By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    Promising Benefits of Predictive Analytics in Asset Management
    11 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: New Technology Is Not an Easy Button for Big Data
Share
Notification Show More
Latest News
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
ai for small business tax planning
Maximize Tax Deductions as a Business Owner with AI
Artificial Intelligence
ai in marketing with 3D rendering
Marketers Use AI to Take Advantage of 3D Rendering
Artificial Intelligence
How Big Data Is Transforming the Maritime Industry
How Big Data Is Transforming the Maritime Industry
Big Data
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > New Technology Is Not an Easy Button for Big Data
AnalyticsBig DataCulture/LeadershipData ManagementDecision Management

New Technology Is Not an Easy Button for Big Data

BillFranks
Last updated: 2012/09/07 at 11:58 AM
BillFranks
6 Min Read
SHARE

Easy ButtonIt is good to remember in today’s hype-filled big data world that there is no “easy” button for big data. In fact, in many ways, big data is quite difficult to deal with. Many organizations seem to be falling for the fallacy that simply implementing new tools or platforms will “automagically” solve their big data problems. Unfortunately this isn’t the case.

Easy ButtonIt is good to remember in today’s hype-filled big data world that there is no “easy” button for big data. In fact, in many ways, big data is quite difficult to deal with. Many organizations seem to be falling for the fallacy that simply implementing new tools or platforms will “automagically” solve their big data problems. Unfortunately this isn’t the case.

For example, there is a common belief that MapReduce platforms such as Teradata Aster or Hadoop can tame big data in and of themselves.  In reality they don’t inherently enable new functionality or analytic logic to be executed. Rather, they allow you to scale certain kinds of functionality and analytic logic in a way that makes the functionality and logic much more powerful and widely applicable.

This is an important distinction – and one I want to explore in detail.

More Read

How Big Data Is Transforming the Maritime Industry

How Big Data Is Transforming the Maritime Industry

Predictive Analytics Helps New Dropshipping Businesses Thrive
Utilizing Data to Discover Shortcomings Within Your Business Model
Small Businesses Use Big Data to Offset Risk During Economic Uncertainty
The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas

Many organizations seem to be thinking of MapReduce as a magic bullet or “easy” button for handling big data. Just set up a system, and your big data problems are solved, right? Wrong. Once the system is in place, it is still necessary to develop the analytic processes that run against it.  There really is no shortcut here. If you want great analytics, you’re going to have to build your processes just like you always have. Organizations that don’t understand this fact will be disappointed when they realize they aren’t instantly getting the value they expected from their investment.

As I said earlier, MapReduce doesn’t inherently enable new functionality. When you hear about MapReduce environments, you will quickly come to a discussion of leveraging languages such as Java or Python. It just so happens that these languages have been around for quite a while. They had strong followings before the concept of MapReduce came into existence. Most users of these languages have never used, and may never use, a MapReduce architecture as part of their work.  However, they code away day to day developing processes just like their big data focused counterparts.

What many people don’t take the time to think about is that whatever logic you develop today in Java to run in a MapReduce environment is something you could have written in Java years ago. The exact same code, the exact same output for a given piece of data. This is why I said that MapReduce doesn’t directly cause any new analytic logic to come into existence. Rather, MapReduce provides a highly scalable platform so that logic can be executed at a scale far surpassing what was possible in the past.

This last point is the value that MapReduce brings. Having a terrific facial recognition or text parsing algorithm doesn’t do much good if there is no way to scale the process to a big data environment. MapReduce provides that ability.  It lets organizations apply algorithms to a much wider base of problems and a much larger amount of data. It allows logic that wasn’t practical to build into your analytic processes to become practical.

This no different than how parallel database platforms provide value. A Massively Parallel (MPP) database system runs on SQL just like a non-MPP system. An MPP system doesn’t enable new functionality in the absolute sense, but it does provide the ability to scale an SQL process.  As a result it enables far more value to be derived and a much wider set of problems to be practically addressed than when using a non-MPP architecture.

In summary, we can expect MapReduce to continue to be a force behind the taming of big data. But, the onus will still be on the organizations that use it to develop and implement the required analytic processes just as they always have had to do in the past. Many analytics that were theoretically possible, but impractical, will no longer be a problem. That will lead to a lot of value. The key is to understand what the architecture will do for you, and to not underestimate the effort required to use it correctly. It will take work to get the benefits. There is no “easy” button for big data.

To see a video version of this blog, visit my YouTube channel.

Originally published by the International Institute for Analytics

 

BillFranks September 7, 2012
Share this Article
Facebook Twitter Pinterest LinkedIn
Share
By BillFranks
Follow:
Bill Franks is Chief Analytics Officer for The International Institute For Analytics (IIA). Franks is also the author of Taming The Big Data Tidal Wave and The Analytics Revolution. His work has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. You can learn more at http://www.bill-franks.com.

Follow us on Facebook

Latest News

ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
ai for small business tax planning
Maximize Tax Deductions as a Business Owner with AI
Artificial Intelligence
ai in marketing with 3D rendering
Marketers Use AI to Take Advantage of 3D Rendering
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

How Big Data Is Transforming the Maritime Industry
Big Data

How Big Data Is Transforming the Maritime Industry

8 Min Read
predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
utlizing big data for business model
Big Data

Utilizing Data to Discover Shortcomings Within Your Business Model

6 Min Read
big data use in small businesses
Big Data

Small Businesses Use Big Data to Offset Risk During Economic Uncertainty

7 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?