BI
Business Intelligence is useful because its
environment is data-driven and supports these process of gathering data, data
storage, and knowledge management. The main characteristics of an executive
information system which its highlights is to analyze big amounts of data for
organizations about their operations (Burstein, Holsapple,
Negash, & Gray, 2008).
The physical state of a computer environment (using
business intelligence) contains a large database infrastructure; using a data
warehouse or data mart as a foundation of information and analysis. The many reasons to use analysis as a means
for reporting, drill down answering queries, real time analysis, and forecasting.
A closer look at many growths in BI includes business performance measurements
(BPM), business activity monitoring (BAM), and many staffing tools currently
used by managers within their organizations (BI for the masses) (Burstein, Holsapple, Negash, &
Gray, 2008).
A closer description of BI systems as previously mention:
§ Data
gathering
§ Data
storage
§ Knowledge
management
This is a way to evaluate complex corporate and
competitive information to improve quality input of accurate planning and
decision making.
BI started in 1989 by Howard Dressner, as a concept and
method to improve business decision making by “fact-based” support for
professionals, vendors, and management (Burstein, Holsapple, Negash, & Gray, 2008).
Competitive intelligence (CI) denotes to monitoring the
competitive environment, which is a subset of BI. CI is used to assist in the
development of action-oriented implications for managers, which can be
delivered in a timely basis and incorporated in the decisions making process. CI is also categorized as primarily including
the collection of competitive data, also known as a library function for market
research towards understanding customer needs (Prescott, PhD, 1999).
Within the research, was found that a survey purposed of
a study to measure the state of competitive intelligence activities from the
perspective of CIOs and CEOs. This project of questionnaires to CIOs and CEOs
from 550 firms in the U.S. The return sample of 55 CEOs and 82 CIOs responded
came from the CI-active firms.
The survey asked all the CEOs reporting CI activity
explaining the reasoning their firms used CI. The majority of the CEOs stated
that CI was very useful in developing, implementing or revising strategies.
They also mention that CI assists in understanding their organizations
performance in relation to their competitors, although, the use of CI
by the competitors was not a major factor in their decision to use it (Vedder, Vanecek, Guynes, and Cappel, 1999).
The survey asked all CIOs reporting CI activity to
evaluate the appropriateness of current ITs level of CI involvement. The figure
below reveals the desired level of participation in CI is appropriate and
wanting more contribution, a smaller percent indicates that the existing level
should be more, but none of the CIOs indicate that they should have less
involvement currently (Vedder, Vanecek, Guynes,
and Cappel, 1999).
Considering back to BI,
organizations measure BI in understanding the performance used for purposes of
decision making, control, guidance, education and learning, and external
communication. BI serves two main purposes 1) proving that it is worth the
investment 2) measuring BI activities to assist BI process ensuring that BI
satisfy the user’s needs and is efficient (Lonnqvist, &
Pirttmimaki, 2006).
Data in-data out: obtaining data in delivers limited
value to an enterprise. At the time when users and applications access the data
to make decisions, the organizations will realize the entire value from the
data warehouse. Data out; commonly refers to BI consisting of business users
and applications accessing data from the data warehouse for enterprise
performances such as Reporting, OLAP, query and predictive analytics (Watson,
& Wixom, 2007).
The illustration below demonstrates the BI framework
including primary activities of getting data in and out.
BI benefits:
§ Reduces
IT infrastructure costs by eliminating redundant data extractions process and duplicate
data housed in independent data marts
§ Saves
time for data suppliers and users based by more efficient data delivery
§ Easy
analyzing of substantial historical data
§ Improvement
of business process
§ Support
for the accomplishment of strategic objectives (Watson, & Wixom, 2007).
Companies that used BI:
§ 3M
justified its multimillion dollar data warehouse platform based by savings from
data mart consolidation
§ Las
Vegas-based gaming corporation Harrah’s Entertainment transformed their
activities of completism in the marketplace with a brand strategy to customers
in promoting cross-casino play through Total Rewards with a loyalty program.
·
Led to casino management to run properties
as independently as marketing was done on the bases of property-by-property
basis.
·
Customer centric data warehouse stored
data on gaming of slot machines (usage), hotels, and special events
·
The analysis of this data made it easy in
understanding customer profitability, lifetime value and preferences of well popular
games and promotional offers in different market segments (Watson, & Wixom,
2007).
Incorporating
real-time BI in an organization has experienced BI professionals for wanting a
demand of fresher data. Enterprise information integration (EII) and enterprise
applications integration (EAI) and real time data warehousing technologies
enabling the delivery of decision support data that is literally minutes old. Using
real-time BI allows changes of these DSS in allowing current decision making,
operational business process and customer –facing applications. An example of a
company using real-time applications; of Continental Airlines using BI to understand
issues of late flights. They use real-time BI for flight manifest, customer
profitability data, real-time flight data from the plane, and current gate and
departure of data all stores in a real-time data warehouse. The company also
identify the high-value passengers who are at risk of missing connections and
assist with special arrangements for the passengers in getting their luggage to
their connecting flights on time (Watson, & Wixom, 2007).
Understanding
the customer needs through data mining tools; is with customer satisfaction and
the link to the growth of the organization. The pro of the situation is the
company having access to the right information and the right moment. The con is
the company fails to fully capitalize on the many benefits, which can be
obtained from this wealth of information and not having the ability to extract
this valuable information from huge databases.
The solution between this links is Data
Mining tools for customer segmentation and profitability, marketing, and
customer relationship management (CRM) (Chopra, Bhambri, and Krishan, 2011).
The ability to know your customers is critical in the industry and if the
company is not able to meet the needs of the customer before their competitors;
will face many issues. Marketing is a thought of production of goods as their necessities
of the customer and then sell those products to them through many channels learning
their behaviors to keeping them as loyal customers.
CRM (customer
relationship management) is the process of predicting customer behavior and
selecting actions to influence that behavior for the benefit of the company by building
and retaining customers through better interaction and service (Rajan,
& Bhatnagar, 2008). The changes in customer’s behaviors and responses to
the product manufacture having an immediate effect on the performance of the
company containing suggestions for decisions making relating to strategies in
the improvement of the relationship with customers.
Effective CRM practices:
§ Obtaining,
analyzing and sharing knowledge about customers, for quick and timely service.
§ Provides
integrated view of customer interactions with software applications taking the
interactions and analysis of the data in revealing information and their
relationships
§ Analysis
can assists retailers in fulfilling the demand of their customers by maximizing
of benefits.
§ Aims
at leveraging investments in customer relations to strength and the competitive
position and maximize returns.
§ Business
strategy utilizing technology with the company’s goal to assists in building
long-term customer loyalty (Rajan,
& Bhatnagar, 2008).
Different Data Mining Techniques are:
§ Association:
this technique of finding patterns based by the connection of events that will
assist in the organizations to make decisions regarding pricing, selling, and
design strategies in marketing. This process is based by direct; such as
purchasing products in correlations to each other (pen and paper); or in
direct.
§ Clustering:
combines the transactions of similar behaviors into one group, or customers
with similar set of queries or transactions into one group.
§ Sequence or Path analysis:
assist in finding patterns of one event that leads to another event such as
birth of a child and purchasing diapers.
§ Forecasting:
this data mining technique assisting in discovering patterns from reasonable
predictions regarding future activities; such as predicting people who join an
athletic club may take exercise classes (Chopra, Bhambri, and Krishan, 2011).
Reference
Burstein,
F., Holsapple, C., W., Negash, S., & Gray, P., (2008) Handbook on Decisions
Support Systems 2. Retrieved from https://link.springer.com/chapter/10.1007/978-3-540-48716-6_9
Chopra,
B., Bhambri, V., Krishan, B., (2011) Implementation of Data Mining Techniques
for Strategic CRM Issues. Int. J. Comp. Tech. Appl., Vol 2 (4), 879-883. ISSN:
2229-6093 Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.433.7522&rep=rep1&type=pdf
Lonnqvist,
A., & Pirttmimaki, V., (2006) The Measurement of Business Intelligence.
Retrieved from http://cjou.im.tku.edu.tw/bi2008/MeasurementOfBI.pdf
Prescott,
J., H., PhD., (1999) The Evolution of
Competitive Intelligence. Designing a Process for Action. Retrieved from http://files.paul-medley.webnode.com/200000023-97ce398c7e/Competitive%20Intelligence%20A-Z.pdf
Rajan,
J., & Bhatnagar, V., (2008) Critical Success factors For Implementing CRM
Using Data Mining. Journal of Knowledge Management Practice, Vol. 9, No. 3
Retrieved from http://www.tlainc.com/articl161.htm
Vedder, R., G., Vanecek, M., T., Guynes, C., S., and Cappel, J., J. (1999)
CEO and CIO Perspectives on Competitive
Intelligence. Vol. 42, No. 8 Retrieved from https://dl.acm.org/doi/pdf/10.1145/310930.310982
Watson,
H., J., & Wixom, B., H., (2007) The Current State of Business Intelligence.
Retrieved from https://www.researchgate.net/profile/Hugh_Watson3/publication/2961945_The_Current_State_of_Business_Intelligence/links/5767e62b08aeb4b9980b0097/The-Current-State-of-Business-Intelligence.pdf
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