Upgrade to Pro — share decks privately, control downloads, hide ads and more …

How can data science benefit your business?

98c35e22a5c8c92bb066efb332e30991?s=47 springcoil
October 26, 2014

How can data science benefit your business?

A talk I gave at the Impactory in Luxembourg on Big Data and it's business implications. All opinions are my own.
#bigdata #datascience

98c35e22a5c8c92bb066efb332e30991?s=128

springcoil

October 26, 2014
Tweet

Transcript

  1. How can Data Science benefit your business? Peadar Coyle Data

    Analytics Consultant @springcoil
  2. My Experience My Experience Expert in Big Data Technologies and

    Data Science Mathematician by training - specialized in Machine Learning Supply Chain Management Customer Churn optimization for B2B in the Pharmaceutical industry Customer Segmentation for Event Management at Letsface.cn Sentiment analysis for a large financial consultancy Web crawl data analysis at Import.io
  3. I did Data in Shanghai I did Data in Shanghai

    Helping an event website target its customers
  4. Data in London Data in London Data geeks need tools

    like these, I helped launch the beta.
  5. I worked with Data in Luxembourg I worked with Data

    in Luxembourg For a small e-commerce website. Doing Supply Chain models.
  6. Now I work with Now I work with Data in

    Luxembourg @ Data in Luxembourg @
  7. In Air Traffic Management In Air Traffic Management BTW Air

    travel produces A LOT of data!
  8. So how come I care about data? So how come

    I care about data? Well I always loved science. Well I always loved science. I wanted to be a neuroscientist I wanted to be a neuroscientist
  9. Then I fell in love with Physics Then I fell

    in love with Physics I studied Quantum Mechanics and I studied Quantum Mechanics and Quantum Optics at Bristol Quantum Optics at Bristol
  10. This gets a bit complicated... This gets a bit complicated...

    And my cat was never much use... And my cat was never much use...
  11. So I ended up in Math & Stats... So I

    ended up in Math & Stats...
  12. Along the way I learned some Along the way I

    learned some programming and other skills... programming and other skills...
  13. I needed to find a career I needed to find

    a career And I decided Academia wasn't for me. And I decided Academia wasn't for me. So I became a data scientist! So I became a data scientist! Now what skills does a data scientist have? Now what skills does a data scientist have?
  14. None
  15. To me data Science is a lot like Science To

    me data Science is a lot like Science
  16. But the hardest thing to learn has been.. But the

    hardest thing to learn has been.. “Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.” Jean-Paul Isson, Monster Worldwide, Inc.
  17. I'm still learning the tech stuff too I'm still learning

    the tech stuff too What is Machine learning? What is Machine learning? Well this next slide might help... Well this next slide might help...
  18. None
  19. What is Data Science? What is Data Science? Data Scientists

    help you harness the Data Scientists help you harness the value of 'big data' value of 'big data'
  20. So, who is talking about Big Data? 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." - , McKinsey report Big data: The next frontier for innovation, competition, and productivity
  21. Who else? Who else?

  22. Gartner says 'Data is the new oil' Gartner says 'Data

    is the new oil'
  23. 'Data are becoming the new raw material of business'

  24. But talk is cheap... 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
  25. So how do you get value out of your So

    how do you get value out of your data? data?
  26. You could hire a data scientist You could hire a

    data scientist This talk is aimed at helping you understand if you This talk is aimed at helping you understand if you need to hire a 'data scientist'. need to hire a 'data scientist'.
  27. 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.
  28. Or this talk could have been titled... Or this talk

    could have been titled... But Peadar where are the business But Peadar where are the business examples? examples?
  29. None
  30. Example: Linkedin Example: Linkedin

  31. But I work in the real world not online! But

    I work in the real world not online! UPS uses data to travel more efficiently and save millions on fuel consumption.
  32. What is "Big Data" anyway? What is "Big Data" anyway?

    Customer peference data is...
  33. What is "Big Data" anyway? What is "Big Data" anyway?

    Wind sensor data is too Wind sensor data is too 'Source: English Wikipedia, original upload 15 July 2004 by - Creative Commons Sharealike license Leonard G.
  34. What is "Big Data" anyway? What is "Big Data" anyway?

    Web crawling data is too
  35. What is "Big Data" anyway? What is "Big Data" anyway?

    Audio and visual data is too
  36. What is "Big Data" anyway? What is "Big Data" anyway?

    Social Media data must be too
  37. What is "Big Data" anyway? What is "Big Data" anyway?

    Not to mention social media metadata
  38. Genomics and health data are too. What is "Big Data"

    anyway? What is "Big Data" anyway?
  39. And what about Particle Physics data? What is "Big Data"

    anyway? What is "Big Data" anyway?
  40. I hope you can see that there is... I hope

    you can see that there is...
  41. Remember this slide? Remember this slide?

  42. 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...
  43. Pick the right methodology for the job 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 Proton-proton collision from LHC -> I have no idea despite having a Physics degree
  44. And then what? Well you can tell stories with visualizations...

  45. 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...
  46. What is a data product? 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
  47. But you said... But you said...

  48. Here is an Example from Mailchimp: Here is an Example

    from Mailchimp: When should I send that email? When should I send that email?
  49. Step 1 to 100: Data Scientists - do lots of

    Step 1 to 100: Data Scientists - do lots of analysis... analysis...
  50. And produce a magic button :) And produce a magic

    button :)
  51. Define Data Science Define Data Science It is the application

    of science and models to really complex human problems Such as: Who is buying our product, why are they leaving our service. Data scientists leverage mathematics and computer science to deliver business value such as smoother operations, enhancing your marketing strategy or forecasting supply and demand. In short data scientists help you prepare for the future.
  52. How does this help you in your business?

  53. None
  54. Case Study - Marketing Analytics: In Case Study - Marketing

    Analytics: In the Game Industry 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.
  55. Example Example Here is a graph of active users on

    an online game. Marketing teams use tools like this to monitor their customers in real-time
  56. What about forecasting? Do you mean the weather? What about

    forecasting? Do you mean the weather? (In Ireland and the UK it is quite easy - just guess rain (In Ireland and the UK it is quite easy - just guess rain all the time!) all the time!) But there are other kinds of forecasts such as supply But there are other kinds of forecasts such as supply chain forecasts or demand forecasts.... chain forecasts or demand forecasts....
  57. Example:Supply Chain Management 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.
  58. But predicting the future is hard But predicting the future

    is hard "It’s Difficult to "It’s Difficult to Make Predictions, Make Predictions, Especially About Especially About the Future" - the Future" - Niels Bohr Niels Bohr
  59. And let data be your guide. So you can do

    an experiment So you can do an experiment
  60. User Conversion after a website change. User Conversion after a

    website change. Luckily they measured it. They learned the website Luckily they measured it. They learned the website change was a bad decision. change was a bad decision.
  61. Sometimes in life you just need pictures Sometimes in life

    you just need pictures or data visualizations, like this... or data visualizations, like this...
  62. Or this: Number of wind turbines by state in the

    US? Or this: Number of wind turbines by state in the US?
  63. But this is Luxembourg... But this is Luxembourg... So we

    need finance examples... So we need finance examples...
  64. Example: Financial Analysis Example: Financial Analysis

  65. Example: Moving average of AAPL Example: Moving average of AAPL

  66. Especially with changes in regulation... Risk is also data science

    challenge Risk is also data science challenge
  67. But I don't work for a corporation But I don't

    work for a corporation Data can be used for NGOs too...
  68. Data can be used for NGO's 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.
  69. Reducing Maternal Mortality Rates in Mexico Reducing Maternal Mortality Rates

    in Mexico - - Mexico - Presidencia de la Republica Mexico - Presidencia de la Republica The maternal deaths in Mexico from pregnancy, childbirth or postpartum complications have decreased from 89 deaths per 100,000 live births in 1990 to 43 in 2011. Despite this improvement, the rate of decline has significantly slowed and Mexico is not on track to achieve its Millennium Development Goal of reducing maternal mortality 75% by 2015.
  70. But what if you're in Politics? But what if you're

    in Politics?
  71. Earlier I showed how data can Earlier I showed how

    data can even predict 49 out of 50 states even predict 49 out of 50 states in the last American election. in the last American election. And that Obama would be the president!
  72. So why would I need a data scientist? 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...
  73. Does this sound like you? Does this sound like you?

    Are you not taking full advantage of your reporting? Are you not taking full advantage of your reporting? Do you need a high level visual overview of your Do you need a high level visual overview of your operations? operations? Are you targeting your marketing efforts effectively by Are you targeting your marketing efforts effectively by using the right customer segmentation - by age or using the right customer segmentation - by age or gender for example? gender for example?
  74. Or this? Or this? Are you losing customers and not

    understanding Are you losing customers and not understanding why? why? Are you making decisions on the basis of data or on Are you making decisions on the basis of data or on the basis of 'gut feeling'? the basis of 'gut feeling'? Are you changing your websites or products on the Are you changing your websites or products on the basis of data driven experimentation? basis of data driven experimentation?
  75. Example: What-if analysis... Example: What-if analysis...

  76. What about communication? What about communication? Mathematically sound communication to

    clients: you may have situations where you need the data scientists to talk directly to clients or to their data scientists. This is yet another reason to make sure you hire someone with excellent communication skills, because they will be representing your business to really smart people.
  77. Data Scientists are like 'translators' Data Scientists are like 'translators'

    A lot of my work at the moment is mathematical communication with external stakeholders and Professors. At Amazon a lot of my work was with Research Scientists in Optimization. Translating their ideas for business stakeholders. I often have to translate from the 'business' to the 'software' team. Do you have someone like that on YOUR team?
  78. If this sounds familiar If this sounds familiar then you

    might need then you might need to hire a data scientist! to hire a data scientist!
  79. I hope the examples helped I hope the examples helped

    I hope it is also clear how data can be used in whatever field you work in. I hope it is also clear that 'big data' is not something to be scared of but should be part of your organizations strategy. I know that developing a data-driven culture is extremely difficult.
  80. Thank You For Listening Thank You For Listening Any questions?

    Reach out to me if you have any data questions. @springcoil Search Peadar Coyle on Linkedin peadarcoyle@googlemail.com