Big Data, Data Analytics

9 Reasons Smart Data Scientists Don’t Touch Personal Data

The production of massive amounts of data as a result of the particular ongoing ‘Big Data’ revolution has changed data analysis. The availability of analysis tools and decreasing storage costs, allied with a drive-by business in order to leverage these datasets with purchased plus publicly available data can bring insight and monetize this particular new resource. This has led to an unprecedented amount of information about the personal attributes of individuals being collected, stored, and lost. This particular data is valuable for analysis of large populations, but there are a considerable number of drawbacks that data scientists and developers need to think about in order to use this data ethically.

Here are just a few factors to take into account before tearing open the predictive toolsets from your cloud provider:

1. Contextual Integrity

Data is collected over different contexts which have different reasons and permissions with regard to capture. Ensure that will the data you capture is valid regarding that context plus cannot be misused for other purposes. There could be unintended side effects associated with mixing public and personal data. An example is notifying other parties of location data without consent, as there are numerous examples of stalkers using applications to track others.

2. History Aggregation

History is an important part of many attempts to defining...

Read More on Datafloq

The production of massive amounts associated with data as a result of the ongoing ‘Big Data’ revolution has transformed data analysis. The availability of analysis tools and decreasing storage costs, allied with a drive-by business to leverage these datasets with purchased and publicly available data can bring insight and monetize this new resource. This has led to an unprecedented amount of data about the personal attributes of individuals being collected, stored, and lost. This data is valuable for evaluation of large populations, but there are a considerable number of drawbacks that information scientists and developers need to consider in order to use this data ethically.

Here are just a few considerations to take into account before ripping open the predictive toolsets from your cloud provider:

1 . Contextual Integrity

Data is gathered over different contexts which have different reasons and permissions for capture. Ensure that the data you capture is valid for that context plus cannot be misused for other purposes. There could be unintended side effects of mixing public and personal data. An example is notifying other parties associated with location data without consent, as there are numerous examples of stalkers using applications to track others.

2 . History Aggregation

History is an important part of many efforts to defining…