week 1
Unit introduction
In all aspects of life, individuals are constantly faced with situations where they must weigh up the available information in order to produce alternatives and make decisions. In the working environment, effective decision making can ensure the successful development of organisations.
Poor decision making can have significant negative consequences and can even lead to the demise of an organisation. In this unit, you will investigate the fundamentals of the decision-making process. You will find out how using data modelling provides the computational ability to compare consequences, and determine a preferred course of action. You will develop the skills and techniques necessary to create complex spreadsheets in order to produce accurate information that informs decision making. You will examine a scenario and then design, develop and test a spreadsheet; you will review your spreadsheet and make refinements based on user feedback, providing an evaluation of the effectiveness of the alternatives produced.
The skills developed in this unit are useful for progression to computing or business-related higher education courses and for use in decision making in the workplace.
Learning aims
In this unit you will:
A - Investigate data modelling and how it can be used in the decision-making process
B - Design a data model to meet client requirements
C - Develop a data model to meet client requirements.
A1 Stages in the decision-making process
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Understanding the scenario.
In any given project or situation, it is key to FULLY understand the "scenario", this is the situation or setting of the problem that needs to be referred to in order to provide a solution. In this unit, you will, for the most part, be providing solutions to enable "data" to be turned in to "information"
When provided with your assignment your scenario will be set and appear in the beginning part of the brief.
Identifying information and sources
In any data model, the need to know where your data is coming from is vital as you will need to ensure that it is accurate and trusted. If your sources are incorrect and inaccurate they will output information that is flawed, GIGO stands for Garbage In Garbage Out.
information required
This is the required information that is needed for the spreadsheet, this could be information such as Profit, Stock levels, Salary, Costs of products and services in a given company.
information that is already available
Information may already be available, this could be things like the tax percentages that are used to calculate VAT(Value Added Tax), or a data set that the client has made available for use by the individual creating the spreadsheet to model the data.
additional information needed
Once you are in a position to FULLY understand the clients data sets and requirements you may find that there are further information requirements that are required to be included in the model, this could be historical spending patterns, "footfall" (the number of people walking in to a shop) the weather over a given period. There may be in some/most situations a requirement to purchase these data sets.
sources of additional information
Identification of the location and source of data and information is vital as the credibility of these could be called in to question. Consider the fact that not all data and information sources are internet based, there are libraries with data is stored and accessed. In university's students use "Athens" accounts to source journals and publications to reference within their work.
Other places of information are personally sourced, this can be collected via surveys of users and people within an institution, again caution should be applied here because the sample of people being surveyed may not be appropriate for the area being reviewed or reflected.
requirements for verifying the information sources.
As discussed above credibility of data and information is key to the successful generation of information from data sets. The reliability of this can have a huge impact on a company's direction and profits. There are obvious measures that can be used to protect companies gathering data and information from external sources, this would be, using trusted sources, for example, BARB in the media sector. Additional to thetrustworthinesss of the information source the age of the data and information, as it may be outdated and useless.
Factors affecting the quality of information
currency of data
The currency of data relates to the age of the data, this is important as mentioned in previous areas the older the data and information the less reliable or useful the results output from the system become.
accuracy of data
Ensuring the accuracy of the data is a simple win or a company, however, the flip of this is that it is easy to have small inaccuracies within data, such as typos or a negative number instead of a positive value.
external factors.
External factors that can impact on may be beyond the control of the data sources control, for example a drugs company may have a data set or model that predicts the sales of a particular drug that they are tracking over the year and they are using old data from the previous 5 years to enable them to predict a trend however an alternative drug has been released by another company that is fractionally cheaper in the past year this could make the data model inaccurate
Analysing the information.
The process of analysing information is done throughout most jobs, and can enable managers and leaders to make informed decisions based on the data presented. Examples of this in an ICT sector could be the Network manager where these need to understand the needs of the users on the system, and through the use of helpdesk requests or issue reports they are able to suggest or impliment solutions and or improvments to the systems to enable the users to be more efficient and effective in thier duties.
Identifying alternatives.
When reflecting and using information and data it is common to make a decision on the face of the outputted results, however, careful consideration should be taken when using this information, could it have been presented in another way? could alternative sources have been used instead? These are factors that have been mentioned throughout this weeks content that could have an impact on a company or institutions decisions.
Identifying the consequences of implementing the alternatives.
The implementing alternatives approached to analysing the data is a decision that requires a careful thought process.
Considering the consequences of the use of the alternative approaches would throw up questions around, the reliability of the data, its relevance, whether it is current, would it take longer for a data model to be produced, and the cost implications.
Making a decision.
Once the possible solutions have been reflected on and discussed a decision should be able to be made on which model would be meet the needs and would have the best balance of quality and practicality.
Justifying the decision.
It is important in any project to be able to justify your choice, as this may link to the increased cost of a project. If you are able to agree/justify why you have made this selection users/owners and companies will be able to quantify why a decision has been made and the suggested benefit of using option A over B.
Last Updated
2019-02-14 11:27:30
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How 2's Coverage
Just a Minute - At the end of the lesson teachers ask learners to summarise their learning. The comparison of these summaries against learning objectives informs planning.
Anonymous Assessment - Learners assess an anonymous piece of work containing deliberate mistakes against given success criteria.
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