UNDERSTAND PEOPLE BETTER. IDENTIFY THEM BY ETHNICITY
Add ethnicity information to personal records
Organizations have a growing need to understand the ethnic diversity of the people they serve. But this task is extremely difficult, since predicting ethnicity it’s a complicated and messy task.
Do you have a large population you need to understand by ethnicity and all you have are their names?
Onolytics can help.
With over a decade of research into the science of the origin of names, we’ve taken out the guesswork and replaced it with an automatic and well-tested method.
Onolytics proprietary algorithm classifies any list of names by the most probable ethnic origin of the person’s full name.
By applying our simple to use tools, within minutes you can quickly identify the ethnic origin of your entire population using only their first name and surname.
The classification is probabilistic and we also provide a score representing how confident we are in each of the individual assignments.
Want more details?.Check out Dr. Mateos’ book Names, Ethnicity and Populations (Springer).
Try Onolytics now!
Introduce a first name and surname (last name) to get a person’s ethnicity prediction in real-time
Enrich. Understand. Act
Onolytics adds ethno-cultural information to people profiles, allowing you to better understand an individual’s background. Using Onolytic’s data, you can adjust your practices to make appropriate accomodations.
Enrich. Understand. Engage
Onolytics adds ethno-cultural information to your customer profiles, allowing you to understand how your users engage with your brand, products, and services
What our clients are saying
- “It was very important for us to be able to identify ethnic origin in a large database of cancer cases in England where ethnicity was not previously recorded. Onolytics offered the most accurate and cost-effective method to append ethnicity to our records and thus understand migration, cultural and genetic factors in certain cancer patterns”
Senior Project Officer, Primary Care Clinical Sciences, at a UK University
- “Canada’s fastest population growth is concentrated in three ethnicity minorities, and we implemented a multicultural marketing strategy in order to reach them better. Onolytics allowed us to find out our most profitable customers by cultural group, in order to expand the market. We now know where and how to get more of them!”
Marketing Segment Analyst, Canada
- “Onolytics software allowed us to get a more accurate picture of the cultural ethnic and linguistic backgrounds of the populations living in electorates. We performed this analysis in order to better help members of parliament understand – and therefore engage and service – the people of their electorate”
Political leader, Australia
- A the Elections Research Centre in our university Onolytics was very useful to classify a survey of political candidates by ethnicity in order to understand more about the extent and nature of under-representation in local government, specifically amongst women and ethnic minorities.
Researcher at Elections Research Centre, UK University
- We used Onolytics to analyze voting behavior in a research project where the aim was to show whether people with a migration background are systematically discriminated at Swiss elections. Onolytics was the only way we could automatically classify the candidates by migration background coding every candidates name and surname.
Professor at a Swiss University
- As a an epidemiologist/ health service researcher working primarily in cancer research, I got frustrated that despite having access to a large dataset of named cancer registration records, I could not examine the patterning of events within the data by measures of ethnicity. Onolytics was the perfect tool to classify patients by ethnicity following a reliable methodology that has external validation and published papers, so it was easy to get ethical committee approval about its use.
Senior Lecturer in Population Health, UK University
- Every three years we invite all eligible women registered with a General Practice for a mammogram, but we wanted to increase the uptake of our service which fell below national targets. A key to understand non-attendance is ethnicity, but whilst we collect data on ethnicity when women attend for screening we do not know the ethnic group of those not attending. Onolytics was key to uncover key patterns behind non-attendance by very specific ethnic minorities and we can now measure our progress towards wider inclusion against a baseline.
National Health Service (NHS) Trust, UK