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: Data Warehouse Design
Share
Notification Show More
Latest News
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
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > Data Warehouse Design
Data Warehousing

Data Warehouse Design

Editor SDC
Last updated: 2009/06/25 at 3:12 AM
Editor SDC
6 Min Read
SHARE
One of the main problems with Data Warehouses is that they are designed to answer any question. The problem is that they usually fail to answer the one someone is asking. DWs are usually good for referential information – meaning I can answer questions like “How many customers do we have that have spent over $100,000?” or “Which customers bought the blue widget?”

There are a number of points of failure that hamper DW projects:

  • They are usually complex and very costly
  • The business changes (regions, product lines, sales hierarchies, etc) in the middle of the process
  • The end use is not well defined
  • Lack of analytical skill and knowledge of data structure in the business users to get the right data
  • The end result is too complex for the users to understand where to go to get the right information
  • No one tells the organization “thou shalt” use the data warehouse – so people get data from all different sources making a common version of the truth difficult to get to
  • There are often no rules of engagement for how to use the environment, or data in general

If organizations only use 6-10% of the data they collect, how do you design the DW for greater adoption?

For starters, …

More Read

What is Data Pipeline A detailed explaination

What is Data Pipeline? A Detailed Explanation

Understanding ETL Tools as a Data-Centric Organization
Differentiating Between Data Lakes and Data Warehouses
How Will The Cloud Impact Data Warehousing Technologies?
Big Data Is More Prevalent in Daily Life Than You Might Think

One of the main problems with Data Warehouses is that they are designed to answer any question. The problem is that they usually fail to answer the one someone is asking. DWs are usually good for referential information – meaning I can answer questions like “How many customers do we have that have spent over $100,000?” or “Which customers bought the blue widget?”

There are a number of points of failure that hamper DW projects:

  • They are usually complex and very costly
  • The business changes (regions, product lines, sales hierarchies, etc) in the middle of the process
  • The end use is not well defined
  • Lack of analytical skill and knowledge of data structure in the business users to get the right data
  • The end result is too complex for the users to understand where to go to get the right information
  • No one tells the organization “thou shalt” use the data warehouse – so people get data from all different sources making a common version of the truth difficult to get to
  • There are often no rules of engagement for how to use the environment, or data in general

If organizations only use 6-10% of the data they collect, how do you design the DW for greater adoption?

For starters, understand the common business questions and the potential levers that can be pulled. For example, one of the areas that always surprises me is the lack of information around the success of marketing campaigns. Marketing campaigns and price are really the only levers we can pull in the short term to increase revenues. What we often fall back to is the sales whip – where we put more pressure on the sales team to perform. This is a strategy of hope (which is not a recognized as a successful strategy practice). We apply the pressure without providing much in the terms of support.

Instead, let’s say we are ending the 3rd quarter and our numbers are a little behind and the pipeline is not as strong as we would like. We know we have some time, but the programs have to be very tactical to find low hanging fruit. Instead of reviewing the potential marketing programs or trying something new, we cross our fingers and yell at the sales team. We could cull the DW to find large groups of customers who had not bought specific groups of products and offer incentives for them to buy. We could identify the groups/verticals of customers with the shortest sales cycle and build a promotion and program for them as well.

Yet why do we not do this… we typically lack the information in a format we can use in a timely manner.

So if we design the data warehouse (or perhaps data marts) around specific business levers we stand a better chance of answering the one question we need. We just might trigger some very interesting questions about our business.

Come visit the blog (http://purestone.wordpress.com) for more information.

Posted in Analytics, Analytics & Business Intelligence, Customer Value, Initiative Management, Operational Performance Management, Process Improvement Tagged: Analytics, Business Questions, Data Mart, Data Warehouse, Marketing Campaigns, Marketing Performance, Report Design


Link to original post

Editor SDC June 25, 2009
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

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
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

What is Data Pipeline A detailed explaination
Big Data

What is Data Pipeline? A Detailed Explanation

8 Min Read
etl for data-driven businesses
Big Data

Understanding ETL Tools as a Data-Centric Organization

8 Min Read
data lake vs data warehouse
Data Lake

Differentiating Between Data Lakes and Data Warehouses

7 Min Read
moving to the cloud
Big DataCloud ComputingData WarehousingExclusive

How Will The Cloud Impact Data Warehousing Technologies?

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
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

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?