Fast forward 2018 and today every CEO, CIO, CDO, CMO Â is seeking answers to questions that don't exist yet. ">
Until a few years ago the work of data scientists was isolated and mattered primarily for research and/or R&D purposes. The industry has been extremely thankful for the contributions of these clever individuals but we need them now in the mainstream! This need is creating a huge demand in the industry and it's radically transforming the hunt for big data and Machine Learning talent.
Fast forward 2018 and today every CEO, CIO, CDO, CMO Â is seeking answers to questions that don't exist yet.
Typical questions in the minds of the leaders could be...
#1 How do I drive innovation within my company, industry or community in a dramatic fashion?
#2 How do we disrupt the industry with our products and services?
#3 How do we change and challenge our workforce's mindset by providing insightful "out-of-the-box" data analytics?
#4 How do we launch a new LOB (Line of Business), product and/or service using these thin/thick/lazy/idle datafied analytics with machine - and deep learning?
#5 For Governmental or CSR-driven businesses - How do we dramatically drive social change, reduce crime using data? For instance see how Memphis reduced its crime rate by 30% using data.
Think about it from your own industry perspective.
An Example: The Oil & Gas sector. How do we enhance and optimise exploration and production (E&P)? With conventional technologies becoming commoditised and depleting traditional resources one has to competitively use unconventional methods such as deepwater, tight oil or shale gas. I won't get deeper into aspects such as seismic plotting, WAZ and NATS, etc. as that is only generating more data waiting to be exploited!
Think what it can mean for your industry if you had the "right eyes" to spot the gold!
No industry today can say that it is not data driven. With vast volume, speed and variety of data coming from external and internal sources, the need to scientifically approach data is paramount for competitive intelligence of organisations. That is what is keeping CxOs awake at odd hours.
data science is the study of the generalizable extraction of knowledge from data,[1] yet the key word is science.[2] It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing and high performance computing with the goal of extracting meaning from data and creating data products. data science is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect of data science.
And who is a Data Scientist?
Data scientists solve complex data problems through employing deep expertise in some scientific discipline.
It is generally expected that data scientists are able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects are not required. However, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. This means that data science must be practiced as a team, where across the membership of the team there is expertise and proficiency across all the disciplines.
Ok, so having addressed the boring what part, lets get on to the why of it. What is really happening in the industry?
I was having an interesting conversation with one of the co-founders of my previous management consulting startup and we were discussing why the consulting industry was getting disrupted so dramatically. If you didn't notice, many consulting firms such as Booz, Monitor Group, etc.
