Business Intelligence & Data
Warehousing in a Business Perspective
Business Intelligence
Business Intelligence has become a very important
activity in the business arena irrespective of the
domain due to the fact that managers need to analyze
comprehensively in order to face the challenges.
Data sourcing, data analysing, extracting the correct
information for a given criteria, assessing the risks
and finally supporting the decision making process are
the main components of BI.
In a business perspective, core stakeholders need to
be well aware of all the above stages and be crystal
clear on expectations. The person, who is being assigned
with the role of Business Analyst (BA) for the BI
initiative either from the BI solution providers’ side
or the company itself, needs to take the full
responsibility on assuring that all the above steps are
correctly being carried out, in a way that it would
ultimately give the business the expected leverage. The
management, who will be the users of the BI solution,
and the business stakeholders, need to communicate with
the BA correctly and elaborately on their expectations
and help him throughout the process.
Data sourcing is an initial yet crucial step that
would have a direct impact on the system where
extracting information from multiple sources of data has
to be carried out. The data may be on text documents
such as memos, reports, email messages, and it may be on
the formats such as photographs, images, sounds, and
they can be on more computer oriented sources like
databases, formatted tables, web pages and URL lists.
The key to data sourcing is to obtain the information in
electronic form. Therefore, typically scanners, digital
cameras, database queries, web searches, computer file
access etc, would play significant roles. In a business
perspective, emphasis should be placed on the
identification of the correct relevant data sources, the
granularity of the data to be extracted, possibility of
data being extracted from identified sources and the
confirmation that only correct and accurate data is
extracted and passed on to the data analysis stage of
the BI process. Business oriented stake holders guided
by the BA need to put in lot of thought during the
analyzing stage as well, which is the second phase.
Synthesizing useful knowledge from collections of data
should be done in an analytical way using the in-depth
business knowledge whilst estimating current trends,
integrating and summarizing disparate information,
validating models of understanding, and predicting
missing information or future trends. This process of
data analysis is also called data mining or knowledge
discovery. Probability theory, statistical analysis
methods, operational research and artificial
intelligence are the tools to be used within this stage.
It is not expected that business oriented stake holders
(including the BA) are experts of all the above
theoretical concepts and application methodologies, but
they need to be able to guide the relevant resources in
order to achieve the ultimate expectations of BI, which
they know best.
Identifying relevant criteria, conditions and
parameters of report generation is solely based on
business requirements, which need to be well
communicated by the users and correctly captured by the
BA. Ultimately, correct decision support will be
facilitated through the BI initiative and it aims to
provide warnings on important events, such as takeovers,
market changes, and poor staff performance, so that
preventative steps could be taken. It seeks to help
analyze and make better business decisions, to improve
sales or customer satisfaction or staff morale. It
presents the information that manager’s need, as and
when they need it.
In a business sense, BI should go several steps
forward bypassing the mere conventional reporting, which
should explain “what has happened?” through baseline
metrics. The value addition will be higher if it can
produce descriptive metrics, which will explain “why has
it happened?” and the value added to the business will
be much higher if predictive metrics could be provided
to explain “what will happen?” Therefore, when providing
a BI solution, it is important to think in these
additional value adding lines.
Data warehousing
In the context of BI, data warehousing (DW) is also a
critical resource to be implemented to maximize the
effectiveness of the BI process. BI and DW are two
terminologies that go in line. It has come to a level
where a true BI system is ineffective without a powerful
DW, in order to understand the reality behind this
statement, it’s important to have an insight in to what
DW really is.
A data warehouse is one large data store for the
business in concern which has integrated, time variant,
non volatile collection of data in support of
management's decision making process. It will mainly
have transactional data which would facilitate effective
querying, analyzing and report generation, which in turn
would give the management the required level of
information for the decision making.
The reasons to have BI together with DW
At this point, it should be made clear why a BI tool
is more effective with a powerful DW. To query, analyze
and generate worthy reports, the systems should have
information available. Importantly, transactional
information such as sales data, human resources data
etc. are available normally in different applications of
the enterprise, which would obviously be physically held
in different databases. Therefore, data is not at one
particular place, hence making it very difficult to
generate intelligent information. The level of reports
expected today, are not merely independent for each
department, but managers today want to analyze data and
relationships across the enterprise so that their BI
process is effective. Therefore, having data coming from
all the sources to one location in the form of a data
warehouse is crucial for the success of the BI
initiative. In a business viewpoint, this message should
be passed and sold to the managements of enterprises so
that they understand the value of the investment. Once
invested, its gains could be achieved over several
years, in turn marking a high ROI.
Investment costs for a DW in the short term may look
quite high, but it’s important to re-iterate that the
gains are much higher and it will span over many years
to come. It also reduces future development cost since
with the DW any requested report or view could be easily
facilitated. However, it is important to find the right
business sponsor for the project. He or she needs to
communicate regularly with executives to ensure that
they understand the value of what's being built.
Business sponsors need to be decisive, take an
enterprise-wide perspective and have the authority to
enforce their decisions.
Process
Implementation of a DW itself overlaps with some
phases of the above explained BI process and it’s
important to note that in a process standpoint, DW falls
in to the first few phases of the entire BI initiative.
Gaining highly valuable information out of DW is the
latter part of the BI process. This can be done in many
ways. DW can be used as the data repository of
application servers that run decision support systems,
management Information Systems, Expert systems etc.,
through them, intelligent information could be achieved.
But one of the latest strategies is to build cubes out
of the DW and allow users to analyze data in multiple
dimensions, and also provide with powerful analytical
supporting such as drill down information in to granular
levels. Cube is a concept that is different to the
traditional relational 2-dimensional tabular view, and
it has multiple dimensions, allowing a manager to
analyze data based on multiple factors, and not just two
factors. On the other hand, it allows the user to select
whatever the dimension he wish to choose for analyzing
purposes and not be limited by one fixed view of data,
which is called as slice & dice in DW terminology.
BI for a serious enterprise is not just a phase of a
computerization process, but it is one of the major
strategies behind the entire organizational drivers.
Therefore management should sit down and build up a BI
strategy for the company and identify the information
they require in each business direction within the
enterprise. Given this, BA needs to analyze the
organizational data sources in order to build up the
most effective DW which would help the strategized BI
process.
High level Ideas on Implementation
At the heart of the data warehousing process is the
extract, transform, and load (ETL) process.
Implementation of this merely is a technical concern but
it’s a business concern to make sure it is designed in
such a way that it ultimately helps to satisfy the
business requirements. This process is responsible for
connecting to and extracting data from one or more
transactional systems (source systems), transforming it
according to the business rules defined through the
business objectives, and loading it into the all
important data model. It is at this point where data
quality should be gained. Of the many responsibilities
of the data warehouse, the ETL process represents a
significant portion of all the moving parts of the
warehousing process.
Creation of a powerful DW depends on the correctness
of data modeling, which is the responsibility of the
database architect of the project, but BA needs to play
a pivotal role providing him with correct data sources,
data requirements and most importantly business
dimensions. Business Dimensional modeling is a special
method used for DW projects and this normally should be
carried out by the BA and from there onwards technical
experts should take up the work. Dimensions are
perspectives specific to a business that could be used
for analysis purposes. As an example, for a sales
database, the dimensions could include Product, Time,
Store, etc. Obviously these dimensions differ from one
business to another and hence for each DW initiative
those dimensions should be correctly identified and that
could be very well done by a person who has experience
in the DW domain and understands the business as well,
making it apparent that DW BA is the person responsible.
Each of the identified dimensions would be turned in
to a dimension table at the implementation phase, and
the objective of the above explained ETL process is to
fill up these dimension tables, which in turn will be
taken to the level of the DW after performing some more
database activities based on a strong underlying data
model. Implementation details are not important for a
business stakeholder but being aware of high level
process to this level is important so that they are also
on the same pitch as that of the developers and can
confirm that developers are actually doing what they are
supposed to do and would ultimately deliver what they
are supposed to deliver.
Security is also vital in this regard, since this
entire effort deals with highly sensitive information
and identification of access right to specific people to
specific information should be correctly identified and
captured at the requirements analysis stage.
Advantages
There are so many advantages of BI system. More
presentation of analytics directly to the customer or
supply chain partner will be possible. Customer scores,
customer campaigns and new product bundles can all be
produced from analytic structures resulting in high
customer retention and creation of unique products. More
collaboration within information can be achieved from
effective BI. Rather than middle managers getting great
reports and making their own areas look good,
information will be conveyed into other functions and
rapidly shared to create collaborative decisions
increasing the efficiency and accuracy. The return on
human capital will be greatly increased.
Managers at all levels will save their time on data
analysis, and hence saving money for the enterprise, as
the time of managers is equal to money in a financial
perspective. Since powerful BI would enable monitoring
internal processes of the enterprises more closely and
allow making them more efficient, the overall success of
the organization would automatically grow. All these
would help to derive a high ROI on BI together with a
strong DW. It is a common experience to notice very high
ROI figures on such implementations, and it is also
important to note that there are many non-measurable
gains whilst we consider most of the measurable gains
for the ROI calculation. However, at a stage where it is
intended to take the management buy-in for the BI
initiative, it’s important to convert all the non
measurable gains in to monitory values as much as
possible, for example, saving of managers time can be
converted in to a monitory value using his compensation.
The author has knowledge in both Business and IT.
Started career as a Software Engineer and moved to work
in the business analysis area of a premier US based
software company.
Article Source:
http://EzineArticles.com/
