Office pc no.1
- The greetings cards are designed by “Graphsoft” – their designs are sent on CD-ROM to offices by post and then uploaded to the personal computer by the Manufacturing Manager.
- Design and quantity is sent to printer and cards are printed and produced
Office pc no.2
- Duplicate Data is received from sales managers laptop
- This is data from potential customers is entered into database with current customer data and is then processed. Such data is likely to be Company name, contact details ( phone,email,fax, and address), reference number (primary key), order quantity or quote quantity, Order specific information like designs.
- As well as customer data we have supplier data entered into the database manually (contact details, reference number , order amount, date, cost)
- Graphs from purchasing manager and cost data is received and entered into spreadsheet along with sales data received from sales manager .
- The sales data and the cost data is processed to show monthly performance as well as future expected sales thanks to forecasting data from the sales manager.
Task 2
Data architecture is defined as the set of rules standards and strategies that determine how data is harvested, arranged and stored in an organisation. It is a high-level map of the information systems likely to be found in a typical commercial organisation.” (Beynon-Davies, 2002). For most modern businesses using some form of information technology, constructing and implementing data architecture is common practice. Using well designed data architecture offers a variety of benefits to an enterprise .It offers an enterprise the opportunity to set and achieve technical objectives as well as numerous other benefits. Using examples from the case study organisation I will set out to discuss the advantages and disadvantages of compiling architecture maps.
Firstly I will look at the technical objectives that data architecture can help an enterprise achieve. Optimising the usage of decision support systems is helpful to a business as it aides the user in the decision making of the business. The classic Decision making theory is defined by Herbert Simon (1955) as having four main stages:
- Intelligence-Awareness of the problem exists and that it must be solved.
- Design-Alternatives are identified and reviewed weighing up the costs and benefits they may have if implemented.
- Choice-The decision is made by selecting the best solution.
- Implementation-The decision is implemented and reviewed for its success
However humans are not as efficient at this for all decisions in Information systems as they are prone to error and poor memory recall so decisions support software is often used , For example the software helps automate processes which in our case study could relate to the manufacturing manager using a stock system which will automatically order when it knows the printer has used stock and its close to empty or an automatic warning when the printer is close to a servicing date or when maintenance is needed. It also provides a business by helping reduce the time of problem solving, Revealing new approaches to thinking about a problem space as well as Improving personal efficiency and organisational control.
The second objective which is a value to an organisation is optimising data value. Stable data architecture exploits the value of data by providing a total view of the business and its clients. Data value needs to be accurate and consistent as otherwise information concerning the business lacks integrity and then cannot be used to support the analysis and reporting needs of workers throughout the organization. Data integrity is data that is complete or whole. All characteristics of the data including business rules, rules for how pieces of data relate dates, definitions and lineage must be correct for data to be complete. Data that has integrity is identically maintained during any operation (such as transfer, storage or retrieval). Put simply in business terms, data integrity is the assurance that data is consistent, certified and can be reconciled.
The final objective that we will look at is optimising ETL processes .ETL stands for extraction, transformation and loading. Etl is a process that involves the following tasks:
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Extracting data from source operational or archive systems which are the primary source of data for the data warehouse
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Transforming the data - which may involve cleaning, filtering, validating and applying business rules
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Loading the data into a data warehouse or any other database or application that houses data
- For our case study, file-based ETL processing would be used where possible, this would benefit the company as Storage costs relatively little, it can be used for testing and debugging, used for calculations with statistics.
As data architecture achieves the technical objectives previously mentioned, it leads to several important business benefits for enterprises, G, White (2009);
- Preserving investment in information –If designed correctly data architecture allows the company in this case to maximise the potential of client data as well as information supplied between the 3 different managers as it is secure and reliable
- Using resources more efficiently – Good data architecture makes it easy for the company to make the most of its data and formulate new strategies as well as forecasting for the future and analysing past sales.
- Maximizing the productivity of knowledge workers – Allows the 3 manager access to vital information relatively quickly and with great ease
- A more efficient IT operation
- Lower software development, support, and maintenance costs
- Increased portability of applications
- Improved interoperability and easier system and network management
- A better ability to address critical enterprise-wide issues like security
- Easier upgrade and exchange of system components
However there also disadvantages whilst using data architecture and data warehousing ,Ralph Kimball ( 1996) found that;
- Data warehouses are not the optimal environment for unstructured data
- Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.
- Over their life, data warehouses can have high costs. Maintenance costs are high.
- Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
- There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.
In conclusion, a well-designed data architecture provides a solid foundation upon which an enterprise can build solutions to meet both the current and future needs of its decision-makers and knowledge workers. This foundation maximizes the value of enterprise data and supports the organization’s investment in information as a strategic resource. By providing more timely, complete and accurate information, a solid data architecture helps an enterprise gain a better understanding of its customers and its markets. A solid data architecture also creates efficiencies in information flow, leading to faster time-to-market performance for organizations.
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References
Simon, H.A (1955) on a class skew distribution functions .Biometrica 42 pp.425-40
White, G. (2009). Information system architecture PowerPoint 12, Slides 13-14.
Beynon-Davies P. (2002). Information Systems: an introduction to informatics in Organisations.
Kimball (1996). The Data Warehouse Toolkit
