Pooling disparate data for better decision-making


Imagine making a business decision without knowing how it
affects your customers, the sale of other products or the bottom line.
Organizations that lack integrated data will be challenged to make decisions
with only partial data or in a potentially untimely manner. Those companies
with integrated data will enjoy the ability to make rapid, informed and
differentiated decisions. As a result, they will emerge as leaders among their


Organizations go to great lengths to provide information
to their employees to help them make the best choices. In fact, companies that
build decision support systems to accomplish that goal tend to stand out as
differentiated leaders. These organizations leverage their data in a way that
gives them greater insight into their business. Accordingly, they can
consistently make smarter, faster decisions that yield streamlined operations,
effective customer service, improved revenue and market growth.


A Complete Picture

Getting to intelligent conclusions often requires information
from different areas of the business, including inventory, sales, marketing and
financial data. Bringing this data together in a timely and meaningful manner
is the key to getting the right information on which to make choices. Companies
that do this will enable differentiated decisions solidly based on all
available facts, which gives them a competitive advantage.


When data is not integrated, it usually means that the
user will make a decision based on information from a subset of total data available
within the organization. Consequently, the user will have old information that
is limited in scope. This in turn reduces the breadth and sophistication of the
questions the user can ask. When data from multiple subject areas is brought
together and integrated within a centralized data warehouse, the number and
sophistication of the questions users can ask will grow dramatically. When data
isn’t integrated, decision makers can only answer a limited number of questions
in various subject areas.


By working toward integrating all of the enterprise’s
data into a centralized data warehouse and allowing users direct access to that
platform, issues of timeliness and propagation can be eliminated.


In addition, an enterprise data warehouse’s value and use,
as well as its benefit to the organization, will increase exponentially as data
elements are integrated into it. The more data subjects in an enterprise data
warehouse, the greater the number of logical combinations among the data
elements. Queries limited to one data subject produce fewer and less intricate
results than queries with a greater range of data subjects.


As a simple example, if we create an Orders” data mart
and a separate Inventory” data mart, the questions to ask would be specific to
that data subject, such as:



* What product orders were placed?

* What products are on back order?



* What is the quantity of the products in inventory?

* What are their expiration dates?

* If we integrate these data marts


Additional questions can be added that reach beyond these
narrow subject areas and expand to the subject areas that intersect and connect


Combined Orders and Inventory

* Which orders can be filled with existing inventory?

* How many days of inventory are required for each

* How will a large order affect current inventory levels?


As the data warehouse environment grows and expands, the
time it takes to develop and deliver new applications will be dramatically
shortened. Established processes and procedures can be leveraged, and the data
that the enterprise data warehouse already holds for existing applications can
be reused as new applications are implemented.


Then, as additional data subjects are integrated into the
enterprise data warehouse, query possibilities are increased and application
delivery schedules are shortened. All of this leads to savings in time and
money by building the application in the enterprise data warehouse rather than
creating a data mart from the ground up.


The Cabela experience


Contending with data sometimes more than three weeks old
and a siloed analytics process, officials at Cabela’s, the world’s largest
direct marketer and a leading specialty retailer, replaced their old data
warehouse with a Teradata solution and successfully launched an internal effort
to integrate Teradata and SAS functionality.


The adoption led to a lot less time and energy being
spent in trying to find out what the truth was. The Teradata/SAS partnership
helps Cabela’s expand its SAS analytics to other areas of the enterprise.
According to Cabela officials, the integration made a huge difference in the
company’s ability to make quick strategic decisions.


“Our statisticians in the past spent 75 percent of
their time just trying to manage data,” Corey Bergstrom, Cabela’s director
of market research and analysis says. “Now they are spending much less
time managing the data and more time analyzing the data. And we have become
more flexible in the marketplace. That is just priceless.”


Improved vision

The more subject areas that are integrated from across
the company and available when a decision is made, the more sophisticated and
relevant the decisions become. There’s a double win. Integrated data enables
more questions to be answered with greater business impact.


By Noel Pettitt, area vice president, South Asia,

[email protected]