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Let’s not get stranded!

In today’s rapidly expanding Electric Vehicle (EV) market, an EV charging company wanted to capture a more significant market share. With recent studies predicting a 10% uptick in EV sales by 2025, rising to 58% in 2040, it’s no wonder that EV charging stations are focused on their strategy now. As the world “goes green”, electric vehicle companies are hard at work besting each other on improved battery life. This is because conversion rates for first-time EV buyers depends so much on alleviation of buyers’ range anxiety - that feeling of being caught short on dead batteries miles from a charging point. This made it imperative for EV charging companies to figure out EV charging stations' best placements in popular driving destinations and routes.

The first wave of Master Data Management (MDM) started in the early 2000s. Early adopters from a few industries which included Banking, Insurance, CPG, and Retail started implementing MDM for customers, products, and a few other master data domains. Over time, MDM became mainstream as companies from all industries realized the need for MDM as part of their data strategy to resolve data inconsistencies across multiple systems and establish a single version of the truth. Over the years, there have been many successes but plenty of big data failures as well, due to the lack of skills and a proper understanding and positioning of MDM solution versus other systems.

Our clients frequently complain that their understanding of their business is hampered by the fact that much of their vital data resides as ‘dark data’ in silos that don’t reside on traditional IT infrastructure (system data).

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?

Many people think that Data Science looks like this: