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Unit 5 Midterm Assignment
IT521-01N: Decision Support Systems (P)
Eleisha Barnett
7-Eleven operates, franchises and licenses close to 7,100 stores in the U.S. and Canada.
Of the 6,000 stores the company operates and franchises in the United States, more than 4,700
are franchised.7-Eleven master franchisees, licensees and affiliates operate more than 32,000 7Eleven and other convenience stores in other countries including Japan, Taiwan, Thailand, South
Korea, China, Hong Kong, Malaysia, Mexico, Singapore, Australia, Philippines, Indonesia,
Norway, Sweden and Denmark (7-Eleven, 2010). 7-Eleven has grown, from a little ice house in
Texas in 1927 to becoming the first and household name in convenience stores. Their slogan,
first introduced in 1969 “Oh Thank Heaven For 7-Eleven” reflects the service, friendliness, and
convenience that the company has become branded for. Branded products such as Slurpee, Big
Gulp, and Big Bite have become the synonymous with products across the country as Kleenex
has been, a generic term for similar products.
7-Eleven has built their presence not just by being a convenient place to buy coffee and a
donut in the morning and milk and eggs on the way home, but by being attentive to customer
needs and wants and tailoring stores to the neighborhoods in which they maintain a presence.
This has been accomplished a numbers of ways such as adding gas to their products offered,
partnering with Anheuser Busch to create a beer called “Chelada” based on what people in
certain Latino neighborhoods created from regular beer and other ingredients. 7-Eleven
introduced a Diet Slurpee which addressed the desires of people watching their weight and
people with other health concerns such as diabetes or even kids with braces (MSNBC, 2008).
In consideration of the data warehouse to be built, we must consider the following
Accuracy: the degree of confidence that data is free of error/defect. This is important
that the data input it correct. One digit off in a part number can send two dozen
Slurpee cups to a store instead of two gallons of mix.
Completeness: the extent to which data is not missing and is of sufficient breadth and
depth for the task at hand. All the data must be available and complete to be able to
make informed and intelligent decisions about any business aspect, whether it be
finance, hiring, putting up a new store, or introducing a new flavor of Slurpee.
Consistency: the degree to which common data across different sources follows the
same definitions, codes and formats. As mentioned in the accuracy, all inventory or
data must follow the same coding. That includes translations from different vendors,
so that no matter where the strawberry Slurpee mix is bought, you will get the
strawberry Slurpee mix when you order it and not the blueberry.
Timeliness: the degree to which data is up to date. This is especially true in the
volatile oil market today. The latest information is important for correct pricing.
Security: the degree to which data confidentiality, integrity and availability has been
maintained. This can be created within the data warehouse itself, whether data
development groups are created which will restrict access to information to these
employees, or user groups. Data needs need to be determined and access granted
based on those needs. .
Fit for Purpose: the degree to which data is relevant, appropriate and meets business
specifications. Accounting has no need for customer related data, nor do individual
stores need travel data for the vice president’s office. Data warehousing, specified for
the individual purpose, will assure that the right information gets to the right people
(Microsoft TechNet, 2010).
An enterprise data warehouse is a large-scale data warehouse that is used across the
enterprise for decision support. The large-scale nature provides integration of data from many
sources into a standard format for effective BI and decision support applications. EDW are used
to provide data for many types of DSS, including CRM, supply-chain management, business
performance management, business activity monitoring, product lifecycle management, revenue
management, and sometimes even knowledge management systems. Based on the size of 7Eleven, growth, and global extensiveness of the concern, we need to be looking at taking 7Eleven, Inc. from a primarily hardware-focused and country-centric organizational model to an
integrated, solution-oriented business structure with a global focus. As such an enterprise data
warehouse is the best choice (Turban, et al, 2010).
Some of the challenges will be the company will become heavily dependent on their data
warehouse, especially when it comes to their daily activities. To be efficient, this data
warehouses need to have very high levels of industrial safety. If the data warehouse development
group is allowed to work independent of other teams, this will reduce the transfer of knowledge
that may be necessary for the industrial safety of data warehouses. Another important decision
that a company will need to face is who should be responsible for administering the databases for
the data warehouses. In most cases, the available options will either be the data warehouse
development group or the DBA group (ExForsys, 2010).
A company will often find that they will have to consistently make changes to the structure
of data warehouse databases. The use of data marts, combined with the wrong mentality by
workers, can create a situation where index and table changes need to be made. If the developers
of the data warehouse are concerned with having problems with the users, they may want
changes to be made frequently, and if they have to deal with the DBA backlog, they could be
irritated. At the same time, the DBA has profound knowledge of the processes that are necessary
for the industrial safety of the databases (ExForsys, 2010).
We will from a top down perspective and decide who has need for what level of access
and then design the data warehouse around this. The president and CEO need access to all data.
The vice presidents and CFO will need access to data as it relates to their particular areas,
whether it is accounting, global licensures, merchandising, logistics, human resources, etc. The
various departments will need access of this nature but only as it relates to their duties.
Information shall be disseminated to the regional offices which will then distribute as needed to
within their office and then to individual stores (Barnett, 2011).
To understand what we are proposing, we are using a star diagram to understand what
data input we are want in our database and what we are hoping to extract and output. We also
need to know how we are obtaining the information; how we are inputting the information and
how we are distributing the data as well as the various sources we are obtaining the information
from and what sources we are distributing the data to. This is important to cut down on irrelevant
data, distributing the correct data to the correct department/personnel, and expedite data in the
most efficient and secure manner possible. However, we must realize that 7-Eleven’s unique
approach to their markets requires data designed for those individual markets (Barnett, 2011).
Figure 1. Star Diagram (Microsoft Technet, 2010)
*----- Design ---------*
*----- Design ------------*
| understand queries
| understand application |
| E-R diagramming
| choose data source
| set data types
| design normalization
| name tables, views, |
| design edits
| and elements
| synchronize with Oracle |
| design tables/views | ---- | code and document
| plan/design security |
| test and verify
| plan production
*----- Design --------------*
| understand business
| map user's language
| map application language |
| define context, tables,
| elements, and values
| write "solutions" queries |
| design delivery to user
*-------- Hardware ----------------------------------------*
| PCs, mainframe, LAN, servers (warehouse, gopher, e-mail) |
Figure 2. Data Warehouse Design Process (University of Colorado System, 2010)
Figure 3. Data Communications Chart (Barnett, 2011)
Human Resources
International Stores
US Stores
In this high speed, globally economically driven world, the need for speed, scalability,
efficiency, and security is imperative in the data warehousing system to be designed for the 7Eleven corporation. It is for these reasons, we have chosen Oracle for 7-Eleven’s data
warehousing needs (Barnett, 2011). Oracle Database 11g offers fast, reliable and secure
reporting, analytics, and data mining on low cost, scalable grids. While many items here are
recommended here are options, we highly recommend the options to optimize the performance
of the data warehouse and for the particular needs of the 7-Eleven corporation.
At the heart of every Oracle Exadata Database Machine are Oracle Exadata Storage
Servers, which combine smart storage software and industry-standard hardware to deliver the
industry's highest database storage performance. To overcome the limitations of conventional
storage, Oracle Exadata Storage Servers use a massively parallel architecture to dramatically
increase data bandwidth between the database server and storage. In addition, smart storage
software offloads data-intensive query processing from Oracle Database 11g servers. The result
is faster parallel data processing and less data movement through higher bandwidth connections.
This massively parallel architecture also offers linear scalability and mission-critical reliability
(Oracle, 2010).
Oracle Data Integrator Enterprise Edition delivers unique next-generation, Extract Load
and Transform (E-LT) technology that improves performance, reduces data integration costs,
even across heterogeneous systems. Unlike conventional ETL tools, Oracle Data Integrator EE
offers the productivity of a declarative design approach, as well as the benefits of an active
integration platform for seamless batch and real-time integration. In addition, hot-pluggable
Knowledge Modules provide modularity, flexibility, and extensibility, something that will be
important in terms of dealing with regional and international markets, each with its unique
market make-ups.
Oracle Data Integrator Enterprise Edition is optimized for Oracle Database to provide
real-time data warehousing with advanced ETL/ELT and data modeling. We can use Oracle Data
Integrator EE together with Oracle Data Profiling and Data Quality for advanced data
governance capabilities. We can also use Oracle Data Integrator EE together with Oracle SOA
Suite to enable service-oriented data integration and management that gives companies even
greater flexibility for their data services (Oracle, 2010). 7-Eleven requires flexibility for the
markets that they enter into. Oracle Data Integration Enterprise Edition, a foundational
component to the Oracle Data Integration Suite is Oracle Fusion Middleware's strategic data
integration offering; which has emerged from combining the best of both Oracle Data Integrator
(ODI) and Oracle Warehouse Builder (OWB) into a single unified product offering (Oracle,
Key requirements are high performance, high availability database environments,
partitioning splits tables and indexes into smaller, more manageable components. The Oracle
Database 11g offers the widest choice of partitioning methods available, including interval,
reference, list, and range. Additionally, it provides composite partitions of two methods, such as
order date (range) and region (list) or region (list) and customer type (list). These partitions are
very important to what 7-Eleven tries to accomplish with customizing their stores to the
neighborhoods in which they are found (MSNBC, 2008). Oracle Partitioning is also the
foundation of Oracle's Information Lifecycle Management strategy, which aligns the business
value of information to cost-effective storage tiers for large data warehousing and transaction
processing applications. Oracle Data Mining (ODM) allows customers to produce actionable
predictive information and build integrated business intelligence applications. Using data mining
functionality embedded in Oracle Database 11g, customers can find patterns and insights hidden
in their data. Application developers can automate the discovery and distribution of new business
intelligence, such as predictions, patterns and discoveries, throughout their organization (Oracle,
Oracle OLAP is a calculation engine that supports the entire spectrum of advanced
analytical applications including planning, budgeting, forecasting, sales, and marketing to help
identify key business trends and model complex business scenarios(Oracle, 2010). OLAP cube
materialized views in Oracle Database 11g dramatically improves the performance of complex
analytic queries accessing multi-dimensional data. Businesses no longer have to replicate data
into a stand-alone, proprietary OLAP server because all of the multi-dimensional data is stored
directly into the Oracle Database relational engine. Decision makers benefit from much faster
access to higher quality information (Oracle, 2010). Costs are reduced and the IT environment is
simplified because the need to purchase additional hardware is eliminated and you only have to
maintain a single server and security model. Oracle OLAP also supports SQL access to multidimensional data types, so you can leverage your existing investment SQL tools, applications,
and skills. Oracle OLAP also provides scale analytics with grid computing. This is accomplished
through the OLAP and Oracle Real Applications Clusters working together to provide the
foundation of Very Large Multi-dimensional Databases (VLDM) which can provide thousands
of concurrent users with continuous access to information (Oracle, 2010).
Oracle Warehouse Builder (OWB) 11g is a data warehousing-centered data modeling and
data integration solution, built into every Oracle 11g database. The full data integration and data
warehousing capabilities of Warehouse Builder require additional licensing for Oracle Data
Integrator Enterprise Edition and ERP application adapters (Oracle, 2010). The Oracle Database
license includes basic ETL features. Warehouse Builder delivers the data warehousing and
business intelligence capabilities as well as data integration and ETL capabilities. On the data
warehousing/BI side is relational and dimensional modeling, support for OLAP and cube
organized MVs and all Oracle data warehousing features, data warehousing specific operators
for loading cubes and dimensions. It also features SCD type 2 and 3 modeling and loading
support, Oracle spatial transformations, Oracle BI tool integration such as Discoverer and OBIEE, and metadata management, data lineage, impact. Other features include analysis and change
propagation for complex designs, fuzzy matching and merging which will continue to take 7Eleven into the future through scalability, and integration with third-party name and address
cleansing products. This system integrates well with what systems 7-Eleven may already have in
On the data integration and ETL side, the Oracle Warehouse Builder features in-database
EL-T processing for Oracle and non-Oracle items, connectivity and data movement choices,
including gateways, JDBC, and bulk data loads, and CDC and realtime data movement options
including Oracle GoldenGate integration (Oracle, 2010). NPR has had real success with this
particular feature and we believe that integrating these features will ensure 7-Eleven’s immediate
and future successes (Turban, et al, 2011). Also featured are native data type support for nonOracle database platforms, best-for-Oracle code generation, including row-based PL/SQL and
set-based SQL options, native SQL code generation for non-Oracle data bases, again very
important to work with what 7-Eleven already has in place and thereby, a real cost savings.
Oracle and SAP ERP application sources, One ETL mapping paradigm for all data
movement/loading modes, flexible flat file and XML loading and unloading options, and basic
Web services support round out the rest of this package (Oracle, 2010).
Barnett, Eleisha 2011
Turban, E., Sharada,R., and Delen, D. (2011). Decision Support and Business Intelligence
Systems. Upper Saddle river, New Jersey: Pearson Education, Inc.