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Notes on company data: how it is collected, structured, licensed and put to work.
150 pieces
- Firmographic, technographic, hiring and intent data: how they fitFirmographic data says who a company is, technographic what it runs, hiring where it invests and intent what it is likely to buy. How the four differ, and how one key joins them.
- How Fokals intent scores are built, and how to read oneEvery Fokals intent score is built from dated things a company did in public, and carries them as evidence. The signals, the scale, the surge flag and how to read a row.
- How to evaluate a company data vendor in ten questionsTen questions for any vendor of company data, from coverage to change management, each with a document to request and a test to run on a sample. Our own answers are included, each with the public document behind it.
- How we detect the technologies on a company websiteWhat stands behind a row of technographic data: the evidence a detection rests on, how a technology is dated, and how an adoption, a confirmed removal and a platform migration are recorded.
- Introducing Fokals: company intelligence data, refreshed dailyFokals supplies firmographic, technographic, hiring and intent data on public and private companies worldwide. What the five datasets deliver, how they reach you and how they are licensed.
- Same-store cohorts: how the weekly market series stay comparableCoverage of a company index expands continuously, and a raw count would carry that expansion into every trend. Here is the cohort rule that keeps it out, and the arithmetic of every measure it feeds.
- The identifiers that make company data joinableCompany data is only useful once it joins to what you already hold. Here are seven identifiers, what each one names, the join each one serves and how a brand reaches its listed parent.
- What a data licence covers: internal use, embedding, redistributionA data licence grants a use, not a dataset. This guide places common plans under internal use, embedding or redistribution and lists what to settle for each before you sign.
- Why we read job postings from the company's own boardA job posting can be read where the employer published it or where it was copied to. The source decides how many records a role produces, whose role it is and what its dates mean.
- Why we write each table once: point-in-time data by designA table corrected in place shows what is known today, not what was known then. The five rules that keep Fokals tables as first written, what they guarantee and how to check them.
- Adding company signals to lead scoringA lead score knows what a person did, not what the company is or is about to do. Here is how to add company features to it without double counting or leakage.
- Adding technographic filters to a prospecting toolA technology filter is a set of definitions before it is a dropdown. What "runs it now", "first seen" and "recently added" should mean, and the datasets and indexes that serve them.
- Benchmarking pay and talent demand for workforce planningA workforce plan needs the pay employers advertise for a role and how many of them are competing for it. Here is how to build both from job postings.
- Benchmarking sales compensation from advertised payQuartiles of advertised base pay and on-target earnings for sales roles, by country and week. Here is how to place a plan against them and how to read the result.
- Building a company knowledge graph from identifiers and eventsA company graph answers questions that span datasets. This guide gives the node and edge tables, the join that finds a brand's listed parent and the way to link your own records.
- Building a point-in-time company dataset for back-testsA back-test is only as honest as the dates in its data. This guide shows how a dated, write-once record lets you key company signals by when they were known and query as of a date.
- Building account-based marketing segments from company signalsA segment is a rule that names accounts and says why they are on the list. Here are recipes that combine fit with timing, the tables behind each and how to keep them current.
- Building company change alerts into your productAn alert is a dated event shown to someone who follows a company. This guide covers the tables to read, the schema to store, how to dedupe and what each message may say.
- Building thematic baskets from technology adoption dataA basket of adopters is only as good as its membership rule. Here is how to build one from website technology data, rebalance on dated events and test it.
- Catching companies as they enter a new marketA new market shows up in a company's site, its job board and its newsroom, each at its own moment. Here is how to join them into one dated timeline per country.
- Commercial due diligence on a target with public signalsBefore the data room opens, public signals can show where a target is hiring, selling and announcing, and how it compares with its competitors. Here is the method.
- Company data as context for AI agents and copilotsDesign the tools, the result format and the tests for an agent that answers questions about companies from dated, sourced rows instead of scraped pages.
- Company signals in credit research and counterparty monitoringPublic signals can flag a borrower under strain before the next report. This guide builds a watchlist from them and shows how to read each trigger beside your own credit file.
- Defining an ideal customer profile from dataDescribing your customers is not enough: a profile has to show what separates them from the market. Here is how to enrich won accounts, measure lift and test the result.
- Detecting go-to-market changes from pricing and key pagesThe key pages of a company website show how it sells. Here is how to turn changes to them, and to its markets and currencies, into a dated watch.
- Detecting restructuring early from postings and announcementsA restructuring leaves a pattern on a company's job board before it reaches a disclosure. Here is the pattern, the announcements that confirm it and the false alarms.
- Enriching company records in your data productEnrichment starts with a match and ends with a licence. Here is how to match records on domain, which fields to add, how to refresh them and what to settle for embedding.
- Event-driven research with Form 8-K filings and newsroomsA company tells you what it has done in two places, its newsroom and its Form 8-K. Here is how to join them into one dated calendar and where the labels stop.
- Expansion signals for account managementCustomers often show growth in public before they ask for more. Here is how to read it from their websites, job boards and announcements, and rank the accounts worth a call.
- Finding accounts by the technology they runThree list patterns on the marketing stack datasets, what account ids add to them, and the checks that keep a technology list from sending a rep to the wrong company.
- Following private funding with SEC Form D filingsForm D is one of the few primary records of private fundraising. This guide explains what it holds, how to read its amounts and dates, and where the record stops.
- Keeping a warehouse in step with incremental API syncA daily sync that resumes after a failure, runs twice without harm and notices a period that arrives late. The design, table by table, with the keys and the checks.
- Mapping a partner ecosystem from technology co-occurrenceCo-occurrence on company websites shows whose audience overlaps yours. A worked method with the datasets, the lift measure and the checks that separate partners from rivals.
- Mapping alternative data to a security masterJoining company data to securities is a many-to-many problem. This guide covers the identifiers every listed company carries, the join order, parents and brands, delistings and check digits.
- Measuring AI adoption across listed companiesNo single public signal measures AI adoption. Here is how to read three of them from company data, combine them into a score you can audit and avoid keyword traps.
- Measuring an installed base and its weekly movementPrevalence per 100 websites, adds, removals and net change for any recognised technology, with the checks that keep coverage growth out of the result.
- Measuring remote and hybrid work trends by industryThe share of new postings that state remote or hybrid work can be read every week by industry and country. Here is what it counts, how to pull it and how to read it.
- Monitoring supplier and vendor risk with public company signalsPublic signals tell a procurement team where to look in a supplier list. Here is how to read announcements, hiring and site changes, and how to turn them into a review queue.
- Personalising campaigns by technology stack and growth stageKey campaign variants on what an account runs, how fast it is hiring and what it announced last week. A rule set, a query and the guardrails that keep copy accurate.
- Portfolio company monitoring for private equity teamsPublic signals give an operating partner a weekly outside view of each portfolio company and its competitors. Here is how to build the watchlist and design the digest.
- Powering account scores in a sales platform with licensed signalsFor product teams that put a score beside each account: what a weekly row carries, the three states a score can be in, how to survive a version change and what to settle in the licence.
- Prioritising accounts with company-level intent scoresA weekly ranking of target accounts from company-level intent scores, with the join to fit, the evidence a rep can read, and the test that shows whether the ranking earns its place.
- Producing market research from weekly aggregate seriesA weekly series is only as good as its footnote. Here is how research and media teams pull the Fokals Market Series, test a number before it is printed and cite it.
- Reading a competitor's strategy from its hiringFunctions, seniority, locations and tools named in a competitor's postings show where it is investing before the product does. Here is a monthly brief built from them.
- Reading technology adoption as a signal of vendor revenueFor a vendor whose product shows on its customers' websites, installs and net adds are a dated proxy for customer growth. Here are the measure, the query and how to read it.
- Recruiting intelligence: finding companies that are hiring nowA company that is hiring is a prospect for a recruiter or an HR-tech seller. Here is how to find them by role and place, rank them by growth and avoid false leads.
- Redistributing company data to your customers under licenceA working list for platforms and product teams that deliver licensed company data onward: what the data carries, what you can promise customers and which questions to put in writing.
- Retrieval-augmented generation over structured company signalsMost questions about companies ask for a count, a date or a list, which a database answers exactly. This guide shows what to embed, what to filter and what to leave to SQL.
- Running competitor displacement plays from technology changesA rival tool leaving a company website is a dated, public change. How to read added and removed events, tell a swap from a flicker, and measure whether the plays work.
- Sector hiring trends for macro and thematic researchA thematic view is a claim about a sector, not one company. Here is how to read hiring series that hold their company cohort fixed, and how to test a theme across industries.
- Sizing a total addressable market with technographic countsCount the companies that run a technology, state how many of your list the data can see, and multiply by your own price. A worked method with its traps.
- Sourcing private companies for venture capital with growth signalsTurn public signals into a short, repeatable list of private companies worth a first call, with the query, the thresholds to start from and how to read what comes back.
- Spotting churn risk when a customer removes your technologyWhen a customer's website stops loading your tag, the record is dated and public. Here is how to turn removals and competitor additions into a churn alert you can test.
- Territory planning with firmographic and hiring dataEqual account counts hide unequal opportunity. How to weight accounts by fit and hiring momentum, balance territories on that weight and check each cell against Market Series.
- Timing outreach with hiring signalsA posting names what a company is about to build and often the tools the role needs. Six hiring signals, where to read each one, the play it supports and how to test the play.
- Tracking competitor launches and partnerships at the sourceA competitor's newsroom, feeds and site changes show what it launches and with whom, in its own words and dates. A watchlist method with the datasets, event types and queries.
- Tracking sales team build-out as a sign of growth plansSales capacity is hired before it produces revenue. Here is how to read a company's sales postings as a weekly record of what it plans to sell, to whom and where.
- Training and evaluating models on dated company signalsA model on company signals is only as honest as its feature table. This guide builds one as of each date, splits by time and names the properties of the record that keep it safe.
- Trigger events for outbound: funding, leadership and launchesA trigger event is a dated change that gives a seller a reason to write now. Here are the 13 event types in company announcements and regulatory disclosures, and how to rank them.
- Using hiring data in equity research between earningsA company reports four times a year and hires every day. Here is how to turn its job postings into a research signal you can test and trust.
- A market dashboard on company signals in Data Studio (Looker Studio)How to shape the market series table for charting, build rate, index and growth charts with an industry filter, and avoid the aggregation and base-date traps that distort a weekly series.
- Alternative data due diligence questionnaires, question by questionThe FISD questionnaire topic by topic, with the evidence each question calls for, and how Neudata and Eagle Alpha handle questionnaires once vendors have answered them.
- Alternative data on Bloomberg: what buyers should knowBloomberg names its alternative datasets and the routes that carry them. This guide sets out what each route gives a buyer and what to test before relying on it.
- Apache Iceberg tables for licensed external dataA table format decides how licensed files are stored, partitioned, loaded again and read back. This guide works through those choices for Fokals bulk exports.
- AWS Data Exchange: a guide for buyers of company dataA buyer's guide to AWS Data Exchange: the five data set types, how subscriptions and entitlements work, how revisions reach your bucket, and how to load company data that is delivered direct.
- AWS Glue pipelines for licensed external dataLand the files, catalogue them without letting a crawler rewrite your schema, process only new days with job bookmarks, and check every load. Worked on the Fokals tables.
- BattleFin: how the alternative data marketplace worksBattleFin runs alternative data events and a platform for finding, testing and buying datasets. This guide describes both from its own pages and gives a plan for each meeting.
- BigQuery sharing (Analytics Hub): a guide for buyers of company dataWhat subscribing to a listing creates, what a linked dataset does and does not allow, how a commercial purchase works, and what to ask a provider before you rely on it.
- Bringing external company signals into HubSpotFokals is delivered direct, and your pipeline writes it to HubSpot through the CRM API. Here is the property design, the domain match, the batch update and the traps, with SQL and the HubSpot calls.
- Company signals as context for Amazon Bedrock applicationsCompany signals are dated rows with sources. This guide compares an Amazon Bedrock knowledge base with a tool call over your own tables, and shows how to keep every answer dated and cited.
- Company signals as grounding data for Vertex AIWhich of Google's grounding options fits company signals, how to expose BigQuery tables to a Gemini model with typed queries, and how to return dates and sources so an answer can be checked.
- Company-level data and identity platforms: where each fitsAn identity platform resolves people and a company-level feed describes organisations. Match the job to the unit first, then join the two at the account.
- Data sharing in Oracle Autonomous Database, explainedVersioned and live shares, Delta Sharing as provider and as recipient, and the controls a licensing team needs to map: who receives the data, for how long, and what is logged.
- Databricks Marketplace: a guide for buyers of company dataFrom listing to read-only catalog: what a Databricks Marketplace listing contains, how instant and request access differ, how non-Databricks buyers connect, and what to ask a provider.
- Datarade: how the data marketplace works for buyersA buyer's reading of Datarade's own pages: free search, listing fields, sample previews, the request form and the contract, with a way to cut a long list to three providers.
- Delta Sharing, explained for data licensing teamsThe open protocol behind Databricks Marketplace: how providers, shares and recipients work, what changed in its name in 2026, and what a licensing team should settle when data is shared.
- Eagle Alpha: a guide for alternative data buyersA buyer's guide to Eagle Alpha as it describes itself: the three buyer tracks, the platform and advisory, the compliance tool and how to test a dataset you find there.
- Exploring bulk company data with DuckDBRead a sample file with read_csv or read_ndjson, then run seven checks on grain, dates, nulls, JSON cells, labels and baselines. The queries are written for the Fokals tables.
- Feature engineering on company signals in DatabricksWhich timestamp to key a feature table on, how to join labels as of a date, and the leakage traps left in daily and weekly company data, with worked SQL and Python.
- Governing licensed data with Unity CatalogA worked Unity Catalog design for licensed data: a catalog per agreement, group grants, licence tags, lineage and share checks, with the limits of each control stated.
- Grounding Azure AI applications on company signalsTwo routes to answers that carry a date and a source: a search index for announcements and a SQL tool for counts and trends, with the tables, the tool definition and the citation rules.
- How to evaluate company data providers on DataradeSix rows, each tied to a field on a Datarade listing and to a check you can run: how to score providers on coverage, freshness, sourcing, delivery, licence and a sample for the companies you track.
- HubSpot Breeze Intelligence, explainedHubSpot introduced Breeze Intelligence in 2024 and now documents data enrichment and buyer intent as CRM features. Here is what it fills, how it behaves and where dated signals fit.
- Ingesting a vendor API with Fivetran or AirbyteFokals is delivered direct by REST API and bulk files. This guide maps a cursor API onto the Fivetran Connector SDK and the Airbyte Connector Builder, setting by setting.
- Joining licensed company data to CRM tables in SnowflakeA worked join model in Snowflake SQL: a domain normaliser, a bridge to the company ID, subdomain matching, a match-rate report and a view for sales teams, with the pitfalls named.
- Listing a data product on Datarade: what providers should knowA provider's reading of Datarade's own pages: the application, the listing fields, the rules on personal and web data, the lead flow and the plans, checked on 4 October 2026.
- LiveRamp Data Marketplace, explained for B2B data teamsLiveRamp's marketplace trades audience data at a person level, beside its identity products. Here is how buyers use it, and where dated company-level signals fit instead.
- Loading company data into Amazon RedshiftWorked SQL for loading company data into Amazon Redshift: a table definition, COPY from S3, the SUPER type for JSON cells, and a daily incremental load that can run twice without duplicates.
- Loading company data into BigQueryA worked path from files in a bucket to partitioned BigQuery tables: LOAD DATA with an explicit schema, JSON columns for the object cells, MERGE for daily files, and checks after each load.
- Loading company data into Databricks with Auto LoaderA worked load of Fokals files into Databricks: landing in a volume, bronze with Auto Loader, typed silver tables kept free of duplicates with MERGE, and a gold join to your identifiers.
- Loading company data into Oracle Autonomous DatabaseA worked load of company CSV and JSON files into Oracle Autonomous Database with DBMS_CLOUD, with the format options that decide the result, the log tables, a load pipeline and JSON queries.
- Loading company data into Snowflake from files and an APIA worked pipeline from delivered files and API pages to history tables: stages, loading CSV by header name, JSON Lines into VARIANT, and an idempotent daily MERGE.
- Microsoft Fabric: bringing external company data into OneLakeWhere external company data goes in Fabric and how it gets there: lakehouse or warehouse, files kept as they arrive, loaded into Delta tables on a daily schedule.
- Modelling licensed company data with dbtA working dbt layout for licensed company data: sources, staging models, tests on keys and versions, freshness set from each table's closing rule, and the one table worth a snapshot.
- Neudata: how funds use it to find alternative dataA guide for funds weighing a data scouting service: what Neudata says it offers, how buyers use its research and compliance tools, and where its reports end and your own tests begin.
- OneLake shortcuts and external data sharing in Microsoft FabricShortcuts point to data without copying it, and external sharing opens it to another tenant. Here is what each does and what to settle in a data licence before you use them.
- Open:FactSet Marketplace: a guide for data buyersFactSet describes its marketplace as one ecosystem for data, applications and workflows. This guide sets out how it works for a buyer and what its entity mapping means for you.
- Power BI reports on company signalsA model for hiring and technology trends in Power BI: which tables are facts, which measures must not be summed, how refresh fits write-once tables and how row-level security keeps viewers in scope.
- Querying company data on Amazon S3 with AthenaHow to query bulk company files with Athena: JSON Lines external tables over your own S3 prefixes, daily partitions with partition projection, and converting to Parquet with CTAS.
- S&P Global Marketplace: a guide for data buyersS&P Global's Marketplace is a discovery platform for its own and curated third-party data. This guide describes it from S&P Global's pages and works through a query profile.
- Salesforce Data Cloud and external company dataData Cloud is now named Data 360. This guide covers the documented ways to bring in company data, the choice between copying and querying in place, and account matching.
- SAP Datasphere: adding external company data to business dataWhere licensed company data lives in SAP Datasphere and how it meets business partner records: a dedicated space, loaded tables, a mapping table and views that expose only what the licence allows.
- Serving company data to AI agents over Model Context ProtocolA team wraps the Fokals REST API as MCP tools in a thin server of its own. This guide covers tool design, paging, keys, rate limits and untrusted text against the current specification.
- Serving company signals from ClickHouseDesign ClickHouse tables for company change events, signals and weekly scores: which engine, which ordering key, and how a second access path is added when a second query needs one.
- Snowflake Marketplace: a guide for buyers of company dataFrom listing page to first query: how a Snowflake listing reaches your account, how trials and billing work, which questions to put to a provider and how to load data delivered direct.
- Snowflake Secure Data Sharing, explained for licensing teamsWhat is copied, who pays, how access ends and what a licence must still say, whether data reaches you as a Snowflake share, a listing, a reader account or as files you load.
- The SAP Datasphere Data Marketplace for data buyersWhat a buyer reads on a product page in the SAP Datasphere Data Marketplace, how licence keys and delivery types work, and the routes for loading data delivered direct.
- Using company signals with Snowflake Cortex AI functionsWhich Cortex functions exist today, worked SQL to summarise a watchlist's announcements and to search them, and the governance, cost and licence questions to settle first.
- What alternative data scouts look for in a new datasetWhat Neudata and Eagle Alpha publish about how funds judge a new dataset, set beside five checks you can run on a company-level dataset, with the Fokals tables as the example.
- What it takes to list a data product on Snowflake MarketplaceA provider-side guide for data teams deciding whether to list on Snowflake Marketplace: prerequisites, listing fields, review, regional delivery, duties after publication and the licence questions to settle first.
- API vs bulk files for company data deliveryMost teams need both: files to build the copy, an API to keep it current and to answer look-ups. How to assign each job to a route, with the HTTP and file-format rules that decide it.
- AWS Data Exchange vs Databricks MarketplaceAWS Data Exchange sells subscriptions billed by AWS and delivers in five forms. Databricks Marketplace lists data shared through OpenSharing and sends commercial transactions through the provider.
- BigQuery sharing (formerly Analytics Hub) vs Snowflake MarketplaceTwo ways to receive data without loading a file, set side by side as Google and Snowflake document them: access, payment, regions, what you may copy, and how data delivered direct sits beside a listing.
- Building crawlers vs licensing company dataA crawler framework fetches pages, and company data needs more work after the fetch. What building takes, what a licence delivers in its place, and how the two combine.
- Company signals vs third-party intent dataBehavioural intent watches people read. Company signals record what a company does. What each method is built to show, and how to join and test the two in one account ranking.
- CSV vs JSON Lines vs Parquet for bulk data deliveryThree file formats, three sets of trade-offs. What each keeps and loses when a vendor hands you a dataset as files, and a tested way to turn a CSV export into typed Parquet.
- Data marketplace vs direct licensingA marketplace can find, bill and deliver a dataset, but the terms of use still come from the provider. What each route settles at each stage, and how to test the data on either.
- Databricks vs Microsoft Fabric for external company dataTwo lakehouse platforms set side by side for a vendor's daily files: Delta Lake with Unity Catalog beside OneLake, OpenSharing beside external data sharing, and the tools each gives you to fence licensed data.
- Datarade vs Snowflake Marketplace: two kinds of marketplaceOne marketplace introduces you to a provider and leaves the contract to the two of you. The other puts the provider's tables in your account and sends the invoice. How to use each, and what both leave open.
- Delta Sharing vs Snowflake Secure Data SharingOne is a protocol that any client can speak, the other a feature inside one platform. A comparison built on two questions a licence turns on: who can be a recipient, and what ends access.
- Eagle Alpha vs BattleFin for alternative data sourcingTwo services a fund can use to find alternative data, set side by side as each describes itself on 4 October 2026, with a test of a candidate dataset that holds on either route.
- Firmographic vs technographic dataWho a company is and what it runs are different facts from different sources. A field-by-field comparison, a segment built on both, and how to read each kind correctly.
- Fivetran vs Airbyte for ingesting a vendor APIA vendor's REST API reaches your warehouse through a connector you build. How the two tools differ, as each documents itself: where the code runs, what you write, where the bookmark lives and how rows are counted.
- ISIN vs LEI vs FIGI: company and security identifiers comparedThree codes that identify three different things. What each one names, who issues it, what you may do with it, how to validate it and which column to join on.
- Microsoft Fabric vs Snowflake for external company dataOne daily vendor file taken into a Fabric warehouse and into Snowflake: the two COPY INTO dialects, a JSON cell on each, what each bills for, and how the platforms read each other's tables.
- Neudata vs Eagle Alpha for alternative data discoveryTwo services that help funds find and vet datasets, compared only on what each publishes about itself, with a way to test both on your own research question before you choose.
- Snowflake Marketplace vs AWS Data ExchangeA share in your Snowflake account, or files, an API and in-place access on AWS: the delivery mechanics, product types and billing of the two marketplaces, set side by side for buyers and providers.
- Snowflake vs BigQuery for external company dataOne vendor file loaded into both warehouses: COPY INTO beside LOAD DATA, VARIANT beside JSON, shares beside linked datasets, and what each documented pricing model means for a daily feed.
- Snowflake vs Databricks for licensed company dataOne daily vendor file taken through both platforms: stages and COPY INTO beside volumes and Auto Loader, VARIANT on each, then sharing, governance and AI functions as each vendor documents them.
- Technographic data vs intent dataFit and timing are separate questions. What technographic and intent data each record, the change in a stack that links them, and how to combine both in one account list.
- 6sense alternatives for account intent signals6sense scores accounts with predictive models inside its platform. If you want the signals themselves, dated and sourced, to score in your own stack, here is how the options compare.
- Alternatives to building your own company data crawlersFour routes to rows of company data from the web: your own crawler, a managed scraping service, open datasets or a licensed feed. What each takes off your hands, and what stays with you.
- Bombora alternatives for B2B intent dataBombora measures what companies research across its Data Co-op. Fokals scores what companies do in public, with the evidence attached. What each is built for, and a join that runs them together.
- BuiltWith alternatives for technographic dataBuiltWith indexes the web for the technologies sites run. If your question is about identified companies, dated changes and what else those companies do, here are the alternatives.
- Clearbit alternatives after the move to HubSpotClearbit is part of HubSpot, and HubSpot documents data enrichment inside its own CRM. For teams whose records live elsewhere, here are the options and a worked design.
- Coresignal alternatives for company and jobs dataCoresignal is built around company, employee and jobs records from the public web. What each product is built for, the sources that fit each part of a workload, and the company and jobs parts mapped dataset by dataset.
- Crunchbase alternatives for company data licensingCrunchbase records who funded a private company, round by round. Here is what it licenses, which databases cover the same ground, and the job that dated company signals do beside a funding database.
- Demandbase alternatives for account intelligence dataDemandbase combines intent, account identification, advertising and sales tools in one platform. If you want account intelligence as tables in your own stack, this guide sets out the options.
- Dun & Bradstreet alternatives for company signalsD&B holds registry, credit and D-U-N-S data. Fokals delivers dated signals of what a company did. A fair map of each, the sources that fit each need, and a worked join from D&B-keyed accounts to those signals.
- HG Insights alternatives for technology intelligenceHG Insights reports the technology companies run beyond what a website shows, with spend and intent data. Here is what it is built for and where a dated, company-level feed fits.
- LinkUp alternatives for job market dataLinkUp and Fokals both take job postings where the employer publishes them. What each is built for, how the two records line up field by field, and four other sources of job market data.
- People Data Labs alternatives for company dataPeople Data Labs documents a person dataset beside its company dataset. Fokals is company-level data throughout. A fair map of the two as documented, a move dataset by dataset, and the sources that fit each need.
- PitchBook alternatives for tracking private company momentumPitchBook answers who invested, in which deal and from which fund. This guide lists other deal databases and works through a momentum screen built from postings, website changes and announcements.
- PredictLeads alternatives for company signalsThe two are close in kind. What each is built for, where the methods differ enough to change your numbers, and how one account alert is built on each feed.
- Revelio Labs alternatives for workforce dataRevelio Labs models who works at a company; job postings show whom it wants next. This guide sets out what each kind of source answers, with four alternatives and a worked measure.
- TheirStack alternatives for jobs and technographic dataTechnographic data comes from job postings or from websites, and the two see different tools. What TheirStack documents, where Fokals fits, and how to check one method against the other.
- Thinknum alternatives for web-sourced alternative dataThinknum indexes the data trails companies leave on the web. Here is what its site states today, which other vendors cover similar ground, and how to test a second feed on the companies you track.
- Veridion alternatives for web-sourced company dataVeridion and Fokals both collect company data from public web sources, for different sets of companies and in different shapes. A fair map of each, the neighbouring sources, and a test to run on your own records.
- Wappalyzer alternatives for technology detection dataWappalyzer identifies website technologies on demand, with lead lists and alerts. If you need detection as a dated dataset to build on, here is how the alternatives compare.
- ZoomInfo alternatives for company-level dataZoomInfo pairs a contact database with sales and marketing applications. If you need company-level data as a feed for your own warehouse, model or product, here is how the alternatives compare.