STRATEGIES TO MEASURE AND COMBAT COMPASSION FATIGUE AND BURNOUT

Executive Summary

The paper seeks to obtain a better understanding on Compassion Fatigue, and Burnout. The paper attempts to explore reason workers become affected with Compassion Fatigue, and Burnout. Additionally the benefits for the organization as a whole of having a controlled level of Compassion Fatigue in employees – also referred to as compassion satisfaction is explored. Various tools and techniques available for managers and workers to identify and measure Compassion Fatigue and Burnout are described. The paper also describes various intervention techniques to prevent, as well as to combat Compassion Fatigue and Burnout in the organization. Finally the paper describes pitfalls in Compassion Fatigue and Burnout measurements and intervention techniques and care that managers need to take when interpreting the results.

Introduction

Compassion Fatigue can be defined as the state of exhaustion and impaired function resulting from prolonged exposure to demanding emotional inter-personal stress in the work environment [2]. The symptoms of Compassion Fatigue include symptoms of secondary traumatic stress, such as intrusive thoughts, avoidant behavior, and hyper-vigilance. Job Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job, and is defined by the three dimensions of exhaustion, cynicism, and inefficacy [6]. Compassion Fatigue is an occupational hazard for health care providers and social workers working with traumatized clients, whereas Burnout is an organizational hazard for employees and managers working in difficult organizational environments. Burnout differs from Compassion Fatigue, as it is not only a result of trauma work but also an outcome of organizational stressors such as workload, work role confusion, tense work relationships with coworkers and supervisors, and lack of resources to do one’s job [14]. Widespread inflection of Compassion Fatigue and Burnout among employees and caregivers results in a dysfunctional organization, mistaken diagnosis and patient neglect. It is important that managers identify the symptoms, measure and address the root cause.

Compassion Satisfaction and Compassion Fatigue and Burnout

Exhibit 1 displays the loose relation between professional quality of life, compassion satisfaction, Compassion Fatigue, Burnout and secondary trauma.

Exhibit 1: Professional Quality of Life, Compassion Satisfaction, Compassion Fatigue, and Burnout [9].

Compassion Satisfaction

Engaging in interpersonal work to help others typically is fulfilling. Most employees experience and are bolstered by satisfaction as they deal with clients in need [5]. Compassion satisfaction is a positive sentiment the provider experiences when able to empathetically connect and feel a sense of achievement in the care-providing process [10].

Compassionate care towards the clients or patients is a primary tenant in social workers and health care providers. It is argued [5] that compassion satisfaction is a vital part of being emotionally fulfilled by one’s works in human services field.

When social workers and health care employees experience compassion, they hold deep feeling about another’s suffering, which prompts them to attempt to relieve the other’s misery [5, 11]. The provider then puts the interests of the patient in the forefront. They are fully engaged in the process of wanting to help, providing assistance to those who are struggling, and providing protection for the victimized. This is known as the compassion process. The provider first notices the emotional state of the patient, then has empathic feelings, and finally responds to attempt to alleviate the pain [5, 12]

Compassion Fatigue

Health care professionals such as physicians, nurses, therapists, and social workers are expected to be compassionate in their work. It is part of the role requirement for which they are being paid. On the other hand, those who are overly responsive in their compassionate role may experience negative consequences if they are spending too much time with traumatized clients.  Figley[2]  defined Compassion Fatigue as a ‘‘state of exhaustion and dysfunction (biologically, psychologically and socially) as a result of prolonged exposure to secondary trauma or a single intensive event.’’ Compassion Fatigue is an occupational hazard for those in the helping professions and is a natural consequence of working with people who have experienced extremely stressful events. Compassion Fatigue is about work-related, secondary exposure to extremely stressful events [5].

Burnout

Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job and is defined by the three dimensions of exhaustion, cynicism, and inefficacy [6]. The significance of Burnout lies to its negative impact on workers job performance and individual health.

The Burnout is result of situations factors (like, job characteristics, occupational characteristics and Organizational characteristics) and individual factors (demographic, personality, job attitudes).

Measuring Compassion Fatigue and Burnout

Social workers experiencing Compassion Fatigue and Burnout are at a higher risk of lower productivity and dysfunction. According to a study conducted by [4], approximately 50% of child protection staff of Colorado County suffered from high or very high levels of Compassion Fatigue.

In order to prevent and combat Compassion Fatigue or burnout, it is important recognize the signs and symptoms of its emergence.

Various measurement instruments to measure Compassion Fatigue and Burnout are described in detail in [12].

Compassion Fatigue Self Test (CFST) was first introduced by Figley in 1995[3]. The original CFST had 40 items divided between CF (23) and Burnout (17). CFST was modified by Stamm and Figley with addition of a series of positively oriented questions paralleling the negative orientation of CF items resulting in a 66-item instrument [12]. The addition of positive oriented questions were intended to measure compassion satisfaction.

Professional Quality of Life Scale (ProQOL) [10] which is most widely used [12] is a revision of CFST and is composed of three discrete subscales. The first subscale measures Compassion Satisfaction, the second measures Burnout and third measures Compassion Fatigue.

The ProQOL is structured as a 30-item self-report measure in which respondents are instructed to indicate how frequently each item was experienced in the previous 30 days. Each item is anchored by a 6-item Likert scale (0 = never, 1 = rarely, 2 = a few times, 3 = somewhat often, 4 = often, and 5 = very often). Scoring requires summing the item responses for each 10-item subscale. A total of 5 items (1, 4, 15, 17, and 29) must be reverse scored prior to computing scores. The subscale scores cannot be combined to compute a total score. The most current scoring guidelines (Stamm, 2005) are based on a conservative quartile method whereby cut scores are based on the 75th percentile. As such, the guidelines suggest that a score of 33 or below on the compassion satisfaction scale may suggest job dissatisfaction. Guidelines for the Burnout scale suggest that a score below 18 reflects positive feelings about one’s ability to be effective in one’s work, and scores above 27 may be cause for concern in that one may not feel effective. Regarding the Compassion Fatigue/secondary trauma scale, scores above 17 should be considered to reflect a potential problem in this domain. Internal consistency reliability estimates for the subscales are reported as .87 for the compassion satisfaction scale, .72 for the Burnout scale, and .80 for the Compassion Fatigue/secondary trauma scale [12].

Appendix 1 provides detailed characteristics of Compassion Fatigue assessments instruments.

Implications for Managerial Practices and Conclusion

After selecting the Compassion Fatigue and Burnout measuring tool, and applying to the front line workers the next step is to implement various managerial steps to reduce the occurrence, mitigate and reduce the impact. The ProQOL method was used in workshops held at Newfoundland and Labrador Housing with frontline social workers and social housing officers. Some of the below mentioned managerial impact were placed at NL Housing.

It is the younger employees who are more susceptible to Burnout and Compassion Fatigue [5, 6]. So a formal mentoring relationship with an experienced employee would be helpful. The mentor chosen by the manager should be willing to work with the mentee. Mentoring process not only helps the mentee overcome the root causes of Compassion fatigue and avoid Burnout but also helps mentor to develop leadership skills.

Employees with low level of hardiness are at a higher risk of experiencing Burnout and Compassion Fatigue [6]. The risk is higher for employees who have an external locus of control rather than an internal locus of control and employees with low self-esteem. One new approach to overcoming Burnout and enhancing employees’ well-being is psychological strength training.

Increasing job engagement decreases risk of Burnout. Job engagement is characterised by energy, involvement and efficacy.

Compassion Fatigue is an occupational hazard for health care providers working with traumatized patients, whereas Burnout is an organizational hazard for employees and managers working in difficult organizational environments. Symptoms experienced by providers experiencing Compassion Fatigue include anxiety, intrusive thoughts, and feelings similar to their traumatized patients, whereas symptoms associated with Burnout involve depersonalization of others, feelings of low personal accomplishment, and emotional exhaustion.

Interventions used by managers need to vary depending on whether they are dealing with Compassion Fatigue or Burnout. For Compassion Fatigue, managers can change the case mix so that the employee does not have to continually deal with horrific experiences. Furthermore, supervisors can arrange for training so that the provider can learn appropriate professional distance and for stress management classes so that the provider can develop healthy personal coping styles. If managers work towards a compassionate organizational culture, they can lessen problems associated with Compassion Fatigue [5].

At the individual level, a person may review personal and work environments. This may be done individually, with family, with a friend or colleague, or with a professional. Regardless of the method, this is a plan about that person and for that person; it is his or hers and not their employer’s or their doctor’s. A plan dictated from outside is likely to lead to dissatisfaction and a marker for Burnout—an organization that dictates personal beliefs is probably an organization that does not value their personnel’s thoughts and feelings. [13]

With Burnout, managers have to deal with burdensome organizational problems. For example, they will want to make sure that the patient volume is not excessive. Beyond role overload, the manager should attend to any dysfunctional cultural issues such as overuse of coercive power. Both Compassion Fatigue and Burnout require intervention by the managers, as both can result in low job satisfaction resulting in lack of organizational commitment. Eventually, both dysfunctions negatively influence retention and productivity [5].

Appendix 1 Characteristics of Compassion Fatigue assessment instruments [12]

References

1] Berzoff, J., & Kita, E. (2010). Compassion Fatigue and Countertransference: Two Different Concepts. Clinical Social Work Journal, 38(3), 341-349.

2] Figley, C.R. (2002). Treating Compassion Fatigue. New York, NY: Brunner-Routledge,

3] Figley, C. R. (1995). Compassion Fatigue: Coping with secondary traumatic stress disorder in those who treat the traumatized. New York: Brunner/Mazel.

4] David Conrada, Yvonne Kellar-Guenther (2006). Compassion Fatigue, Burnout, and compassion satisfaction among Colorado child protection workers. Denver, CO

5] Slatten, Lise Anne DM; David Carson, Kerry PhD; Carson, Paula Phillips PhD. (2011). Compassion Fatigue and Burnout: What Managers Should Know

6] Christina Maslach, Wilmar B. Schaufeli, and Michael P. Leiter, JOB BURNOUT, Annual Review of Psychology

7] Jeffrey E. Lewin , Jeffrey K. Sager, A process model of Burnout among salespeople: Some new thoughts

8] Joan Berzoff & Elizabeth Kita, Compassion Fatigue and Countertransference: Two Different Concepts

9] http://proqol.org/

10] Stamm BH. Measuring compassion satisfaction as well as fatigue: developmental history of the Compassion Fatigue and satisfaction test.

11] Bateman TS, Porah C. Transcendent behavior. In: Cameron KS, Dutton JE, Quinn RE, eds. Positive Organizational Scholarship. San Francisco, CA

12] Brian E. Bride, Melissa Radey, Charles R. Figley. Measuring Compassion Fatigue

13] Beth Hudnall Stamm, PhD, The Concise ProQOL Manual

14] Jodi M. Jacobson, Risk of Compassion Fatigue and Burnout and Potential for Compassion Satisfaction among Employee Assistance Professionals: Protecting the Workforce

Master Data Management (MDM) – Strategies, Architecture and Synchronisation Techniques

1.     Abstract

In this term paper the author first introduces the concepts of Master Data Management (MDM), Master Data, Data Domain, Customer Data Integration (CDI), Product Data Management (PDM) and One Master Data. Next a business case in support of MDM is presented. In the business case studies various industry scenarios that would require or benefit from a MDM initiative. The implementation of Master Data Management requires business initiative and an IT initiative. The paper will therefore explain various implementation architecture and management framework for MDM implementations that are published in journals and books.  The most important part of MDM is data synchronisation techniques. The data synchronisation is required to maintain the integrity of Master Data in a steady state scenario. The paper will explain data synchronisation techniques that could be used. In the conclusion the paper will provide a MDM implementation solution using a case study which will use the concepts explained in the paper. The problem statement in the case study is derived from the authors work experience. In order to complete the term paper multiple articles from various management and technology journal and books were reviewed. These articles are listed in the references table..

2.     Introduction

The increasing amount of data is creating challenges to companies' data management practices, causing data quality problems which are very common in today's companies. Additionally today's technology allows storing more data than a company can manage and different enterprise solutions often lead to further data confusion [8].

Disparate systems create potential for data error; data errors are these inconsistencies in data that cause data quality issues which could result in lost consumer cross selling opportunities, invoicing problems, or even failed products. It is estimated that incorrect data in retail industry lead to a loss of approximately $40 billion annually [9].

Master Data Management also called Reference Data Management, is an integrated business and IT function that focuses on the management and interlinking of reference or master data that is shared by different systems and used by different groups within an organization [4].

Gartner defines Master Data Management as below.

“Master Data Management is a technology enabled business discipline that helps organisation achieve a “single version of truth” in such important areas as customers, product, accounts etc.

In MDM, the business and IT organisation work together to ensure the uniformity, accuracy, semantic persistence, stewardship and accountability of enterprise’s official, shared master data. Organisation apply MDM to eliminate the costly debates on “whose data is right” which can lead to poor decision making and business performance” [6]

1.            Master Data

Master data provides a foundation and a connecting function for business Intelligence(BI) by the way in which it interacts and connects with transactional data from multiple business areas such as sales, service, order management, purchasing, manufacturing, billing, accounts receivable, and accounts payable(AP)  [1].

According to master data (also called reference data), is any information that is considered to play a key role in the core operation of a business, typically shared by multiple users and groups across an organization and stored on different systems [4].

Master Data is complementary to BI and can provide an excellent source of dimensional data [11].

2.            Data Domains

Master Data consists of information critical to a company’s operations. The data is usually categorised master data entity such as customer, products, vendors, partners, employees, inventory etc. These categories are called Data Domains [1 and 9]. The concepts of Master Data Management apply to each of the domains in general. Each of the domains has different implementations challenges.

3.            Master Data Management

In an article on Enterprise Data Management (EDM), Cohen [5] describes MDM as one of the main components of an effective enterprise data management (EDM) program. There are six components in enterprise data management (EDM). Figure 1 describes the components of enterprise data management (EDM) which when managed well together help companies to take advantage of the latest technological innovations and more effectively manage their information.

Figure 1: Enterprise Data Management [5]

MDM is the process of helping a company to standardize the definition and attributes of all of its critical data elements (customer, vendor, product, etc.) to create a common point of reference enterprise wide. MDM can facilitate the sharing of data among all a company's disparate business functions, departments and even divisions - not to mention across all information systems, platforms and applications. Without an effective enterprise wide MDM implementation the other components for EDM will not be as effective. The business cases defined in the next section provide some examples to support this statement.

A MDM solution therefore creates a single view of data in any targeted data domain. This is also referred to as the golden record. For example, if the master data management is for Customer Data then any record will refers to the “single truth” or “single customer view” which is an authoritative customer record that has usually been generated by extracting, cleansing the data from multiple channels of enterprise. This process is called Customer Data Integration (CDI). CDI is the subset of MDM and encompasses every aspect of customer touch points in the organisation. CDI is the most widely used implementation of MDM [1] while [8] mention that customer master data is a common starting point for an organization’s MDM.. An effective CDI means that any customer attribute is uniquely identifiable and there exists no multiple versions of customer attribute in any of the company’s enterprise IT systems.

Product data management (PDM) systems are used to manage all product-related data and also product master data. Product master data is far more complex than customer master data [8].

3.     Building a Business Case for MDM

In this section four Business Case scenario are provided. These business cases present a problem statement the solution to which is implementation of MDM.

1.            Business Case #1: Merger of two companies.

In U.S.A, a major telecom service Provider Company A bought telecom service provider company B. The two major telecom service providers merged in 2005. Each of the company individually provided mobile phone services to approximately 20 million subscribers each – one used CDMA technology and other used GSM technology.

The challenges for the newly merged companies where multiple, the chief among them being to consolidate their customer service department such that the outward projection to the customer was one brand. This was in addition to normal integration related problems like, HR, finance and regulatory etc.

The challenges resulting out of this merger that MDM could address are:

a.    How do we accomplish consolidation of all customer bases such that there is single source of truth on all the customer attributes? This is CDI part of MDM.

b.    Because the two companies had different price plan, devices, and products – they need to be consolidated into one product reference. This is part of PDM or MDM.

2.            Business Case #2: Replacement of an ERP application

In the year 2008, a major crown corporation, managing social housing portfolio replaced its legacy ERP system with a Commercial off the shelf (COTS) implementation. This had a unintended impact on the downstream applications when the migration to new ERP was completed. The downstream applications that used the original ERP’s unique identifier (UiD) to cross reference were now out of sync with the corporate property master data in the corporate ERP because the new application did not use the same Unique Identifier (UiD). Additionally the new ERP did not integrate with the downstream application. That is whenever a attribute is modified in the new ERP, that modification is not communicated to the downstream applications. The new ERP being COTS product, cannot be modified without incurring huge cost.

Thus the challenge here is to ensure to synchronise the data in the down steam applications whenever data is the main ERP is modified. This is a classic case for MDM implementation.

3.            Business Case #3: New application introduced

In year 2012 a new application was introduced in a organisation that manages building repairs. This application was a web based application which was used to order jobs. The application used the job costing information from the ERP system and communicated back to the ERP system when the order was completed. This required that the job costing information that is actually maintained in the corporate ERP is correctly communicated to the new web based application. This is problem can be tackled using MDM.

4.            Business Case #4: The Homeless Shelter Network

There are many homeless shelters in a big city. Big urban center could have upwards of 100s of such shelters. All of them are mostly funded either directly by provincial government or a provincial government funding agency and/or individual city councils.  However each one of shelter house is a mostly independent not-for-profit organisation or a charity run entity. Each one of the shelters would have their own distinct business processes, and data collection methods with varying degree of sophistication.

Each shelter would be able to provide the number of clients it served in a particular time period. However if the funding agency or the governments wants to know how many unique homeless individual were served by the all the shelters funded by it, there is no way of knowing it unless every shelter uniquely and uniformly identify the homeless individual it serves i.e. if each one of them run same software application to manage their shelter or at least use same identity proof. However, in practice not everyone uses same software or same method of indentifying the homeless client.

MDM can be used in scenario like this to overcome data duplication problem.

4.     MDM Approaches and Architecture

Before proceeding with MDM architecture it is important to review the types of data and tables in a modern database application. Enterprise systems deal with and generate different types of data. These data are classified into data domains like, customer, products, accounts, vendors etc. Additionally the data can be classified as transactional data and non-transactional data. Transaction data are generally stored in transaction tables. Examples of transaction data include call records of a subscriber (CDRs), or line items in a purchase order or a bank transaction in ATM machine. Normally transaction data tables have large number of records. The data in transaction tables is dynamic and the tables are frequently updated with new rows. The data in the transaction table are generally critical for regulatory reporting. However before the advent of virtual server and cheap storage the transaction data used to be archived in tape drives or sometimes simply deleted after certain time period. The transaction tables provide the point in time information and therefore are at the heart of any Business Intelligence initiative.

Non-transaction data is also called reference data are stored in tables called reference table. The reference table contain such information as customer unique identifier details (name, address, account number etc), vendor details (vendor name, vendor number, vendor address etc), and company employees, company address etc. This information is critical to the organisation. The data in reference tables are used for referential integrity in transaction table. Reference tables are normally never archived or deleted.

Another way of categorising data is operational and non-operational. Operational data is the real-time collection of data in support of a company’s need in their daily activities. Nonoperational data is normally captured in a data warehouse on a less frequent basis and used of business intelligence (BI) [1]. 

Accordingly this particular classification of data is used to divide MDM into two sections Operational MDM and Analytical MDM [1, 3, 4, 8 and 13]. A third category is a combination of operational and analytical MDM and is called enterprise MDM [1, 4]. Operational MDM integrate operational applications such as enterprise respirce planning (ERP), customer relationship management (CRM), and supply-chain management (SCM) in upstream data flow [8]. Analytic MDM is seen in practices which reminds data warehousing (DW) such as customer data integration and financial performance management. The enterprise MDM system is used for maintain and publishing all the organisation master data.

The architecture of enterprise MDM is shown in figure 2. The main components of MDM system are MDM applications, a master data store, a metadata store and a set of master data integration services [14]. This is shown in figure 3.

Figure 2: Enterprise MDM Architecture [3]

Figure 3: MDM components [14]

Enterprise MDM is the most intrusive implementation, while analytical MDM is least intrusive reason being enterprise MDM encompasses both operational and non-operation data. As a result the gain is highest in enterprise MDM implementation.  Additionally while implementing MDM, it makes sense to break down the MDM initiative into phases and target just a few applications at a time to avoid disruption.

5.     MDM Framework

It is important to understand how master data is created, used, maintained and integrated with multiple applications. The MDM frameworks mentioned in this section describes various ways to store, process and synchronise master data. The main components of MDM are:

1.            Composite applications

The applications are the IT applications which will collect, use and maintain master data. An example of this application could be customer service software used in a call-center, an ERP system, a down stream application, a front end web application etc. Each composite application will have its own database or two applications could share a database.

2.            Business Process Orchestration

This is the most critical part of MDM initiative. Business Process Orchestration is a set of rules, guidelines, workflow or regulations created by the business owners and leaders such that the data being entered in the applications are consistent, and accurate. An example of this role could be as below. To eliminate discrepancies in name (Michael vs. Mike, Robert vs. Robert) of the same person, a homeless shelter clerk would verify the name of the client with his MCP card or any government issued valid ID. This is a an example of a simple business process and correct implementation of this is critical for success of MDM. A complex example of business process could be a set of Microsoft SharePoint workflow steps that would be required by a clerk to be completed before an new vendor or a supplier is added to the SCP application.

3.            Enterprise Service Bus

This is the technology component of the MDM. This could be a complex middleware products like Software AG’s Webmethods, IBM websphere or Tuxedo. Or it could be a simple solution as a network share with xml reader products. 

4.            MDM data synchronisation services

These are services that will synchronise master data between the applications or between application and the master data store. These could be triggered based service or could be message based service.

5.1.          Single Central Repository Architecture (SCRA)

The Single Central Repository Architecture is shown in figure 4. In this architecture, the master data is stored in a single central repository which will be updated by the MDM services, and the applications. The applications will not hold a copy of any master data. Applications refer to master data from the central repository.  There are no local versions of Master Data anywhere.

Figure 4: Single Central Repository Architecture (SCRA) [1]

Advantages of SCRA are that it guarantees data consistency [1], and some of the applications may become redundant one SCRA repository is up and running therefore enabling to retire legacy applications.

However the disadvantage is the massive upfront cost. The upfront implementation and migration to SCRA is costly because it requires massive data conversion effort and migration of data from multiple disparate systems. This can also be disruptive to business.

The prevalence of COTS products could possibly make implementation of SCRA difficult if not impossible. However once SCRA is implemented, the cost of maintenance would be minimal [1].

5.2.          Central Hub and Spoke Architecture (CHSA)

The central Hub and Spoke Architecture is a variation of SCRA [1]. It contains a central repository (central hub) while also providing ability to the individual application to maintain an extension of the data. Therefore some application would access master data from the central hub and not keep a local copy, others might only use the central hub as a reference [15].

Figure 5: Central Hub and Spoke Architecture (CHSA) [1]

The biggest advantage of CHSA is its flexibility to relatively decouple by supporting spoke systems. This flexibility is really important when we have COTS applications which cannot be coupled with the central hub [1].  The flaw of CHSA hub-and-spoke is that it doesn’t address issues of timeliness and latency [15]. Additionally the data conversion effort is still required.

5.3.          Virtual Integration (VI)

This pattern uses data virtualization to provide one or more on-demand integrated views of master data entities such as customer, product, asset, employee etc. even though the master data is fractured across multiple underlying systems. Applications, processes, portals, reporting tools and data integration workflows needing master data can acquire it on-demand via a web service interface or via a query interface such as SQL [16].

Figure 6: The Virtual Master Data Management pattern

5.3.1.   Data Service Federation (DSF)

Data Service Federation is a common Virtual Integration architecture. The virtual integration pattern aggregates data from multiple sources into a single view by maintaining metadata definition for all the sources [1].

Figure 6: Data Service Federation (DSF) [1]

The advantages of DSF is less costly than the SCRA and CHSA because the data does not have to be physically copied from one location to another nor any additional storage space is required. However the biggest disadvantage is that the data improvements are not propagated back to the source application [1].

6.       Data Synchronisation Techniques

The data synchronisation is the critical step to maintain the consistency of master data. Synchronization step is mostly required regardless of what type of MDM framework is implemented. In this paper three different types of data synchronisation techniques are described. However it must be noted that there can multiple other ways of synchronisation data and that database or data synchronisation is not unique to MDM.

6.1.          Trigger based 

Trigger based approach is described in the figure below. In this approach the trigger for data synchronisation is an update or insert event on the source database table record.

In this step when a candidate record is modified in source database, a service polls the event and propagates the modified data to other database tables.

Figure 8: Trigger based [13]

This kind of synchronisation ensures that all the data in multiple tables are always synchronised. However while it is easy to implement in a small scale setting, the process is extremely computation intensive in a large scale. It is also dependent on high availability of networks.

6.2.          Message-based Data Synchronisation and Integration Framework (MDSIF)

The message based data synchronisation and Integration Framework is detailed in the article [13]. In this process message oriented middleware (MOM) is used to propagate the data from multiple data

Figure 7: Message-based Data Synchronisation and Integration Framework (MDSIF)­

6.3.          Confidence Tables Approach

Figure 9: Confidence Tables Approach [13]

7.       Case Study

In this section we will revisit a business case mentioned in the previous section and apply MDM principles to achieve one Master Data. We will select case study on multiple homeless shelters. The business case is restarted below for easy reference.

8.       Conclusion

It can be concluded based on the findings in this paper that MDM cannot be classified as only an IT problem but it is a managerial challenge which requires structural changes to managing business processes, and managerial decision making.

9.       References

1.    Master Data Management in Practice: Achieving True Customer MDM. Cervo, Dalton, Allen, Mark.  ISBNs: 9780470910559. 9781118085660. [Wiley Corporate F&A].Hoboken, N.J.: Wiley. 2011

2.    Management of the master data lifecycle: a framework for analysis. Ofner, Straub, Otto and Oesterle.

3.    Practical Approach for Master Data Management, Chandra Sekhar Bhagi, World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 1, No. 5, 213-216, 2011

4.    Enterprise Master Data Management Trends and Solutions. APOSTOL, Constantin-Gelu, http://revistaie.ase.ro, Vol. XI, no. 3/2007

5.    Understanding the Scope of Data Management: The Components of a Robust Enterprise Program. Cohen, Rich.

 http://www.information-management.com

6.    Gartner Inc,.”Hyper Cycle for Master Data Management, 2010” Andrew White and John Radcliff.

https://www.gartner.com/doc/1464920/hype-cycle-master-data-management

7.    The transverse information system: new solutions for IS and business performance, Rivard, François. 1-84821-108-2, 978-1-84821-108-7  Date: 2009 Page: 49 – 84

8.    Managing one master data – challenges and preconditions, Risto Silvola, Olli Jaaskelainen, Hanna Kropsu-Vehkapera, Harri Haapasalo. Industrial Management & Data Systems, ISSN: 0263-5577, Volume 111 issue 1

9.    Methodologies for data quality assessment and improvement, Batini, C., Cappiello, C., Francalanci, C., Maurino, A. , ACM Computing Surveys, Vol. 41 No.3

10. How Kijiji's data threw off Ottawa’s math on skills shortages. http://www.theglobeandmail.com/news/politics/how-kijijis-wonky-data-threw-off-ottawas-math-on-skills-shortages/article17675622/

11. Introduction to Master Data Management. Mark Rittman,

www.rittmanmead.com

12. National Homelessness Information System http://hifis.hrsdc.gc.ca/initiative/index-eng.shtml

13. Message-Based Approach to Master Data Synchronization among Autonomous Information Systems, Dongjin Yu and Hangzhou Dianzi. International Journal of Enterprise Information Systems, 6(3), 33-47, July-September 2010.

14. Using Master Data in Business Intelligence Colin White, BI Research available at www.fm.sap.com and www.broadstreetdata.com

15. The Flaw of the Hub-and-Spoke Architecture, Evan Levy, Information Management Jounal available at

http://www.information-management.com/blogs/business_intelligence_bi_hub_and_spoke_architecture_analytics10015083-1.html

Data Federation- Master Data Patterns - The Virtual MDM Pattern, Mike Ferguson, available at  http://www.b-eye-network.co.uk/blogs/ferguson/archives/2009/12/data_federation-_master_data_p.php

Crude Oil Price. Factors impacting Crude Oil Prices. A fundamental analysis.

Oil Price Forecast - Fundamental Analysis.

Balance of Supply and Demand:

In the most generic economic explanation, crude oil being a globally traded commodity, is subjected to the basic price mechanism of supply and demand. Two major activity centres: downstream and upstream largely dictates the fluctuation in both supply and in demand. Demand for crude oil has a tendency to become more distributive as oil consuming countries increasingly trying to establish their own refining capacity in order to minimize on foreign import dependencies as well as to capture the economic value added during the refining process. A key trend in recent years, with new technology developments such as hydraulic fracturing which allows oil trapped in tight rocks to be exacted economically, the United States has become more are more self-dependent and hence, reducing pressures on international supply of crude oil.

On the supply side, there are two main players: OPEC countries and non-OPEC countries. Having rather contrasting policies with regards to pricing crude oil, these two groups can at times, cancelling out each other’s effort to maintain selling price. Canada for example, while gradually becoming a major exporter of crude oil, for years did not align their pricing policies with OPEC’s due to their strong belief on an open global market economy.

A key concept used in the industry is price-elasticity of demand for crude oil. This measures the sensitivity or responsiveness of oil demand to changes in price. According to the US Federal Energy Office’s estimation, the long run price elasticity of demand for oil consuming countries ranges from -0.2 to -0.6 with the US remains around -0.5. One could expect the short run price elasticity to remains in an even lower range considering the time lag it takes for adjustments. As such, it is visibly clear that the demand for crude oil remains very steady despite any potential price increases.

Gross Domestic Product Growth

Crude oil price is in correlation with Real GDP growth since growth in general requires crude oil to fuel economic activities. The world economy has been expanding at about 5% annually since 2004 until 2008 and averaged out at nearly 3.5% afterwards. In essence, the emergence of BRICS countries is largely responsible for such robust growth observed and while the world GDP growth has not necessarily been consistently high, growth nevertheless, places crude oil price into upward pressure. At this point, the emerging economies are still consuming crude oil at a relatively low rate on per capita basis so it is expected that the upward pressure on oil price will continue as their GDPs keep on catching up with richer countries.

Apart from the price shock experienced due to the global financial crisis in 2009, patterns of crude oil price movement and world real GDP growth have been markedly similar. Hamilton (2009) demonstrated that strong growth in world income was the main cause of oil price surge in 2007-08 and equally the dramatic collapse of oil price in 2009 was related to the financial crisis in the same year.

            REAL WORLD GDP GROWTH AND BRENT CRUDE OIL PRICE

Crude Oil Production and Inventories

Despite a period of stagnation from 2004 to 2008, global production has been increasing steadily ever since. Large contributors to the overall production traditionally are OPEC and Russia. In recent years, Canada and the U.S. are sharply improving their production and becoming more and more influential in the market. However, the production level has been strictly close to consumption level on daily basis without taking into account the demand to accumulate inventories. Consequently, supply and demand will not balance once the net production capacity is compared with the real demand in consumption and inventories accumulation. This imbalance of oil supply and demand and more importantly, the persistent expectation that this imbalance will hold for a foreseeable future have contributed to the general upwards trend of crude oil price.

Crude oil inventory level is carefully watched by traders as it has a very strong influence on Market Expectation. Low crude oil inventories may cause uncertainty about the ability of the market to meet demand, which supports higher prices. Conversely, high crude oil inventory levels support lower oil prices. In recent years, on a “days of supply” basis, crude oil inventories have been on the declining trend due to continuous growth in demand. Demand for crude oil to accumulate inventories is significant and putting additional pressure on prices. As an example, in an effort to quarantine the threat of supply disruptions, the US in particular has been steadily increasing their Strategic Petroleum Reserve, the largest government-controlled inventory in the world. Despite this growing accumulation, the U.S. inventories alone do not reverse the global declining trend and in the long term prospects, traders expect demand for inventories will further putting upwards pressure on prices.

Data source: EIA – Independent Statistics & Analysis – U.S. Energy Information Administration

Production Spare Capacity

It terms of spare capacity, much of this capacity is possessed by OPEC with Saudi Arabia holding the majority. Therefore, the industry has long viewed spare capacity outside Saudi Arabia as an important indicator of supply tightness. It can be seen from the chart below that spare capacity is staggeringly low compare to the steady increase of the demand curve. Without a significant spare capacity, market participants cannot mitigate price shocks and supply disruptions effectively via pure supply and demand balancing. The total combination of these factors should in general put an upward pressure on crude oil price.

Analysts in general consider Production Spare Capacity a contributor to market expectation and hence, contribute to the price setting mechanism via market expectation. A report of high spare capacity within OPEC generally give market a comfort of knowing that supply is in a plentiful state whereas, a negative report of spare capacity could fuel fears of supply disruptions and price shocks. Noting a persistent global demand growth, spare capacity is still rather stagnating, it is common sense to consider that this trend is also putting crude oil price in an upwards pressure. 

           

Market Expectation:

Apart from the level of supply and demand for the physical crude oil, market expectation has frequently been cited as an important element contributing to price movement. In October 2014, Goldman Sachs forecasts that despite OPEC’s latest price reducing efforts in order to maintain market share, oil price will still be further reduced due to the U.S. increased capacity via non-traditional oil exploration route. In essence, Goldman Sachs believes that the U.S. shale oil deposit and its latest extraction technology have enabled it to become the new first mover swing producer instead of OPEC, and hence, oil price could continue to decline. Upon the release of this forecast, crude oil price fell further, hitting a 28-month low of $79.44 per barrel mainly due to market expectation and sentiments. Market expectation while intangible, can create volatility in the market as demonstrated above. Many scholars believe that market expectation is strongly reflected in the futures contracts market by corresponding activities of trading participants. Hence, they consider that by analyzing movements in the futures contract market, traders can visualize the current and future market expectation regarding the price movement patterns.

Crude Oil Futures Market

A futures contract is an agreement to buy and sell a specified amount of crude oil at an agreed upon future date, at an agreed upon price and location. Unless offset, the parties are obligated to complete the agreed transaction at the expiration date. Market expectation therefore is reflected in the futures market where buyers and sellers fix future prices corresponding to the delivery times. Apart from providing much needed liquidity, large international futures markets also serve as price-signalling centres to worldwide traders as a whole. Due to their standardized format, futures may not a very efficient instrument to allocate the trading of the physical commodity. While less than 3% of futures contracts result in the delivery of crude oil, futures still remain a good indicative benchmark of market expectation.

On the other hand, whether price expectations reflected in futures contracts really contribute to the price setting mechanism is still a highly controversial issue. Business analysts in general are skeptical against correlating these two variables. Participants in the futures market in general are hedgers (commercial) and speculators (non-commercial) who we can distinguish by their exposures to the physical crude oil traded in the futures contracts. Some academic scholars also share similar views with business analysts. Bahattin, Jeffery, Buyuksahin and Harris (2009), using linear Granger tests, fail to find causality from traders’ positions to prices. On a behavioural aspect, most traders alter their positions following a price change which suggests that their activities in the market do not systematically affect the price but rather, traders are responding to new information reflected in price changes due to the market efficiency.

Geo-political, economics and natural disasters

Historically, crude oil price has a strong tendency to react to geo-political and economic events including weather related developments. In the current landscape, there is a high degree of uncertainty of future oil supply adequacy and market participants are continuously trying to assess the size and duration of possible disruptions. As discussed above, demand tends to be very “inelastic” to price change in the short term. At the same time, while supply is concentrated (within some countries with healthy crude oil deposits), demand is very distributive as other countries need crude oil and its products to drive their economic activities, any projection of future supply disruptions should result in a large price hike in order to balance the physical demand and supply.

Weather can also play a significant role in creating high price volatility. Hurricanes, earthquakes have been shutting down major economic activities on both sides of crude oil supply and demand. Severe cold weather is also an upwards price driver as suppliers struggle to meet demand for crude oil.  In summer, gasoline use increases during the travel season, increasing demand for oil, leading to an increase in prices.

OPEC Intervention

OPEC is an international organization consisting some of the major oil exporting countries in the world. Currently OPEC has 14 members with Saudi Arabia being the largest producer. On average OPEC contributes some 40% of the total global crude oil production and its export represents about 60% of the total crude oil traded globally. Having such behemoth supply power, OPEC frequently use this position to place upwards pressure on crude oil price by continuously adjusting the production capacity and inventory. Apart from the global financial crisis in 2009, the world has hardly seen any other significant discrepancy between OPEC output and crude oil price.

At the time of writing, crude oil price is continuing on a declining trend, much at the irritation of OPEC. For almost 40 years, OPEC was a major force that can influence crude oil price at the level that could benefits member countries. In recent years, with Canada and especially the US, increasing on production capacity, OPEC can no longer assert its authority as before. Canada is one of a few develop countries that maintain a net export of petroleum products, whereas huge deposit of shale oil means that the U.S. is gradually approaching the point where they can become a net exporter. The political landscape of crude oil is further reformed as the U.S. and the European Community implementing heavy sanction on Russia which put the oil extraction industry in Russia under heavy obstacles to find buyers in the U.S and in Europe. Hence, sanctions on Russia further increase excess production capacity in non OPEC countries. Experts are predicting that OPEC may have to take action in order to reverse this declining trend. Some OPEC nations are producing crude oil at breakeven point well below the current trading price. However, the large majority of OPEC members are producing crude oil at breakeven points close to well above the current trading price meaning that they cannot sustain this trend for long.  According to Citi group, Venezuela is producing crude oil at breakeven of $161 and Libya at $185. Kuwait and Qatar respectively require $44 and $71 to break even. These staggering differences again highlight the potential volatility of crude oil production cost from within OPEC and in the short term, it is prudent to consider that OPEC’s influence on price is waning.

Crude Oil Price and OPEC Petroleum Production

Interest rate

Interest rate traditionally is viewed by many scholars as having a negative correlation with crude oil price. In many cases, a decline in interest rate signals a new boost in terms of economic activities and hence, increases the demand for crude oil to fuel these activities. The effect of busy economic activities is further consolidated by the higher incentive to accumulate inventories and hence, increases demand for inventories. This influential theory was published by Frankel in 2008 which shows that the declining interest rate is a decrease in opportunity cost of holding on crude oil inventories, and hence, bolsters the demand for crude oil used to accumulate inventories.

Exchange rate        

Crude oil trading is denominated in US dollar and hence, any fluctuation in the dollar exchange value theoretically can have an effect on the dollar selling price of crude oil. Oil producing countries place a great interest in monitoring the price movement of the dollar value. The dollar value directly impacts their oil revenue, and hence, international purchasing power. Oil consuming countries which represent the majority of the world demand are, on the other hand, sensitive to any fluctuation in the US dollar exchange rate due to the change in net expenses used to purchase oil for consumption and inventories. Fillip Novotny (2012) proved that the global demand for crude oil is negatively correlated with the US dollar exchange rate. This phenomenon is explained as oil consuming countries would import more in the event of a depreciating US dollar exchange and would refrain from importing in the event that the US dollar exchange appreciates. As the general demand changes, so too does crude oil price in a negative correlation and hence, it was concluded that crude oil price and US dollar exchange hold an inverse relationship and numerically a 1% depreciation of US dollar exchange rate would induce an increase of 2.1% in crude oil price.

Test of Correlation and Causality

Further to theories described above, a multiple linear regression is conducted in order to examine the true extent of correlation and causality between oil prices and several other fundamental factors. The data are obtained on a 73 months basis available at the U.S Energy Information Agency which collect various date on crude oil consumptions as well as macroeconomics data.

-       WTI Spot: refers to spot price of WTI traded in the U.S (New York Mercantile Exchange). WTI Spot is used as a dependent variable in order to test causality of other fundamentals.

-       Real World GDP Growth: the annual real growth rate on a global scale.

-       Total World Petroleum Consumption: represents total demand. This variable does not take into account demand for accumulating inventories. Inventories fluctuation is reflected by another variable which is Net Inventories Withdrawals.

-       Total World Petroleum Production: represents the total supply in the analysis.

-       Real U.S Dollar Exchange Rate: reflects the real value of U.S. dollar in the exchange market. The time series data shows fluctuations which corresponds to fluctuation in the value of the U.S dollar.

-       Net Inventories Withdrawals: represents withdrawals from global inventories including the U.S Strategic Petroleum Reserve.

-       U.S Prime Lending Rate: is the bank prime lending rate in the U.S. This rate in the U.S was chosen to reflect the WTI Spot price in analysis is the spot price traded in the U.S.

-       OPEC Production: OPEC’s means to control the price by varying the production, and hence, vary the supply side of crude oil. Hence, OPEC production can be considered as OPEC’s authority on influencing the price.

The non-hierarchical multiple regression (stepwise) was used to check the statistically significant contribution to the variability in crude oil price. Durbin-Watson residual is also chosen as the data in analysis has time-series characteristics. The regression shows that based on available data, despite all independent variables have strong correlations with WTI Spot prices, only Real World GDP Growth is statistically significant in contributing to the variability of WTI Spot prices. Since the data set consists of only 73 month data, it is suspected that power of this regression is not enough to reflect a strong causality as well as correlation as in reality.

Descriptive Statistics
	Mean	Std. Deviation	N
WTI Spot	92.84	8.653	73
Real World GDP Growth	3.340	.8682	73
Total World Petroleum Consumption	89.8892	2.25444	73
Total World Petroleum Production	89.8711	2.11484	73
Real U.S. Dollar Exchange Rate	104.3527	4.59357	73
Net Inventory Withdrawals Total World	.0185	1.01412	73
U.S. Prime Lending Rate	3.2897	.14355	73
OPEC Production	29.8796	.64372	73

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	Real World GDP Growth	.	Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).
a. Dependent Variable: WTI Spot

Model Summaryb
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.587a	.344	.335	7.056	.463
a. Predictors: (Constant), Real World GDP Growth
b. Dependent Variable: WTI Spot

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1856.210	1	1856.210	37.278	.000b
	Residual	3535.359	71	49.794
	Total	5391.568	72
a. Dependent Variable: WTI Spot
b. Predictors: (Constant), Real World GDP Growth

Collinearity Diagnosticsa
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions
				(Constant)	Real World GDP Growth
1	1	1.968	1.000	.02	.02
	2	.032	7.873	.98	.98
a. Dependent Variable: WTI Spot

Residuals Statisticsa
	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	81.38	99.51	92.84	5.077	73
Residual	-20.359	18.794	.000	7.007	73
Std. Predicted Value	-2.258	1.313	.000	1.000	73
Std. Residual	-2.885	2.663	.000	.993	73
a. Dependent Variable: WTI Spot