Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

‘Big Data Started a Mentality Revolution’: An Interview with Data Scientist Ignacio Elola

by Peadar Coyle
May 30, 2016
in Conversations, Industry, Technology & IT
Home Conversations
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

Ignacio Elola is a self-proclaimed ‘data nerd, data punk and data scientist’ at import.io , a young startup that’s shaking up the world of data. With their free app, you can transform any website into a table of data or an API in minutes. Recently voted Best Startup by O’Reilly Strata Santa Clara, GigaOM and Web Summit, it has been backed by top European VCs and Silicon Valley-based angel investors.

Follow Peadar’s series of interviews with data scientists here.


Table of Contents

  • What project have you worked on do you wish you could go back to, and do better?
  • What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
  • What do you wish you knew earlier about being a data scientist?
  • How do you respond when you hear the phrase ‘big data’?
  • What is the most exciting thing about your field?
  • How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

What project have you worked on do you wish you could go back to, and do better?

All of them. I’m constantly learning and improving and if I could go back I could do all past projects much better. That doesn’t mean I wish to re-do all past projects, as when something is working is working and is done, but for important projects is a good practice on my opinion to keep iterating and re-factoring code, as every month I learn something new that could help doing this better


What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Two words: do it. The only way to really learn something is by doing; so be proactive and start getting things done and learning in the process. I would also advice against specializing too much into something unless you have things very clear, a generalist can always get specialized  something later on, but the other way is harder. Plus it would be much beneficial in any early stage career to learn as much as possible from any related disciplines and any business aspects, not only the algorithm or statistics you are working on. Know your environment and learn from everybody around you.


Join the Partisia Blockchain Hackathon, design the future, gain new skills, and win!


What do you wish you knew earlier about being a data scientist?

I really haven’t find any bad surprises on my journey, things that I wish I knew early. I think keeping an open mind approach about your role and your company and everything else help a lot on this.

How do you respond when you hear the phrase ‘big data’?

Well, I think “big data” really change the data and the technology space in terms of what tools (databases, search indexes, and so) we need to use to deal with these amounts of data. But the real revolution it started is a mentality revolution: the “all data is useful” thinking, the data driven approach for decision making… it is al related, we can see how it is already having a real impact in startups, medium companies and big enterprises. That is an approach that can be used in “big” or “small” data, it doesn’t matter and most of the time people actually work with small or medium data, not so many companies are actually doing “big data”. But that is okay!

What is the most exciting thing about your field?

The thing I find the most exciting is to be able to work with different teams and departments and help everyone in their decision process by using data. I just love to improve processes and open everybody mind to the data driven world!
Having the freedom to came-up with new ideas and projects to create value out of the data you have in unexpected ways is also something very challenging but rewarding, and I think is a must have in any data science role.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

The starting point need to be the business: what question are you trying to solve. I’m very pragmatic in framing data problems, and very output oriented. First thing is to formulate a question that makes sense and that will help you in some way, and understand the business problem you are trying to solve or improve – otherwise you won’t be able to know how good is your answer later on!
Then is the turn of the data itself: what data do you have and how you can use it to answer that question, how close can you get to answering that question? What algorithm do you need to use or how to clean the data are things of technical difficulty, but where you’ll find many resources to help you in the way: courses, books, tutorials, blogs… That’s why I find those first steps the most important ones.

 

Follow @DataconomyMedia

unnamedPeadar Coyle is a Data Analytics Professional based in Luxembourg. He has helped companies solve problems using data relating to Business Process Optimization, Supply Chain Management, Air Traffic Data Analysis, Data Product Architecture and in Commercial Sales teams. He is always excited to evangelize about ‘Big Data’ and the ‘Data Mentality’, which comes from his experience as a Mathematics teacher and his Masters studies in Mathematics and Statistics. His recent speaking engagements include PyCon Sei in Florence and he will soon be speaking at PyData in Berlin and London. His expertise includes Bayesian Statistics, Optimization, Statistical Modelling and Data Products.


 

(Image Credit: Jon Gosier / Periodic Table of World Internet Facts / CC BY 2.0 )

 

Tags: data scienceimport.ioInterview

Related Posts

What is digital maturity: Model, scale, level

Fostering a culture of innovation through digital maturity

January 30, 2023
5 best office automation tools: Examples

The significance of office automation in today’s rapidly changing business world

January 24, 2023
Examples of artificial intelligence in supply chain management

Unleashing the power of AI with the rise of intelligent supply chain management

January 23, 2023
What is smart robotics: Benefits and challenges

Unlocking the full potential of automation with smart robotics

January 19, 2023
Business intelligence consultant: Salary, job description, role and more

The data-smart consultants: The significance of BI experts in today’s business landscape

January 12, 2023
CES 2023 robots, CES 2023 smart home techs, and CES 2023 top products are here! We summarized the CES 2023 highlights for you.

Robot uprising started at CES 2023

January 9, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

LATEST ARTICLES

Fostering a culture of innovation through digital maturity

Nvidia Eye Contact AI can be the savior of your online meetings

How did ChatGPT passed an MBA exam?

AI prompt engineering is the key to limitless worlds

Transform your data into a competitive advantage with AaaS

Google code red: ChatGPT, You.com and rumors of Apple Search challenge the dominance of search giant

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.