Data Scientists Must Prioritize Client Confidentiality

Data Scientists Must Prioritize Client Confidentiality

Many organizations are reluctant to create data science teams (internally or externally) because of information confidentiality and privacy concerns.

It is dangerous to open the kimono to competition - disclosing high-value information about inner workings of the firm may cause significant damage.

There is real fear of exposing valuable confidential information to data scientists who may leave the firm and share key knowledge with competitors. Moreover, externally hired data scientists could potentially share critical information with their other clients who may be direct or indirect competitors.

One solution is hiring only data scientists who are governed by the data science Code of Professional Conduct and are required to maintain strict client confidentiality.

Rule 5 of the Data Science Association Code of Professional Conduct includes the following confidentiality provision:
Rule 5 - Confidential Information

(a) Confidential information is information that the data scientist creates, develops, receives, uses or learns in the course of employment as a data scientist for a client, either working directly in-house as an employee of an organization or as an independent professional.

It includes information that is not generally known by the public about the client, including client affiliates, employees, customers or other parties with whom the client has a relationship and who have an expectation of confidentiality. The data scientist has a professional duty to protect all confidential information, regardless of its form or format, from the time of its creation or receipt until its authorized disposal.

(b) Confidential information is a valuable asset.

Protecting this information is critical to a data scientists reputation for integrity and relationship with clients, and ensures compliance with laws and regulations governing the client's industry.

(c) A data scientist shall protect all confidential information, regardless of its form or format, from the time of its creation or receipt until its authorized disposal.

Share it:
Share it:

[Social9_Share class=”s9-widget-wrapper”]

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

You Might Be Interested In

Smart Data Is a Bigger Priority Than Big Data for FinTech Companies

27 Jul, 2016

In the financial technology world, collecting the right information is critical. Even though big data is often heralded as the …

Read more

Machine learning may supercharge enterprise architecture

4 Apr, 2017

APIs as products in their own right, serverless architectures, and “legacy in a box” are the trends shaping IT management …

Read more

Who do you trust? How data is helping us decide

10 Oct, 2017

My first lesson in the dangers of trusting strangers came in 1983, not long after I turned five, when an …

Read more

Do You Want to Share Your Story?

Bring your insights on Data, Visualization, Innovation or Business Agility to our community. Let them learn from your experience.

Get the 3 STEPS

To Drive Analytics Adoption
And manage change

3-steps-to-drive-analytics-adoption

Get Access to Event Discounts

Switch your 7wData account from Subscriber to Event Discount Member by clicking the button below and get access to event discounts. Learn & Grow together with us in a more profitable way!

Get Access to Event Discounts

Create a 7wData account and get access to event discounts. Learn & Grow together with us in a more profitable way!

Don't miss Out!

Stay in touch and receive in depth articles, guides, news & commentary of all things data.