# EVALUATING A BUSINESS INTELLIGENCE SOLUTION. FEASIBILITY ANALYSIS BASED ON MONTE CARLO METHOD

Save this PDF as:

Size: px
Start display at page:

## Transcription

3 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method opportunities to increase revenue, lower costs and improve asset utilization. BI system implementation success measures rely on process performance (budget, time schedule) and infrastructure performance (system quality, information quality, system use). Delphi experts consider that BI system implementation is a continual information improvement program to leverage decision support (Yeoh, 2008). A business-driven methodology is recommended in any BI project management approaches, project scoping and planning being vital for the project success. According to a Delphi expert the success of 90percent of the BI projects is determined prior to the first day. A well-communicated scope, realistic expectations and time-lines and an appropriate budget will be conclusive (Yeoh, 2008). A business-driven approach of a BI project implementation starts with a feasibility study. The decision-making process for large projects is very complicated, and will not be subject of this paper. Having in mind a middle-sized BI project, a feasibility study based on the Monte Carlo simulation method will be conducted. According to (Gonzalez, 2009), project management best practices recommend the most suitable probabilistic, statistical and simulation tools for the project analysis. 2 Monte Carlo Method 2.1. Theory fundaments Today, the concept Monte Carlo Method has become something very unspecific, because you can find Monte Carlo methods in almost every domain, from medicine to economy and from chemistry to regulating the flow of traffic. It s obvious that the way these methods are applied varies substantially from field to field and there are dozens of subsets of Monte Carlo in each of these fields. Finally, to call something a Monte Carlo experiment all you need to do is use random numbers to examine some problem (Woller, 1996). Upon the whole, Monte Carlo methods allows us to examine more complex systems than we otherwise can (Mode, 2011). The Monte Carlo method relies on using random occurrences for approximation calculi. The beginnings of Monte Carlo methods can be related to the year 1873, when Hall published a paper about the determination of number Pi by means of Buffon s needle. PERCENTBuffon's needle problem asks to find the probability that a needle of length a will land on a line, given a floor with equally spaced parallellines a distance d apart (Weisstein, 2002). Actually, the innermost crux of the method consists in revealing the association which could be established between some thorough deterministic phenomena and some random experiments. The Monte Carlo method developed systematically starting with the second world war, when it was used at the blanketing of the atom bomb, in conjunction with direct modelling of probabilistic problems regarding the random diffusion of

4 Mihaela I. Muntean, Cornelia Muntean neutrons from a fissile material. The possibility of applying Monte Carlo methods to deterministic problems was first announced by E. Fermi, J. Von Neumann, S. Ulam and put forth by them hard upon the second world war. At bottom, the Monte Carlo method is a method of computational disposal of mathematical problems, based on the modelling of random variables. We presume z to be a random variable. We perform n independent experiments so that each should end with a value of z (we can imagine that in every experiment, simply and solely, the value of z is measured). This process of constructing for z a number of n values x 1, x 2,, x n represents the modelling of the random variables, and the values x i are called the achievments of z. If it is about studying real phenomena, then the modelling of random variables connected with them is called simulation. The main procedure of elaborating a Monte Carlo method for solving a problem consists in reducing this problem to the determination of mean values. Rather, for calculating the approximate value of a scalar a (which could be the area of a surface, the root of an equation, the value of a definite integral etc.) we must find a random variable z, so that we can have z med = a. Then, by modelling z, that is building n achievments for it x 1, x 2,, x n, we will consider: a 1 n n x i i 1 We want to make obvious the method by considering that we would like to estimate the area S A of a plane bounded surface A (Postaru, A., 2004). To figure this out we will fix on a rectangle D with the area S D which should enclose A (Figure 1).. D A Figure 1. Area S A estimation (A -a plane bounded surface included in D) In D we choose randomly n points. We name with n(a) the number of points that got in A. Certainly if n is great-sized, then: n A n S S A D,

5 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method Hence, we can determine the estimation: n A S A S D n In other words we can calculate the deterministic value S A by using a known value n A S D which we multiply through the incidence, a random variable which n represents the number of favorable events n A related to the total number of events n, produced by the experiment of generating random points in the area D. In this example the random variable z is by default present and has two possible values: S D if the point gets in A and 0 if the point gets in D\A. We can easily verify that: z med = S A, and thus: n 1 n A xi SD n n i 1 Based on this reasoning the Monte Carlo method has four parts: 1. The definition of a domain of possible entries; 2. The construction of the probabilistic model of the real analysed process (system); 3. The generation of random entries with a given distribution law and the execution of deterministic computations with the random generated entries; 4. The use of the statistical estimation theory for aggregating the results. Especially due to the third item in the above list, the Monte Carlo methods lend oneself best to be approached with computer programs and tend to be used especially when it is impossible to calculate an exact result with a deterministic algorithm (Wang, 2010). In economy, the Monte Carlo methods are especially useful for modelling phenomena with uncertain entries, such as risk evaluation in business, feasibility studies, financial forecasting, portfolio analysis and much more (Evans, 2009). 2.1 Evaluating BI projects. Establishing a general theoretical approach Nowadays, organizations have adopted more prudent policies requiring a financial justification for nearly every IT initiative, including Business Intelligence system implementation. Therefore, a feasibility analysis is determinant in the decision of going further with a BI initiative. The precision and reliability of the feasibility analysis relies on the information used in the analysis. Based on the input data, the financial condition and performance of the investment will be evaluated and forecastings will be made. Expected return and expected risks will ground the final financial decision (Björnsdóttir, 2010). Best practises (Matson, 2000; Helfert, 2001; Park, 2002; Lee, 2009) show how financial feasibility analysis should be conducted. A project can be considered

7 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method initiatives, in fact SaaS approaches, are gaining advantage over the traditional ones, lower costs being the main reason for this phenomena (Reyes, 2010). 2.3 Business Intelligence as a shared service SaaS is a model of software delivery that allows companies to deliver solutions to its customers in a hosted environment over the Internet (Joha, 2012). SaaS is generally associated with business software and is typically thought of as a low-cost way for businesses to obtain the same benefits of commercially licensed, internally operated software without the associated complexity and high initial cost (Hurbean, C., 2010). Aspects like: 1 low cost of entry; 2 the responsability is on the vendor; 3 less risky investment; 4 vendors must provide a secure data environment; 4 the worls is flat; 5 Saas is safer; 6 SaaS products are automatically backed up; 7 SaaS vendors innovate faster; 7 - SaaS is more stable, especially for SMEs; 8 packaging and pricing, have been recognized by the IT specialists community ((Jakovljevic, 2006), (Peterson, 2012)) as general characteristics of the SaaS family. All major analysts, including IDC, Garnter, and Forrester, predict for the SaaS BI market a major growth through 2013 (Neubarth, 2011). Comming back to the TCO for a BI initiative, all the upfront and ongoing fees associated with the BI project implementation should be taken into consideration (Oco, Inc., 2007). Based on the proposed general approach (Figure 2), TCO will be calculated as part of a concret feasibility analysis regarding a BI project proposal for a midsized Limited Liability Company (LLC), that will ground the practical study case in paragraph 3. Figure 3a shows a TCO calculation for a SaaS BI initiative vs. a traditional BI implementation in Figure 3b. Figure 3a. TCO calculation for a SaaS BI initiative

8 Mihaela I. Muntean, Cornelia Muntean Figure 3b. TCO calculation for a traditional implementation As expected, the implementation year has a huge cost and the next eight years have also much greater costs then the SaaS variant. This because ETL (Data Integration License), System Integration Costs, Database License and Infrastructure/ Hardware Costs are not zero and because the internal IT Personnel costs are much higher for a traditional BI solution implementation comparing with the SaaS alternative Practical Case Study The most powerful argument when implementing a BI solution is the substantial growth of visibility over business performance (Mircea, 2012). The greatest restriction that limits the adoption of a BI solution is the existence of a limited organizational culture (Nicolau, 2009). In Romania, the local business culture related to BI is not so developed, only few managers have invested in BI initiatives. Recording to the last year statistics, less than 10 percent from the eligible firms acquired a BI solution (Edelhauser, 2011). In the nowadays Romanian business environment, small and medium sized enterprises proved to be a major source of innovation, flexibility and growth (Raşca, 2007), (Voicu, 2009), (Păunescu, 2012). It is also encouraging that entrepreneurs begin to identify the advantages brought by the BI systems in supporting decision-making processes (Mircea, 2008). Based on a request formulated by a Romanian midsized LLC, a complete feasibility analysis of a desired BI initiative has been conducted. The demarche was deployed according to the defined theoretical approach (Figure 2) and a convenient, popular Monte Carlo simulation based tool, was used. 3.1 Establishing predictions for the inputs Without any BI initiative, based on the real figures of the company for the last couple of years, the scenarios are those presented in Figure 4.

9 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method Figure 4. No BI initiative. Prediction of the operational profit Based on historical data and/or expert judgment, a distribution function for the annual sales growth rate is introduced. Predictions for Year 1, for example, have in mind a Pert distribution (Figure 5) with a certain base percentage and a provisioned Min Max range for possible extreme situations. Based on these assumptions, the probable evolution scenario together with the pessimistic and the optimistic one will be deployed. In a similar way, adequate distribution functions for the next years have been chosen. Figure 5. Pert distribution function for Sales growth rate in Year 1 Having in mind this state of art (Figure 4), the desired Business Intelligence initiative will be introduced under the form of a SaaS alternative (Figure 6).

10 Mihaela I. Muntean, Cornelia Muntean The predicted sales growth rate has suffered some adjustments regarding the considered Base, Min and Max assumption; the Pert distribution remains in actuality. The adoption of a SaaS BI initiative is supposed to increase the sales considerable and to diminish the personnel costs due to the increased operational efficiency. Personnel costs savings are presumed to be enclosed in a range from a minimum of 5% to a maximum of 14 percent and a base value of 10 percent (Figure 6), a triangular distribution function being associated (Figure 7). Figure 6. SaaS BI initiative. Prediction of the operational profit/ ROI / IRR Figure 7. Triangular distribution function for the personnel costs savings

11 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method All previously established outputs (Figure 2) are calculated. A mean ROI value of 5,71 and a mean IRR of 95 percent has been obtained after iterations executed during the Monte Carlo simulation process. The simulation was also performed for a traditional BI initiative (Figure 8). As expected, the results are not encouraging. Figure 8. Traditional BI initiative. Prediction of the operational profit/ ROI / IRR after eight years of implementation 3.2 Analysing and interpreting the results The two main outputs ROI (Figure 9) and IRR (Figure 10) will be analyzed based on a histogram, respectively a graph with cumulative descending distribution. Interactions are possible moving the sliders over the diagrams in order to identify the probability to obtain a certain output value (ROI or IRR). In our case, according to Figure 9, the probability to obtain a ROI smaller than 1.5 is 1.6 percent, fairly sufficient for the company to go ahead with the project investment. The graph in Figure 10 indicates a mean value for the IRR of percent and a 2.3 percent probability to obtain an IRR smaller than 20 percent; 20 percent for IRR is generally accepted to be fairly sufficient for a new project investment in a Romanian company (Popescu, 2009). The probability to get losses is lower than 1 percent, but not zero. This result is a direct consequence of the fact that the minimum sales rate was presumed to be below 100 percent for the second, third

12 Mihaela I. Muntean, Cornelia Muntean and fourth year. Nevertheless, if the minimum sales growth rate can be increased to 100 percent, the risk of the project vanishes for good at all. Figure 9. SaaS BI initiative. Results histogram for ROI Figure 10. Saas BI initiative. Result graph for IRR The mean values for ROI and IRR being profitable, the recommendation of a SaaS BI initiative as an advisable solution will be reinforced. When considering the second variant, that is adopting a traditional BI solution, it is necessary to calculate the IRR and ROI similar to the SaaS variant, but for a few more years instead of just four. Even considering eight years instead of four, the investment is far too big for a midsized company and the probability to obtain losses is impermissible high. The result histogram for the obtained ROI is shown in

13 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method Figure 11, and the graph with cumulative descending distribution for the IRR is shown in Figure 12. Figure 11. Traditional BI initiative. Results histogram for ROI Figure 12. Traditional BI initiative. Result graph for IRR Here, the probability to get a ROI below 1.5 is 18.7 percent, a pretty big value (in contrast to 1.6 percent, as it was for the SaaS implementation), and even worse is the probability of 85.1 percent (compared with 2.3 percent when adopting the SaaS variant) to get an IRR smaller than 20 percent. Although the mean value obtained for the ROI (2.6) is not definitely bad and the mean value of nearly 12 percent obtained for the IRR is also acceptable, the probability of getting losses is much too high in this case. A traditional BI initiative is not an option for the considered company.

14 Mihaela I. Muntean, Cornelia Muntean 4. Conclusions and future work Business Intelligence is the process for increasing the competitive advantage of a company by intelligent use of available data in decision-making. Only a revolutionary solution, like a Business Intelligence initiative, can solve the complex issues faced when evaluating decision support applications and ensure the availability of any business-critical information. Small and medium sized firms have demands for BI solutions, needing systems that take into account users involved in operational actions, not only top managers, using scorecards, key performance indicators, analytical grid, dashboard analysis. But a rigorously feasibility analysis should be performed before starting any BI initiative. To avoid losses, a carefully monetary analysis is necessary. Therefore, a general theoretical approach will be proposed; outputs like ROI and IRR will be determined based on the specified input values and their predictions over the considered time period. Using Monte Carlo simulation techniques, pessimistic, probable and optimistic scenarios are deployed. The theoretical considerations have been applied to a concrete study case on a Romanian LLC. Predictions of the inputs have been established, simulations have been fulfilled and results have been analysed. As expected, the SaaS BI initiative can be implemented with almost no risks at all. Future researches have in mind an extended theoretical unitary approach of further financial indicators in order to improve the proposal s capabilities. Thereby, the necessary support for evaluating BI initiatives will be guaranteed. This is an essential first step in helping firms, in particularly Romanian small and medium sized organizations, to become competitive by accumulating the right business intelligence. REFERENCES [1] Bennett, F.L. (2003), The Management of Construction: A Project Life Cycle Approach. 1st ed. Burlington: Butterworth-Heinemann; [2] Björnsdóttir A.R. (2010), Financial Feasibility Assessments. Building and Using Assessment Models for Financial Feasibility Analysis of Investment Projects; Thesis submitted in partial fulfillment of a Magister Scientarium degree in Industrial Engineering, University of Iceland, Reykjavik; [3] Brohman, D.K. (2000), The Business Intelligence Value Chain: Data Driven Decision Support in A Warehouse Environment. An Exploratory Study; Proceedings of the 33rd Hawai International Conference on Systems Science; [4] Edelhauser E. (2011), IT&C Impact on the Romanian Business and Organizations. The Enterprise Resource Planning and Business Intelligence Methods Influence on Manager s Decision. Study case. Revista de Informatica economica, 15(2);

17 Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte Carlo Method solve.com/downloads/literatura/metoda%20monte Carlo.doc >, Accessed on May. 23, 2012 [35] Raşca, L. and Deaconu A. (2007), Romanian Small and Medium Sized Enterprises Challenges Upon Accession into the European Union; Analele Universitatii din Oradea, 2007; [36] Reyes E.P. (2010), A System Thinking Approach to Business Intelligence Solutions Based on Cloud Computing, submitted to the System Design and Management Program in partial fulfillment of the requirements for the degree of Master of Science in Engineering and Management, Massachusetts Institute of Technology, 2010; [37] Voicu V., Zirra D. and Ciocirlan D. (2009), Business Intelligence Effective Solutions for Management; the 10 th WSEAS Conference on Mathematics and Computers in Business and Economics, Prague, 2009; [38] Wang Y., Li L. and Lim E.P. (2010), Trust-Oriented Composite Service Selection with QoS Constraints; Journal of Universal Computer Science, 16(13), ; [39] Weisstein, E.W. (2002), Buffon's Needle Problem. From MathWorld--A Wolfram Web Resource., <<a href='http://mathworld.wolfram.com/ BuffonsNeedleProblem.html>, Accessed on May. 23, 2012; [40] Woller J. (1996), The Basics of Monte Carlo Simulations; University of Nebraska-Lincoln, Physical Chemistry Lab (Chem 484), Spring 1996, <<a href='http: //www.chem.unl.edu/zeng/joy/mclab/mcintro.html>, Accessed on May. 23, 2012; [41] Yeoh W., Koronios A. and Gao J. (2008), Managing the Implementation of Business Intelligence systems: A Critical Success Factors Framework; International Journal of Enterprise Information Systems, 4(3), IGI Publishing.

### Theory and Practice in Business Intelligence

MPRA Munich Personal RePEc Archive Theory and Practice in Business Intelligence Mihaela Muntean West University of Timisoara, Faculty of Economics and Business Administration, Department of Business Information

### Cloud Computing Technology - Optimal Solution for Efficient Use of Business Intelligence and Enterprise Resource Planning Applications

Scientific Papers (www.scientificpapers.org) Journal of Knowledge Management, Economics and Information Technology Cloud Computing Technology - Optimal Solution for Efficient Use of Business Intelligence

### Calculating ROI for Business Intelligence Solutions in Small and Mid-Sized Businesses

Calculating ROI for Business Intelligence Solutions in Small and Mid-Sized Businesses Introduction Successful business intelligence implementations can unlock key information within a company s data vaults

### Return on Investment in Business Intelligence in Small and Mid-Sized Businesses

Return on Investment in Business Intelligence in Small and Mid-Sized Businesses ZELJKO PANIAN The Graduate School for Economics and Business University of Zagreb J.F. Kennedy Sq. 6 CROATIA zpanian@efzg.hr

### A collaborative approach of Business Intelligence systems

A collaborative approach of Business Intelligence systems Gheorghe MATEI, PhD Romanian Commercial Bank, Bucharest, Romania george.matei@bcr.ro Abstract: To succeed in the context of a global and dynamic

### The expression better, faster, cheaper THE BUSINESS CASE FOR PROJECT PORTFOLIO MANAGEMENT

Cloud Solutions for IT Management WHITE PAPER THE BUSINESS CASE FOR PROJECT PORTFOLIO MANAGEMENT How Progressive IT Organizations Are Using Hosted Solutions To Deliver On Time, On Budget, On Quota and

### THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS

THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS ADRIAN COJOCARIU, CRISTINA OFELIA STANCIU TIBISCUS UNIVERSITY OF TIMIŞOARA, FACULTY OF ECONOMIC SCIENCE, DALIEI STR, 1/A, TIMIŞOARA, 300558, ROMANIA ofelia.stanciu@gmail.com,

### Project Management. Systems Analysis and Design, 8e Kendall & Kendall

Project Management Systems Analysis and Design, 8e Kendall & Kendall Learning Objectives Understand how projects are initiated and selected, define a business problem, and determine the feasibility of

Industrial Manufacturing 7 things to ask when upgrading your ERP solution The capabilities gap between older versions of ERP designs and current designs can create a problem that many organizations are

### Why Cloud BI? of Software-as-a-Service Business Intelligence. Executive Summary. This white paper explores the 10 substantial

of Software-as-a-Service Business Intelligence Executive Summary Smart businesses are pursuing every available opportunity to maximize performance and minimize costs. Business Intelligence tools used to

SUCCESSFUL IMPLEMENTATION OF BUSINESS INTELLIGENCE AS A TOOL FOR COMPANY MANAGEMENT Tomáš Mandičák, Peter Mesároš, Karol Hrubý. INTRODUCTION Nowadays, situation on the markets is not easy for companies.

### STRATEGIC INTELLIGENCE WITH BI COMPETENCY CENTER. Student Rodica Maria BOGZA, Ph.D. The Bucharest Academy of Economic Studies

STRATEGIC INTELLIGENCE WITH BI COMPETENCY CENTER Student Rodica Maria BOGZA, Ph.D. The Bucharest Academy of Economic Studies ABSTRACT The paper is about the strategic impact of BI, the necessity for BI

THE ROLE OF BUSINESS INTELLIGENCE IN BUSINESS PERFORMANCE MANAGEMENT Pugna Irina Bogdana Bucuresti, irina_bogdana@yahoo.com, tel : 0742483841 Albescu Felicia Bucuresti albescu@inde.ro tel: 0723581942 Babeanu

### ANALYTICS PAYS BACK \$13.01 FOR EVERY DOLLAR SPENT

RESEARCH NOTE September 2014 ANALYTICS PAYS BACK \$13.01 FOR EVERY DOLLAR SPENT THE BOTTOM LINE Organizations are continuing to make investments in analytics to meet the growing demands of the user community

### Supply chain intelligence: benefits, techniques and future trends

MEB 2010 8 th International Conference on Management, Enterprise and Benchmarking June 4 5, 2010 Budapest, Hungary Supply chain intelligence: benefits, techniques and future trends Zoltán Bátori Óbuda

### Next Generation Business Performance Management Solution

Next Generation Business Performance Management Solution Why Existing Business Intelligence (BI) Products are Inadequate Changing Business Environment In the face of increased competition, complex customer

### Why Cloud BI? The 10 Substantial Benefits of Software-as-a-Service Business Intelligence

The 10 Substantial Benefits of Software-as-a-Service Business Intelligence Executive Summary Smart businesses are pursuing every available opportunity to maximize performance and minimize costs. Business

### Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com

Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com Advanced Analytics Dan Vesset September 2003 INTRODUCTION In the previous sections of this series

### I D C M a r k e t S c a p e : W o r l d w i d e B u s i n e s s A n a l y t i c s B P O S e r v i c e s 2 0 1 2 V e n d o r A n a l y s i s

Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com C O M P E T I T I V E A N A L Y S I S I D C M a r k e t S c a p e : W o r l d w i d e B u s i n e

### THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA. Thomas Roland Managing Director. David Roggen Director CONTENTS

THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA David Roggen Director Thomas Roland Managing Director CONTENTS Shared Services Today 2 What Is an Analytics Hub? 3 Analytics Hub

1 3 Business Intelligence Support Services Service Definition BUSINESS INTELLIGENCE SUPPORT SERVICES Service Description The Business Intelligence Support Services are part of the Cognizant Information

### White Paper March 2009. Government performance management Set goals, drive accountability and improve outcomes

White Paper March 2009 Government performance management Set goals, drive accountability and improve outcomes 2 Contents 3 Business problems Why performance management? 4 Business drivers 6 The solution

### Customer-Facing Business Intelligence in Wholesale Distribution

Customer-Facing Business Intelligence in Wholesale Distribution September 3, 2012 In partnership with one of our premier IT wholesale distribution customers, I completed a Benchmarking Study on Software-as-a-Service

### BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining

BUSINESS INTELLIGENCE Bogdan Mohor Dumitrita 1 Abstract A Business Intelligence (BI)-driven approach can be very effective in implementing business transformation programs within an enterprise framework.

### Prescriptive Analytics. A business guide

Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications

### Amajor benefit of Monte-Carlo schedule analysis is to

2005 AACE International Transactions RISK.10 The Benefits of Monte- Carlo Schedule Analysis Mr. Jason Verschoor, P.Eng. Amajor benefit of Monte-Carlo schedule analysis is to expose underlying risks to

### Mitigating Risk through OEM Partnerships. Leveraging OEM to Drive the Bottom Line

Mitigating Risk through OEM Partnerships Leveraging OEM to Drive the Bottom Line Executive Summary Incorporating third-party technology to add new capability to existing applications is not a new concept

### Blue: C= 77 M= 24 Y=19 K=0 Font: Avenir. Clockwork LCM Cloud. Technology Whitepaper

Technology Whitepaper Clockwork Solutions, LLC. 1 (800) 994-1336 A Teakwood Capital Company Copyright 2013 TABLE OF CONTENTS Clockwork Solutions Bringing Cloud Technology to the World Clockwork Cloud Computing

### Optimization applications in finance, securities, banking and insurance

IBM Software IBM ILOG Optimization and Analytical Decision Support Solutions White Paper Optimization applications in finance, securities, banking and insurance 2 Optimization applications in finance,

### On the Interaction and Competition among Internet Service Providers

On the Interaction and Competition among Internet Service Providers Sam C.M. Lee John C.S. Lui + Abstract The current Internet architecture comprises of different privately owned Internet service providers

### Innovative Analysis of a CRM Database using Online Analytical Processing (OLAP) Technique in Value Chain Management Approach

Innovative Analysis of a CRM Database using Online Analytical Processing (OLAP) Technique in Value Chain Management Approach ADRIAN MICU, ANGELA-ELIZA MICU, ALEXANDRU CAPATINA Faculty of Economics, Dunărea

### Additional Case Study Two: Property Risk Assessment: A Simulation Approach

Additional Case Study Two: Property Risk Assessment: A Simulation Approach This case study focuses on analyzing the risk of investing in income properties. The key point is that measures of central tendency,

### Eric.kavanagh@bloorgroup.com. Twitter Tag: #briefr 8/14/12

Eric.kavanagh@bloorgroup.com Twitter Tag: #briefr 8/14/12 ! Reveal the essential characteristics of enterprise software, good and bad! Provide a forum for detailed analysis of today s innovative technologies!

### Data Virtualization: Achieve Better Business Outcomes, Faster

White Paper Data Virtualization: Achieve Better Business Outcomes, Faster What You Will Learn Over the past decade, businesses have made tremendous investments in information capture, storage, and analysis.

### ISV Strategy for Revenue & Customer Growth

ISV Strategy for Revenue & Customer Growth Online Store @ Cloud Bhavin Raichura, Vijith Vayanippetta Abstract On-demand software distribution & retail e-commerce is one of the key growth strategies for

### ACCOUNTING MODERNIZATION PREMISE OF AN EFFECTIVE GOVERNANCE SYSTEM OF ENTERPRISE

ACCOUNTING MODERNIZATION PREMISE OF AN EFFECTIVE GOVERNANCE SYSTEM OF ENTERPRISE MIHAELA UNGUREANU ALEXANDRU IOAN CUZA UNIVERSITY OF IASI myhaella5@gmail.com Abstract: Value of company is maximizing insofar

### CUSTOMER SERVICE THE IMPORTANT GOAL OF LOGISTICS

CUSTOMER SERVICE THE IMPORTANT GOAL OF LOGISTICS Associate Professor Adriana SCRIOŞTEANU, PhD University of Craiova Faculty of Economics Craiova, Romania Professor Daniela POPESCU, PhD University of Craiova

### WHITE PAPER OCTOBER 2014. Unified Monitoring. A Business Perspective

WHITE PAPER OCTOBER 2014 Unified Monitoring A Business Perspective 2 WHITE PAPER: UNIFIED MONITORING ca.com Table of Contents Introduction 3 Section 1: Today s Emerging Computing Environments 4 Section

International Journal of Latest Research In Engineering and Computing (IJLREC) Volume 3, Issue 1, Page No. 1-7 January-February 2015 www.ijlrec.com ISSN: 2347-6540 BENEFITS AND ADVANTAGES OF BUSINESS INTELLIGENCE

### Market Maturity. Cloud Definitions

HRG Assessment: Cloud Computing Provider Perspective In the fall of 2009 Harvard Research Group (HRG) interviewed selected Cloud Computing companies including SaaS (software as a service), PaaS (platform

### The Power of Business Intelligence in the Revenue Cycle

The Power of Business Intelligence in the Revenue Cycle Increasing Cash Flow with Actionable Information John Garcia August 4, 2011 Table of Contents Revenue Cycle Challenges... 3 The Goal of Business

### The 5 Questions You Need to Ask Before Selecting a Business Intelligence Vendor. www.halobi.com. Share With Us!

The 5 Questions You Need to Ask Before Selecting a Business Intelligence Vendor www.halobi.com Share With Us! Overview Over the last decade, Business Intelligence (BI) has been at or near the top of the

### The New Branch Office Network

By Jim Metzler Ashton, Metzler & Associates This paper was written by Dr. Jim Metzler, Vice President of Ashton, Metzler & Associates. Dr. Jim Metzler is widely recognized as an authority on both network

### Profit Forecast Model Using Monte Carlo Simulation in Excel

Profit Forecast Model Using Monte Carlo Simulation in Excel Petru BALOGH Pompiliu GOLEA Valentin INCEU Dimitrie Cantemir Christian University Abstract Profit forecast is very important for any company.

### Major Trends in the Insurance Industry

To survive in today s volatile marketplace? Information or more precisely, Actionable Information is the key factor. For no other industry is it as important as for the Insurance Industry, which is almost

### BUSINESS INTELLIGENCE: APPLICATIONS, TRENDS, AND STRATEGIES. Business Intelligence: aplicaţii, tendinţe şi strategii

ANALELE ŞTIINŢIFICE ALE UNIVERSITĂŢII ALEXANDRU IOAN CUZA DIN IAŞI Tomul LII/LIII Ştiinţe Economice 2005/2006 BUSINESS INTELLIGENCE: APPLICATIONS, TRENDS, AND STRATEGIES LUMINIŢA HURBEAN * Business Intelligence:

Rethinking Your Finance Functions Budgeting, Planning & Technology BDO Canada Daniel Caringi ( dcaringi@bdo.ca ) September 25th, 2014 A journey of a thousand miles must begin with a single step. - Lao

### Quantifying Outsourcing Intangible Benefits

Quantifying Outsourcing Intangible Benefits This paper presents the basis for quantifying the intangible benefits of outsourcing inititiatives in economic terms. Abstract This white paper provides outsourcing

### Rootstock Software White Paper How Cloud Computing is Changing the Manufacturing ERP Landscape

Rootstock Software White Paper How Cloud Computing is Changing the Manufacturing ERP Landscape By Pat Garrehy Executive Summary The introduction and success of cloud computing in recent years has dramatically

### Chapter 11 Building Information Systems and and Managing Projects

1 Chapter 11 Building Information Systems and and Managing Projects LEARNING TRACK 1: CAPITAL BUDGETING METHODS FOR INFORMATION SYSTEM INVESTMENTS Traditional Capital Budgeting Models Capital budgeting

### ASIAN JOURNAL OF MANAGEMENT RESEARCH Online Open Access publishing platform for Management Research

Online Open Access publishing platform for Management Research Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Review Article ISSN 2229 3795 Business analytics a tool to business

SOLUTION BRIEF Converged Infrastructure Management from CA Technologies how can I deliver innovative customer services across increasingly complex, converged infrastructure with less management effort

### THE TOP 5 RECRUITMENT KPIs

BETTER DATA, BETTER BUSINESS: THE TOP 5 RECRUITMENT KPIs Jointly written by: COPYRIGHT 2012, BULLHORN + INSIGHTSQUARED, ALL RIGHTS RESERVED. Companies today are increasingly competing in a data-centric

### Methodology Framework for Analysis and Design of Business Intelligence Systems

Applied Mathematical Sciences, Vol. 7, 2013, no. 31, 1523-1528 HIKARI Ltd, www.m-hikari.com Methodology Framework for Analysis and Design of Business Intelligence Systems Martin Závodný Department of Information

### A Knowledge Management Framework Using Business Intelligence Solutions

www.ijcsi.org 102 A Knowledge Management Framework Using Business Intelligence Solutions Marwa Gadu 1 and Prof. Dr. Nashaat El-Khameesy 2 1 Computer and Information Systems Department, Sadat Academy For

### Strategic Management System for Academic World

2011 International Conference on Software and Computer Applications IPCSIT vol.9 (2011) (2011) IACSIT Press, Singapore Strategic Management System for Academic World Expert System Based on Composition

### View Point. Lifting the Fog on Cloud

View Point Lifting the Fog on Cloud There s a massive Cloud build-up on the horizon and the forecast promises a rain of benefits for the enterprise. Cloud is no more a buzzword. The enabling power of the

### CLOUD COMPUTING SOLUTION - BENEFITS AND TESTING CHALLENGES

CLOUD COMPUTING SOLUTION - BENEFITS AND TESTING CHALLENGES PRAKASH.V, GOPALAKRISHANAN.S Assistant Professor Department of Computer Applications, SASTRA University Associate Dean Department of Computer

### BIG DATA THE NEW OPPORTUNITY

Feature Biswajit Mohapatra is an IBM Certified Consultant and a global integrated delivery leader for IBM s AMS business application modernization (BAM) practice. He is IBM India s competency head for

### SALES AND OPERATIONS PLANNING BLUEPRINT BUSINESS VALUE GUIDE

Business Value Guide SALES AND OPERATIONS PLANNING BLUEPRINT BUSINESS VALUE GUIDE INTRODUCTION What if it were possible to tightly link sales, marketing, supply chain, manufacturing and finance, so that

### Improving claims management outcomes with predictive analytics

Improving claims management outcomes with predictive analytics Contents: 1 Executive Summary 2 Introduction 3 Predictive Analytics Defined 3 Proven ROI from Predictive Analytics 4 Improving Decisions in

### ROI CASE STUDY IBM COGNOS BI COMPETENCY CENTER MARTIN S POINT HEALTH CARE

ROI CASE STUDY IBM COGNOS BI COMPETENCY CENTER MARTIN S POINT HEALTH CARE THE BOTTOM LINE Establishing a business intelligence competency center (BICC) enabled Martin s Point Health Care to significantly

### INTRAFOCUS. DATA VISUALISATION An Intrafocus Guide

DATA VISUALISATION An Intrafocus Guide September 2011 Table of Contents What is Data Visualisation?... 2 Where is Data Visualisation Used?... 3 The Market View... 4 What Should You Look For?... 5 The Key

### Tapping the benefits of business analytics and optimization

IBM Sales and Distribution Chemicals and Petroleum White Paper Tapping the benefits of business analytics and optimization A rich source of intelligence for the chemicals and petroleum industries 2 Tapping

### EMC ADVERTISING ANALYTICS SERVICE FOR MEDIA & ENTERTAINMENT

EMC ADVERTISING ANALYTICS SERVICE FOR MEDIA & ENTERTAINMENT Leveraging analytics for actionable insight ESSENTIALS Put your Big Data to work for you Pick the best-fit, priority business opportunity and

### The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,

### Aligning IT to the Strategic Plan

RG Perspective Aligning IT to the Strategic Plan Why it s permanently number one on your to-do list 11 Canal Center Plaza Alexandria, VA 22314 HQ 703-548-7006 Fax 703-684-5189 www.robbinsgioia.com 2013

### Data Mining Solutions for the Business Environment

Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

### How IT Can Help Companies Make Better, Faster Decisions

How IT Can Help Companies Make Better, Faster Decisions How It Can Help Companies Make Better Faster Decisions Of the many different groups that make up a business organization sales, finance, human resources

### PRIME DIMENSIONS. Revealing insights. Shaping the future.

PRIME DIMENSIONS Revealing insights. Shaping the future. Service Offering Prime Dimensions offers expertise in the processes, tools, and techniques associated with: Data Management Business Intelligence

### Eighty Twenty Thinking in Traditional and Cloud Environment

International Journal of Enhanced Research in Management & Computer lications, ISSN: 2319-7471 Eighty Twenty Thinking in Traditional and Cloud Environment 80/20 Thinking Mitesh Soni Research & Innovation

### Using Predictive Analytics to Increase Profitability Part III

Using Predictive Analytics to Increase Profitability Part III Jay Roy Chief Strategy Officer Practical Intelligence for Ensuring Profitability Fall 2011 Dallas, TX Table of Contents A Brief Review of Part

### Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are

### Practical Calculation of Expected and Unexpected Losses in Operational Risk by Simulation Methods

Practical Calculation of Expected and Unexpected Losses in Operational Risk by Simulation Methods Enrique Navarrete 1 Abstract: This paper surveys the main difficulties involved with the quantitative measurement

### Using Predictive Analytics to Increase Profitability Part II

Using Predictive Analytics to Increase Profitability Part II Jay Roy Chief Strategy Officer Practical Intelligence for Ensuring Profitability Fall 2011 Dallas, TX Table of Contents A Brief Review of Part

### A DIFFERENT KIND OF PROJECT MANAGEMENT

SEER for Software SEER project estimation and management solutions improve success rates on complex software projects. Based on sophisticated modeling technology and extensive knowledge bases, SEER solutions

### The Total Economic Impact Of SAS Customer Intelligence Solutions Intelligent Advertising For Publishers

A Forrester Total Economic Impact Study Commissioned By SAS Project Director: Dean Davison February 2014 The Total Economic Impact Of SAS Customer Intelligence Solutions Intelligent Advertising For Publishers

### Outcomes-Based Investing:

Outcomes-Based Investing: Large-Scale Monte Carlo Simulation for Investment Management Christopher Jones Executive Vice President, Financial Research & Strategy Financial Engines INFORMS Conference on

### Database Marketing simplified through Data Mining

Database Marketing simplified through Data Mining Author*: Dr. Ing. Arnfried Ossen, Head of the Data Mining/Marketing Analysis Competence Center, Private Banking Division, Deutsche Bank, Frankfurt, Germany

### IT&C support for decision making in notary offices

IT&C support for decision making in notary offices George Schin (Corresponding author) Dunarea de Jos University Str. Domneasca. 47, Galati, Romania E-mail: george.schin@mail.com Margareta Racovita Dunarea

### DECISION SUPPORT SYSTEMS OR BUSINESS INTELLIGENCE. WHICH IS THE BEST DECISION MAKER?

DECISION SUPPORT SYSTEMS OR BUSINESS INTELLIGENCE. WHICH IS THE BEST DECISION MAKER? [1] Sachin Kashyap Research Scholar Singhania University Rajasthan (India) [2] Dr. Pardeep Goel, Asso. Professor Dean

### Professional Services Organizations (PSO) Need Professional Services Automation (PSA)

RTM Consulting Professional Services Organizations (PSO) Need Professional Services Automation (PSA) PSOs Eat Your Own Dog Food Randy Mysliviec President & CEO 1-855-786-2555 RTM Consulting 2 2007-2015

### Using Predictive Analytics To Drive Workforce Optimization. New Insights From Big Data Analysis Uncover Key Drivers of Workforce Profitability

Using Predictive Analytics To Drive Workforce Optimization New Insights From Big Data Analysis Uncover Key Drivers of Workforce Profitability Using Predictive Analytics To Drive Workforce Optimization

### A DIFFERENT KIND OF PROJECT MANAGEMENT: AVOID SURPRISES

SEER for Software: Cost, Schedule, Risk, Reliability SEER project estimation and management solutions improve success rates on complex software projects. Based on sophisticated modeling technology and

### Projected Cost Analysis of the SAP HANA Platform

A Forrester Total Economic Impact Study Commissioned By SAP Project Director: Shaheen Parks April 2014 Projected Cost Analysis of the SAP HANA Platform Cost Savings Enabled By Transitioning to the SAP

### Transforming Accounts Payable into a Profit Center

PROFIT FROM PAYMENTS: Transforming Accounts Payable into a Profit Center Leveraging Business Process Automation to Capitalize on Dynamic Discounts a New Breed of Early Payment Incentives BEGIN Table of

### Design of a Customer-Centric Balanced Scorecard Support for a Research on CRM Strategies of Romanian Companies from FMCG Sector

Design of a Customer-Centric Balanced Scorecard Support for a Research on CRM Strategies of Romanian Companies from FMCG Sector MICU ADRIAN MICU ANGELA ELIZA CAPATINA ALEXANDRU NISTOR COSTEL Management-Marketing

### Capitalizing on Change

White paper Capitalizing on Change Capitalizing on Change One Network Enterprises www.onenetwork.com White paper Capitalizing on Change These big bang implementations take months and years to complete,

### Three Fundamental Techniques To Maximize the Value of Your Enterprise Data

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Prepared for Talend by: David Loshin Knowledge Integrity, Inc. October, 2010 2010 Knowledge Integrity, Inc. 1 Introduction Organizations

### CUSTOMER RELATIONSHIP MANAGEMENT - A MAJOR ELEMENT OF THE COMPANY S BUSINESS STRATEGY

CUSTOMER RELATIONSHIP MANAGEMENT - A MAJOR ELEMENT OF THE COMPANY S BUSINESS STRATEGY Associate Professor Nicoleta Rossela Dumitru, Ph.D Romanian American University 1B, Ezpoziţiei Avenue, Sector 1, Bucharest

### Lawson Healthcare Solutions Optimization of Key Resources Forms a Foundation for Excellent Patient Care

Lawson Healthcare Solutions Optimization of Key Resources Forms a Foundation for Excellent Patient Care Healthcare organizations continue to experience an alarming erosion of their operational foundation,