Does L&D Need Data Science
Some time ago I wrote some articles here relating to data and L&D. I ended one of those articles with a question which is, does L&D need data science? I intend to answer that question with this post. Without wasting much time, I will say that the answer is yes. Now before you roll your eyes, let's answer another question, what really is data science? Yes, we've all heard the news about data science being the sexiest job of the century and how there is a skills shortage which has resulted in a mad rush by universities, MOOCs and development bootcamps to train up data scientists. Almost every university runs some variant of a data science degree programme in various guises such as business analytics, big data, text analytics and of course data science.
A lot of it really is hype, but the truth is data science is important considering the amount of data we generate. Okay, let's go back to the question, what is data science? Simply put data science is the process of using scientific techniques such to uncover patterns in data for better decision making. Such scientific techniques may include hypothesis generation, testing and experimentation. By applying scientific processes to data, not only will one be able to understand the data more, but you can also model the data to aid various types of decision making. Data science goes beyond just analysing data, into more complex fields such as natural language processing, neural networks and artificial intelligence. Two things are important to know about data science, firstly it is an applied science. It is composed of a set of skills that you can apply to almost any domain. Secondly it is not a subject in itself, rather it is made up of a number of different subjects that come together to form the core of data science.
Whenever there is discussion about what one needs to know to become a data scientist three circles which intersect are usually used to illustrate what data science is made up of.
- Circle 1: knowledge of maths and statistics. This is the foundation of data science as the analytic techniques and algorithms used in data science are applied mathematics and statistics.
- Circle 2: Ability to program and use appropriate technology. data science is not computer science, but computer science is needed to apply mathematical and statistical algorithms to data. This may be through using programming languages such as Python, R and Scala or software such as tableau, Weka and SAS. Programming languages tend to be the preferred way for doing data science.
- Circle 3: This third circle is what makes data science really useful and it's having domain knowledge or understanding of a subject area to which you will be applying data science to. That subject area could be medicine, education or genomics. It doesn't matter how much technical knowledge you have, if you don't have a domain to apply it to, then your data science skills become meaningless.
Learning and development is a domain that data science can be applied to and data science is already being applied to HR and education. Why not learning and development? So my answer is yes, L&D does need data science and we need it because good data analytics can help us understand what we do better, improve our decision making to have more impact on our organisations and subsequently become more influential. In my next posts I will discuss some of the poster words of data science and how they may relate to L&D. The words I am talking about are big data and machine learning.