By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    football analytics
    The Role of Data Analytics in Football Performance
    9 Min Read
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Improving BI Development Efficiency: Standard Data Extracts
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > Improving BI Development Efficiency: Standard Data Extracts
Business IntelligenceData Warehousing

Improving BI Development Efficiency: Standard Data Extracts

EvanLevy
Last updated: 2009/10/06 at 1:49 PM
EvanLevy
5 Min Read
SHARE
Mars by jason42882

A few years ago, a mission to Mars failed because someone forgot to convert U.S. measurement units to metric measurement units. Miles weren’t converted to kilometers.

I thought of this fiasco when reading a blog post recently that insisted that the only reasonable approach for moving data into a data warehouse was to position the data warehouse as the “hub” in a hub-and-spoke architecture. The assumption here is that data is formatted differently on diverse source systems, so the only practical approach is to copy all this data onto the data warehouse, where other systems can retrieve it

I’ve written about this topic in the past, but I wanted to expand a bit. I think it’s time to challenge this paradigm for the sake of BI expediency.

The problem is that the application systems aren’t responsible for sharing their data. Consequently, little or no effort is paid to pulling data out of an operational system and making it available to others. This then forces every data consumer to understand the unique data in every system. This is neither efficient nor scale-able.

More Read

ai low code frameworks

AI Can Help Accelerate Development with Low-Code Frameworks

Tackling Bias in AI Translation: A Data Perspective
How AI is Boosting the Customer Support Game
AI-Based Analytics Are Changing the Future of Credit Cards
Enterprises Are Leveraging the Benefits of AI-Driven ERPs

Moreover, the hub-and-spoke architecture itself is also neither efficient nor scalable. The way manufacturing companies …

Mars by jason42882

A few years ago, a mission to Mars failed because someone forgot to convert U.S. measurement units to metric measurement units. Miles weren’t converted to kilometers.

I thought of this fiasco when reading a blog post recently that insisted that the only reasonable approach for moving data into a data warehouse was to position the data warehouse as the “hub” in a hub-and-spoke architecture. The assumption here is that data is formatted differently on diverse source systems, so the only practical approach is to copy all this data onto the data warehouse, where other systems can retrieve it

I’ve written about this topic in the past, but I wanted to expand a bit. I think it’s time to challenge this paradigm for the sake of BI expediency.

The problem is that the application systems aren’t responsible for sharing their data. Consequently, little or no effort is paid to pulling data out of an operational system and making it available to others. This then forces every data consumer to understand the unique data in every system. This is neither efficient nor scale-able.

Moreover, the hub-and-spoke architecture itself is also neither efficient nor scalable. The way manufacturing companies address their distribution challenges is by insisting on standardized components. Thirty-plus years ago, every automobile seemed to have a set of parts that were unique to that automobile. Auto manufacturers soon realized that if they established specifications in which parts could be applied across models, they could reproduce parts, giving them scalability not only across different cars, but across different suppliers. 

It’s interesting to me that application systems owners don’t aren’t measured on these two responsibilities:

  • Business operation processing—ensuing that business processes are automated and supported effectively
  • Supplying data to other systems

No one would argue that the integrated nature of most companies requires data to be shared across multiple systems. That data generated should be standardized: application systems should extract data and package it in a consistent and uniform fashion so that it can be used across many other systems—including the data warehouse—without the consumer struggling to understand the idiosyncrasies of the system it came from.

Application systems should be obligated to establish standard processes whereby their data is availed on a regular basis (weekly, daily, etc.). Since most extracts are column-record oriented, the individual values should be standardized—they should be formatted and named in the same way.

Can you modify every operational system to have a clean, standard extract file on Day 1? Of course not. But as new systems are built, extracts should be built with standard data. For every operational system, a company can save hundreds or even thousands of hours every week in development and processing time. Think of what your BI team could do with the resulting time—and budget money!

photo by jason b42882

Link to original post

EvanLevy October 6, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Shutterstock Licensed Photo - 1051059293 | Rawpixel.com
QR Codes Leverage the Benefits of Big Data in Education
Big Data
football analytics
The Role of Data Analytics in Football Performance
Analytics Big Data Exclusive
smart home data
7 Mind-Blowing Ways Smart Homes Use Data to Save Your Money
Big Data
ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

ai low code frameworks
Artificial Intelligence

AI Can Help Accelerate Development with Low-Code Frameworks

12 Min Read
data perspective
Big Data

Tackling Bias in AI Translation: A Data Perspective

9 Min Read
How AI is Boosting the Customer Support Game
Artificial Intelligence

How AI is Boosting the Customer Support Game

6 Min Read
AI analytics
AnalyticsArtificial IntelligenceExclusive

AI-Based Analytics Are Changing the Future of Credit Cards

6 Min Read

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

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?