Advanced analytics: opportunities and challenges
| Pages | 155-172 |
| Date | 13 March 2009 |
| Published date | 13 March 2009 |
| DOI | https://doi.org/10.1108/02635570910930073 |
| Author | Ranjit Bose |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Advanced analytics:
opportunities and challenges
Ranjit Bose
Anderson School of Management, University of New Mexico,
Albuquerque, New Mexico, USA
Abstract
Purpose – Advanced analytics-driven data analyses allow enterprises to have a complete or
“360 degrees” view of their operations and customers. The insight that they gain from such analyses is
then used to direct, optimize, and automate their decision making to successfully achieve their
organizational goals. Data, text, and web mining technologies are some of the key contributors to
making advanced analytics possible. This paper aims to investigate these three mining technologies in
terms of how they are used and the issues that are related to their effective implementation and
management within the broader context of predictive or advanced analytics.
Design/methodology/approach – A range of recently published research literature on business
intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their
current state, issues and challenges learned from their practice.
Findings – The findings are reported in two parts. The first part discusses a framework for BI using
the data, text, and web mining technologies for advanced analytics; and the second part identifies and
discusses the opportunities and challenges the business managers dealing with these technologies face
for gaining competitive advantages for their businesses.
Originality/value – The study findings are intended to assist business managers to effectively
understand the issues and emerging technologies behind advanced analytics implementation.
Keywords Data analysis, Competitive advantage
Paper type Research paper
1. Introduction
What differentiates companies in today’s highly competitive markets is their ability to
make accurate, timely, and effective decisions at all levels – operational, tactic al, and
strategic – to address their customers’ preferences and priorities. Increasingly,
companies in virtually every industry around the globe have started using advanced
(also known as predictive) analytics to analyze their data (both structured and
unstructured), combining information on past circumstances, present events,
and projected future actions. By incorporating advanced analytics into their daily
operations, these organizations gain control over the decisions they make every day, so
that they can successfully meet their business goals (Apte et al., 2003).
The advanced analytics driven data analyses allow enterprises to have a complete
or “360 degrees” view of their operations and customers. The insight that they gain
from such analyses is then used to direct, optimize, and automate their decision
making. It results in successful achievement of a variety of specific organizational
goals, whether they are associated with an increase in cross-sell revenue generation,
a decrease in production or service cost, a reduction in fraudulent behavior, or an
increase in promotional campaign response rates.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Advanced
analytics
155
Received 24 July 2008
Revised 18 August 2008
Accepted 18 September
2008
Industrial Management & Data
Systems
Vol. 109 No. 2, 2009
pp. 155-172
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570910930073
Advanced analytics is a general term which simply means applying various
advanced analytic techniques to data to answer questions or solve problems. It is not a
technology in and of itself, but rather, a group of tools that are used in combination
with one another to gain information, analyze that information, and predict outcomes
of the problem solutions. Data integration and data mining are the basis for advanced
analytics. The more information that is gathered and integrated allows for more
pattern recognition and relationship identification. Statistical analysis is another very
important component to see trends and patterns in the data. Some other techniques
used to manipulate the data is fuzzy logic, to deal with incomplete or ambiguous data,
and neural networks to anticipate decisions and assist in predictive analytics which
helps predict likely outcomes (Wu et al., 2006).
Data mining is a powerful emergent technology for the automatic extraction of
patterns, associations, changes, anomalies and significant structures from data. These
uncovered patterns from data play a critical role in decision making because they
reveal areas for process improvement. Most of the value of data mining comes from
using data mining technology to improve predictive modeling (Wang and Wang, 2008).
For example, data mining can be used to generate predictive models automatically,
which predict how much profit prospects and customers will provide and how much
risk will entail from fraud, bankruptcy, charge-off and related problems.
Recentadvances have led to the newest and hottesttrends in data mining – text mining
and webmining (Hearst, 2003; Fanet al., 200 6). These two data mining t echnologies open a
rich vein of enterprise performance and customer data in the form of textual comments
from survey research or e-mails and log files from Web servers, which were previously
unusable. Applying text and web mining to these data adds a richnessand depth to the
patterns already uncovered through the company’s data mining efforts. Text mining
applies thesame analytical functions of data miningto the domain of textual information,
relying on sophisticated, text analysis techniques that distill information from free-text
documents (Dorre et al., 1999; Oliveira et al., 2004). Web mining is a form of data min ing
used to discover patterns from the web. There are three categories of web mining: web
content mining, webstructure mining, and web usage mining.
This study aims to investigate these three mining technologies in terms of how they
are used and the issues that are related to their effective implementation and
management within the broader context of predictive or advanced analytics.
2. Current state of business intelligence
The managerial view of business intelligence (BI) is getting the right information to the
right people at the right time so they can make decisions that ultimately improve
enterprise performance. The technical view of BI usually centers on the process of,
or applications and technologies for, gathering, storing, analyzing and providing
access to data to help make better business decisions.
This section summarizes the evolution of BI over the years to its current state.
Figure 1 diagrammatically captures the stages in the BI evolution. Each of these stages
is briefly discussed below.
Rapid advances over the last several years in data capture, processing power, data
transmission, data transformation, and storage capabilities have enabled organizations
to integrate their various databases into data warehouses. Data warehousing is
conceptualized as a process of centralized data management and retrieval. The core of
a well developed BI program is the data warehouse.
IMDS
109,2
156
To continue reading
Request your trial