Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Reinventing the BI Solution You Already Have – A Series of Unfortunate Data Warehousing/Business Intelligence Events #1
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > Reinventing the BI Solution You Already Have – A Series of Unfortunate Data Warehousing/Business Intelligence Events #1
Business IntelligenceData Warehousing

Reinventing the BI Solution You Already Have – A Series of Unfortunate Data Warehousing/Business Intelligence Events #1

RickSherman
RickSherman
5 Min Read
SHARE

(This is part of our ongoing Series of Unfortunate Data Warehousing and Business Intelligence Events. Click for the complete series, so far.)

Series_unfortunate A fundamental flaw of many business intelligence solutions is recreating what the company is already using for reporting and analysis. This takes one of two paths:

1)    The data warehouse is built using essentially the source systems’ data model. It may be “cleaned up” with new names and use only a subset of the source data, but it is really just a retread of what you already have.

It does shift your reporting from the source systems to a DW, but you have not taken advantage of the advanced dimensional modeling techniques that have grown to provide superior analytic performance. An entity-relationship (ER) model or third normal form (3NF) is indeed best practice for transactional systems, but not for business intelligence or data integration. IT knows 3NF and hence figures that is what they should do; many experienced practitioners starting off using 3NF and they continue to do so.

More Read

Twitter to Add Analytics? Now it MUST Be Cool
Social Media vs Social CRM vs Social Business vs Enterprise 2.0
Sugarcon Day 2, Re-cap of Paul Greenberg’s Keynote on Social CRM
Council to Counter Web Content Generators’ Growing Clout?
Some thoughts on the IRM(UK) DW/BI conference

The cost is longer development times and more labor-intensive maintenance. It also performs slower than best practice design, so …


(This is part of our ongoing Series of Unfortunate Data Warehousing and Business Intelligence Events. Click for the complete series, so far.)

Series_unfortunate A fundamental flaw of many business intelligence solutions is recreating what the company is already using for reporting and analysis. This takes one of two paths:

1)    The data warehouse is built using essentially the source systems’ data model. It may be “cleaned up” with new names and use only a subset of the source data, but it is really just a retread of what you already have.

It does shift your reporting from the source systems to a DW, but you have not taken advantage of the advanced dimensional modeling techniques that have grown to provide superior analytic performance. An entity-relationship (ER) model or third normal form (3NF) is indeed best practice for transactional systems, but not for business intelligence or data integration. IT knows 3NF and hence figures that is what they should do; many experienced practitioners starting off using 3NF and they continue to do so.

The cost is longer development times and more labor-intensive maintenance. It also performs slower than best practice design, so many companies compensate by buying more infrastructure such as CPUs, memory, storage and network bandwidth. If you sell or resell hardware then using this design is fine, but for the consumers of BI solutions you should try another way.

2)    The other end of the spectrum from 3NF is recreating your current reporting solutions, often data shadow systems or spreadmarts, that basically flatten out the data. It is easy to see why people recreate the spreadsheets the business people are using for reporting, but it leads to inflexible reports that require more and more reports to be built every time the business changes or expands their reporting requirements.

The fundamental concept behind dimensional modeling and OLAP (online analytical processing) design was to provide business people with the flexibility in their reporting and analysis. This is how a company can enable business self-service reporting rather than have a large group of BI developers designing, building and maintaining dozens or hundreds of custom reports.

Just as with 3NF the “flat world” approach to data mart design results in a much higher TCO and the huge queue of report development one sees at many BI implementations. Most assume that queue and costs come with the territory, but it does not have to be that way.

I will follow up with more unfortunate events I have observed. Feel free to e-mail with the unfortunate events you have seen.
Link to original post

TAGGED:business intelligencedata warehousing
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive
data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

InformationWeek’s “10 Most Strategic IT Vendors” Includes Teradata

4 Min Read
tips for making the most of business intelligence
Business Intelligence

5 Vital Business Intelligence Tips All Companies Should Embrace

7 Min Read

BI’s Place in Sustainability Reporting

4 Min Read
big data scientist skills
AnalyticsBig DataHadoopMapReduce

Is Hadoop Knowledge a Must-Have for Today’s Big Data Scientist?

3 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 and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?