This paper is loosely based on the Wikipedia article entitled “Data Analysis” and the book Mastering Genealogical Proof.
Genealogists use raw data to accumulate and analyze patterns and trends toward establishing a Genealogical Proof. Evidence in the genealogical community is generally understood as pieces of data that are arranged through collection, sifting, and arranging. Evidence, positive or negative, is acquired through examining and modeling data using generally accepted processes. One such process is to use the computer application Evidentia. Other processes enabling the development of evidence are those used in the legal and forensics professions (e.g., DNA analysis).
Each point of data genealogists use is inspected, cleansed, transformed, and modelled. Most serious genealogists use the Genealogical Proof Standard. While this standard is more qualitative than quantitative, the results are the same, actionable information used to formulate decisions.
The Genealogical Proof Standard follows, simply: Formulating a research question, gathering data sources, considering the information in those sources, formulating evidence from that information, and finally constructing a proof statement. The process is generally iterative since there is no such thing as a final statement of proof in genealogy.
While traditional data analysis is generally thought to be quantitative, there is much similarity to the genealogical research process. The steps in data analysis are analogous to the process used by genealogy professionals. Data analysis begins with a research question, followed by compiling source information, and finally, generating actionable conclusions.
Sometimes thought of as a hypothesis, the research question is the beginning of both genealogical research and data analysis. Genealogists formulate a question by asking something such as “Who was Joan Jones’ mother?” Data analysts ask, “How is product A better than product B?” The answers come in basically the same way for both.
Data Collection, Processing, and Cleaning
To answer the research question, both genealogists and data analysts collect, process and classify data relevant to the issue. Almost all data is seen as relevant to analysts, but genealogists often go further, collecting source material relevant not only to the issue, but also surrounding the issue. Data analysts, on the other hand, are more focused on the question itself, locating only data relevant to products A and B.
The difference between traditional and genealogical data analysis is that genealogists have much more fuzzy information to deal with. Items like local and regional history books may include data about their question. Such items are generally not relevant to a data analyst focused on a product research project, unless it involves cultural appropriation, i.e., the Korean car makers’ KIA Tucson vehicle. 😊
Genealogists often explore different sets of data to glean information and evidence relevant to their questions. Similarly, a traditional data analyst will do the same, focusing more on specific items than general items.
Modelling and Algorithms
There are no “real” algorithms for genealogists to apply to their data findings. There is, however, a Genealogical Data Model, which was constructed to help genealogists apply their data to real-world projects. The Genealogical Data Model was originally constructed to be a basis for software, but since it was completed, no software has used the GDM (except for The Master Genealogist, which used large parts of it).
Data Products and Communications
Genealogists use a proof model to present data and their formulation of the evidence they’ve compiled. A traditional data analyst uses a tool such as business intelligence software to present their findings. The only real difference between the two is that they present findings in a different way.