By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: EMC Survey Differentiates BI and Data Science
Share
Notification Show More
Latest News
big data mac performance
Data-Driven Tips to Optimize the Speed of Macs
News
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
Artificial Intelligence
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > EMC Survey Differentiates BI and Data Science
Business IntelligenceR Programming Language

EMC Survey Differentiates BI and Data Science

DavidMSmith
Last updated: 2011/12/16 at 1:51 PM
DavidMSmith
5 Min Read
SHARE

EMC last week published the results of a survey of 462 IT decision makers who self-identified as either a data scientist or business intelligence professional (plus 35 invitees who were attendees at the EMC Data Scientist Summity and/or Kaggle competitors).

EMC last week published the results of a survey of 462 IT decision makers who self-identified as either a data scientist or business intelligence professional (plus 35 invitees who were attendees at the EMC Data Scientist Summity and/or Kaggle competitors). There’s a nice summary of the conclusions at the EMC blog, (where data scientists are described as “The New Rock Star”) and you can also find writeups at eWeek and ITBusinessEdge. Here are a few of my takeaways from the report and how they pertain to the R language:

The world needs more data scientists, stat*! According to the survey, 65% of data science professionals believe demand for data science talent will outpace the supply over the next 5 years. What’s more, most think that new data scientists will be found from graduating classes. R is the de-facto standard for statistics teaching at universities (and with many academic institutions no longer able afford SAS or SPSS licensing, more are adopting free statistical software for teaching and research), and with more than 2 million users worldwide may of these new data scientists will be already be trained in R. In our experience with Revolution Analytics customers, this is a key factor in the growing adoption of R in corporations.

There will be more data — and more drive to analyze it. Data from mobile sensors, social media, surveillance, medical imaging — combined with traditional customer and transactional data — has created an explosion in the opportunity to generate value and insights from the data. But according to the survey, only 1/3 of companies are able to effectively use new data to assist their in decision-making process. This is exactly where the R language shines — to give data scientist the freedom to explore and combine diverse data sets and come up with novel ways to make all this data — data companies are making big investments to collect and store — finally pay its way. And since there’s so much data, being able to apply big-data analytics with the R language makes Revolution R Enterprise a fundamental tool in this process.

Data Science and Business Intelligence aren’t the same thing. One of the most interesting aspects of the survey for me was how it highlighted the differences between data science and business intelligence, given that the survey participants identified themselves as one or the other. This is especially revealed in the choices of data analysis tools by BI professionals (dark blue) and data scientists (light blue) in the chart below taken from the EMC report:

More Read

3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows

3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows

5 Proven Tips for Utilizing AI with PPC Advertising in 2023
5 Ways AI Technology Has Disrupted Website Development
Fortifying Enterprise Digital Security Against Hackers Weaponizing AI
10 Ways How Artificial Intelligence Is Changing the Content Writing Landscape

Data science - BI tools

That 20% of data scientists use R but only 5% of self-described business intelligence professional do so isn’t much of a surprise, and illustrates the key difference between BI and Data Science. (BTW, I’m surprised Excel wasn’t an option for Data Analysis as well as Data Management — I’d expect to see similar levels of usage amongst BI professional for that use case.)  While data science is about exploring and learning from data, BI is a process with limited flexibility to answer a fairly narrow range of questions. But as businesses start reaping the benefits of data scientists to extract answers to more complex questions from big data, there’s no doubt that there will be a need to get these models, predictions, and visualizations in the hands of a BI audience that wouldn’t normally use a tool like R. That’s why being able to integrate R into BI frameworks and other end-user applications is so important.

* Pun very much intended.

EMC Press Release: New Global Study: Only One-Third of Companies Making Effective Use of Data


DavidMSmith December 16, 2011
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

big data mac performance
Data-Driven Tips to Optimize the Speed of Macs
News
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
Artificial Intelligence
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
Artificial Intelligence

3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows

6 Min Read
ai in ppc advertising
Artificial Intelligence

5 Proven Tips for Utilizing AI with PPC Advertising in 2023

10 Min Read
ai in web design
Artificial Intelligence

5 Ways AI Technology Has Disrupted Website Development

7 Min Read
Digital Security From Weaponized AI
Security

Fortifying Enterprise Digital Security Against Hackers Weaponizing AI

11 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?