Communication is a key skill in any field. Without communication, we would be hard-pressed to drive change in an organization. We need to communicate ideas well to ensure that the people that help us do what we do, can help us do what we do. One of the key functions of any data scientist is to encourage data-driven change to effect some positive collective outcomes (increasing revenues, reducing costs, improving resiliency). If you can’t communicate the work you’ve done in a concise, meaningful, contextualized way,…you may fail in making your work actionable. The right people responsible for driving change won’t get to onboard your train, and your data science will go nowhere. No person is an island. We need the help of thousands of people to do what we do effectively, and to do that, communication is an absolute key.

“I cannot live without brain-work. What else is there to live for?” – Sherlock Holmes

I saw this challenge (minus the solution) being passed around in a WhatsApp message, and after solving it, it struck me as a fantastic way to illustrate the difference between data, information, knowledge, and insights, under the pretext of not getting shot.

Let’s get started, shall we?

“My name is Sherlock Holmes. It is my business to know what other people don’t know.” – Sherlock Holmes

Many people think that Data Science looks like this:

In many meetings, I’ve been asked by customers, “How do you demonstrate an ROI for an MDM solution to manage customer data.”   They all get the merits of what MDM can do - a single source of truth for customer data that is the foundation of many customer-centric business initiatives.

But how do you create a business case for this?

To start, we need some basic metric assumptions that will be useful for our calculation:

As your enterprise moves toward being data driven, the ability to derive a domain ontology from your company’s data will become ever more important. In order to move to this deep analytical process, it is important to understand the amount of data required and the state the data must be in before you should attempt any deep analytics studies.

The goal of data science is to translate ALL business problems into scientific problems which can be managed and/or improved in a systematic data-driven way. It is the job of a data scientist to do exactly this. All business problems will benefit from the application of a more scientific and data-driven approach.

When you read the marketing spin on Big Data and the tools available today, you may deduce that there is much upside and not much downside to implementing a Big Data project. Nevertheless, you will quickly find that this is not the case. It’s not as simple as typing “apt-get install Hadoop” into a Linux command window, everything installs, you ask it complex questions, and it gives you sage advice. It is tough to get a Big Data project working.

I often struggle to explain the value of ontologies to my clients. The word ‘ontology’ itself sounds complicated, academic, pompous, bombastic, and irrelevant. For that matter, the word bombastic always struck me as, well,
. . . bombastic.

Nevertheless, when you try to look up ‘ontology’ in everyone’s favorite dictionary, err, google, you get an even less helpful definition:

NOUN

1. 1. the branch of metaphysics dealing with the nature of being.

It really can’t get much more clearly unhelpful than that. At least their 2nd definition seems a little bit more relatable for our purposes:

1. 2. a set of concepts and categories in a subject area or domain that shows their properties and the relations between them.

There are a certain set of timeless skills that one should aim to develop over the course of their career in the field of data science:

A two-blog series on what data science can do, with real world examples.