The
importance of Data warehouse and best practices
Businesses are recognizing the essence of databases and
enterprise data warehouse. The data
warehouses help to offer a three-dimensional view of the business apart from
offering a powerful platform for ensuring a wide spectrum, of business
intelligence tasks including predictive analysis, real-time strategic support,
and decision support throughout an enterprise.
They can scale up and help the organizations to garner the desired
performance as the company data grows.
The managers and the system analysts need to have the ability to connect
to the data warehouse from their PCs, and the connection should be immediate
with high performance, and the tools should be easy to use (Weir, Peng &
Kerridge, 2003). The data warehouse assembles the data from a variety of
sources within an enterprise, and it then cleans up the data, assures its
quality, and releases it when it is ready for analysis.
Best
Practices
The management of quality of the data in the warehouse is
vital to the data integration process.
We can consider it as the first step in this integration process because
quality data is the key to ensuring profitable insights are achieved. The integration of data analysis cannot be
successful unless there is good data quality scheme in place. Business intelligence depends on upon the
dashboards and analytical tools that need the integration of data from various
source systems (Ullrey, 2007). The
assurance of data quality is a must before nay integration can take place.
Therefore, to make sure that there is effective data quality management,
specific best practices are in requirement.
Data quality management should be
included in the data lifecycle
Before the integration of data takes place, there must be
the checking of the condition of data and that data should be raised to a
minimum level of quality. Even though there are many tools that are used for
data quality checking, the database managers are unaware of the most suitable
tools that can help achieve the best data quality management (Mohanty, Jagadeesh
& Srivatsa, 2013). They should, therefore, analyze the tools available in
the market and select the ones that can achieve the highest quality management
for the data in the warehouse. The Hadoop tool is becoming very common and
useful in this process. The assurance of
data quality is an imperative step in the overall implementation of a data
warehouse.
Do the initial architecture
envisioning
The initiation of the data warehouse implementation project,
the initial architecture modeling, is necessary so as to identify the potential
vision on how the implementation team will construct the data warehouse. At this stage, the designer does not have to
create a comprehensive data model, but one only needs to ensure a high-level
vision at the start of the project while the details can then be decided on a
just-in-time basis through model storming. Occasionally a simple wireframe
sketching can help you in understanding the architectural vision. It can capture all the technologies to be
used as well as a high-level domain modeled that shows the entities and
relationships between the entities (March & Hevner, 2007).
Model the DW details just in time
The most appropriate tie for modeling the details is not at
the beginning of the project, but they should rather be model stormed through
the project in a just-in-time manner (Lawyer & Chowdhury, 2004). Several reasons can support this. The first reason is that requirements always
change throughout the process of project development. The second reason is that by waiting to
assess the project details just-in-time, one can have more domain knowledge as
compared to analyzing them during the beginning of the project. Thirdly, the delivery of regular software to
clients can give the stakeholders a good amount of long-term experience with
the system under development.
Focus on the Usage
When one wants to develop a data warehouse system in an
effective manner, one should understand how the organization or individuals
will be using it to support the business objectives. That means that a user-centered approach is
in requirement and development is driven by the use cases or the usage
scenarios. Many developers get it wrong
by leveraging a data-centered approach driven by the data models. Although data is an important part of the
data warehouse architecture, it is only one of the several parts (March &
Hevner, 2007). Focusing on the data
rather than the usage can make an organization risk building something that
people will not be interested in using, a too common occurrence in the
traditional data warehouse efforts.
There should be active participation of the users of the data warehouse.
Adopt a lean approach to the data
governance
The traditional command-and-control approaches have provided
to work very poorly Watson, Fuller & Ariyachandra, 2004). The DDJ 2006 Report examined the current
state of the data management practices, and it found out that 66 percent of the
development teams opt to work around their enterprises’ data group. When they
do so, 75 percent of their time is wasted because the data groups are too
difficult to work with, they are also too slow to respond, or they do not offer
adequate value that can justify the effort of collaborating with them. The lean
approach can help solve that problem
The Refined Project Plan
|
Identifier
|
Activities
|
Tasks
|
Milestone/Deliverable
|
|
PRQ-001
|
Requirements
|
Define the technical,
business, as well as the staffing requirements
|
The technical include: Hardware and
peripherals; Vendor Contract;
Licensing requirements;
Acquire data for data loads;
Business deliverables include: ACD business rules and the data
load requirements
The staffing requirements are:
vendor resource identification and the ITD DW resource identification.
|
|
PRQ -002
|
Acquisition
|
Purchase software and
hardware.
|
Vendor selection and
acquisition process.
|
|
PRQ -003
|
Implementation
|
Install the software and hardware.
|
Fully installed and working
software and hardware
|
|
PRQ -004
|
Inventory
|
Load the Data
|
An inventory with fully loaded
data
|
|
PRQ -005
|
Application Deployment
|
Develop the application code
for integration.
|
A coding for systems
integration and a working system
|
|
PRQ
-006
|
Implementation/Testing
|
Test
the software and hardware.
|
A
Test Plan and test cases
|
|
PRQ
-007
|
Training
|
Train
users.
|
Documentation
for the users and support personnel.
|
References
Lawyer,
J., & Chowdhury, S. (2004, January). Best practices in data warehousing to
support business initiatives and needs. In System Sciences, 2004.
Proceedings of the 37th Annual Hawaii International Conference on (pp.
9-pp). IEEE.
March, S.
T., & Hevner, A. R. (2007). Integrated decision support systems: A data
warehousing perspective. Decision Support Systems, 43(3),
1031-1043.
Mohanty,
S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives:
Enterprise big data warehouse, BI implementations and analytics. New York:
Apress.
Ullrey, B.
R. (2007). Implementing a data warehouse: A methodology that worked.
Bloomington, Ind: AuthorHouse.
Watson, H.
J., Fuller, C., & Ariyachandra, T. (2004). Data warehouse governance: best
practices at Blue Cross and Blue Shield of North Carolina. Decision Support
Systems, 38(3), 435-450.
Weir, R.,
Peng, T., & Kerridge, J. (2003). Best practice for implementing a data
warehouse: a review for strategic alignment. VLDB.
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