Maven’s data challenge for April-May 2022 involves the analysis of a set of data related to “Unicorns”.
A few of my work colleagues saw my LinkedIn post related to the announcement of this challenge, and thought I was looking at data related to the horsey-type unicorns, and they were pleasantly amused at the thought.
Needless to say, they now understand what the term means in a business sense (as I had learned what it meant a mere 48 hours beforehand).
Anyway, this is a shorter than usual blog post related to my initial approach to this challenge, and again links back to an earlier post I made in LinkedIn about a technique I picked up from Ben Jones in a SWD podcast he had with Cole Nussbaumer Knaflic.
In it, he described how we can take on the role of a journalist, and list out the questions we would like to “ask” the data in order to draw out information that you could use to build a structured story or article with real depth of meaning.
Step 1 – Looking at the type of data
I did not wish to look directly at the data initially in case it skewed or biased my view on the questions I might be able to ask. This is where a data dictionary comes in very handy. It helps give an overview and context for the subject without giving too much away about the content.
The below is the data dictionary provided with the dataset.
|Valuation||Company valuation in billions (B) of dollars|
|Date Joined||The date in which the company reached $1 billion in valuation|
|City||City the company was founded in|
|Country||Country the company was founded in|
|Continent||Continent the company was founded in|
|Year Founded||Year the company was founded|
|Funding||Total amount raised across all funding rounds in billions (B) or millions (M) of dollars|
|Select Investors||Top 4 investing firms or individual investors (some have less than 4)|
From an initial review, I divided the types of data up into several categories which could help me form a list of questions
- Dates – These can help form time and trend related questions
- Money – These can form queries on average values, return on investment and growth
- Geography – These can help make comparisons at several hierarchical levels
- Companies – These can assist in analysis at both high and detailed levels from both and company and investor viewpoint
Now this initial breakdown was complete, I could move on to developing a list of questions I could potentially “ask” the data and form the crux of my analysis.
Step 2 – Ask the Questions Already!
OK, so here are a shortlist of questions I made, along with some “subquestions”. I explored and used some of them in my final design, but left them all listed here in case they help fire the imagination of anyone else in their journey.
Questions Related to Date/Time
- Are unicorns a recent phenomena, and is there any trend over the past number of years in the number of unicorns being created? How is this trending with the overall economy?
- How long does a company typically take to become a unicorn? Is this impacted by the type of industry or country? Is there any apparent trend over time?
- Are there certain industries which have become more prevalent in recent years as opposed to say 10 or 20 years ago?
Questions Related to Companies, Money, Funding and Growth
- Which industries make up the majority of unicorns and have the highest average valuations?
- Which industry or country is seeing the most funding from venture capitalists, and are there any particular industries providing high capital investment return rates?
- Which industry or company has experienced the highest or lowest value annual growth rate since joining the unicorn club?
- Are investors typically attracted to particular industries, or do they “spread their bets”?
Questions Related to Location
- Where in the world (cities, countries or continents) are the highest prevalence for unicorns? Has this changed over time?
- Is there any connection between these locations and the type of industry?
- Where in the world has the highest value unicorns or those that provide the highest investment return rates?
- How do unicorns measure up against % GDP of GDP per capita in particular countries? (might need some additional data for this one!)
- Are unicorns confined to historic world powers, or are emerging markets and countries taking a lead?
Step 3 – Start your analysis
It would maybe not be wise to attempt to answer all these questions, but maybe focus on a selection that you think will tell a story to “illustrate the current landscape of unicorn companies around the globe“.
I will leave you here with at least an alternative technique to approaching these challenges, and hopefully also a little inspiration or some ideas to start or evaluate your own approach.
Below is my final result of answering at least one or two questions from each section.
2 thoughts on “Maven Challenge – Unicorns – Interviewing the Data”
Great Insight, Gerald. This is such a great headstart for people like me just joining the challenge.
Thanks Oltundun – I am glad it is able to help give some ideas 👍