Thursday, October 15, 2015

Data Science vs. Decision Science

Either of them is not the end, but the means to the end - for problem-solving, improvement, and innovation.

From a business perspective, "Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies"(Whatis.com). Decision science or engineering is not "just a new buzzword." It is a knowledge revolution for proactive structural decision simulation analysis and strategic decision analysis for crisis early warning and proactive feedforward reactors control against process operation uncertainties. The typical challenge seen with the traditional analytics approach is to arrive at insights, but not necessarily affect actual business decisions, and not always in a timely manner. Decision Engineering approach embeds analytics in actual business decisions - rather than leaving it to the receiver of insights to use or dump. There is a further difference between Data Science and Decision Science. The latter is also called Business Data Science, combining the instrumental (data science tools & technique), social (business context) and functional (information process) axes to add value to the data and information within a company.


Data science has to be able to contribute towards the ability or capacity to enable change. Usually data-gathering is driven by the need to make decisions. Many are questioning the ROI on data science. But in the operational context of many organizations, data is collected as part of an important business function: human resources, logistics, quality control, and accounting. This means the data will be collected even if the ROI is not apparent. Strategic alignment, however, dictates constant change either in the nature of the data collected or its handling; This is not as simple as scanning all sorts of historical data that might now be outdated and inapplicable. While certain segments of the data science community might be focused on this historical learning, businesses generally seek out guidance to deal with future developments. So in certain respects, the only surviving lessons from the past are the most abstract; and the lessons also lead to the development of capacity to make use of intellectual capital; this remains with an organization in perpetuity, unless deliberately impaired. As such, a data scientist actually wields not just data but capital (capitalized intelligence) as evidenced by persistent artifacts associated with production.


There are three general pillars in Decision Science: Modeling/Simulation (stochastics and probability), Data Analysis (an application of statistics principles and data science), and Optimization (mathematical modeling with generally discrete results and an opportunity for sensitivity analysis for skeptics). These three pillars are different methods an analyst could apply to a particular problem based on the information at hand and the desired methodology. All three methods provide an opportunity to gain insight to make an informed quantitative decision and evaluate if a certain risk is acceptable. And there is an opportunity to explore a mixture of the "pillars," it doesn't matter if you are calling yourself a decision of data scientist, you are still applying scientific and mathematical principles to gain further insight or arrive at some results.


What is the goal of doing both data science and decision science? The pyramid of wisdom separate: data - information - knowledge - understanding - wisdom. This is in order of largest to smallest. These elements are not the same, but the one cannot be without the other. Nor is the one better nor equal they are all relevant and to be taken into account in decision making. Too often, businesses lean towards data science as the solution, building unbalanced teams which fall somewhat short of the fundamental skill of business problem definition and structuring. A solution is somewhere between data science and decision science. Businesses needs insights that drive real value, data science is only one of many enablers, and decision science could put more focus on decision analysis, but always keep in mind: either of them is not the end, but the means to the end - for problem-solving, improvement and innovation.


24 comments:

I really appreciate information shared above. It’s of great help. If someone want to learn Online (Virtual) instructor lead live training in Data Science, kindly contact us http://www.maxmunus.com/contact
MaxMunus Offer World Class Virtual Instructor led training on Data Science. We have industry expert trainer. We provide Training Material and Software Support. MaxMunus has successfully conducted 100000+ trainings in India, USA, UK, Australlia, Switzerland, Qatar, Saudi Arabia, Bangladesh, Bahrain and UAE etc.
For Demo Contact us.
Nitesh Kumar
MaxMunus
E-mail: nitesh@maxmunus.com
Skype id: nitesh_maxmunus
Ph:(+91) 8553912023
http://www.maxmunus.com/


Hello,
The Article on Data Science vs. Decision Science is nice give detail information about it .Thanks for Sharing the information about Caparison of Data Science and Decision Science. big data scientist

Really useful information. we are providing best data science online training from industry experts.

Really helpful to develop my knowledge and understand the datascience and decision science. This explanation are very clear so easy to understand..
Also Check out the : https://www.credosystemz.com/training-in-chennai/best-data-science-training-in-chennai/

your article on data science is very good keep it up thank you for sharing.
Data Science Training in Hyderabad

thank you for the valuable information giving on data science it is very helpful.
Data Science Training in Hyderabad

nice information on data science has given thank you very much.
Data Science Training in Hyderabad

Attend The Data Science Courses From ExcelR. Practical Data Science Courses Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Courses.
Data Science Courses
Data Science Interview Questions

wonderful article. Very interesting to read this article.I would like to thank you for the efforts you had made for writing this awesome article. This article resolved my all queries.
Data science Interview Questions
Data Science Course

Great post i must say and thanks for the information. Education is definitely a sticky subject. However, is still among the leading topics of our time. I appreciate your post and look forward to more.

Correlation vs Covariance

Great post i must say and thanks for the information. Education is definitely a sticky subject. However, is still among the leading topics of our time. I appreciate your post and look forward to more.
Data Science Institute in Bangalore

Very interesting to read this article.I would like to thank you for the efforts you had made for writing this awesome article. This article inspired me to read more. keep it up.
Correlation vs Covariance
Simple linear regression

Thank you so much for ding the impressive job here, everyone will surely like your post.
Data Science Course in Bangalore

You actually make it look so easy with your performance but I find this matter to be actually something which I think I would never comprehend. It seems too complicated and extremely broad for me. I'm looking forward for your next post, I’ll try to get the hang of it!

Data Science Course

I have express a few of the articles on your website now, and I really like your style of blogging. I added it to my favorite’s blog site list and will be checking back soon…

Data Science Training

Very interesting blog. Many blogs I see these days do not really provide anything that attracts others, but believe me the way you interact is literally awesome.You can also check my articles as well.

Data Science In Banglore With Placements
Data Science Course In Bangalore
Data Science Training In Bangalore
Best Data Science Courses In Bangalore
Data Science Institute In Bangalore

Thank you..

Post a Comment