Big Data and Internet of Things for Managers

Peadar Coyle



Who am I?

  • Keynote, Speaker and Teacher at conferences throughout Europe
  • Industry work and 'big data' blogger  for several years
  • Co-organizer of Data Science Meetup in Luxembourg 

Who do I work for?

I work in the Product team as a Data Scientist for JobTODAY We're a VC-backed recruitment app based in Luxembourg 


So how come I care about data?

Well I always loved science.
I wanted to be a neuroscientist

Then I fell in love with Mathematics

I studied Quantum Mechanics at Bristol and Mathematics in Luxembourg

This gets a bit complicated...

And my cat was never much use...

Along the way I learned some programming and other skills...

I needed to find a career

And I decided Academia wasn't for me.

So I became a data scientist!

Aims of the talk

  • Explain the substance behind the phrase 'big data'

  • Tell you how you can use data in your business.

  • Help you understand the importance of data in your business strategy.

What is a data scientist?

  • A statistician who lives in San Francisco
  • A data analyst who lives in New York
  • Data Science is statistics on a Mac.
  • A data scientist is someone better at statistics than any software engineer and better at software engineering than any statistician. 


Why am I talking about this?

What is the competitive advantage?

  • Massively personalised online experiences
  • 'Data moats' are hard to copy


Amazon: Supply chain and warehouse models

Selecting 'House of Cards'

Source: Netflix

Idea: Netflix knew that David Fincher fans liked political dramas, and that Kevin Spacey was very popular - from analysing video playing data, and customer preferences

HR Analytics

Predictive analytics of who would leave your firm

So, who is talking about Big Data?

"We project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively." -  Big data: The next frontier for innovation, competition, and productivity, McKinsey report

Who else?

Gartner says 'Data is the new oil'

But talk is cheap...

Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... -

Professor Dan Ariely - Duke University

 Example: Linkedin

But I work in the real world not online!

UPS uses data from the Internet of Things to travel more efficiently and save millions on fuel consumption.

So what other areas?

  • These are case studies from various market segments
  • The aim is to help you brain storm applications 
  • Happy to answer any questions afterwards

How do you recommend books?

Use Machine Learning!

Preventative Maintenance for Windmills

Siemens are leveraging IoT data

  • 'Source: English Wikipedia, original upload 15 July 2004 by Leonard G. - Creative Commons Sharealike license

Web Crawling and Scraping

Zalando the German fashion e-commerce company  are scraping millions of websites to find trends.

Audio and visual data

Spotify uses sound data in their recommendations!

Social media data

Fashion - Image Analysis leverage novel human-in-the-loop recommendation engines, recommend on the basis of colours of clothes, and do cutting-edge image analysis with deep learning 
all to provide value in Fashion-Tech. 

Recruitment data

How do you disrupt recruitment for blue-collar work?

One way is by leveraging 'big data' to deliver better personalised experiences, optimise the spend of marketing budget and reduce customer churn.

What about Metadata?


Healthcare data

Genomics and health data are too.

Even in Food...

  • Monsanto CEO recently said 'data science is part of our growth strategy'
  • Monsanto leverages Climate Corps algorithms to up-sell fertiliser and other services

I hope you can see that there is...

So why good data analysis is hard?

  • Getting data is hard

  • Building models is hard

  • Asking the right business questions is even harder

I often have to borrow lots of peoples brains to get to
the right business questions...

 Pick the right methodology for the job

  • Text -> topic modelling, sentiment analysis, information extraction
  • E-commerce data -> prospensity analysis, collaborative filtering
  • Multimedia -> speech-to-text, audio fingerprinting, face recognition
  • Clickstream logs -> frequent pattern mining, sequence analysis
  • Yield optimisation -> Optimisation models, weather forecasting
  • Proton-proton collision from LHC -> I have no idea despite having a Physics degree
And then what? Well you can tell stories with visualizations...
But Data Scientists don't just produce reports

They produce data products too.

So what is a data product?

Well I'm glad you asked...

What is a data product?

A data product provides actionable information without exposing decision makers to the underlying data or analytics.

Examples include: Movie Recommendations, Weather Forecasts,Stock Market Predictions, Production Process Improvements, Health Diagnosis, Flu Trend Predictions, Targeted Advertising.
– Mark Herman, et al., Field Guide to Data Science

    Here is an Example from Mailchimp:
    When should I send that email?

    And produce a magic button :)

    Case Study - Marketing Analytics: In the Game Industry

    1) Uses gamers play data to optimize marketing
    communications across channels. - Customer segmentation modelling

    2) Building Personalization Engine Rules for 1:1
    communications with individual gamers. To help reduce customer churn.

    3) Predicts gamers likelihood to churn or to respond to
    up-sell offers.  


    Here is a graph of active users on an online game. Marketing teams use tools like this to monitor their customers in real-time

    Example:Supply Chain Management

    • These are examples from Tableau an excellent data science product - based on laptop sized data sets. Similar to my Amazon work.  However these can also be built with open source tools.

    Electricity Demand prediction

    Analysing IoT data for demand prediction


    So you can do an experiment

    And let data be your guide.

    User Conversion after a website change.


    Example: Financial Analysis

    Quantopian Inc

    Leveraging big data to better assess the efficiency of trading algorithms. 

    Quantopian Inc is building a crowd-sourced hedge fund they give the infrastructure, capital and research to compete with Wall Street. 

    Example: Moving average of AAPL

    Risk is also data science challenge

    Especially with changes in regulation...

    Data can be used for NGO's

    This is a web app of house prices and commutes, done for an NGO in London who wanted to show the effects of
    changes in house prices on peoples commutes.

    Politics: Hilary Clinton

    Data Scientists for her campaign help optimise media buys, analyse the effect of communication and optimise the donor acquisition strategy

    So why would I need a data scientist?

    You may already have one. I know numerous business intelligence, data analysts, business analysts, risk analysts who ARE data scientists.

    Alternatively you can hire a data analytics consultant to help you get started.

    But what signals should I look for?

    Well there are many answers... Like...

    Does this sound like you?

    • Are you losing customers and not understanding why?

    • Are you making decisions on the basis of data or on the basis of 'gut feeling'?

    • Are you changing your websites or products on the basis of data driven experimentation?

    If you want Interviews with experts  or

    Thank You For Listening

    Any questions?

    Reach out to me if you have any data questions.

    I'll be doing a training on 'big data' at Nyuko soon.

    Search Peadar Coyle on Linkedin


    By springcoil


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