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Business Intelligence Solutions for Dafydd Hardy

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Business Intelligence Solutions for Dafydd Hardy

Executive Summary

Business intelligence solutions have vanquished most of the old standalone solutions because they run in modules that are linked to ease operational burdens traditionally caused by information redundancy. The solutions are flexible and allow installation of some modules to run without others. It allows share-ability of information which speeds business transactions. Other benefits include reliability and consistency of transferring business data across the firm, a streamlined conduction of processes in and out of the firm and an inbuilt operational level of reporting that enables timely decision-making. This paper proposes business intelligence for Dafydd Hardy where the proposal entails a description of the Business Case, and theoretical Review of business intelligence solution as a dimensional Approach. Two business intelligence solutions have been compared that is Pentaho community open source business intelligence and MAIA Intelligence solution. MAIA Intelligence solution has features that allow data to be accessed from multiple sources, and also updates the database automatically when an inquiry is made by an authorized person. It reports that can be imported into Excel, HTML, XML and PDF in and are Multi-formatted. Pentaho community open source business intelligence allows third parties to modify but submission of the modification is necessary. It offer on-demand processing and storage to remotely hosted and managed data sources and can now use the GPL applications.

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Discussions on the sources of all resources, management and implementation timescale, strategy for implementation and project monitoring and evaluation are also presented. While business intelligence will not be superior to changes that Dafydd Hardy make in its ways of doing business, the value embedded in the software, if appropriately used, will be hard to pin down. This means that more than being the mainstay of businesses, the firm will use business intelligence to facilitate adoption of the best services and industry’s specific practices. Dafydd Hardy will need to tailor their services to manageable levels to yield the desired results.

Introduction

What matters in really estate agency is the utilization of data to maximise profits. Profitability will grantee business sustainability since it’s the only source of competitive advantage among the industry players, and Dafydd Hardy (DH) is not exceptional. Currently the company is utilising customer Jupix online system which does not provide enough information for decision making, thus organisation requires a more elaborate system which produces information that can be relied upon to increase business profitability. This system proposed is business intelligence solution which is capable of extracting transforming and loading data from Jupix system to a data ware house.

DH has many clients who supply different kinds of properties under various categories of real estate. Since there are too many clients to supply the various properties, DH wants to change their system to help in proper record keeping, maintaining a record of all transactions.

The business intelligence solution as a dimensional Approach- A Theoretical Review

For it to be effective, business intelligence must be aligned with business structure and strategy to increase the capability of responding to internal and external environmental changes which may affect business operations. This needs proper and systematic approach to make the strategy sustainable. This implies that in business intelligence design the data extraction is more important to ensure that no data is lost. The process of change must be captured in the design and all components should be considered as a continuum of the change process (Bertsekas and Gallager, 1992). The process will start with the problem identification followed by development and evaluation of alternative solutions after which the best approach is selected and designed to meet the required specifications. The system is then implemented and evaluated for its operability.

Data warehousing

This is the collection of data from different sources to a data base which can be easily used. Therefore, Dafydd Hardy must have the ability to collect data to a coordinated and periodical copying from Jupix online system into a business intelligence that uses open source for analytical and processing (Montani and Jain, 2010). The main aim of data ware housing is to create quality information which can be collected in a timely manner from a variety of sources. The system should be able to provide consistent and easily available information to authorized users. The designed data warehouse should support a complete data communication, command and control capability and ability to assemble and analyse data by using quality and analytical standards (Galliers and Leidner, 2002).

A Data warehouse must have the ability to give accurate and true information for analysis and decision making. This means that each data element from the house should be traceable to its source which in turn should be creatable. This data should have a unique way of storage in the warehouse so that it can be reproduced and in case of any changes it should be reported with explanation for changes. The ware house should have a mechanism of routing and scheduling all incoming data based on specific rules meeting organizational objectives (Haag, 2006).

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Extract, transform and load

Extract transform and loading is the process of using data stored in data warehouse or any other source for decision making. This process will assist Dafydd Hardy to dig out the desired information from various sources and transform it into usable form before transporting it to where it is required. Loading as well is the process of storing the information that has been extracted, transformed and transported. Transforming of data may include clustering of data to provide appropriate information which can be used for decision making (Newman, 2010).

Open source business intelligence solution

To design this system, a dimensional approach is used. Crucial to this approach are planning, analysing designing, development testing, implementation and maintenance. Open source business intelligence differs in other comparable systems in that they are developed to extract transform and load data to a data warehouse and it is able to reproduce all reports that are useful for decision making (Kapoor, 2009). Additionally, open source business intelligence solution has more of an outward focus and higher projector at risks than Jupix online systems. It is also innovative and therefore not easily duplicated or affected by authorized users. Its application in Dafydd Hardy is of much interest in ensuring that nothing is lost. This is because there are no doubts about its impracticality and manageability thus, progressively outmanoeuvred. The implementation of business intelligence system will help the assimilation, extraction, transformation and loading of data into a data ware house. In this way the system is able to provide cross- functional information for report production ((Kapoor, 2009). Data from various sources will be stored safely in a single data base while supporting various systems. The information stored will cover customers and their details, products for sale or hire, clients and other functions.

Here Open source tools will be used to develop proprietary applications – a programming model for processing and generating large data sets. The application is a very efficient, distributed and independent processing is available in this application. Applications that do large dataset processing in a distributed environment often are developed using open source software (Kapoor, 2009).

Dimensional approach

In this case of Dafydd Hardy, dimensional data approach will be used as opposed to normalisation approach. To begin with normalisation approach is where data is organised in an efficient with an aim of eliminating redundancy. Various forms called normal forms are prepared where data is organised. In the case of dimensional approach which is of interest, data is organised in a manner that we have facts tables. The dimensional approach ensures that there is optimisation in use of data as well as querying. It ensures that users of the system get proper reports. It is a star like where each star represents a specific element of the data. It may classify Dafydd Hardy customer, clients, properties and staff. Each fact table will contain numerical figure of an element this will improve query performance. Dimensional approach has a set of guidelines to extract, transform and load data into data warehouse.

Changing attributes

Changing attributes are identified and classified into mini dimensional table and are joined to the main table. The mini dimensional table will only contain change in facts thus it will provide easier accessibility of the information. This aids the communication and instruction between information users. This system will not cause much more confusion due to strained quantity of exchangeable text and inconsistency in the period of delivery. The following table has provided mini table for DH facts above.

Splitting changing attributes into a new mini dimensions

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Comparison of two open source business intelligence solutions

The two open source business intelligence system compared are MAIA Intelligence solution and Pentaho community open source business intelligence. MAIA Intelligence solution allows usage by many users with minimum cost. It allows the users access multiple data sources with easy. It allows remote monitoring with a lot of flexibility, reliability and reduction in costs for the user. It uses various performance indicators in producing reports that are Multi Formatted in a flexible manner. It allows all levels of users to ‘drill down, sort, filter, group, alert, schedule reports & then quickly monitor and navigate relevant information’. It also allows the information to be exported to Excel, HTML, XML and PDF(MAIA Intelligence Pvt. Ltd, 2008)

Unique reports can be written and may be developed with existing software applications on the server of the user. The software application used in these cases mostly would be a GNU Public Licence application and again there would be no violation of the reciprocal clause as in this case, the GPL compliant application may have been downloaded by the service provider and since he is not developing the application but offering it has a tool to the application provider there is no violation of the reciprocal clause (MAIA Intelligence Pvt. Ltd, 2008).

MAIA business intelligence manages all data from all sources (web servers, legacy and operational systems, diverse data stores or applications. In even the most complicated IT environments) thus enabling users to ‘capture, transform and flow data throughout the enterprise’. It will help businesses transform their server infrastructures into dynamic environments. In such a dynamic environment the servers expand or reduce their capacities based on their requirements. In MAIA, a person inherits the same rights to the source code of the software. The functioning of the application does not depend on the client’s system’s architecture and it works mainly on server components that are usually outside the user’s network(MAIA Intelligence Pvt. Ltd, 2008).

Pentaho community open source business intelligence offer on-demand processing and storage to remotely hosted and managed data sources. Third party users individuals who use the Pentaho community open source business intelligence can now use the GPL’ed applications without agreeing to the licensing terms of GPL and still use the GPL’ed application for creating proprietary data base applications and modules.

Pentaho community open source business intelligence requires people who have made modifications to the source to release those modifications because it prevents the sale of the modified open source software if not released (Pentaho Corporation, 2011).

Configuration

Creation of database

user ~ $ sudo su postgres

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postgres ~ $ psql

postgres=# CREATE USER DH WITH PASSWORD ‘secret’;

postgres=# CREATE DATABASE DH WITH OWNER = DH ENCODING ‘UTF8’;

postgres=# q

postgres ~ $ exit

user ~ $

Creation of tables

user ~ $ cd /var/lib/tomcat6/webapps/DH/WEB-INF/classes/setup/sql/create

user ~ $ psql -d DH -U DH -W -f create-db-postgres.sql

user ~ $ cd /var/lib/tomcat6/webapps/DH/WEB-INF/classes/setup/sql/data

user ~ $ psql -d DH -U DH -W -f data-db-postgres.sql

database configuration

user ~ $ cd /var/lib/tomcat6/webapps/DH/WEB-INF/DH/WEB-INF

user ~ $ vi config.xml

user ~ $ sudo /etc/init.d/tomcat6 restart

main-db

jeeves.resources.dbms.DbmsPool

DH

secret

org.postgresql.Driver

jdbc:postgresql://127.0.0.1/DH

<poolSize>10

3600

Costs Analysis

The process of identifying costs related to resources used in the project development will incorporate all departments in the business to ensure ‘buy-in’. For a Dafydd Hardy to implement business intelligence, expenditure in the tune of $100,000 may suffice but will depend on the needed and available infrastructure prior and after the implementation process. The bulk of the project development expenditure will go to software consulting costs. This is also the highest budget risk for implementing the business intelligence in a Dafydd Hardy. Again, whereas most of other business intelligence budget items will be directly predictable, the cost of consulting may not be easily correlated with clients’ ability to appreciate its value. The budgeting will therefore transcend the normal cost to include the increment of funding to ensure higher success probability. Cost analysis for the project will take two dimensions- resources and training for greater success (Khosrowpour, 2006).

Resources

Costing for the resources will include analysis of overruns for software consulting and developing the baseline budget for managing the project. In many instances, business intelligence software will cost between 20-25% of total project budget. That shall leave 75-80% for development of hardware, assistive software and project management costs. Further, the rule-of-thumb in business intelligence implementation is that consulting cost that will be about twice the cost of the business intelligence close to 40% of the total project budget (Liu, 2007). Taking this as a comfortably optimistic value, 60% of the project budget will be allocated to software and consulting. Many business intelligence projects are liable to several failures before eventually picking up, thus, up to 200% of the project budget may be allocated as an overrun costing. In essence, any business intelligence project must take into consideration the quality and costing in the project implementation budgeting. The firm will be careful in using acceptable software quality no matter the cost, as the huge consulting cost overruns to ensure that the project succeeds. An alternative will be to find an acknowledged consultant and agree on a fixed price contract. Such contracts will enable the firm to obtain all its deliverables on time and at lower market prices.

Development of the business intelligence consulting budget will involve constructing two budgets- one in which approval is justified and another upon which project management will be possible. This will involve many prior assignments (Khosrowpour, 2006). Furthermore, all consultants will be compelled to disclose as much detail about their costing as possible to ensure that quality is observed. The next step will be the comparison of details behind every quote submitted. This is to assist in reconciling differences existing in assumptions and quoted hours. Assumptions should be as identical as possible and no huge gaps should exist in schedules and hours quoted by the consultants.

Whereas this may indicate over-budgeting, it will be a better measure during the justification phase and is a much more conservative ratio for analysis ROI. Developing the baseline budget for managing the project shall follow approval of the consulting budget. The budget will be developed in line with projects details and starts at the project-planning phase or earlier.

Staff training and development

Dafydd Hardy is apparently a potential candidate for operational transformation and change management. The technical team’s reliance on one person for instructions and technical expertise means all information must be channelled and derived from him. Strategically, the company should maintain its competitive services, introduce new services within its competitive service channel and get rid of redundancy and overreliance on certain offices. It demands proper and effective communication to maintain a competitive advantage. To implement a successful business intelligence to leverage Dafydd Hardy’s opportunities, the firm must invest in training and consultancy for the system.

Budget allocations for training will cater for needs assessment, task analysis, designing, implementation and assessments. The mode of training adopted for purposes of improving employee productivity must rhyme with identified needs for such training. Task analysis will involve evaluations aimed at enhancing learning of the business intelligence system- identification of tasks that have been performed well or otherwise and instructing on how best to improve or redesign the performance of such tasks. Design, implementation, and evaluation will take a huge chunk of the training process. For implementation of business intelligence at Dafydd Hardy, it is estimated that the total cost for training and employee development will cost roughly 8-10% of the total project budget. This may however be varied depending on the nature and progress of system implementation.

Sources of all Resources

Because of limited resources, Dafydd Hardy may have problems in implementing the business intelligence. However, these problems may be mitigated through constant reference to the vendor’s resources and support. The system should be made manageable to impact only marginally, on the company’s adaptability. The company may also need to take advantage of individual experiences from its employees and free operation granted by less strict compliance in procedure by the consulting firm. Nevertheless, the company will have to change its business logistics to attain as much as possible the financial resources from its pool. It will also have to drop its strict definition of procedures and roles so as not to lose any flexibility. Rather, it may use its advantage as Dafydd Hardy to be more flexible in implementing the business intelligence system in an informal environment or in an atmosphere that does not conform to industry standards while remaining ethical. This is because the competitive edge of Dafydd Hardy is in the knowledge and experience of its employees and in the intrinsic way through whom they perform tasks rather than on formal competitive procedures. The company needs to map this knowledge into the steps of the business intelligence process.

Management and Implementation Timescale

Implementation of the business intelligence would follow the conventional business intelligent life cycle with an exception of the first four steps- planning, analysis, design, and development. Dafydd Hardy will have to test, implement, maintain, and/or monitor the progress of the business intelligence’s implementation. Testing of the program may have to run for a period, and it will include writing the conditions under which it must occur and be performed. Depending on the success of the tests, the firm may decide to implement the project or drop it. Implementation would involve writing up detailed user instructions and documentation, determining optimum operational procedures and providing training for the program users. Maintenance strategy may also be included at this stage but may all the same come later as the program is put to proper use. Therein, help desks, procedures for program maintenance as well as favourable environments to support change if the latter should be necessary will also be spelt out (Webb and Schlemmer, 2008).

Management and implementation of the project will largely depend on the responsibility and speed of consultation and the progress made thereof. Consulting will take the bulk of the earlier phases of the project, being higher at first. The duration should however ramp down and then slightly back up immediately after the go-live of the business intelligence system (O’Brien and Marakas, 2008). This will allow clients to acquire greater knowledge and adopt greater responsibility as the project advances. With this schedule, the consultants are able to back-off and come back only to support the clients as the project ends. By the testing phase, for instance, the clients should already be able to do most of the work on the project on their own. A schedule of 48 consulting hours per week for a start, an 8 hours per week during the training and 12-24 hours per week during implementation and a final 36-48 hours per week during go-live is proposed for business intelligence systems project for Dafydd Hardy.

Strategy for Implementation

Employees’ resistance is the greatest challenge to many Business intelligence systems implementation. Therefore, effective Business intelligence is possible if five core competencies are established, chief of which is the use of change management strategies. Workers’ perception of the Business intelligence as a threat to their jobs is often a negative net outcome to its implementation. Positively, however, the project is an exchange process like normal marketing in which the sellers, buyers, and products are equivalent to the Business intelligence implementers, its potential users and the Business intelligence system itself (Galliers and Leidner, 2002).

A comprehensive understanding of the evolution of the firm over time will be necessary in tailoring the Business intelligence to adapt to the needs of DH. These would include a review of the company’s socio-technical systems, effectiveness of work categorization, solidification and redundancy practices, distinctions and repetitions in processes and the praxis tradition of the company. This can be followed by a determination of which of the broader categories of services suffer most and segment Business intelligence according to the various niches that the company belong in.

Based on the analysis above, DH would adopt Business intelligence that incorporates changes in seniors management’s mode of communication, process and participation, support and participation, establishment of a common understanding among all employees, process reengineering, instructions on the systems operation and appreciation. Advantages of this software include an improved information and communication system, the creation of new business opportunities, and improved organizational productivity and sharpened competitive edge.

DH will need Business intelligence that provides effective control to most of its business parameters while increasing rationalization and decision making. The Business intelligence must be able to support sophisticated analytical forecasting, unified information storage and business processes integration. Additionally, the company will have to integrate into its processes, the latest technologies to match the large organizations that use Business intelligence and to keep with the pace of the global competitive market in which it wants to venture. Tailor-made Business intelligence is increasingly becoming available to DH. Most vendors are speeding up their integration processes and adding capacities to Business intelligence to make them easily installable and implementable.

Project Monitoring and Evaluation

Monitoring and evaluation is the last process in business intelligence implementation. The process is necessary to ensure that the outcomes of the business are achieved according to plan. For this process, a performance system will be necessary to monitor the business intelligence system progress. Workers resistance and anxiety should be monitored just as the status evaluation of the project. The feedback process should also be timely systematic and accurate so that the top management may take appropriate action to mitigate any deviations. When the feedback from status evaluation is positive, the recorded performance will be maintained, otherwise, operational changes will be made on the business intelligence implementation process. The management should understand what it entails for the system to make allocations for quality management. They will, for instance, re-identify the needs of users or re-evaluate how change management strategies are executed and adopt fitting strategies to replace the failing ones (Irani, Themistocleous and Love, 2003).

In the course of project implementation, it will be necessary to complete phase reviews. The process will involve determining if the stakeholders have completed project deliverables and signed off. Thereafter, it will be necessary to determine if the project’s full scope has been delivered too and if the objectives of the project have been met. Only then will the project be formally closed. At Dafydd Hardy, guidelines will be formulated to undertake several backward-looking evaluations of the consultant and Dafydd Hardy’s performance against stipulated commitments that were made in the structured contracts. The company will also periodically determine performances in similar structured contracts and compared the same to metrics established previously.

A post-completion review will also be necessary to assess, ex post, the effectiveness, and efficacy of the resources budgeting project and decisions as well as how its implementation was managed. The process will compare costs and capital usage, planned and actual actions, results and benefits. It will also review all assumptions made during the implementation process by looking at the actual outcomes. Through that, Dafydd Hardy will learn to improve its take on future projects and risk management. For its post completion review, Dafydd Hardy will develop a framework to monitor future performance of present portfolios of contracts under changed predicted scenarios for accessibility to its services. In this task, it will also review the processes leading to the definition of capital costs utilized during the decision process.

Conclusions and possible recommendations

DH must assess their database management and report production requirements and makes a choice from existing database management software. However, preference depends on what DH considers most cost effective since they are a profit making organization. However, it suffices to note that database management and network connectivity opportunities and associated infrastructure technologies are numerous and what to adopt depends on how much DH is willing to commit in terms of funding to this project. It is recommends that DH should adopt open source technologies that are less costly and easily scalable even when they are considering expansion to other states. Based on the fact that many firms are adopting open source, DH has a lot to gain from incorporating these network connectivity opportunities as well as accompanying infrastructure technologies.

Reference List

Bertsekas, D. and Gallager P, 1992. Data networks. New Jersey: Prentice hall.

Galliers, R. D and Leidner, D. E. 2002. Strategic information management: Challenges and Strategies in Managing Information Systems. New York: A Butterworth-Heinemann.

Haag, C. 2006. Management information systems for the information age. Toronto: McGraw-Hill Ryerson.

Irani, Z., Themistocleous, M. and Love, P. 2003. The impact of enterprise application integration on information system lifecycles. Information & Management, 41: 177–187.

Kapoor, C., 2009. Open Source vs. Cloud Computing. Who cares?’ Edgeofthecloud. Web.

Khosrowpour, M., 2006. Emerging trends and challenges in information technology management. Hershey Penn: Idea Group.

Liu, C, 2007. Modelling Consumer Adoption of the Internet as a Shopping Medium: An Integrated Perspective. Youngstown: Cambria Press.

MAIA Intelligence Pvt. Ltd, 2008. MAIA Intelligence. Web.

Montani, S. and Jain, L., 2010. Successful Case-Based Reasoning Applications. Berlin: Spinger.

Newman, R. 2010. Computer security: protecting digital resources. Sudbury: Jones and Barlett Publishers.

O’Brien, J. A. and Marakas, G. M. 2008. Management information systems. New York: McGraw-Hill Irwin.

Pentaho Corporation, 2011. Pentaho Open source business intelligence. Web.

Webb, B. and Schlemmer, F., 2008. Information Technology and Competitive Advantage in Small Firms. New York: Routledge.

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