Data enrichment is the process of adding verified outside facts, such as company filings, funding history, product activity, or work history, to a raw name or company record, so the record holds enough evidence to be scored, compared, or acted on.
How data enrichment works
A record usually starts thin: a founder's name and an email from an inbound form, a company name from a conference list, a candidate's CV. Nothing in it is enough to score. Enrichment fills it in, in five steps.
- Match the entity. Work out which real person or company the record refers to. "Acme" could be dozens of companies. This step is called record linkage or entity resolution, and after decades of research it remains a hard problem.
- Append facts. Add fields from outside sources: founding date, headcount, funding rounds, past roles, product launches.
- Reconcile conflicts. When two sources disagree on headcount or a job title, pick a rule (most recent, most authoritative) and record which one won.
- Standardize. Put dates, currencies, titles, and locations into one format. This overlaps with normalization.
- Stamp and refresh. Record where each fact came from and when. Re-check on a schedule, because companies and careers change.
Combining records from different systems into one view is a classic data integration problem. Enrichment is the part of it aimed at making each record decision-ready.
Why data enrichment matters for scoring
A score can only use what is in the record. If half the companies in a pipeline are missing headcount, any criterion that depends on team size is scored on guesswork for half the list. Enrichment is what lets evidence-based scoring point at evidence.
- Venture capital. A fund turning a list of 2,000 companies into a market map needs stage, sector, and founding team for each one.
- Private equity. Firms watching a target market need ownership, size, and leadership changes kept current.
- Hiring. A CV lists titles. Enrichment adds context: what the employer did, how large it was, what the candidate shipped.
- Sales. Go-to-market teams enrich accounts with firmographics and intent data. Investors reading a company's sales motion often ask how clean that data is.
Each enriched fact can become a signal: a new senior hire, a funding round, a product launch. Without enrichment there is nothing to detect.
Worked example: enriching an inbound pitch
Northwind Ventures receives a one-line inbound, the usual starting point for inbound deal scoring: "Dana Okafor, CEO, Tillwise, dana@tillwise.example." Before enrichment the record has three fields.
| Field | Before | After enrichment | Source noted |
|---|---|---|---|
| Company | Tillwise | Tillwise Ltd, point-of-sale software for independent cafes | Company website, registry filing |
| Founded | 2024 | Registry filing | |
| Funding | Pre-seed, angel round | Founder's deck | |
| Headcount | 6 | Public team page, checked against founder | |
| Founder history | Former payments product lead at a mid-size fintech | Public profile, reference call |
The partner can now score thesis fit and team in minutes. The "source noted" column matters as much as the facts: when the founder says headcount is actually 8, the team knows which source to correct.
Data enrichment vs data cleansing vs normalization
| Data enrichment | Data cleansing | Normalization | |
|---|---|---|---|
| Goal | Add missing facts | Fix wrong or duplicate facts | Make values comparable |
| Changes | Adds new fields | Corrects or deletes existing fields | Rescales or reformats existing fields |
| Example | Adding founding year | Merging two records for the same company | Converting all revenue to US dollars |
In practice the three run together, and the order matters: clean first, enrich second, normalize last.
Enrichment also sets up two other jobs. Complete, comparable fields let a team set a company against its peers, which is the basis of benchmarking. And where enrichment adds facts about a person, relationship intelligence adds who on the team knows them and how well.
Keeping enriched data accurate and compliant
Enrichment about people is personal data processing. In the EU, the General Data Protection Regulation applies, and the UK has its own version with the same core rules. Among other things, it requires a lawful basis for processing (Article 6), data that is accurate and kept up to date (Article 5), and, where data was not collected from the person directly, telling them about it within set time limits (Article 14). This is a summary, not legal advice.
Common mistakes:
- Bad matches. Attaching another person's career history to a candidate is worse than leaving the field empty.
- No source or date on each fact. You cannot fix or defend what you cannot trace.
- Collecting everything. Pull the fields the rubric uses. Extra personal data adds risk without adding signal.
- Enrich once, never refresh. A company's headcount from last year can flip a score this year.
How ScoringFactory handles enrichment
ScoringFactory fills in the record on each founder, company, or candidate before scoring, and keeps the source attached to every fact, so each score is cited to the line. Data handling and retention practices are described on the trust page. Your team sees what the score rests on and decides what to do with it.
Frequently asked questions
What is data enrichment?
Data enrichment is adding verified facts from outside sources to a basic record, such as a name, email, or company name. Typical additions are company size, funding history, industry, and work history. The goal is a record complete enough to score, segment, or act on, with each added fact traceable to where it came from.
What sources are used to enrich company or candidate records?
Common sources include company registries and filings, company websites, press releases, funding announcements, public professional profiles, and information the person or company provides directly. The right mix depends on the decision. Whatever the source, record it next to the fact with a date, so errors can be traced and corrected later.
How do you keep enriched data accurate and compliant?
Match entities carefully, store the source and date for every fact, refresh on a schedule, and collect only the fields your scoring uses. For personal data in the EU or UK, GDPR requires a lawful basis, accuracy, and transparency toward the people concerned. Get legal advice for your specific use.
Is enrichment the same as scraping?
No. Scraping is one way to collect raw data from websites. Enrichment is the broader job of matching that data to the right record, reconciling conflicts, standardizing it, and keeping it current. Enrichment can use scraped data, licensed datasets, public filings, or information people provide directly, and each source carries its own terms and legal limits.
Sources
- Regulation (EU) 2016/679 (General Data Protection Regulation), EUR-Lex, European Union, current text
- An overview of end-to-end entity resolution for big data (Christophides et al., 2020), ACM Computing Surveys
- (Almost) all of entity resolution (Binette and Steorts, 2022), Science Advances