Dear Member,
Welcome to TDWI FlashPoint. In this issue, Wayne Eckerson
offers five steps to help you bridge the divide between analytic modelers
and IT administrators.
CONTENTS
FlashPoint Snapshot
FlashPoint Snapshots highlight key findings from TDWI's wide
variety of research.
Vendor Products Related to BI Search and Text Analytics
The list is representative and not meant to be comprehensive. (View
larger image.)
Source: BI Search and Text
Analytics: New Additions to the BI Technology Stack (TDWI Best
Practices Report, Q2, 2007). Click
here to access the report.
Based on 370 Internet-based respondents. Rounding and
multiple-choice questions are responsible for percent totals that
do not equal exactly 100 percent.
Top
Five Steps to Bridge the Business/IT Gap
Wayne W. Eckerson, TDWI
The Problem
There is a cultural divide between analytical modelers and IT administrators.
Anyone who has managed or mediated problems between these two groups
knows how different they are -- culturally, organizationally, and
functionally.
Analytical modelers view IT as gatekeepers to the data and bottlenecks
to getting things done. They see IT as paralyzed by process and blinded
by methodology. From the perspective of the IT department, on the
other hand, analytical modelers are CPU hogs who issue runaway queries
and kick off CPU-intensive scoring routines that kill query performance
on the database server and slow query response times for everyone.
Solutions
Most of the solutions for bridging the divide are organizational
and cultural in nature. In other words, the solution lies in changing
attitudes and behaviors, and this type of change doesn't come easily
or quickly.
There are five ways to bridge the divide between IT and analytical
modelers.
- Find a Liaison. Every organization that has
successfully aligned the two groups has had one or more liaisons
who straddle both worlds -- they can talk the language of business
and IT and align the two groups through force of personality and
partnerships.
- Foster Dialogue. Bring the two groups together
so that they can begin to understand each other and overcome prejudices
and knee-jerk reactions. This can be done by 1) having the groups
socialize outside of work, 2) conducting formal meetings where the
two sides air their grievances and acknowledge common objectives,
and 3) establishing formal channels for regular communication between
executives and managers in the two groups.
- Compromise. Once the two sides understand each
other better, it's time for both to roll up their sleeves and change
their habits to accommodate the fundamental needs of the other group.
Often, this means that analysts should learn the language of IT,
such as SQL, and adopt some IT processes, such as modeling rules
and naming conventions, especially if they want IT to deploy their
models in a quick and efficient way. Conversely, IT needs to give
analysts access to the corporate jewels (i.e., the data warehouse)
and give them space within the database server to add, delete, and
manipulate tables and data as they see fit without being restricted
by storage capacity, CPU threads, or memory.
- Training. To make the changes just described
requires that both groups pick up new skills. Analysts need to learn
how to write SQL and create and manipulate tables in the database.
Database administrators need to learn how to configure and tune
a mixed workload environment that supports a combination of casual
users, power users, and analysts. This may require hiring consultants
to show the database administrators how to configure a mixed workload
system in their own environment.
- Establish an Analytic Sandbox. These dedicated
environments let analysts do all the things analysts love to do.
This way, they can create tables; load, merge, and aggregate data;
combine fields; and run queries -- without impinging on other users
of the data warehousing environment.
- An analytic sandbox partitions spaces within a data warehousing
database, giving modelers read-only access to any data in the warehouse
so they don't have to download it to a data mart or desktop workbench.
Using partitions and workload management utilities, IT can also
give modelers free reign to load and manipulate data in the sandbox
without affecting performance for other users of the system or jeopardizing
the integrity of data within the data warehouse. Recent advances
in database technology -- specifically workload management -- have
made this an increasingly popular option.
- Virtual sandboxes require a database that supports partitions
and robust workload management. This can be challenging for database
administrators who don't know how to configure a mixed workload
environment or whose database environment isn't powerful enough
to support dozens or hundreds of partitions for analytical modelers.
As a result, companies may need to spend money to hire consultants
and upgrade their database environment to support analytic sandboxes.
Summary
To overcome the cultural and organizational divide and get these
groups working together harmoniously for the good of the organization,
business and technology managers should find a liaison, foster dialogue,
compromise, train, and implement analytical sandboxes.
Top
FlashPoint Rx
FlashPoint Rx prescribes a "Mistake to Avoid" for business intelligence
and data warehousing professionals from TDWI's
Ten Mistakes to Avoid series.
Ten Mistakes to Avoid When Selecting and Deploying ETL Tools
Mistake 3. Taking a Myopic View of ETL Usage
What does the future look like for your environment? From a data
perspective, this may mean dealing with time-sensitive data. At first,
you might process data in a nightly batch, but chances are good that
you will need to add low-latency data in the near future, or provide
on-demand access to just-in-time BI tools or dashboards. If you don’t
think about such future needs, then you may ignore features like messaging,
EAI or Web service integration, or the broader set of data federation
features provided by EII tools or components.
When evaluating ETL tools, it also pays to look beyond basic ETL
to broader data integration needs. For example, use beyond a single
project will drive the need for multi-project source control and security -- features
you wouldn’t consider if your scope is a single data warehouse.
Many organizations are realizing how useful ETL tools can be outside
the data warehouse environment. The tools are good at getting data
from one place to another, which means ETL tools can be a great fit
for system migrations and consolidations.
Moving data from one application to another or merging several different
applications into one system is a lot of work, particularly if it
involves dealing with packaged applications like SAP. These data migration
efforts are often underestimated. If you are having trouble justifying
an ETL purchase based on data warehouse savings, take a step back
and look at reuse outside of the data warehouse. You may find that
other projects have similar needs, which could provide the ROI you
need to justify your purchase.
This excerpt was pulled from the Q2 2006, TDWI
Ten Mistakes to Avoid series, Ten Mistakes to Avoid
When Selecting and Deploying ETL Tools, by Mark Madsen. Top
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Editorial Inquiries
Have a Comment or Question about TDWI FlashPoint?
If you would like to respond or comment on the articles above, or
submit an article idea or case study, please e-mail James E. Powell or call 206.284.1762. |