The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

Lessons In Data Science From Bugs Bunny And Wile E. Coyote

January 20, 2016 at 5:33 PM

I fondly remember watching as a child “The Roadrunner Show” cartoon series where the coyote (Wile E. Coyote) was devising elaborate schemes to try and catch the roadrunner. Although his schemes appeared to be clever and creative (to a six year old at least), he always failed. So, in one of the episodes he ordered a giant mainframe type super computer (this was the 1950s after all) to apply “data science” to devise a more effective scheme. This time his prey was Bugs Bunny.

You can watch the cartoon here for a refresher or to catch up if you've never seen it. Funny, even for adults. (Viewing time approximately 7 minutes).

In his first attempt, Bugs had crawled down his rabbit hole and locked the rabbit hole entrance with a giant combination lock. Wile E. Coyote input the data into the UNIVAC super computer to get an answer to his problem – in his case:

  1. Rabbit
  2. in Hole
  3. Combination Lock

He pulls on a lever and the computer sputters, lights up and spits out a piece of paper with the solution: the code to the combination lock.

Albeit a very rudimentary approach to data analysis and data-driven decision making, it was probably the earliest exposure to the concept of data science to many of us kids watching. Predictably, the coyote fails in this attempt to catch Bugs Bunny, as well as in his subsequent attempts. Why? One could argue it was due to bad luck or insufficient data, but *spoiler alert* the actual reason was due to the UNIVAC super computer being operated by the very bunny the coyote was trying to catch.

Key Lessons Learned

So what do we learn from this? Here are some key lessons I took from re-watching this episode which serve as simple reminders for all of us applying data analytics to our decision making: 

  1. Make sure you are basing your decisions on sufficient data.
    In the example above, Wile E. Coyote entered "Rabbit. In hole. Combination lock." With no data to work with and an ambiguous request, one would expect an unreliable answer.
  2. Don't allow bias within the data to affect the outcome.
    Ask yourself, the following questions: Was there bias in the collection of the data? How accurate is your data and how much do you trust it? Was a known group with a particular background targeted for a survey? Who is collecting the data? Do they have an interest in the outcome? With Bugs Bunny collecting the data and doing the data analysis rather than the UNIVAC computer, the recommended action was definitely biased against the coyote.
  3. Use data-driven insights as a guide for your decisions, but don’t act on them blindly.
    Add common sense and experience as part of the mix to your data-driven decision making. Always keep in mind that although data science can be highly accurate and reliable, the 2 points above could result in some misdirection. Wile E. Coyote blindly acted on the super computer's final recommendation ("Go back and take your medicine.") when asked "Rock. Falling. What'll I do?" and went back and allowed the falling rock to land on him.

What would you ask the UNIVAC Super Computer?

Do you wish you had a UNIVAC super computer that could answer any question you have? If you did, what burning business question would you ask it? If you could use data to answer ANY question you have about your business, what is the ONE question you would ask that could transform your business?

Challenge our data scientists to determine whether an answer to your one question can be answered using your data. If you have the right data or have access to alternate data, we could derive the answer for you using a combination of 3rd party data and advanced data analytics techniques.

predictive analytics guide

Image credit: "To Hare is Human" Warner Brothers / Merries Melodies

Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

Latest Posts

[Slideshare] How To Make Your Business Data Work For You

Common barriers to success: Skills shortage: data scientists are in high demand and in low supply. Companies lack the skills to develop advanced data analytics or machine learning applications. Cost: recruiting and building up or training a team, as well as infrastructure costs are immense. Inefficiency and low ROI on: acquisition campaigns; re-activation and retention campaigns; outbound sales calls and debt collection. Resulting in: No or ineffective use of data. High cost to get insights from data. Low returns from campaigns. What’s the alternative? Machine Learning as a Service (MLaaS): removes infrastructure skills and requirements for machine learning, allowing you to begin benefiting from machine learning quickly with little investment. Subscription based pricing, allowing you to benefit using machine learning while minimising your set-up costs and seeing returns sooner. Answers as a Service: Use historic data and machine learning to allow answers to increase in accuracy with time. MLaaS with predictive models pre-developed to answers specific questions: Genius Call Connect: What is the best time and number to call customers? Genius Customer Growth: Which customers are most likely to respond to cross-sell? Genius Re-activation: Which dormant customers are worth re-activating? Genius Customer Retention: Which customers are most likely to churn? Genius Leads: Which contacts are likely to respond to my campaign? Genius Risk Classifier: Which debtors are most likely to pay or roll? Benefits of Genius: Quick and cost-effective ability to leverage machine learning: Minimal set-up time Minimal involvement from IT Subscription based service Looking to make your data work for your business? Read more on Genius to see how it can help your business succeed. 

5 Must-Join Facebook Pages For Data Science, Machine Learning And Artificial Intelligence In 2019

While LinkedIn has traditionally been thought of as the business or work focussed social platform, Facebook has been making headway into gaining market share in the space as well. With company pages and groups, Facebook is catering to every interest and aspiration that people might have – and combining that with their social interactions and news sources. Facebook aims to give users a one-stop-shop experience, and it’s very good at doing it.

Our 2018 Customer Acquisition And Engagement Blog Roundup

Our final roundup this year covers two of our main topics: customer acquisition and customer engagement. We’ve not covered these topics in depth this year, and so decided to combine these two to provide a roundup of the best of both.