Data and Business Intelligence Systems: Prospects and Challenges

This report, sourced from Mercados y Negocios, explores the critical role of Data and Business Intelligence (BI) systems in achieving a competitive advantage, particularly for Small and Medium-sized Businesses (SMBs). It highlights the increasing recognition of BI's importance in a turbulent global market, moving beyond the historical reluctance of SMBs due to perceived complexity and cost. The report defines BI and Analytics, emphasizing their roles in transforming data into actionable insights, improving decision-making, and driving operational efficiencies. It details the architecture of BI systems, including data warehousing, ETL tools, and various BI tools like reporting, ad hoc query, visualization, OLAP, dashboards, and data mining. The report also outlines the benefits of a well-implemented BI strategy, such as faster decision-making, improved customer experience, cost reduction, and identification of new business opportunities. It also covers the technologies and tools used in BI systems, providing a comprehensive overview of their applications and impact on business performance.

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Mercados y Negocios
ISSN: 1665-7039
ISSN: 2594-0163
[e-mail]
Universidad de Guadalajara
México
Data and Business Intelligence Systems
for Competitive Advantage: prospects,
challenges, and real-world applications
Djerdjouri, Mohamed
Data and Business Intelligence Systems for Competitive Advantage: prospects, challenges, and real-world
applications
Mercados y Negocios, no. 41, 2020
Universidad de Guadalajara, México
Available in: https://www.redalyc.org/articulo.oa?id=571861494009
This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International.

Mohamed Djerdjouri. Data and Business Intelligence Systems for Competitive Advantage: prospects, c...
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Artículos de investigación
Data and Business Intelligence Systems for Competitive Advantage: pro
challenges, and real-world applications
Sistemas de datos e inteligencia empresarial para una ventaja competitiva: perspectivas, desafío
mundo real
Mohamed Djerdjouri
State University of New York, Estados Unidos
[e-mail]
Redalyc: https://www.redalyc.org/articulo.oa?
id=571861494009
Received: 12 October 2019
Accepted: 09 December 2019
Abstract:
is paper is intended as a short introduction to Business Intelligence (BI) and Analytics systems. e main aim
raise awareness of organizations in the developing world, about the benefits of these technologies and the c
the survival and competitiveness of the firm in the complex and turbulent global market. For many years, m
sized businesses (SMBs) have not followed large organizations in the implementation of BI technologies. e
by SMBs is the complexity and high cost of deploying and managing BI systems. However, according to rece
of SMBs executives, they now realize the crucial role BI systems play in the company’s performance, and co
are now increasingly investing in and implementing BI technologies.
Jel Code: M15.
Keywords: SMBs, turbulent global market, managing BI systems, IT industry.
Resumen:
El objetivo principal del documento es sensibilizar a las organizaciones en desarrollo sobre los beneficios de
crucial que desempeña en la supervivencia y competitividad de la empresa ante el complejo y turbulento m
muchos años, las pequeñas y medianas empresas (PYMES) no han seguido a las organizaciones grandes en
modelo Business Intelligence (BI). La razón principal declarada por las pymes es la complejidad y el alto cos
y administrar sistemas de BI. Sin embargo, según una encuesta reciente de la industria de TI a los ejecutivo
se dan cuenta del papel crucial que juegan los sistemas de BI en el rendimiento y la competitividad de la em
invirtiendo cada vez más en su implementación.
Código Jel: M15.
Palabras clave: PYME, turbulento mercado global, gestión del Business Intelligence, Industria IT.
INTRODUCTION
Second to its people, a company’s most valuable asset is information. Information is a crit
organization. In this rapidly changing global market, consumers are now demanding quick
service from businesses. To stay competitive, companies must meet or exceed the expec
Moreover, the world has witnessed an information explosion. Data is being generated at a
more and more of this Data is unstructured, which makes its analysis challenging to say th
Data is seen as a new class of economic assets, just like currency or gold.

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FIGURE 1
e Information Explosion
(zettabyte = unit of information equal to one sextillion (1021) or, strictly, 270 bytes)
Source: Own elaboration.
So to stay competitive and to improve its own performance, a company must m
promptly, based on timely and accurate information. To this end, many leading innovative
adopting and relying on Business Intelligence systems to stay ahead of trends and future
Intelligence (BI) expedites decision making. is, in turn, helps companies to act quickly an
information before competing businesses do. e result of all this is a competitively superio
for the company, which allows for an appropriate and timely response to customer proble
concerns.
e ultimate achievement is improved customer experience. BI refers to technologies, ap
approaches practices for the collection, integration, analysis, and presentation of bu
(Hedgebeth, 2007). BI helps managers gain insights into their own business as well as into
general, and it provides them with valuable facts and information that improves the qualit
(Chaudhuri, Dayal & Narasayya, 2011)
Analytics, on the other hand, is defined as the scientific process of transforming
making better decisions. A sound BI system provides the decision-maker with valuable inf
appropriate time and in the right format. e ability to mine and analyze big data gives org
and richer insights into business patterns and trends, helping drive operational efficiencie
advantage in manufacturing, security, marketing, and IT (Ghasemghaei, 2019). Sun and W
that big data have become a strategic resource for industry, business, and national securi
Wang (2017) affirm that data nowadays have also become a strategic enabler of exploring
and the economics of services.
FIGURE 2
Data mining
Source: Own elaboration.
BI systems merge data with different formats and from various sources and gather it int
or data marts. en they use Analytics to process these data to provide historical, current a

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outlook of business operations and the market in which they operate. e information is us
through a dashboard or analytics interface. BI soware makes analysis and report-making
more reliable.
In her article, Loshin (2012) reported that BI is used to understand and improve perform
costs and identify new business opportunities, this can include, among many other things
o Analyzing customer behaviors, buying patterns, and sales trends
o Identifying opportunities to reduce costs
o Measuring, tracking and predicting sales and financial performance
o Budgeting and financial planning and forecasting
o Tracking the performance of marketing campaigns
o Optimizing processes and operational performance
o Improving delivery and supply chain effectiveness
o Web and e-commerce analytics
o Customer relationship management
o Risk analysis
o Strategic value driver análisis
Jennifer Lonoff Schiff (2013), reports that CIO.com surveyed a sample of BI experts and
about the benefits of investing in BI systems. e consensus among these experts is that B
bottom line of businesses. And the fundamental reasons for that are that BI helps organiza
answers to critical business questions; align business activities with corporate strategy; em
reduce time spent on data entry and manipulation; gain insights into customers; benchma
partners; identify areas for cost-cutting; and boost productivity.
BI simplifies information discovery and analysis, making it possible for decision-makers
organization to quickly and more easily access, understand, analyze, collaborate, and act
anytime and anywhere. BI helps move from just consuming information to developing in-d
knowledge about that information. By tying strategy to metrics, organizations can
advantage by making better decisions faster, at all levels of the organization. BI i
transforms data into meaningful, actionable information.
BI soware consolidates data from different sources and assembles it in “data warehous
that eliminate distinctions in data formats. It then presents the results through a r
dashboard interface. BI soware thus serves as a common platform for shared, company-w
soware makes analysis and report making much faster and more reliable.
TECHNOLOGY AND TOOLS
A typical architecture for supporting BI within a firm is shown in figure 3 below. A BI archit
framework for organizing the data, information management, and technology components
to build BI systems for reporting and data analytics. e underlying BI architecture plays a
projects because it affects the development and implementation of timely decisions. e d
tasks are performed are typically loaded into a repository called the data warehouse that
multiple data warehouse servers. e data oen comes from different sources, operational
departments within the firm, as well as external sources. e data have different formats a
both structured and unstructured data may be used. All these data need to be standardize
in preparation for BI tasks. e technologies for preparing the data for BI are known as Extr
Load (ETL) tools. Also, a popular engine tool for storing and querying data wareho
Database Management Systems (RDBMS). Large data warehouses usually deploy parallel
so that SQL queries can be executed over large volumes of data.

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FIGURE 3
Typical Business Intelligence (BI) architecture
Source: Own elaboration.
e technology components, referred to as BI tools in figure 4 above, are used to present
business users and enable them to analyze the data. is includes the BI tools (or BI sowa
used within an organization as well as the supporting IT infrastructure such as hardware, d
and networking devices. ere are various types of BI applications that can be built into an
reporting, ad hoc query, and data visualization tools, as well as online analytical processin
dashboards, performance scorecards, data mining engines, and web analytics, to name a
FIGURE 4
Data Integration Architecture
Source: Own elaboration
Reporting tools are an essential way to present data and easily convey the resu
are increasingly business users who need quick, easy-to-understand displays of informatio
2019). And report writers allow users to design and generate custom reports Ad hoc query
user tool that accepts an English-like or point-and-click request for data and constructs an
retrieve the desired result. Visualization tools: help users create advanced graphical repre
via simple user interfaces. is tool help users uncover patterns, outliers, and relevant fact
Processing (OLAP) tools enable users to analyze different dimensions of multidimensional
server understands how data is organized in the database and uses special functions for a
Examples of analysis tools are time series and trend analysis.
Dashboards typically highlight key performance indicators (KPIs), which help mana
metrics that are most important to them. Dashboards are oen browser-based, making th
by anyone with permission. Performance scorecards attach a numerical weight to p
progress toward goals. ink of it as dashboards taken one step further. Scorecards are an
keep tabs on key metrics.
Data mining tools allow users to analyze data from many different dimensions or angles
and summarize the relationships identified. Technically, data mining is the process of find
or patterns among dozens of fields in large relational databases
Web analytics tools enable users to understand how visitors to a company’s web
pages (Imhoff, Galemmo & Geiger, 2003; Shen, 2013). ey perform the measurement, col

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and reporting of Web data for purposes of understanding and optimizing Web usage. ey
business and market research, and to assess and improve the effectiveness of a web site.
BENEFITS
A well-implemented BI strategy can deliver real insight for an organization. BI systems he
make better decisions with higher speed and confidence; recognize and maximize
shorten marketing efforts; improve customer relationships; align effort with the firm’s stra
revenues and profits (Williams & Williams, 2010).
Moreover, BI systems help firms quantify the value of relationships with suppliers and cu
gives them more leverage during negotiations. Jennifer Lonoff Schiff (2013) reports that in
of “500” companies, they revealed a variety of benefits these firms, the main ones includ
get faster answers to your business questions; get key business metrics reports when and
them; gain insight into customer behavior; identify cross-selling and up-selling opportuniti
streamline operations; improve efficiency; learn what your real manufacturing costs are; m
better and; see where your business has been, where it is now and where it is going.
Without business intelligence, a firm runs the risk of making critical decisions based on
or inaccurate information. Robert Eugene Miller (2013) also reports that executives that a
BI strategy helps firms in the following ways:
- Quickly identify and respond to business trends
- Empowered staff using timely, meaningful information and trend reports
- Easily create in-depth financial, operations, customer, and vendor reports
- Efficiently view, manipulate, analyze, and distribute reports using many familiar tools
- Extract up-to-the-minute high-level summaries, account groupings, or detail transactio
- Consolidate data from multiple companies, divisions, and databases
- Minimize manual and repetitive work
It is reported in the literature that successful implementation and usage of BI has shown
all sectors of the economy- healthcare, e-commerce, government, industry, etc. On avera
reported an ROI of $10.66 for every dollar spent on business intelligence/analytics. Real-w
in different sectors of the economy will be presented in section 5 below.
CHALLENGES
According to the Garner Analytics firm research, 70% to 80% of corporate BI projects fail.
many challenges when developing and implementing a BI strategy. e two main ones are
for adoption, Poor data quality, and Others challenges
User resistance for adoption
Like for any new IT system, user resistance is one significant barrier to BI success. Users
the way they do things unless their current methods are tedious and time-consuming. Als
the mistake of believing that if they implement the system first, people will use it (build it
cliché). e way around this pitfall is for the firm to involve all the stakeholders from the be
project and throughout the implementation process. Users should define what they really
project. When the implementation ends, the majority of the users will already be familiar
and know how to use it. ey also feel empowered when their suggestions are implemente
success, the firm must high rates of user adoption.
Poor data quality

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Without the collection, storage, and access to reliable data, a firm cannot get any valua
insights into their business and the business environment. Data is the most essential com
system. e main challenge here is for the firm to make sure the data stores and
good working order before they can begin extracting and acting on insights. e risk is that
done correctly, critical and strategic decisions will be made based on unreliable informatio
establish and maintain an appropriate level of data quality to feed into the BI system.
Others challenges
e other challenges include breaking down departmental knowledge silos; integrating th
other operational, performance management and transactional system; transforming the
a culture of ‘gut feel’ to one of data-based decision-making; securing executive sponsorshfinancial backing,
Finally, measuring the performance of BI is a significant challenge and can be problema
develop and employ a set of key metrics to help evaluate performance and return on inve
many firms use metrics such as the time it takes to answer user queries, the dep
information obtained from the BI tool and, the number and quality of decisions made as a
generated via the BI tool
BUSINESS AND GOVERNMENT APPLICATIONS
Proper implementation of BI technologies can reap many benefits for the firm. Excellent r
reported across all sectors of the economy: healthcare, government, and industry. It is es
each dollar spent in BI technologies and Analytics technology, there is, on average a ten d
investment. In this section, a few successful implementations of BI will be presented. e s
are “literally” taken from the articles in which the cases were published.
New York State Department of Taxation and Finance: Using Business Intelligence to imp
revenues and citizen equity .(IBM Smarter Planet Leadership Series, 2011)
e New York State Department of Taxation and Finance resolved to make its pro
driven. e Tax Audits department has a team of 1600 auditors. Research has shown that m
of U.S. taxpayers willing to take liberties with their taxes when they sense that the govern
information to catch them. e core of the deterrent is the incorporation of more data sour
with the use of predictive intelligence capabilities–to accurately identify potentially questi
e main flaw with the current process (“pay and then chase”) was that the problems we
only aer refund checks had been sent and cashed. Also, the process was time-consuming
resource and was oen fruitless. e department wanted to change the process to catch an
refunds before they were sent out.
e system: e New York State Department of Taxation and Finance achieved this goal b
a BI system called Case Identification and Selection System (CISS). e system is not mere
for questionable returns patterns with historical data stored in the department’s warehous
e analytics are embedded directly into the mainstream return process. e department
intelligence to determine dynamically when to process a refund request and when to set i
analysis or to reject the refund directly. In a nutshell, the system compares each open cas
past similar cases to recommend which cases should be pursued and through which mean
the overall amount of revenue collected. e results were outstanding. e New York State T 
Finance Department reported the following critical results and benefits:
- $1.2 billion reduction in improper or questionable refunds paid from the State of New Y
plus another $400 million reduction projected in 2011
- Dramatic reduction in the costs and inefficiencies associated with “pay and chase” pol

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- $100 million increase in delinquent tax collections through the use of optimization algo
- Over a 350% increase in criminal tax fraud investigations due to greater interdepartme
collaboration on cases.
Business Intelligence and Analytics in Politics: e Real story behind President OBAMA Ele
Victory (Siegel, 2013)
Barack Obama’s 2012 campaign for a second term employed more than 50 Busi
Analytics experts. e traditional political campaigns up to now spent large amounts of mo
trying to sway swing voters in swing states. e Obama campaign management hired a mu
team of statisticians, predictive modelers, data-mining experts, mathematicians, soware
quantitative analysts. It eventually built an entire Business Intelligence/Analytics departm
large as that of its 2008 campaign.
What the Obama BI team realized is that presidential campaigns must focus even more
that. ey applied predictive analytics (BI technology) that pinpoints truly persuadable vote
moved beyond simple poll analysis. Its real power came from in trying to influence the fut
speculate on it. Forecasting calculates an aggregate view for each US state, whereas pred
technology) delivers predictions for each individual voter.
During the six months leading up to the election, the Obama team launched a full-scale
campaign, leveraging Web, mobile, TV, call, social media, and analytics to directly micro-t
voters and donors with tailored messages. Instead of focusing on just “swing” voters (mos
voters who have not made up their minds and are persuadable to vote one way or anothe
Obama BI team realized that a “persuadable voter” (swingable) is a person who will be infl
the candidate by a call, a door knock, flyer, or TV ad?
e benefits: e Obama BI team predicted an entirely new thing. Beyond predicting which
was destined to vote, they also predicted whether each individual voter would be persuad
contact. e best way to do persuasion is to predict it. Beyond identifying voters who will c
Obama if contacted, the BI models had to distinguish those voters who would come out fo
case as well as those who were at risk of being turned off by campaign contact and switch
for the opponent.
e necessity was to learn to discriminate, voter by voter, whether contact would persua
only four especially close states in the 2012 election. Only Florida, North Carolina, Ohio, a
decided by less than 5 percentage points. e smallest number in 30 years (Reagan vs Mon
e results: More voters were convinced to choose Obama, in comparison to trad
targeting. Most people predicted the election to be very close, but in fact, Obama won a d
Obama got 51.1 percent of the popular vote to Mitt Romney's 47.2 percent, a four-point m
President Obama won 26 states and the District of Columbia, and he also won 332 elector
206 for Romney (It takes 270 electorate votes to win the Presidential election). It is widely
use of BI/Analytics by Obama’s Campaign led to the landslide victory by Barack Obama. (S
Improving Financial Reserve Management in the Insurance Industry (Microso, 2019)
EM Insurance company located in the state of Iowa employs more than 2100 pe
approximately $3 billion, it sells its products through independent insurance agencies thro
States. EMC Insurances Companies struggled with pinpointing the right amount of
reserve against potential case payouts; keeping too much or too little could be disadvanta
performance.
Aer experience a run-up in reserves, EMC took steps to improve its financial re
e company had a great deal of insurance claim data but a limited ability to ana
Unexpected fluctuations of financial reserves prompted EMC to use BI technologies to unc

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correlations, relationships, and patterns hidden within the firm’s warehouse of claim data.
included predictive modeling for improved claim outcomes.
Results/Benefits: e company can identify casualty and worker’s compensation claims th
to have a negative outcome. ere is also an apparent enhancement of the accura
data. Executive decision making is supported with improved analysis. Expenses are now m
controlled.
ere are many more success stories in business and government of organizations which
and quality improved significantly with the appropriate implementation of BI technologies
for these organizations was the improvement of their competitiveness in the Marketplace.
e Gartner report (2019) mentioned that the benefits of fact-based decision-maki
business managers in a broad range of disciplines, including marketing, sales, supp
manufacturing, engineering, risk management, and finance and HR. Significant changes a
world of BI and analytics, including the dominance of data discovery techniques, more ext
time streaming event data, and the eventual acceleration in BI and analytics spending wh
matures, said Roy Schulte, vice president, and distinguished analyst at Gartner. As the co
storing and managing data continues to fall, companies are finding it practical to apply BI
more extensive range of situations. Nowadays thousands of businesses in all sizes, in all i
the world are implementing and utilizing Strategic Business Intelligence (Stackpole, 2011)
e Chief Information Officers focus on BI, and analytics looks set to continue through 20
to Gartner (2013). Gartner's user surveys show that "improved decision making" is
purchases. Capabilities that will evolve BI from an information delivery system to a decisio
increase the value of BI and drive its growth (Gartner Report, 2011 and 2019).
CONCLUSION
According to the 2019 Gartner report, by 2020, the number of data and analytics experts
will grow at three times the rate of experts in IT departments, and by 2021, analytics and
increase from 35% of employees to over 50%, including new classes of users, particularly
BI is essential for the firm’s growth and decision-making. It gives companies a more stru
at data while providing in-depth interpretations. It aids decision making via real-time, inte
and analysis of vital corporate information. e business and technological advances promi
being developed, explored, and enhanced.
For many years, many small and medium-sized businesses (SMBs) have not followed lar
in the implementation of BI technologies. e main reason stated by SMBs is the complexit
of deploying and managing BI systems. However, according to recent IT industry survey o
they now realize the crucial role BI systems play in the company’s performance, and comp
they are now increasingly investing in and implementing BI technologies.
In the majority of developing economies, firms face much more significant and n
because most organizations do not have access to the latest technologies. However, the b
implementing BI systems stems from the lack of reliable and quality data. As mentioned e
data is the lifeblood of BI systems. Today’s data-driven business culture has given organiz
and competitive advantages through the integration of data into everyday operations and
decisions.
However, the managerial culture should change to adopt more a data-driven decision-m
Organizations should realize the importance of collecting, storing, and analyzing internal a
data to harness the information obtained from BI systems and Analytics to improv

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uncover insights into customer buying patterns, internal cots, revenues, and profitability t
critical business issues.
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Alternative link
http://mercadosynegocios.cucea.udg.mx/index.php/MYN/article/view/7537 (pdf)