We’re starting a Data Science practice at Making Sense. The decision was made because we believe that we can offer more value to our partners than just products. Let’s learn more about it!
What is Exactly ‘Data Science’?
There is added value inherent in every business, in every market – but where is it and how do you go about finding it?
Data Science – a very broad term as you’ll learn soon – is the science of identifying this added value by looking into the data of your business. It’s transforming data itself into a tool so you can make better decisions.
And why “science”? Well, we could look at some numbers and make arbitrary decisions, but those would only be educated guesses. Sometimes they’re just hunches (numbers can be tricky to interpret). The “science” part of data science entails running data experiments to validate how certain we are of our interpretations of the results.
‘Data Science’ as a Buzzword
Even though the concept has been around for a long time, the term “Data Science” has gathered a lot of attention lately. A few factors have played a role in this:
- Storage has become cheaper and more readily available
- Big Data infrastructures have become more common, allowing for even more massive volumes of data
- IoT (internet of things) and “fog” networks are able to provide more spread-out, decentralized, data
- Network speed averages have gone up, allowing for more data transmissions
- Strategies for processing distributed data have become more efficient
- Computational improvements have also allowed for certain algorithms to flourish and be production-feasible when they weren’t before (deep learning, for example).
Some argue that the concept has always existed, and that it is a new term that just refers to business analytics. In that way, it is really not new at all if you want to argue the point. But the very same thing has happened at least twice before: when the hype was “the cloud” and when the hype was “internet 2.0”. Neither of these concepts was truly new either, but they were suddenly ‘revolutionary’ once they became popular buzzwords. We predict the same thing will happen with “Data Science”, regardless of what name it eventually takes on.
“The scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.”– David Donoho, 50 Years of Data Science
Now, an increasing number of companies are finding out that they can learn more about their own business and get better at it by just gathering the right data and performing careful analysis.
How Does One ‘Do’ Data Science?
- Identify your goal: what do you want to know?
- Gather data
- Manipulate the data so it can help answer your question
- Answer the question / build a model that will answer the question
- Take action: make a decision based on the response or deploy the model
This is the very basic process of a data science feature / iteration / project. Good communication, documentation and collaboration are very important if you want a team to work towards the goal, but business domain is crucial too, as that will lead to better interpretations of the data.
Of course, the devil is in the details. The challenging parts are how to make each of these steps happen and how to identify whether you’re carrying them out in the right way.
Where is the Added Value?
We started out talking about “added value”. This added value is found when we get answers to the questions we propose and we know more about our business than we did before. This is about transforming raw data into knowledge. And knowledge… knowledge is power.
How advanced would you be in your business if you could just ask about something which you didn’t know and get the answer with statistical rigor backing it up?
How much happier would your customers be if you had a way to predict what they want? Imagine if this could happen even before you get to know them.
This is not science fiction – this is already happening. And we’re going to be a part of it.
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