I created a survey question on Twitter to look at data trends. I was interested to see whether people felt that improving the quality of their data was more important than AI data ethics. Data quality is heavily influenced by data ingest so I added this as an option, as i felt it is often over looked, but is a foundation stone of good data quality.
A few definitions:
“Data Ethics describe a code of behaviour, specifically what is right and
wrong, encompassing the following: Data Handling:
generation, recording, curation, processing, dissemination, sharing, and use."
“Data Quality (DQ) as stated
in the DAMA International, Data Management
Book of Knowledge "Refers to
both the characteristics associated with and to the processes used to measure
or improve the quality of data.” Data is
considered high quality to
the degree it is fit for the purposes data consumers
want to apply it."
“Data
ingestion is the process of obtaining and importing data for
immediate use or storage in a database. To ingest something is to
"take something in or absorb something." Data can be streamed
in real time or ingested in batches.”
“Data
ingestion tools provide a framework that allows companies to collect,
import, load, transfer, integrate, and process data from a wide range of data
sources.”
The survey question had 267 votes.
In additions to the results above I received a few additional
comments.
- Neither
- The biggest thing in my opinion is just ethics. How is the data collected?
- Also, what is it being used for. What are the impacts of high or low accuracy models.
- Improving quality and ethics seem to me, to be related tasks
- All of the above?
The results are quite interesting with AI Data Ethics and Improving Data
Quality being the trends that the respondents thought were the most important.
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