synonyms, hierarchies and associations) used to describe and filter information consistently, beyond keywords.

In competitive intelligence and technology watch, a thesaurus makes it possible to define what a document is “about” through standardised concepts (e.g., polyamides, lithium-ion batteries, EUDR), including synonyms, variants and translations. This makes filtering explainable (you can see why a signal came in) and maintainable (you can adjust the vocabulary as the environment changes).

Why a strong, multilingual thesaurus improves filtering:

• It reduces noise: it distinguishes meanings through context and allows you to exclude unwanted senses (polysemy).

• It increases coverage without losing precision: it captures more specific concepts, variants, acronyms and synonyms without “opening the tap” indiscriminately.

• It improves early detection: when a technology emerges, it often appears with unstable terminology; the thesaurus helps capture those variants.

• It aligns teams and avoids internal ambiguity: marketing, R&D and strategy share the same operational “dictionary”.

• Multilingual = less geographical blindness: it lets you monitor sources in multiple languages with the same focus, reducing language bias and avoiding reliance on English (or only the local language).

Example: If your thesaurus links “EUDR” to “EU Deforestation Regulation” and its Spanish/French equivalents, and also connects raw materials and supply chains, you can capture relevant regulatory changes even if the article uses a different label or is published in another language.

Analyst

In a sentence: An analyst is anyone (or an AI agent) who turns information into intelligence, adding context, judgement and conclusions that are useful for decision-making.

In practice, an analyst does not “consume news”: they interpret signals, assess their relevance to an intelligence hypothesis, identify opportunities/threats and propose actions. This can be a formal role within an Intelligence Function, but in real organisations it is often distributed: “we are all analysts” when expert knowledge is spread across R&D, marketing, quality, procurement, operations or regulatory affairs, and each area can contribute interpretation of what is happening in its domain. The Intelligence Function coordinates that heterogeneous effort so that analysis has real impact on decisions.

In Antara, the Analyst role is associated with concrete operational capabilities: subscribing to channels, accessing/receiving signals, collaborating in comments, and proposing signals for inclusion in corporate reports, leaving a trace of who analysed what and why.

Example: A regulatory affairs lead identifies a relevant regulatory signal, explains the potential impact on product/market, and proposes it for the next intelligence report; an R&D colleague adds technical implications and prioritises actions.

Intelligence Directive

In a sentence: The Intelligence Directive is the document that defines and prioritises what the organisation needs to know (and for which decisions), acting as a compass to guide environmental monitoring and analysis work.

Within an Intelligence Function, the directive is the starting point of the cycle: before collecting information, you need to specify what you want to know and which decisions you aim to support. Without that definition, organisations tend to accumulate information without a purpose, generate noise, and lose focus.

A well-designed directive makes it possible to define, organise and prioritise monitoring activities in order to anticipate critical changes in the market, technology or regulation. In practice, it typically includes:

• Decisions and objectives to be supported (tactical and strategic).

• Monitoring areas (competitors, customers, technology, regulation, etc.) and their priority.

• Intelligence hypotheses derived from the deployment of objectives (which changes to observe and under which criteria).

• Responsibilities and roles (who monitors, who analyses, who validates).

• Cadence and deliverables (alerts, dashboards, intelligence reports; frequency).

• Prioritisation criteria and metrics (for continuous improvement and periodic review).

Example: (You can read this specific article and download a complete template.)

Intelligence Hypothesis

In a sentence: An intelligence hypothesis is an explicit formulation of what you should monitor and why, derived from the Intelligence Directive, to detect relevant signals and reduce noise.

Intelligence hypotheses should not be born “from intuition” or from open-ended lists of topics. They are derived from the deployment of objectives that the organisation defines in its Intelligence Directive: which decisions need to be supported, which risks or opportunities should be anticipated, and which priorities leadership sets. That deployment turns strategic objectives (often broad) into operational monitoring focuses, useful for capture, filtering and analysis.

A good hypothesis translates a business concern or decision into an actionable focus: it defines which changes in the external environment (market, technology, regulation, competitors, customers) may affect the organisation, what evidence would indicate that those changes are happening, and how they will be prioritised. Instead of “monitoring everything”, it narrows down the topic, scope, time and geographical horizons, and relevance criteria, so that the system and analysts can filter with precision.

In some contexts, intelligence hypotheses are treated as equivalent to Critical Monitoring Factors (CMFs): that is, they are framed as a set of key questions that guide what should be observed and with what priority. This equivalence is useful when you want to quickly turn objectives into monitoring focuses that teams can understand and act on.

Example: Objective in the directive: “Ensure regulatory continuity for the EU packaging portfolio”. Hypothesis/CMF: “New initiatives to restrict additive X in food-contact applications”. Evidence: regulatory drafts, public consultations, industry position papers, competitors’ reformulations.

Competitive intelligence

In a sentence (definition): Competitive Intelligence (CI) is the action of defining, collecting, analysing and distributing intelligence on products, customers, competitors and any aspect of the external environment; it is necessary to support tactical and strategic decision-making in an organisation.

CI turns dispersed information from the external environment (market, technology, regulation, competitors’ moves, customer signals) into actionable conclusions to make better decisions. Its value is not in “accumulating news”, but in clarifying your view of the environment: prioritising what matters, adding context and anticipating impacts.

In an operational approach, CI is guided by an Intelligence Directive, which deploys objectives and priorities, from which intelligence hypotheses (or, in some contexts, critical monitoring factors) are derived to steer capture and analysis. CI relies on a combination of automation and human judgement: systems that filter and structure, and analysts (often distributed across several areas) who interpret and validate.

Typical CI outputs include prioritised alerts (signals), shared dashboards and intelligence reports that synthesise findings, implications and recommendations. When CI is well designed, it reduces noise, speeds up response time and improves coordination between R&D, marketing, strategy and operations.

Example: Before and during a project to launch a new packaging material, CI monitors competitors’ moves, regulatory changes in food-contact applications, and technological substitutes. The goal is to anticipate compliance risks, identify differentiation opportunities, and make evidence-based decisions on the product roadmap.

Technology watch

In a sentence (definition): Technology watch (TW) is a specific branch of competitive intelligence, focused on monitoring technological advances, scientific publications and patents, as well as the activities of key players in the scientific and technological domain of a sector: universities, technology centres, start-ups and companies’ R&D departments.

Technology watch (TW) aims to detect early signals of technological change: new approaches, materials, processes, architectures, research results, patent families, investments and collaborations that anticipate where a field is heading. Its purpose is not to “follow trends out of curiosity”, but to support concrete R&D and innovation decisions: what to explore, what to prioritise, what to discard, and which partners or technology routes to compete with.

As part of an Intelligence Function, TW is guided by the Intelligence Directive and operationalised through intelligence hypotheses (or critical monitoring factors, depending on the framework). This makes it possible to define a clear focus (technology scope, applications, time horizon, geography), design relevance criteria and reduce noise. In industrial environments, TW is also often integrated with market and regulatory monitoring, because a technology’s viability depends on adoption, costs, supply chains and regulatory compliance.

Example: An R&D team monitors “recyclability of barrier materials in packaging”. TW tracks coating patents, papers on new additives, materials start-ups and moves by technology centres. The result is a set of prioritised signals and a report with implications: differentiation opportunities, patent-blocking risks and recommended next experiments.

Market intelligence

In a sentence: Market intelligence is the analysis of information about customers, demand, channels, pricing and market dynamics to support commercial decisions; in practice, the term is used ambiguously and often overlaps (or is confused) with Business Intelligence and competitive intelligence.

In many corporate environments, “market intelligence” is used as a convenient label (widely promoted by consultancies) to refer to anything that helps understand the market. The problem is that the label is too generic: sometimes it means market research; sometimes sales analysis; sometimes competitor tracking; and sometimes internal dashboards. That ambiguity creates confusion with expressions such as “business intelligence”, “business insight”, or even “market intelligence” itself, which can be used differently from one organisation to another.

To make it concrete, it helps to distinguish it from Business Intelligence (BI):

• BI typically focuses on quantitative, structured and often internal information (ERP/CRM/finance) to measure performance and optimise operations and commercial decisions.

• “Market intelligence” sometimes draws on BI (sales, margins, funnels), but it also incorporates external sources (competitors, prices, demand trends, regulation, announcements, trade press, tenders).

Put simply: if your goal is to understand the market from the inside using controlled numerical data, you are usually doing BI. If your goal includes understanding the market from the outside (competitors, customers, regulation, technology) and anticipating what comes next, you are moving into the domain of competitive intelligence (CI).

That is why, in this glossary, we use Competitive Intelligence as the canonical concept: it is the most complete and least ambiguous framework to describe the process of defining, capturing, analysing and distributing intelligence about the external environment (including the market), with a focus on tactical and strategic decisions. If someone arrives looking for “market intelligence”, what they typically need is CI, or a combination of CI + BI.

Example: A sales-by-channel dashboard (BI) tells you what is happening in your business. An analysis of why a competitor is gaining share with a new positioning, which regulatory changes may affect your category, and which signals indicate a shift in demand (CI applied to the market) helps you decide how to respond and where to invest.

Tactical intelligence

In a sentence: Tactical intelligence is intelligence focused on the immediate horizon: identifying and analysing what is already happening (or has just happened) in the external environment and reacting quickly with operational decisions.

Tactical intelligence is activated when the focus is not “what could happen” but what has happened and requires a response: spotting a business opportunity, a reputational incident, a supply disruption, a labour dispute at a key player, and so on. That is why its value depends less on lengthy reports and more on speed, prioritisation and coordination: capturing relevant signals, validating them with judgement, and putting them in the hands of those who can act.

In terms of decision horizon, it operates in the “now” (hours/days). The typical “customer” is the department (sales, procurement, operations, quality, marketing…), and analysis is often distributed: people “in the trenches” are best placed to interpret the relevance of a signal in their context and turn it into action. In this framework, waiting for a quarterly report often leads to late action.

When it is hard to specify “what to monitor” in tactical mode, it helps to break the problem down with a five-forces-type framework (adapted): monitoring signals in an organised way across competitors, customers, suppliers, substitute products and regulation. The idea is not to “monitor everything”, but to formulate concrete focuses (hypotheses) and useful combinations: competitor + customer, supplier + technology, regulation + new entrants, etc., to reduce noise and increase precision.

Example: You detect a prospective customer’s interest in a technology or service you can provide: the sales team validates the opportunity and gets moving. Or you detect an agreement between a competitor and one of your external R&D collaborators: the legal team contacts the latter and audits compliance with confidentiality agreements. Action is decided within hours or days, not weeks.

Strategic intelligence

In a sentence: Strategic intelligence is intelligence focused on the medium term: interpreting trends and changes in the external environment that could affect the organisation (or that it could take advantage of) and translating them into leadership decisions.

Unlike tactical intelligence (which reacts to what has already happened), strategic intelligence works with signals that have not yet fully materialised: trends, early moves in other markets, technological shifts with adoption potential, and regulatory turns that can reshape competition. Its typical horizon is 0.5 to 2 years, and its natural “customer” is management and corporate strategy, because the output usually affects priorities, investments, positioning and resource allocation.

At this level, the challenge is not only to “capture” information, but to build meaning: connect scattered signals, identify plausible causal links, and estimate impacts and options. This is where a well-defined Intelligence Directive makes the difference: it narrows down which decisions must be supported, and from there intelligence hypotheses are derived to guide filtering (so you do not fall into infobesity) and prioritisation.

As a bridge between the tactical horizon and strategic foresight, strategic intelligence often monitors forces similar to those in the “five-forces-type” framework used in tactical intelligence (competitors, customers, suppliers, substitutes and regulation), but with one difference: the focus is on dynamics and second-order effects (for example, how a regulatory change alters the cost structure and opens the door to new entrants; or how a key supplier repositions bargaining power over the next 12–18 months).

Example: You detect that, in another geographic market, several competitors begin to offer a “low-carbon certified” solution and that a draft regulation points towards requiring emissions traceability. Strategic intelligence assesses likelihood and timing, impact on portfolio and pricing, and recommends options: accelerate certifications, adjust the technology roadmap, and prepare commercial messaging and partnerships.

Strategic foresight

In a sentence: Strategic foresight is a discipline for anticipating plausible futures in a highly uncertain environment (without pretending to predict) and using that exploration to make better decisions today.

Within the horizons of intelligence framework, strategic foresight works on the long term (typically >10–15 years) and its natural “customer” is corporate strategy / senior leadership: it does not answer “what has happened” (tactical) nor only “0.5–2 year trends” (strategic), but what could happen and how to prepare.

A key point: strategic foresight is not prediction. It starts from learning from the past and the present in order to build a “story of the future” that is revised over time. The goal is to manage uncertainty and avoid strategic thinking being reduced to extrapolating trends. In that logic, detecting discontinuities (regime shifts, shocks, tipping points) becomes especially important, not only “trends”.

In corporate foresight, the most widely used method to make that “open” future actionable is Scenario Planning: a foresight method aimed at managing uncertainty and improving decisions. Scenarios are not forecasts: they are plausible visions and narratives that describe contexts; several are developed in parallel to maximise strategy robustness and to share a common view across the organisation.

Typical benefits of strategic foresight (in organisations):

• Proactivity (differentiation and tackling “wicked problems”).

• Resilience (extending the life of the business and getting ahead of crises).

• Adaptation (adjusting strategy and monitoring the environment).

• Reinforcement (seizing opportunities and training the team).

Scenario planning

In a sentence: Scenario planning is a strategic foresight method to manage uncertainty and make better decisions, by building several plausible scenarios (not predictions) and designing robust strategies against them.

Scenarios are plausible visions of the world and narratives that describe contexts; several are developed in parallel to maximise success and to share a common view in the strategic conversation. Within the horizons of intelligence framework, it fits the foresight horizon (long term, >10–15 years) and its natural “customer” is Corporate Strategy / senior leadership.

How it is executed (operational summary)

1. Identify the focal question: a problem or decision today that is critical for the future, and choose a time horizon (typically >10 years).

2. Scan the outside and the inside (signals, trends, internal hypotheses, capabilities). 

3. Select the drivers of change and their dynamics (for example, PESTEL).

4. Create a scenario matrix (combining the critical and uncertain drivers).

5. Consider strategic implications (options, bets, hedges, early-warning signals).

6. Review the scenarios (keep them alive, update them, and learn).

The 5 keys to a good scenario (so it is actually useful)

A useful scenario should be:

• Plausible (it includes surprises, but no magic).

• Consistent (it makes sense to the people who build it and to those who read it).

• Have a creative title (memorable for strategic discussion).

• Have a narrative (what the world looks like and what your organisation looks like in that context).

• Have a short presentation (an elevator pitch in ~5 minutes).

What it produces (outputs) and how it connects to Competitive Intelligence

• Scenarios + implications: “which decisions change” if the world moves towards A/B/C.

• Deployed intelligence hypotheses: “where to look and what to look for” to detect in time which scenario is gaining traction.

• Scenario tracking: a follow-up that feeds the strategic conversation and extends the useful life of scenario work (preventing it from becoming a “dead PowerPoint”).

Example (industrial)

Focal question: “How could our market change if sustainability and traceability regulation tightens and recycling technology scales?”

Scenarios: (A) strict regulation + mature technology; (B) strict regulation + immature technology; (C) lax regulation + adoption driven by costs; etc.

Outcome: a robust strategy (portfolio, partnerships, investments) and a set of early-warning signals to understand which scenario is materialising.

Intelligence report

In a sentence: An intelligence report is a structured deliverable that synthesises a set of signals and analysis on a topic, and turns them into conclusions and recommendations that are useful for decision-making.

A report is not a “dump of links” nor a news summary. Its purpose is to create meaning: to explain what is happening, why it matters, what implications it has, and which courses of action it opens up. In Antara, the report is also understood as the culmination of the intelligence flow: first comes the organisation’s distributed expert knowledge, and then the report, which incorporates it and makes it actionable for decision-makers.

Who is it for? It depends on the horizon and the “internal customer”:

• In the tactical horizon, the typical recipient is the department (immediate decisions).

• In the strategic horizon, the typical recipient is management / corporate strategy (0.5–2-year trends).

In both cases, the report helps align judgement and speed up decisions, avoiding debates based on impressions or on information overload.

What makes it effective (and why, in Antara, it is designed as an “interactive web page”)

• Trust and auditability: the reader can access the underlying information, see who worked on it, and review the individual analysis, which increases confidence in the conclusions.

• Security: reports should not circulate uncontrolled by email; in Antara, email is used as a notification and link, not as the container for the analysis.

• Updatable: mistakes can be corrected after publication and successive editions can be maintained.

• Collaborative: analysts can propose signals; the writer/editor accepts or rejects contributions, and traceability is embedded.

Example: “Monthly report on competitive and regulatory moves”: it groups selected signals, explains patterns (e.g., M&A, new partnerships, regulatory drafts), estimates impact by product line, and proposes actions (commercial adjustments, R&D prioritisation, compliance mitigations).

Intelligence function

In a sentence: The intelligence function is the cross-functional process that the organisation designs and governs to turn information from the external environment into useful competitive intelligence, aligned with strategy and oriented towards decisions.

The intelligence function is not “a report” nor “a person”: it is a way of working. Just as the quality control function defines how standards are ensured across the company, the intelligence function defines how intelligence is monitored, filtered, analysed and distributed in a repeatable way. That implies designing processes, relying on manuals, templates and tools, and establishing a continuous improvement cycle: what is monitored, under which criteria, who validates, how it is prioritised, and how impact is measured.

By nature, it is cross-functional: it affects sales (competitors’ and customers’ moves), procurement (supplier risks), operations (disruptions), R&D (technology watch), legal (regulatory changes), marketing (positioning), and also less obvious functions such as talent management (future critical skills, identifying opportunities to upskill the team). The intelligence function coordinates that distributed contribution and prevents each area from “monitoring in its own way” without consistency.

The function responds to strategy: it is made concrete through an Intelligence Directive that deploys objectives and priorities, and from there it derives hypotheses, relevance criteria and deliverables (signals, alerts, dashboards, reports). Its success, however, depends less on imposed rules and more on team culture: curiosity, discipline, collaboration, and the habit of justifying why something matters. Formal enforcement helps, but without culture, intelligence turns into bureaucracy or noise.

That is why it requires leadership and governance: someone (or a committee) to drive the process, resolve priority conflicts and ensure quality. And, to sustain it over time, that leadership must be supported by objective metrics: adoption (participation across areas), precision (noise vs useful signals), response times, report usage, decisions supported, and impact cases.