MY DATA ACTION

PART 1- How to use Data Science in everything we do

key takeaways > use the data you deal with > add to business value using data science

Data science is about empowering ourselves with data. In today’s world, we are anything but short of data. Data is the 21st century’s renewable resource. Resources when used sustainably, open doors to new opportunities; if you wish to knock on the one that says, “data science”, this article is for you.

The question remains- how one can inculcate data science in everything they do? Where do we each begin?
A Venn diagram.

The first circle(green in color) asks:
— What kind of data do you deal with in your everyday life and work?
— What are your areas of data expertise?
— What kind of data can you bring to the table?
Think about your skills, resources, and networks.

The second circle(pink in color) asks:
— What kind of problems do you deal with? can any be solved using Data Science?
— What kind of predictions will be helpful to you?

The goal is to make an impact and add to the business(personal) value, so ask yourselves how you can deploy data to improve business(personal) value. That makes the third and final circle(yellow in color).

The intersection of the three circles will provide you with your data action!

Feel free to edit this Venn diagram, maybe add more circles, more questions anything that floats your boat.

Let’s understand the Venn diagram with an example, a good example is IBM’s Green Horizon Project, wherein environmental statistics from varied assets and sensors are leveraged to produce pollution forecasts. The aim is to bring down the environmental impact and the corresponding Venn diagram would look like this:-

Let’s say a team in IBM is responsible for collecting and managing environmental data, the team acquires this data from varied assets and sensors. They wish to somehow add value to their business using this data and so, come up with pollution forecasting algorithms that help reduce IBM’s negative environmental impact.

A simple Venn diagram can help one appreciate the power of data (and data science) and identify the data scientists within themselves.

Lastly, it is important to note that even though we have the knowledge and expertise, data science and data analytics might still fail!
That is because the quantity and quality of data depend on people.
One can acquire data, but to make it work, one needs more than just data science, one requires data to be everyone’s responsibility and everyone’s business.

References:-
https://mediacenter.ibm.com/media/Green+Horizons/1_f9ftqtn1
https://youtu.be/xC-c7E5PK0Y
https://omdena.com/blog/machine-learning-examples/