1. ANALYTICS
An imperative for Sustaining and
Differentiating.
“A little knowledge that acts is worth infinitely more than much knowledge
that is idle.”
Khalil Gibran
Submitted by:
Madhuja Mukherjee
Nikhil Kansari
PGP2, BIM TRICHY
TEAM NAME- B3 (BONG, BHARTI,
BUSINESS)
2. Summary:-
With global economy tumbling around contingent issues, industries giving up with their implemented
strategies, organizations are tumbling to deliver an efficient value chain. Be it a B2C market or a B2B
market everyone wants to offer superior business value. Nobody wants to become next SATYAM,
PRICEWATERHOUSECOOPERS, CITIBANK or LEHMAN BROTHERS. In an era where head to
head competition is growing, marketers need something different to sustain. So the question for the
hour is WHAT NEXT? Well the answer lies in Business Analytics. Today when everyone offers
similar kind of products and services, business processes can be the point of difference. Organizations
often face issues in areas like: Customer segmentation, Buyer behavior, Customer profitability, Fraud
detection, Customer attrition and Channel optimization. Various Analytic Applications have been
develop to address those issues, but still there are some areas where we cannot use analytics e.g.
Personnel relations. Enterprise Resource Systems (ERP), Point-of-Sale (POS) systems and Web sites,
have created better transaction data that can be utilized to sustain a healthy Bottom Line. A new
generation of technically literate executives is coming into organizations and looking for new ways to
manage them with the help of technology.
Purpose/Goal:-
Generation next is moving to Cloud, every single organization wants to utilize the Utility Business
model to become more cost effective and customer centric. Rapidly growing organizations have
recognized the potential of business analytics and have aggressively moved to realize it. The purpose
of this white paper is to provide an in-depth view for importance of Analytics. How organization can
achieve sustainability and differentiation and use Analytics as a critical success factor in next
generation technology. It will give you insights regarding risks while choosing options to run: whether
to run with numbers or with guts.
Introduction:-
Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in
areas rich with recorded information, analytics rely on the simultaneous application of statistics,
computer programming and operations research to quantify performance. The most common
application of analytics is the study of business data with an eye to predicting and improving business
performance in the future. Analytics is unique in that it leverages a number of competencies and assets
that can typically be applied to multiple discrete value-creating activities in an organization.
Organizations often delve in questions like:-
Q) What market segments do my customers fall into, and what are their characteristics?
Q) Which customers are most likely to respond to my promotion?
Q) What is the lifetime profitability of my customer?
Q) How can I tell which transactions are likely to be fraudulent?
Q) Which customer is at risk of leaving?
Q) What is the best channel to reach my customer in each segment?
The initial phase of computerized decisions were implemented using (DSS) Decision support systems
like enterprise information systems (EIS), Group support systems (GSS), enterprise resource
management (ERM), enterprise resource planning (ERP), supply chain management (SCM),
3. Knowledge management systems (KMS) and Customer relationship management (CRM). Then came
an era of Business intelligence where data and systems both were used to take decisions and intelligent
tools were built to mine and extract information from past collected data. However, data is just the
baseline and requires additional tools to make it work for you and your line of business. This is where
the term analytics comes into play.
Basically analytics is observed by inclusion of at least one model. Model is a simplified representation
or abstraction of reality. They are classified, based on their degree of abstraction, as Iconic, Analog or
Mathematical model. But merely application of those models doesn’t provide any thumb rule to come
to a decision. Data mining is the next generation tool to apply business intelligence at its best.
Organizations have huge amount of data in there data warehouses which should be utilized by data
mining algorithms. Big Data is the pretty contemporary concept in line with data mining in Analytics.
Data mining in contrast:
Data mining is the nontrivial process of identifying valid, novel, potentially useful, and ultimately
understandable patterns in data stored in structured databases. Vastly it has 3 major components which
are used extensively in Analytics i.e. Prediction, Association and Clustering. Areas where data mining
can be applied as application;
A) Customer Relationship Management
i) Maximize return on marketing campaigns
ii) Improve customer retention (churn analysis)
iii) Maximize customer value (cross-, up-selling)
iv) Identify and treat most valued customers
B) Banking and Other Financial
i) Automate the loan application process
ii) Detecting fraudulent transactions
iii) Maximize customer value (cross-, up-selling)
iv) Optimizing cash reserves with forecasting
C) Retailing and Logistics
i) Optimize inventory levels at different locations
ii) Improve the store layout and sales promotions
iii) Optimize logistics by predicting seasonal effects
iv) Minimize losses due to limited shelf life
D) Manufacturing and Maintenance
i) Predict/prevent machinery failures
ii) Identify anomalies in production systems to optimize the use manufacturing capacity
iii) Discover novel patterns to improve product quality
E) Brokerage and Securities Trading
i) Predict changes on certain bond prices
ii) Forecast the direction of stock fluctuations
iii) Assess the effect of events on market movements
iv) Identify and prevent fraudulent activities in trading
F) Insurance
i) Forecast claim costs for better business planning
ii) Determine optimal rate plans
iii) Optimize marketing to specific customers
iv) Identify and prevent fraudulent claim activities
4. All the aforementioned applications of data mining are being capitalized by organizations. Business
analytics are the parts and parcel of these applications where the analysts apply various tools &
algorithms to extract useful content and take decisions. The demand for the generation next technology
is to increase the AQ (analytical quotient) of organizations. If we consider the situation in India it can
be a Megatrend, according to a recent discussion in IIM Bangalore panels it was found; if we look at IT
offshoring, half the CMM Level 5 companies are in India but our domestic penetration and application
of IT is abysmal. If you measure the IT spend in India versus Capital expenditure, we rank at number
30 in the world. It is also true that the application of IT domestically may be lagging behind because of
the lack of demanding customers. However, one must make a beginning and it would be a very good
idea if the B-Schools in the country were to take leadership here1.
Business analytics in simple terms refer to the using of hindsight to better the insight and create a more
sound foresight into business planning.
The types of business analytics in existence are:
Reporting or Modelling or
Descriptive Predictive
Analytics Analytics
Affinity
Clustering
Grouping
----------------------------------------------------------------------------------------------------------------------------- ------------------------------------
1- Murthy, Ishwar; “Business Analytics in India -- Opportunities and Challenges: Discussion”; IIMB Management Review
(Indian Institute of Management Bangalore); Jun2006, Vol. 18 Issue 2, p175-191, 17p.
5. Descriptive Analytics basically help to mine data to provide business insights. Predictive analytics on the other
hand refers to the predictions about future events based on the historical data and facts with the aid of statistical
techniques like modeling, machine learning, data mining and game theory. In business it is used to identify
risks and opportunities by exploiting the patterns evolved of historical data. Clustering is mainly utilized in
explorative data mining and is deemed to be a common technique for statistical data analysis used in varied
fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics.
Last but not the least affinity grouping is a business tool used to organize ideas and data. Commonly used
within project management, it helps to sort large number of ideas into groups based on their natural
relationships for review and analysis.
Good Data Won’t Guarantee Good Decisions
It is being found that most of organizations have three categories of employees: - ―Visceral decision
makers‖, who seldom trust analysis, they rely on intuitions and make decisions unilaterally. Second
category is ―Unquestioning empiricists‖ – They are kind of people who trust analysis over judgment,
and values consensus. Third kind is called ―Informed Skeptics‖, who applies judgment to analysis; they
listen to others but are willing to dissent. In most of the organizations there is always a skill deficit
among the employees, do they know what data to use and when to use effectively. It is being observed
that organizations face four kinds of problems while deciding over Big Data investments.
1. Analytic skills are concentrated in too few employees. Instead of searching new talent for
adapting analytics organization should train the existing employees at various levels.
2. IT needs to spend more time on the ―I‖ and less on the ―T.‖Firms should not always focus on
streams like Finance, HR or supply chain where business needs are clearly defined. Rather they
should focus in areas where the business needs are ambiguous; at this stage they should use
behavioral understanding and anthropological skills.
3. Reliable information exists, but it’s hard to locate. Organizations lack an accessible structure
for the data they have collected.
4. Business executives don’t manage in-formation as well as they manage talent, capital, and
brand. Executives consider data as something to handle by the IT department only and do not
want to deep dive into it.
So the need of the hour is to develop more of Informed Skeptics in your organization. Organize
knowledge management programs where you can develop Knowledge repository which can be easily
accessed by employees and executives both. Those trained knowledge workers can definitely
overcome those above stated four problems and contribute to the bottom line effectively. Because it
doesn’t matter how many Big Data analytics you have in your organization until and unless they are
backed by big decision makers.
Pros and Cons of Customer Analytics
In service industry a customer is everything, most of service organization devote major pie of their
investments in satisfying customers and building relationships with them. That is what we often call as
CRM (customer relationship management), organization gather customer centric data from point of
sales and various other interactions then those data are mapped in dashboards or scorecards to
understand the trend and the gaps. Today’s distracted consumers, bombarded with information and
6. options, often struggle to find the products or services that will best meet their needs. Advances in
information technology, data gathering, and analytics are making it possible to deliver something like
or perhaps even better than the proprietor’s advice.
Suppose we consider example of Retail chains like Bigbazar and Spencers where daily lakhs of
customers come for shopping they even get loyalty cards for their purchases. Now if a Credit Card
Company or an Insurance Company buys or hires access to point of sales & Loyalty card holders
data/information it can unleash new chambers for both the companies to understand their customers
better and provide better service than their competitors. Credit histories, demographic studies, analyses
of socioeconomic status, and so on can be used to predict depression, back pain, and other expensive
chronic conditions. Now this information can be mined and analyzed deeply to unveil credit worthiness
and insurers value by various customer centric credit card and insurance companies.
It’s not only about those credit cards or insurance company; customer analytics can be developed in IT
and ITeS, hospitals, hotels, Banks etc. But there needs a decorum to be built while collecting customer
centric information, because if the customers once gets to know that his/her data is shared among
organization there can be a difficulty in maintaining the relation once again. Therefore it is imperative
for organizations to consider the confidentiality of the customer data which is used in analytics.
Consider Microsoft’s success with e-mail offers for its search engine Bing. Those e-mails are tailored
to the recipient at the moment they’re opened. In 200 milliseconds—a lag imperceptible to the
recipient-advanced analytics software assembles an offer based on real-time information about him or
her: data including location, age, gender, and online activity both historical and immediately preceding,
along with the most recent responses of other customers. These ads have lifted conversion rates by as
much as 70%—dramatically more than similar but not customized marketing efforts. So technology
and strategies are used to create next best offers in order achieve differentiation.
Analytics means business so we can move to a next level to decide over a model that can be used to
provide better customer oriented services. In Service marketing we have three value proposition
models that are used by organization with respect to the product/service they offer.
1. Operational excellence: - Companies excel at competitive price, product quality and on-time
delivery.
2. Customer intimacy: - Companies excel at offering personalized service to customers and at
building long-term relation with them.
3. Product leadership: - Companies excel at creating unique product that pushes the envelope.
In generation next technology where almost every business model becoming obsolete day by day,
bottom line and top line of organizations are on peril . Organizations need to choose an effective model
to sustain. We can recommend Customer Intimacy model as most effective to implement, as be it
product or service, ultimately companies spend a lot in creating value propositions and value chains to
satisfy their customers.
7. Using the above model, customer centric organizations can create value proposition for their
customers. They can differentiate and sustain on the aforementioned attributes and relations. Customer
Analytics can be applied to the data that is being collected in warehouses and accordingly we can apply
our models. Now for such kind of value proposition there must be an equally apt value chain which
should have components to satisfy the customers more effectively than competitors. Due to reverse
engineering process imitators can copy your product or services, so to create the differentiation one
needs to emphasis on value chain too.
Figure shows value chain with respect to business analytics value and opportunity space.
8. Retail Sales Financial Services Risk and Credit
Talent Analytics
Analytics Analytics Analytics
Marketing Analytics Behavioral Analytics Collections Analytics Fraud Analytics
Supply Chain Transportation
Pricing Analytics Telecommunications
Analytics Analytics
Domains of Business Analytics
The very variation in the domains itself explains the importance that analytics enjoys in the
contemporary business scenario. It has practically pervaded every field enhancing the performance and
yield of the field in concern. An edge over the competitors is what every business seeks, business
analytics categorically responds to that need. The following examples will help comprehend better
exactly how indispensable it is in the process of creating differentiation and providing the necessary
competitive edge.
Marketing it the right way to grasp the target customers mind has always been a challenge in itself.
However, the perk of marketing lies in its challenges. Nowadays retail business with its terrific boom
has enhanced this competition as different brands are available under the same roof. The chance of
becoming shifters according to market changes have increased exponentially. Hence comes in the retail
sales analytics. In the recent past Oracle has set forth an exemplary release with its ―Oracle Retail
Merchandising Analytics‖ that helps to pull data from multiple retail systems and enable retailers to
quickly decide if they should change pricing, product orders, or take other actions to meet sales and
profit performance goals, thereby attesting the mandate necessity of such an web-based business
intelligence application in the given scenario of cut throat competition.
Roping in Oracle yet again the ―Oracle Financial Analytics‖ helps to portray well the role of analytics
in financial services. It helps front-line managers improve financial performance with complete, up-to-
the-minute information on their departments' expenses and revenue contributions. With its numerous
key performance indicators and reports it also enables the financial managers to improve cash flow,
lower costs, meanwhile increasing profitability. It also helps to maintain more accurate, timely, and
transparent financial reporting that helps ensure Sarbanes-Oxley compliance.
The risk and credit analytics can be done using SAS. It helps to access and aggregate data across
disparate systems, seamlessly integrates the credit scoring/internal rating processes with the concerned
companies overall credit portfolio risk assessment, accurately forecasts, measures, monitors and reports
potential credit risk exposures across the entire organization on both counterparty and portfolio levels,
allowing seamless integration of credit scoring with credit risk, evaluating alternative strategies for
pricing, hedging or transferring credit risk, optimizing allocation of regulatory capital and economic
9. capital, meeting the reporting and risk disclosure requirements of regulators and investors for a wide
variety of regulations, such as Basel II and finally managing the entire life cycle of a loan from
origination, to servicing, to collection/recovery. Other example includes that of CMSR Hotspot
Profiling Analysis. This helps to drill-down data; systematically and detects important relationships,
co-factors, interactions, dependencies and associations amongst many variables and values accurately
using Artificial Intelligence techniques, and generate profiles of most interesting segments. Hotspot
analysis can identify profiles of high (and low) risk loans accurately through thorough systematic
analysis of all available data.
The Cognos Talent Analytics as a module for IBM Cognos Workforce Performance helps to provide
standard reports that help in simplifying the analysis and assessment of talent management programs,
providing the industry's most comprehensive workforce performance solution.
The SAP CRM Analytics helps to get to the bottom of marketing analytics. The analysis of information
concerning markets, rivals, and past marketing initiatives, help one to assess and thereby affect the
success of future advertising initiatives and campaigns proper from the planning phase. Advertising
Analytics lets one achieve detailed insights and arrive at detailed analysis results that one can then
deploy within the operational processes in marketing.
Quantivo Behavioral Analytics enables to give behavioral analytics a new shape. It helps to identify
what behaviours are highly correlated and what types of affinities exist in the data, delivers a
comprehensive view of customer behaviours across multiple data sources, and provides query results in
―train-of-thought‖ speed.
Collection Analytics can be best exemplified by the Redwood Analytics Business Intelligence-Billing
and Collections. The billing and collection software helps to make more proactive and informed
decisions on inventory management by a better comprehension of the billings and collections history.
It helps attorney firms to target and track attorney work effort, client billings and collection trends
along with daily and total inventory balances.
Kappa Image LLC Fraud Detection Software is a single package wherein written analysis is done on
all variable data fields and not only the signature. This helps to prevent fraud and also helps to detect in
case of any committed. It ensures completely automated profile creation and maintenance including
representations of multiple stocks types and writers per account.
In terms of Pricing Analytics ACEIT (Automated Cost Estimating Integrated Tools) has indeed proved
beneficial. It is a premier tool in analyzing, developing, sharing, and reporting cost estimates,
providing a framework to automate key analysis tasks and simplify/standardize the estimating process.
In fact Accenture with its shift from descriptive to predictive analytics have also further attested the
fact that pricing analytics is not only necessary but also indispensable in the current business scenario.
In a world where marketing communications success is driven by the perceived relevance to the target
audience, predictive analytics becomes a key to growing and gaining market share.
10. Genpact has also allowed the telecommunication companies to drive effectiveness, deliver outstanding
sustainable customer satisfaction through smarter analytics. It helps the telecommunication companies
to eliminate inefficiencies, improve operational performance and thereby profit, be cost effective and
enhance operational excellence through our deep granular telecom process management expertise and
Lean Six Sigma rigor, increase customer loyalty and operational effectiveness through our suite of
smarter telecom analytics solutions and accelerate expansion into developing economies through our
innovative global delivery platform spread across 64 centers in 17 countries.
Supply Chain Analytics helps to combine technology with human efforts to identify trends, perform
comparisons and highlight opportunities in supply chain functions despite huge data being involved. It
helps in decision making in terms of inventory management, manufacturing, quality, sales and
logistics. Tools like OLAP play a major role in this sphere.
Analytic capabilities within a ―Software-as-a-Service‖ (SaaS) transportation management system
(TMS) provides insight into shipping operations by compiling and analyzing value-added data from the
network of shippers throughout the life of your contracts, orders, shipment, transactions, and freight
payment activities, providing access to network benchmarks. Business intelligence capabilities within a
TMS gives the edge needed to accurately manage and analyze the transportation costs and execution
performance against the network to help make better operational decisions. The examples will include
procurement and transportation, delivery performance by carriers and suppliers and tracking key
performance indicators in the freight payment and audit process.
11. Product
Management
Market/Sales Customer
Management Management
Supplier/ Business
Human
Partner Analytics
Resource
Management Management
Enterprise Services/
Management Operations
Management
The figure shows how business analytics is intertwined with the high-impact business processes. The
areas where analytics partake in the processes are as follows:
1. Product Management: the impact of analytics are namely in product pricing, product
profitability and the portfolio optimization of the product.
2. Customer Management: the sections taken care of by analytics in terms of customer
management are namely customer segmentation, customer lifetime value, customer loyalty,
customer profitability, and churn as well as customer experience. It helps one to gauge and
comprehend them better.
3. Human Resource Management: analytics help to analyze the behavioral pattern of
employees who may be contemplating a switchover. This analysis when done with respect to
previous data; gives an insight into such employee decisions. It therefore helps to curb attrition
through employee motivation and employee retention measures.
4. Services and Operations Management: herein analytics take care of the capacity
planning/demand forecasting, customer experience, capital expenditure, workforce
effectiveness, performance, and leakage/shortfall.
5. Enterprise Management: analytics ensure better operations in terms of fraud, revenue
assurance, asset utilization, security, collections and advanced forecasting.
6. Supplier and Partner Management: the benefits of analytics extend in the fields of contract
compliance, vendor efficiency and vendor optimization.
7. Market and Sales Management: analytics play a vital role in channel optimization, up-
selling, cross – selling and campaign performance.
12. Business
Constraints Solutions
Challenges
Efficiency Budget
CRISP-DM, SQL Server, UNIX,
CART, SVM, SOLARIS,
Cost Staffing WINDOWS, SAS, S/CMM,
ORACLE, SPSS, REGRESSION,
Experian, Clustering,
Risk Infrastructure RAPIDMINER, Linux
Licensing
Risk Tolerance
Urgency
Security
End Users
The above figure depicts: Analytics Solutions based on Challenges and Constraints
It’s imperative for an organization to align decision making with fact-based inputs, but those facts
should also be collected with some kind of analytical tool. Due to wide availability of those tools in the
market, availability of talent has drastically gone down. So organizations should keep in mind the
business challenges and constraints to the corporate strategy that can help in finding a right fit analytics
solution. To get the right fit, it's essential to look at organization as a whole. Determine the budget
constraints, staffing levels, and resource availability for the analytics efforts. Consider risk tolerance
for making decisions. Develop an understanding of data privacy and regulatory issues regarding data
security.
13. The Competition: Google Analytics (GA) being top in the e-commerce is a free service offered by
Google that generates detailed statistics about the visitors to a website. A premium version is also
available for a fee. The product is aimed at marketers as opposed to webmasters and technologists from
which the industry of web analytics originally grew. It is the most widely used website statistics
service, currently in use on around 55% of the 10,000 most popular websites. Another market share
analysis claims that Google Analytics is used at around 49.95% of the top 1,000,000 websites (as
currently ranked by Alexa).
GA can track visitors from all referrers, including search engines, display advertising, pay-per-click
networks, e-mail marketing and digital collateral such as links within PDF documents. If your site sells
products or services online, you can use Google Analytics e-commerce reporting to track sales activity
and performance. The e-commerce reports show you your site’s transactions, revenue, and many other
commerce-related metrics.
SiteTrail lets you see a quick snapshot of any competitor website at no cost.
Omniture has various enterprise website analytic tools.
InQuira from ORACLE provides an integrated software platform that has three core capabilities:
knowledge base management (including authoring and workflow), natural language search, and
advanced analytics and reporting.
Adometry is the leading provider of ad analytics, delivering actionable insight to improve the
performance of online advertising. Adometry provides scoring, auditing, verification, and fractional
cross-channel attribution metrics to optimize results and improve return. Formerly known as Click
Forensics, Inc., Adometry has been improving online traffic quality for over half a decade.
14. Survey of Literature:-
The Literature review further helps in understanding the utility and relevance of business analytics’ in
the real world scenario.
1) An analytic capability is especially critical in healthcare because lives are at stake and there is
intense pressure to reduce costs and improve efficiency. We can use antecedents and catalysts
for developing an analytic capability based on an in-depth study of the cardiac surgical
programs.
Ghosh, Biswadip , Scott, Judy E ―Antecedents and Catalysts for Developing a Healthcare
Analytic Capability‖ Communications of AIS; 2011, Vol. 2011 Issue 29, p395-410.
2) It is imperative that rather than having the right tools, technology and people, organizational
factors is one of the most important predictors of the ability to create competitive advantage.
Data-oriented organizational cultures have three key characteristics: (1) analytics is used as a
strategic asset, (2) management supports analytics throughout the organizations and (3) insights
are widely available to those who need them.
KIRON, DAVID, SHOCKLEY and REBECCA ―Creating Business Value Analytics‖ MIT
Sloan Management Review; Fall2011, Vol. 53 Issue 1, p57-63, 7p.
3) Business analytics turns traditional retail experience from pushing products to empowering and
pulling customers on products based from their buying activity. The analytics require continual
update of consumer’s data to better know their spending habits and limits. Experts says that
organizations will need to have clear objectives or identifying how they will harness the
analytics to their business problems and make sure that their service delivers consumers'
expectation. Benefits for using social media like Facebook to gather consumer’s response and
analyze their sentiments regarding a company or its brands.
Hodge, Neil: ―Harnessing analytics‖ Financial Management (14719185); Sep2011, p26-29, 4p.
4) Business users, while expert in their particular areas, are still unlikely to be expert in data
analysis and statistics. To make decisions based on the data collected by and about their
organizations, they must either rely on data analysts to extract information from the data or
employ analytic applications that blend data analysis technologies with task-specific
knowledge. Analytic applications incorporate not only a variety of data mining techniques but
provide recommendations to business users as to how to best analyze the data and present the
extracted information. Unfortunately, the gap between relevant analytics and users' strategic
business needs is significant. The gap is characterized by several challenges like cycle time,
analytic time and expertise, business goals and metrics and goals for data collection and
transformations.
Kohavi, Ron, Rothleder, Neal J &Simoudis, Evangelos ―EMERGING TRENDS IN BUSINESS
ANALYTICS‖ Communications of the ACM; Aug2002, Vol. 45 Issue 8, p45-48, 4p.
5) Analysis of consumer-related and consumer-generated data is a very important way to measure
the success of on-line retailing. The software packages for data analysis have two major
shortcomings: (1) solutions are not offered as a service reachable by standard procedures over
the Internet, but as isolated standalone applications or ERP system modules; (2) privacy
restrictions need to be integrated into a framework of business analytics for Web retailers. The
first aspect can be addressed with standardized developer software for Web services, but the
second must consider privacy legislation, privacy specifications on Web sites (P3P), and data re
identification problems.
15. Berendt, Bettina, Preinbusch, Sören, Teltzrow, Maximilian: ―A Privacy-Protecting Business-
Analytics Service for On-Line Transactions‖ International Journal of Electronic Commerce;
Spring2008, Vol. 12 Issue 3, p115-150, 36p.
6) HR analytics' benefits and strategic value to business, pointing out the wrong notions about the
concept, and explaining the proper way to execute the process to achieve maximum value.
Mondare, Scott, Douthitt, Shane, Carson, Marisa: ―Maximizing the Impact and Effectiveness of
HR Analytics to Drive Business Outcomes‖ People & Strategy; 2011, Vol. 34 Issue 2, p20-27,
8p.
7) Web analytics as a process for making better decisions in business as well as notes the essential
role of the web analyst in translating information into relevant key performance indicators
(KPI).
Stoller, Jacob: ―Not just for techies anymore Web analytics goes mainstream‖ CMA Magazine
(1926-4550); May2012, Vol. 86 Issue 3, p18-19, 2p.
8) Managers have used business analytics to inform their decision making for years. And while
few companies would qualify as being what management innovation and strategy expert
Thomas H. Davenport has dubbed 'analytic competitors,' more and more businesses are moving
in that direction. Which best practices do the most experienced project managers involved in
business analytics projects employ, and how would they advise their less experienced peers?
The authors found that the most important qualities could be sorted into five areas: having a
delivery orientation and a bias towards execution; seeing value in use and value of learning;
working to gain commitment; relying on intelligent experimentation; and promoting smart use
of information technology. Although many of the business analytics project managers the
authors interviewed report to the IT department, they identify with the business side of their
organizations. Best-in-class CIOs realize that IT and business can't afford to continue to be at
loggerheads with one another. IT should pursue opportunities to deliver faster implementation
cycles, maintaining just enough process and architectural hygiene to ensure quality and
professional support.
VIAENE, STIJN,DEN BUNDER, ANNABEL VAN: ―The Secrets to Managing Business
Analytics Projects‖ MIT Sloan Management Review; Fall2011, Vol. 53 Issue 1, p65-69, 5p.
9) Chief information officer (CIO) FilippoPasserini at the Procter and Gamble says that he has
created the Decision Cockpits, the illustration of the business conditions for making faster
business decisions. Passerini believes that he faced difficulty in implementing the business
tools due to culture change. He notes that he is expanding business intelligence where there is
competition.
Watson, Brian P: ―How P&G Maximizes Business Analytics‖ CIO Insight; Jan2012, Issue 121,
p18-20, 3p.
10) The article offers the author's insights on predictive analytics. The author states that business
enterprises draw generalizations from analyzed data in predictive or business analytics to adjust
business strategy and customer experiences. He mentions that the practice of predictive
analytics is more beneficial to small companies than large firms.
Kirchner, Matthew: ―Predictive Analytics‖ Products Finishing; Mar2012, Vol. 76 Issue 6, p52-
53, 2p.
11) The article explores the potential of automated web analytics for deriving business intelligence
(BI). BI is defined as the ability to apprehend the links of facts to guide action towards an aim.
16. It interprets data and transforms it into insights that can be used to guide strategy formulation.
The common elements for effective measures and outcomes using online analytical tools are
also discussed, including dashboard usage and customer relationship management.
Bhatnagar, Alka: ―Web Analytics for Business Intelligence‖; Online; Nov/Dec2009, Vol. 33
Issue 6, p32-35, 4p.
12) Probability can augment the application of predictive analytics. Businesses have used predictive
analytics to prevent losses that may result from fraud, operational errors, or low productivity.
Analysts convey that business predictions should also be supported with probabilities and an
awareness of various reactions to probabilities. This article explains how actions for using
predictive models can be supported by probability in real case decisions such as customer
lifetime value (CLV), clinical treatment, and churn management.
McKnight, William; ―PREDICTIVE ANALYTICS: BEYOND THE PREDICTIONS‖;
Information Management (1521-2912); Jul/Aug2011, Vol. 21 Issue 4, p18-20, 3p.
13) The article discusses how big data changes the way organizations use business intelligence and
analytics. It states that big data has characteristics that add to the challenge including high
velocity, high volume and a variety of data structures. Early adopters of big data include
scientific communities with access to expensive supercomputing environments which aimed to
analyze massive data sources. An exciting source of big data is said to be social network data
which companies would like to leverage. The article discusses an open source framework
created by Doug Cutting called Hadoop that has become the technology of choice to support
applications supporting petabyte-sized analytics utilizing large numbers of computing nodes.
Rogers, Shawn; ―BIG DATA is Scaling BI and Analytics‖ ; Information Management (1521-
2912); Sep/Oct2011, Vol. 21 Issue 5, p14-18, 5p.
14) Visual analytics (VA)—the fusion of analytical reasoning and computational data analysis with
rich, interactive visual representations—promises to provide many relevant techniques for
business-ecosystem-intelligence systems. However, the effectiveness of such systems requires
the careful vigilance of complex, heterogeneous, and distributed data; an in-depth
understanding of the business ecosystem context and end-user domain; and the corresponding
design of relevant visualizations and metrics.
Basole, Rahul C, Hu, Mengdie; ―Visual Analytics for Converging-Business-Ecosystem
Intelligence‖; IEEE Computer Graphics & Applications; Jan2012, Vol. 32 Issue 1, p92-96, 0p.
15) About the opportunities and challenges faced by business analytics in India. Issues that were
discussed including infrastructure and manpower needs for India, user needs in business
analytics and technological challenges associated with integrating data from multiple sources;
Challenges in the field of analytics in financial services in India.
Murthy, Ishwar; ―Business Analytics in India -- Opportunities and Challenges: Discussion‖;
IIMB Management Review (Indian Institute of Management Bangalore); Jun2006, Vol. 18
Issue 2, p175-191, 17p.
16) The paper investigates the relationship between analytical capabilities in the plan, source, make
and deliver area of the supply chain and its performance using information system support and
business process orientation as moderators. The findings suggest the existence of a statistically
significant relationship between analytical capabilities and performance. The moderation effect
of information systems support is considerably stronger than the effect of business process
orientation. The results provide a better understanding of the areas where the impact of business
analytics may be the strongest.
17. Trkman, Peter, McCormack, Kevin; ―The impact of business analytics on supply chain
performance‖ ; Decision Support Systems; Jun2010, Vol. 49 Issue 3, p318-327, 10p.
17) The article explains deep analytics and the role of tools and technologies in predictive analytics
and modeling. It defines business analytics as the skills, technologies, applications and
practices for continuous, iterative exploration and investigation of previous business
performance in order to obtain insight as well as drive business strategy. Investment in more
advanced analytics technology solutions is said to be prompted by the need to remain
competitive. The core principles that support an effective implementation of deep analytics
technologies are discussed including signal detection and visualization. It emphasizes the need
to promote high quality information across the enterprise.
GRIFFIN, JANE; ―Deep Analytics: What is it, and how do I do it?‖Information Management
(1521-2912); Sep/Oct2010, Vol. 20 Issue 5, p53-54, 2p
18) Good Data Won’t Guarantee Good Decisions: by Shvetank Shah, Andrew Horne, and Jaime
Capellá.
19) The Dark Side of Customer Analytics: by Thomas H. Davenport and Jeanne G. Harris
Relevance/Usefulness:-
The relevance of business analytics lies in the very fact that innovation is the mother of differentiation,
and it is the differentiation that provides the cutting edge in this era of survival of the fittest. The above
examples amply prove the fact beyond a shadow of doubt that it is not a mere coincidence that business
analytics has become the be all and end all of efficient and speedy operations irrespective of its field.
Real-time dashboards to monitor every detail and highlight areas that require immediate attention are
but one of the miracles that business analytics is performing. With wafer-thin margin of two to three
percent cost effectiveness has become a rule to live by for all operating in the market, the supply chain
analytics help managers to understand key issues in the field of :
Correctly analyzing barriers to market entry, which vary widely from product to product
Responding to competition within a well-defined supply tier structure
Dealing with high threat of product substitutes
Continually driving product innovation
Managing product life cycles to maximize returns
By leveraging the power of technology even fraud detection can turn out to be a proactive process
allowing organizations to detect potential frauds thereby reduce the negative impact of significant
losses owing to fraud.
Use of business analytics in billing and collection can help in enabling the analytical skills across
businesses in the most contemporary fashion; help to automatically update data at regular intervals as
per requirement. These tools are also subject to customization providing functionalities specifically
useful to the concerned organization. The relevance of the financial analytics is even more prominent
when the example of Oracle is taken into account. The benefits rendered are:
Payables: assess cash management and monitor operational effectiveness of the payables
department to ensure lowest transaction costs.
Receivables: Monitor DSOs and cash cycles to manage working capital, manage collections,
and control receivables risk
General ledger: Manage financial performance across locations, customers, products, and
territories, and receive real-time alerts on events that may impact financial condition
18. Profitability: Identify most profitable customers, products, and channels and understand
profitability drivers across regions, divisions, and profit centers
Retail analytics came into prominence and relevance owing to the fact that the current business focus
has shifted from mass marketing to target marketing. Target marketing requires slicing the potential
market into segments. It helps businesses to promote the right product or service to the right segment
of customers; thereby saving costs pertaining to efforts and space of targeting the customers who may
never be interested in buying the product. This requires effective customer intelligence and actions in
alliance with the same. This is performed by the retail analytics.
The SAP CRM tool will help to plan market financing, market campaigning, target group optimization.
It will also ensure campaign monitoring and success analysis, advertising plan evaluation, lead analysis
and external record evaluation.
All these put together will create an invincible edge beyond a shadow of doubt that will not only help
create business but also retain customers and sustain business in the competitive market scenario.
Data/Method Analysis:-
In order analyze the power of analytics we have collected data from National Institute of Diabetes and
Digestive and Kidney Diseases, a data set of Diabetic patients which can be used for various analysis.
We have downloaded the ARFF (Attribute relation file format) ―diabetes.arff‖ and used WEKA 3.7 as
a mining tool. After feeding the data to Classification and clustering algorithms we got the outputs
which we will observe with the screen shots. Before we move into analysis, let us understand the basic
components of the file diabetes.arff.
Number of Instances: 768
Number of Attributes: 8 plus class
For Each Attribute: (all numeric-valued)
1. Number of times pregnant (preg)
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test (plas)
3. Diastolic blood pressure (mm Hg) (pres)
4. Triceps skin fold thickness (mm) (skin)
5. 2-Hour serum insulin (mu U/ml) (insu)
6. Body mass index (weight in kg/ (height in m) ^2) (mass)
7. Diabetes pedigree function (pedi)
8. Age (years) (age)
9. Class variable (0 or 1) (class- 1 means tested positive, 2- means tested negative)
Missing Attribute Values: None
Doctors were fairly certain that diabetes does not cause "number of times pregnant," age, and
―diabetes pedigree function" (heredity). But still there is need for more in depth analysis for
root cause.
The "plasma glucose concentration" and the "serum insulin" measurements are both tests for
diabetes, so they have been included.
An interesting part of the dataset is that it has two measures related to being overweight:
"triceps skin fold thickness" and "body mass index." These measurements don't cause you to be
overweight, rather being overweight causes these measurements to be high. Unfortunately, this
makes "overweight" a hidden variable in the network. After further examination, skin fold
thickness looked like very poor evidence for diabetes, so they used body mass index as the
value of overweight.
19. Analysis:-
1) We fed the diabetes.arff file into WEKA 3.7 and applied the Classification algorithm OneR to
it, and it gave a following output.
Now there are 182 incorrectly classified instances, which gave an error rate of 23.7%. At the
bottom of the window is ―Confusion Matrix‖. The rows in this matrix correspond to the correct
classes (a = does not have diabetes; b = has diabetes). Hence, there are a total of 447 + 53 = 500
patients without diabetes in the test data, and 129 + 139 = 268 patients with diabetes. The
columns correspond to the predicted classes. Hence, 447 of the 500 negative patients were
correctly classified as negative and 53 of them were incorrectly classified as positives (called
"false positives"). This gives a false positive rate of 0.48. Conversely, 129 of the 268 positive
patients were falsely classified as negatives (called "false negatives") and 139 were correctly
classified as positives.
2) Now to improve the correctly classified instances we have fed the data set to another algorithm
called J48. It can be observed that the correctly and incorrectly classified instances have
improved by application of this algorithm. We can analyze the output in similar way as we did
in the previous one.
20. 3) Similarly we can apply Clustering algorithm SimpleKmeans to analyze the clusters for tested
negative and tested positive people. Those who are more prone to diabetes are having relation
between the attributes. A visualized graph is attached so that we can estimate relation between
insulin level and Age.
21. 4) Above output of the data set can be utilized by Doctors and pharmacists to determine the main
root causes of diabetes and the derived problems which arouses due to diabetes. The data set
can be analyzed with more number of mining algorithms with analytics involved for new
findings. It can not only provide insights for cure, also can led to new areas which can be
considered while treatment of a diabetic patient.
5) Not only Hospitals, Pharmaceutical Companies who are dealing with Sugar supplements, E.g.
Sugar Free etc. can utilize this data and redefine their products and improve the value
proposition for their target group.
ConclusionsRecommendations:-
The future potential being:
Business analytics is broad enough to include capabilities and solutions that benefit a variety of
disciplines. Interestingly, it is observed that business analytics is not just primarily an IT or business
function, but is a function of both IT and business. With this approach, there is an increased need for
collaboration across organizations on issues relating to business analytics, as well as the need for cross
departmental management teams for oversight.
22. From the study now it is clear how Analytics is imperative for sustaining and differentiating in the
generation next technology. We have come up with some recommendations after the study which is as
follows:-
1) Organizations should transform into learning organization and imbibe Analytics into the
employees rather than searching for new talents in the market. Train every member to
fit into best analytical practices in order to align their goals and objectives with that of
the organization.
2) Provide better practices to fresh minds from technical/Business schools by means of
internships or corporate lectures so that they can provide better insights in the new era
of Analytics.
3) Develop Analytics oriented strategies at strategic, tactical and operational levels.
4) Whatever business you are be it product or services; understand your customer better
for competitive advantage with better analytical tools. Develop a value chain that must
be superior to competitors. This in return will create superior customer lifetime value
(CLV).
5) Implement HR analytics and Identify the resources who can take Analysis based data
oriented decisions.
6) Trans-creativity and Innovation in Analytics is the demand of the hour. There is a vast
opportunity of predictive analytics in India due the diversity in demography, consumer
behavior, and regional preferences.
7) Develop Analytics based Innovative business models for sustaining and differentiating
because business model contains the core competencies. Improving capabilities is
another option but they can be copied easily. The bar for entry level barriers can be
raised with the help of analytics.
8) Not only corporations, Economies and Industries can also implement Analytics to
forecast economic activities that can sustain growth and development.
9) Cost based optimized Analytics can contribute to both Top and Bottom lines of
business.
10) In Technology trends Analytics goes at par with cloud computing, organizations can
sort out solutions to so many kinds of problems, for which often they don’t have any
answer.
To quote Benjamin Franklin ―An investment in knowledge pays the best interest‖. It therefore becomes
mandatory for every manager to have a clear understanding and firm grip over business analytics. This
further vindicates Peter Drucker’s thought that a manager is responsible for the application and
performance of knowledge.
