Business Information Management Lecture Notes PDF

Business Information Management Dr. Michael P. O’Brien Module: MI4007 Week 2 (Lecture 1 of 2) 1 Data Data are raw facts and figures that on their own have no meaning These can be any alphanumeric characters...

Business Information Management Dr. Michael P. O’Brien Module: MI4007 Week 2 (Lecture 1 of 2) 1 Data Data are raw facts and figures that on their own have no meaning These can be any alphanumeric characters i.e. text, numbers, symbols 2  Data is Everywhere! For the most part, data in organisations is not managed properly and is hoarded and forgotten about and its benefits are lost to the company or organisation. 3 Data is Everywhere! Every activity that an organisation engages in will require data in some form. E.g: Serving a customer Manufacturing a product Stock control Admitting a patient to a hospital Etc. Data should be managed as a resource in the same way as employees and finance. Combinations of data may be used to generate new opportunities such as: New market niches Product & process improvement New products or services 4  Data Examples Yes, Yes, No, Yes, No, Yes, No, Yes 42, 63, 96, 74, 56, 86 111192, 111234 None of the above data sets have any meaning until they are given a CONTEXT and PROCESSED into a useable form. 5 Data Into Information To achieve its aims the organisation will need to process data into information. Data needs to be turned into meaningful information and presented in its most useful format. Data must be processed in a context in order to give it meaning. 6  Information Data that has been processed within a context to give it meaning OR Data that has been processed into a form that gives it meaning 7 Information The three main uses of information in business: 1. Communication share ideas co-ordinate actions transmit information to inform shareholders 2. Support business processes A business process is an activity or set of activities required to achieve a specific goal or outcome 3. Decision-Making 8  Examples In the next three examples, explain how the data could be processed to give it meaning. What information can then be derived from the data? 9 Example 1 Raw Data Yes, Yes, No, Yes, No, Yes, No, Yes, No, Yes, Yes Responses to the market Context research question – “Would you buy brand X at price Y?” Processing Information ??? 10  Example 2 Raw Data 42, 63, 96, 74, 37 John’s marks in the five Context modules for Semester 1 Processing Information ??? 11 Example 3 Raw Data 111192, 111234 The previous and current Context readings of a customer’s gas meter Processing Information ??? 12  Suggested answers to examples Example 1 – We could add up the ‘yes’ and ‘no’ responses and calculate the percentage of customers who would buy product X at price Y. The information could be presented as a chart to make it easier to understand. Example 2 – We could convert John’s individual module results into letter grades. Example 3 – By subtracting the second value from the first we can work out how many units of gas the consumer has used. This can then be multiplied by the price per unit to determine the customer’s gas bill. 13 Information Quality 14  Knowledge Knowledge is the understanding of rules needed to interpret information. “…the capability of understanding the relationship between pieces of information and what to actually do with the information” Debbie Jones – www.teach-ict.com 15 Knowledge Examples Using the 3 previous examples: – A Marketing Manager could use this information to decide whether or not to raise or lower price Y. – John’s lecturer could analyse the results to determine whether it would be worth him re-sitting a module. – Looking at the pattern of the customer’s previous gas bills may identify that the figure is abnormally low and the gas meter may be faulty or possibly being interfered with! 16  Knowledge Workers Knowledge workers are workers whose main capital is knowledge. Knowledge workers must employ a combination of convergent, divergent, and creative thinking as part of their work. Examples include healthcare professionals, software engineers, pharmacists, architects, engineers, scientists, design thinkers, public accountants, managers, lawyers, and academics, and any other white-collar workers, whose line of work requires one to ”think for a living”. 17 Expert Systems Because many rules are based on probabilities computers can be programmed with “subject knowledge” to mimic the role of experts/knowledge workers. One of the most common uses of expert systems is in medicine – The ONCOLOG system shown here analyses patient data to provide a reference for doctors, and help for the choice, prescription and follow-up of chemotherapy. 18  Data, Information, Knowledge Data Information Knowledge Human Beings Information Technology 19 The DIKAR Model The “Technology” View process interpret decisions drive Data Information Knowledge Action Results Mainly operational significance Mainly strategic significance 20  The RAKID model The “Business” View obtain defines require metrics Data Information Knowledge Action Results Mainly operational significance Mainly strategic significance 21 Rate of Motion towards Knowledge What is this (note the point when you realise what it is but do not say) – I have a box. – The box is 3' wide, 3' deep, and 6' high. – The box is very heavy. – When you move this box you usually find lots of dirt underneath it. – Junk has a real habit of collecting on top of this box. – The box has a door on the front of it. – When you open the door the light comes on. – You usually find the box in the kitchen. – It is colder inside the box than it is outside. – There is a smaller compartment inside the box with ice in it. – When I open the box it has food in it. 22  Rate of Motion towards Knowledge It was a refrigerator! At some point in the sequence you connected with the pattern and understood. When the pattern connected the information became knowledge to you. If presented in a different order you would still have achieved knowledge but perhaps at a different rate. 23 Knowledge Types 24  Wisdom There is one final category: Wisdom Evaluated understanding Essence of philosophical probing – critically questions, particularly from a human perspective of morals and ethics – discerning what is right or wrong, good or bad A mix of experience, values, contextual information, insight In computing terms may be unachievable – can a computer have a ‘soul’ – feel empathy?? 25 Knowledge/Wisdom 26  We know more than we can tell! “Knowledge is experience, everything else is just information” – Albert Einstein 27 28