#ARTEFACT VS ARTIFACT MEANING SOFTWARE#

Summaries - results of an analysis on a single cohort, utilizing one or more variables.Query results - products of structured queries and functions, often producing derivative tables.Vohorts - subsets of variables, otherwise known as signatures or feature sets.Cohorts - subsets of entities, otherwise known as clusters or segments.Analysis parameters - the arguments and settings by which you configure and run an analysis - i.e., the question or request you asked of the data.

You are probably already creating UDATs now, here are some examples:

Useful! For reporting, making decisions, applying to other data sources or exporting for use in external systems.

FAIR (Findable, Accessible, Interoperable, Reusable).

explicitly attributable to the persons and processes that created it.

Digitally storable in a structured schema.

Within most organizations, data artifacts are scattered around like this.

Because you don’t want your organization’s discoveries and trade secrets sequestered in the brains of your employees or scattered about in different places or incompatible digital formats.

Because the investment you put into collecting, storing and utilizing data sources is wasted if you don’t have a systematic process for creating recordable, attributable, reproducible and useful outputs from analysis.

#ARTEFACT VS ARTIFACT MEANING SOFTWARE#

Because the state-of-the-art outputs from data analysis software are visualizations and dashboards, which are highly overrated - specifically because visualizations fail to generate UDATs.There is a significant Last Mile Problem. Because people have far more questions of - and needs for - data sources than current processes and systems can handle.Because data is expanding at an exponential rate, both in scale and complexity.Why the generation and management of UDATs are critically important right now. Our industry loves acronyms, so I’m going to use UDATs to refer to Useful Data Artifacts from here on out. More technically - a Useful Data Artifact is a nonrandom subset or derivative digital product of a data source, created by an intelligent agent (human or software) after performing a function on the data source. It’s not in your brain - it’s a structured data object, created when you or an algorithm do something with data. It is not an idea, a thought, a realization, or an insight. I’d been using the term “Data Artifacts” for some time, but what’s the value of one, if it’s not useful ? What is a Useful Data Artifact?įirst and foremost, a Useful Data Artifact is an actual digital thing. A brief introduction to Useful Data Artifacts - and the next generation of data analysis systemsįor starters, I’d like to acknowledge Josh Dunn for first using the term “Useful Data Artifacts” in a conversation over lunch at the Boston Seaport a few weeks back.

Artefact vs artifact meaning