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  1. 1. Customer Relationship Management <ul><li>Customer Relationship Management is ‘an organisational discipline which includes the identification, attraction and retention of the most valuable customers in order to sustain profitable growth’. (the Economist) </li></ul><ul><li>It could also be the process of making and keeping customers and maximising their profitability, behaviour and satisfaction. </li></ul>
  2. 2. Customer Relationship Management <ul><li>There are some other ‘givens’: </li></ul><ul><li>1. As a general rule (which seems to be accurate in many instances), 80% of revenue (or profit) is derived from 20% of a Company’s customers. </li></ul><ul><li>2. A Company needs customers </li></ul><ul><li>3. A Company needs to make profits from those customers </li></ul><ul><li>4. Customers should have high levels of satisfaction in transacting their business </li></ul>
  3. 3. Customer Relationship Management <ul><li>5. The return on investment requirement leads to the selection and recognition of ‘the most valuable’ customers. </li></ul><ul><li>A general analysis of customers will probably show that </li></ul><ul><li> 80% of customers come from these groups </li></ul><ul><li>small (purchases) </li></ul><ul><li>inactive (on mailing lists, or Direct Buy catalogues) </li></ul><ul><li>prospective - identified clients which could lead to a sale </li></ul><ul><li>Inactive - have been customers, but have not bought anything for a period </li></ul>
  4. 4. Customer Relationship Management <ul><li>In the ‘top 20%’ are these customers </li></ul><ul><li>the best customers make up about 1% </li></ul><ul><li>the big customers make up about another 4% </li></ul><ul><li>medium customers make up about 15% </li></ul><ul><li>(notice the unquantified ‘best’, big’, medium’) </li></ul>
  5. 5. Customer Relationship Management <ul><li>This could be set up as in the diagram </li></ul>1% of customers 4% of customers 15% of customers 80% Top Big Medium Small Inactive Prospects Suspects
  6. 6. Customer Relationship Management <ul><li>A survey of a European company showed: </li></ul><ul><li> 2150 customers Revenue $M10 </li></ul><ul><li>Profit $900,000 (approx) </li></ul><ul><li> 80% of its customers provided 20% of revenue </li></ul><ul><li>Another 5% of its customers provided 29% </li></ul><ul><li>Another 4% of its customers provided 27% </li></ul><ul><li>and 1% of its customers provided 24% </li></ul><ul><li> </li></ul><ul><li>Some rough calculations showed that the average revenue per customer was $4650, profit per customer was $418, and Return on Investment (ROI) was about 9% </li></ul>
  7. 7. Customer Relationship Management <ul><li>One of the aims of CRM analyses is to ‘accurately’ assess which grouping of customers would provide the optimum result </li></ul><ul><li>or, what percentage of each group could be targetted for improvement </li></ul><ul><li>This utilised ‘what if ’ modelling - what would be the result of say increasing the top 1% from by 6 customers, the next 4% (big) by 12 customers …. and so on. </li></ul>
  8. 8. Customer Relationship Management <ul><li>The main problem is ‘knowing’ which groups and the relative numbers in each group to concentrate on </li></ul><ul><li>This is where well planned and constructed CRM databases are are essential </li></ul><ul><li>They should contain the required information </li></ul><ul><li>Information is one aspect BUT the major effort is involved in extracting ‘intelligence’ from the information stored. </li></ul>
  9. 9. Customer Relationship Management <ul><li>Marketing and Sales statistical process control techniques are needed. </li></ul><ul><li>What ‘data’ is required ? </li></ul><ul><li>Try this :- </li></ul><ul><ul><ul><ul><li>customer value (profit per customer, lifetime value, NPV) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>customer behaviour (revenue/customer, lifetime, share of customer) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>customer satisfaction (satisfaction scores, defection risk, cross selling potential) </li></ul></ul></ul></ul>
  10. 10. Customer Relationship Management <ul><li>Customer Marketing: Basic Data </li></ul><ul><li>Name </li></ul><ul><li>Address </li></ul><ul><li>Purchasing dates </li></ul><ul><li>Purchasing Amounts </li></ul><ul><li>Purchasing values </li></ul><ul><li>Patterns of Purchasing </li></ul><ul><li>Subsidiary organisations </li></ul><ul><li>Methods of selling </li></ul><ul><li>Names of sales representatives …………….. </li></ul>
  11. 11. Customer Relationship Management <ul><li>Customer marketing : Diagnostic data </li></ul><ul><li>Interviews with customers and prospective customers </li></ul><ul><li>Value of customers </li></ul><ul><li>Behaviour of customers </li></ul><ul><li>Satisfaction of customers </li></ul><ul><li>Customer focus </li></ul>
  12. 12. Customer Relationship Management <ul><li>Customer Marketing : Decisions </li></ul><ul><li>Proceed or Not to Proceed on results </li></ul><ul><li>Which are the target improvement groups </li></ul><ul><li>Plan the processes for data collection and aalyses </li></ul><ul><li>Customer Marketing : Audit </li></ul><ul><li>Measure, remeasure and confirm results </li></ul><ul><li>Analyse the results </li></ul>
  13. 13. Customer Relationship Management <ul><li>A working profile: </li></ul><ul><li>Many organisations collect and generate large volumes of data to assist them in their day to day operations. </li></ul><ul><li>Many organisations have ‘data warehouses’ to access this collected data </li></ul><ul><li>However, the difficult part is the detection of ‘important’ content of the stored data. </li></ul><ul><li>And this is where ‘data mining’ techniques are useful </li></ul>
  14. 14. Data Mining ? <ul><li>Data mining is being increasingly used to to assist in Management making better decisions in daily operations </li></ul><ul><li>One example is that of identifying ‘suitable candidates’ and products for cross selling </li></ul><ul><li>Association analysis (or market basket analysis) identifies relationships and associations among the items (or services) which customers purchase. </li></ul><ul><li>There is now awareness that the combination of profitability analysis and basic associations analysis can be very effective </li></ul>
  15. 15. Data Mining <ul><li>Cross selling can be a major strategy for some organisations (- is it applicable to Monash University ?) </li></ul><ul><li>It is know that when customers have multiple association with a business, such as a bank, they are less likely to move their business to a competitor. </li></ul><ul><li>The loss rate for customers who have 2 accounts with a bank is estimated to be about 55% </li></ul><ul><li>For Customers who have 4 or more products and services with a bank, the loss rate is close to zero. </li></ul>
  16. 16. Data Mining <ul><li>There are 2 other aspects : </li></ul><ul><li>1. Cross selling improves customer retention </li></ul><ul><li>2. It is more profitable to sell more products or services to an existing customer than to obtain a new customer </li></ul><ul><li>Did you know that it is generally accepted that credit card companies only start to make money in the 3rd year of doing business with a customer ? </li></ul>
  17. 17. Data Mining <ul><li> Two of the BIG questions are : </li></ul><ul><li>What is the product to sell ? </li></ul><ul><li>To whom is the product sale directed ? </li></ul><ul><li>They can be part answered by a combination of ‘intuition’ (which you saw way back in Lecture 1), and by the use of data mining analysis or analyses. </li></ul><ul><li>In the banking industry, mortgage owners are encouraged to think about home equity loans - this is ‘intuition’ but the bank (or company) may be unaware of other opportunities with the customer </li></ul>
  18. 18. Data Mining <ul><li>Data mining can be seen as a technique of deriving information from data </li></ul><ul><li>One of the techniques is called ‘association analysis’ </li></ul><ul><li>This can identify products (or services) which can be highlighted and cross-sold to to customers </li></ul><ul><li>A company’s business strategy will lead to some selected products being promoted for marketing </li></ul>
  19. 19. Data Mining <ul><li> The combination of ‘intuition’ and data mining is a sound decision </li></ul><ul><li>Let’s assume that the ‘cross product’ has been decided </li></ul><ul><li>The next step is to decide who is the ‘prospective customer’ </li></ul><ul><li>This requires more research and analysis </li></ul>
  20. 20. Data Mining <ul><li>There are several approaches : </li></ul><ul><li>1. To use association analysis to rework those customers who have been previously targeted but have not taken up the cross-sell offer </li></ul><ul><li>2. Another approach is to build a predictive model to show who is likely to buy specified products or services </li></ul><ul><li>3. Another approach is to build a model to predict the likelihood of customers, identified by association rules only, buying a product </li></ul>
  21. 21. Data Mining <ul><li>What are Association Rules ? </li></ul><ul><li>There are 3 factors explored </li></ul><ul><li>Confidence </li></ul><ul><li>Support </li></ul><ul><li>Leverage (sometime called lift) </li></ul>
  22. 22. Data Mining <ul><li>Confidence : </li></ul><ul><li>Is based on the probability that if customers buy Product a, they will also buy Product B (or in SQL like terms A determines B). </li></ul><ul><li>Support: </li></ul><ul><li>This is the frequency of occurrence of the rule in the set of records available </li></ul><ul><li>Leverage: </li></ul><ul><li>This is a complex item, but it can be stated as it being a multiplier of the probability of B in the presence of A, as opposed to the probability of B with no influence of A </li></ul>
  23. 23. Data Mining <ul><li>You are aware that most organisations are interested in ‘profitability’ which is linked to ‘return on investment’. </li></ul><ul><li>2 of the ‘danger’ indicators are low or negative profitability </li></ul><ul><li>It’s a good move to include some form of profitability analyses with association analyses </li></ul>
  24. 24. Data Mining <ul><li>Let’s go back to our starting example of the ‘company’ which had analysed its consumer base. </li></ul><ul><li>The top 1% of its customers resulted in an average of $114,000 revenue, $45,600 profit and 114% Return on Investment </li></ul>
  25. 25. Data Mining <ul><li>The ROI reduces as the analyses approach the 80% of customers who create to 20% of revenue </li></ul><ul><li>At this level the revenue from each customer (average) is $1160,the profit drops to $500 and the ROI drops to -53% or (53%) - not a good number as a previous Australian Treasurer was heard to say. </li></ul>
  26. 26. Data Mining <ul><li>An interesting aspect is that in the 80% customer bracket, experience shows that about 5 to 10% of the inhabitants have a high potential to move ‘up the ladder’ and become high-revenue, high-profit and high ROI customers </li></ul><ul><li>Conversely, customer identified as having ‘downwards’ profiles are normally discarded or dropped from the next marketing campaign.They may be encouraged by email or by a special promotion to ‘do better’. </li></ul>
  27. 27. Data Mining <ul><li>As a typical profit embedded association rule :- </li></ul><ul><li>Visa Gold with high profitability  house loan with high profitability with </li></ul><ul><li>support of 0.22 </li></ul><ul><li>confidence 10.7 </li></ul><ul><li>leverage 13.3 </li></ul><ul><li>This is interpreted as : </li></ul><ul><li>when a customer has a Visa Gold card (a high profitability item), the customer is also likely to have a housing loan (high profitability) in 10.7% of cases, and this is 13.3% more likely in the overall record population of the data warehouse </li></ul>
  28. 28. A Data Mining Process <ul><ul><li>Extract product holding and service information for each customer </li></ul></ul><ul><ul><li>Calculate profit for each product or service </li></ul></ul><ul><ul><li>Categorise profit level for each product or service </li></ul></ul><ul><ul><li> Prepare data in a format for data mining tool use </li></ul></ul><ul><ul><li> Run association analysis with product/service embedded with profitability </li></ul></ul><ul><ul><li> Profile customer characteristics based on identified rules </li></ul></ul><ul><ul><li> Calculate the number of customers who can be cross-sold </li></ul></ul><ul><ul><li>Set up Communication channels and messages </li></ul></ul>
  29. 29. Data Mining <ul><li>These are some of the Data Mining functions, techniques and applications </li></ul><ul><li>Category Function Algorithm Application </li></ul><ul><li>Predictive Model Classification Decision Tree Targetting Marketing </li></ul><ul><li>Neural Networks Risk Analyses </li></ul><ul><li>Classification Customer Retention </li></ul><ul><li>Discrimination Fraud detection </li></ul><ul><li> Logistic Regression Bankruptcy Prediction </li></ul><ul><li>Forecasting Time series Statistical Time Sales Forecasting, </li></ul><ul><li>Forecasting series </li></ul><ul><li>Box-Jenkins model Interest Rate predictions </li></ul><ul><li> Company Loss Forecasts </li></ul>
  30. 30. Data Mining <ul><li>The previous overheads showed you the ‘highs’ of the application of computer bases models linked with Customer Relationship Management applications </li></ul><ul><li> Information Technology is an incurably ‘super-optimistic’ environment </li></ul><ul><li>On the next overheads there are some items which may cause you to wonder ‘are the new IT techniques are successful as they seem to be ?’ </li></ul>
  31. 31. Data Mining <ul><li>The message to Management seems to be </li></ul><ul><li> ‘ learn everything about your customers’ and somehow you will be guided by all that information to deliver the goods and services which will make them happy and loyal to your company’. </li></ul><ul><li>Loyalty can produce profits, reduction of costs, growth and other benefits including a good return on Investment </li></ul>
  32. 32. Data Mining <ul><li>However there is a risk involved. </li></ul><ul><li>There may be a wide gap between the gathering of all of the customer information and insight which may be revealed of the customers’ preferences </li></ul><ul><li>There is the possibility of alienating, or turning off, more customers than are being satisfied </li></ul><ul><li>There is a possibility that energy is being spent of what may be counter-productive results </li></ul>
  33. 33. Customer Relationship Management <ul><li>The type of relationship between a business and its customers will vary from by the type of business and of course by individual customers </li></ul><ul><li>Is interaction necessary ? </li></ul><ul><li>Why should the customers feel that it is important to them that efforts are being made to discover more about their buying habits - and perhaps their lifestyles ? </li></ul><ul><li>Could customers feel that this is ‘intrusive’ and ‘not necessary’ ? </li></ul>
  34. 34. Customer Relationship Management <ul><li>Would the customers like to be not part of the data gathering industry - and the resulting analyses ? </li></ul><ul><li>A conundrum: if a business fails to build a relationship with customers who value relationships, and instead focuses on what are seen as cost cutting measures, they may go elsewhere </li></ul><ul><li>Alternatively, if attempts are made to build relationships with customers who are more focussed on products and services, they also may go elsewhere. </li></ul>
  35. 35. Data Mining <ul><li>Information Technologists, because of their skills, tend respond to a problem with technology, and particularly in so in the current environment where there is a high level of interest in Customer Relationships and their Management. </li></ul><ul><li>But it may be that more technology, or expensive technology, may not the the solution - it may make the problem worse. </li></ul>
  36. 36. Customer Relationship Management <ul><li>What if a relationship was defined as : </li></ul><ul><li> A vendor with every company which made or sold every product a customer used last month, or last quarter ? </li></ul><ul><li>Where would the ‘data gathering’ stop ? </li></ul>
  37. 37. Customer Relationship Management <ul><li>Is the focus on customer relationship manipulation rather than customer relationship management ? </li></ul><ul><li>The largest scale data gathering system is not necessarily the best one on the grounds that it is available. </li></ul><ul><li>A smaller-scale model might produce better results </li></ul>
  38. 38. Customer Relationship Management <ul><li>A few suggestions: </li></ul><ul><li>Profile the best customers. </li></ul><ul><li>Determine who they are ( ? criteria) and what they buy. </li></ul><ul><li>Use this as the starting point of mapping the full life cycle of the ‘valuable’ customers’ dealing with the company </li></ul><ul><li>Map onto a timeline what events happen, and when they happen - and note the time intervals for each ‘valued’ customer. </li></ul>
  39. 39. Customer Relationship Management <ul><li>Loyalty is more than capturing an account code, an email address, a telephone number at each transaction </li></ul><ul><li>Good relationships and trust are a 2 way mechanism - which take time, flexibility and minimum pressure </li></ul><ul><li> Hopefully, you are not totally confused, but are garnering some ideas which indicate that much skill, as well as effort, is required in successful CRM applications. </li></ul>
  40. 40. Customer Relationship Management <ul><li>Our attention will turn now to another aspect of Information and this is the need to ensure high levels of ‘data quality’ - or the quality of data must be very high </li></ul><ul><li>Data quality is essential if information about customers is to produce clear, accurate and consistent information </li></ul>
  41. 41. Data Quality <ul><li>How could data not be accurate ? </li></ul><ul><li>(or, if you like, inaccurate ?) </li></ul><ul><li>Missing content of fields </li></ul><ul><li>‘ Old’ or rarely used data </li></ul><ul><li>An incorrect, but logical numeric address e.g. 90 Dandeong Road, instead of 900 Dandenong Road </li></ul><ul><li>Reversal of number in a phone contact </li></ul><ul><li>An unnotified change of address </li></ul><ul><li>Changed item numbers </li></ul><ul><li>Illogical or non-active web addresses </li></ul>
  42. 42. Data Quality <ul><li>What are some of the effects ? </li></ul><ul><ul><li>Multiple mail outs to the same address </li></ul></ul><ul><ul><li>No mailouts to an important address </li></ul></ul><ul><ul><li>Which is likely to be the worse of these two alternatives ? </li></ul></ul>
  43. 43. Data Quality <ul><li>In the health system, it is common for multiple record systems to exist - and this can mean multiple records for the same person, BUT there may be no way of tying all of the records for the same patient together </li></ul><ul><li>CRM applications, by their nature, invariably ‘bring together’ many pieces of data about the same entity - a customer, a supplier, a product, a process ….. </li></ul>
  44. 44. Data Quality <ul><li>There are software based ‘data cleaning’ services </li></ul><ul><li>Madison Information technologies </li></ul><ul><li>Evoke Software </li></ul><ul><li>MetaRecon from Metagenix </li></ul><ul><li>Group 1 Software - Enterprise Data Quality and HotData </li></ul><ul><li>Their objective ? To redo or reconstitute data so that it becomes suitable to produce </li></ul><ul><li>clear </li></ul><ul><li>accurate </li></ul><ul><li>consistent information </li></ul>
  45. 45. Customer Relationship Management <ul><li>Some Pointers for Success with CRM </li></ul><ul><li>1. Determine and Maintain the focus of the application </li></ul><ul><li>2. Design the CRM territory correctly </li></ul><ul><li>3. Balance Detail and Summary data sets </li></ul><ul><li>4. Use the correct data for the application </li></ul><ul><li>5. Stay in synchronisation - develop the whole CRM strategy before using technology (which rarely addresses everything). </li></ul>
  46. 46. Customer Relationship Management <ul><li>6. Plan for Today - Anticipate benefits of emerging technology </li></ul><ul><li>7. Develop and Action Plan </li></ul><ul><li>8. Integrate and associate Customer data </li></ul><ul><li>9. Share, don’t put data in walled environments </li></ul><ul><li>10. Use all communication channels available </li></ul>
  47. 47. Acknowledgements <ul><li>J.L. Weldon - EDS CRM Services, New York </li></ul><ul><li>B.Grime - Customer Marketing Institute </li></ul><ul><li>F.Reichheld - Bain and Company </li></ul><ul><li>S.Liu - IBM Global Business Services </li></ul><ul><li>J.Yap - IBM International Global Services </li></ul>
  48. 48. Database Security
  49. 49. Database Security <ul><li>DATABASE SECURITY is the protection of a database from </li></ul><ul><ul><ul><li>unauthorised access </li></ul></ul></ul><ul><ul><ul><li>unauthorised modification </li></ul></ul></ul><ul><ul><ul><li>destruction </li></ul></ul></ul><ul><li>Privacy is the right of individuals to have some control over information about themselves </li></ul><ul><li>Integrity refers to the correctness, completeness and consistency of stored data </li></ul>
  50. 50. Database Security <ul><li>Some Random Ideas: </li></ul><ul><li>Physical Access controls - badges, closed circuit TV, guards... </li></ul><ul><li>Terminal Authentication User I/D’s, Passwords </li></ul><ul><li>(System Level and Database Level) </li></ul><ul><li>Authorisation - Authorisation Rules </li></ul><ul><li>(which users can access what information </li></ul><ul><li> What operation users can invoke </li></ul><ul><li> Read Only, Read/Write, Update, Delete </li></ul><ul><li>User Views - non updatable access, but access to latest </li></ul><ul><li> level of information </li></ul>
  51. 51. Database Security Other Tools: Security Logs, Audit Trails, Encryption • Data Encryption Standard • Public Key Encryption
  52. 52. Security User Application Database Security Table user name Authority Checks (grants) Access authority
  53. 53. Security <ul><li>Some perceptions: </li></ul><ul><li>1. Security is often an afterthought </li></ul><ul><li>2.Organisations often have no upfront planning of system-wide security </li></ul><ul><li>3.When systems are distributed, security reaches beyond individual databases and into the operating systems </li></ul><ul><li>4.No tools specifically available for either client/server or distributed database </li></ul>D.Burleson, DBMS. Author of Distributed Databases
  54. 54. Server Security <ul><li>1. First layer - LAN or Host Computer Operating System </li></ul><ul><li>(1) Login / valid username / password </li></ul><ul><li>(2) Privileges / permissions on directories </li></ul><ul><li> and files (read/write/execute/delete) </li></ul><ul><li>Operating System controls </li></ul>
  55. 55. Server Security <ul><li>2. Second Layer - Database Server </li></ul><ul><li>(1) Valid user accounts / password </li></ul><ul><li>(some servers use operating system authentication </li></ul><ul><li>- eliminates a level of security checking) </li></ul><ul><li>(2) Privileges / permissions </li></ul><ul><li> Database Administrator - GRANT and REVOKE </li></ul><ul><li>commands </li></ul><ul><li>Examples: Create, Alter, Drop database objects ..... </li></ul><ul><li>(Databases, Tables, Views, Procedures ..) </li></ul>
  56. 56. Server Security <ul><li>More examples: Create, Alter, Drop Database Users </li></ul><ul><li>Start Up and Shut Down the Database Server </li></ul><ul><li>Customise Specific Jobs or Locations Privileges </li></ul><ul><li>Different Administrators and Different Functions </li></ul>
  57. 57. Server Security <ul><li>OBJECT PRIVILEGES </li></ul><ul><li>All database servers control access to : </li></ul><ul><li>Tables, Views, Procedures with Object Privileges </li></ul><ul><li>Examples: Select, Insert, Update, Delete privileges on </li></ul><ul><li>tables and views </li></ul><ul><li>References privilege (associated with referential </li></ul><ul><li>integrity constraints and Rules/Procedures </li></ul><ul><li>Execute - controls the ability to execute a Procedure </li></ul>
  58. 58. Server Security <ul><li>Some syntax: </li></ul><ul><li>GRANT select, update on nameinfo </li></ul><ul><li>to user1, user2, user3 </li></ul><ul><li>GRANT execute on deletenameinfo to user4 </li></ul><ul><li>with GRANT OPTION </li></ul><ul><li>[2 items here - deletenameinfo is a Procedure </li></ul><ul><li>and the GRANT OPTION delegates the privilege to other users.(User4 can pass on the privilege) </li></ul><ul><li>GRANT select (userid, username) on business to </li></ul><ul><li>user1, user3, user4 </li></ul>
  59. 59. Server Security <ul><li>A result of the application of attribute lists and object privileges. </li></ul><ul><li>IF a server cannot insert a value for a not-null attribute , AND the attribute does not have a default attribute value, all INSERT statements on the table will : </li></ul><ul><li>(a) be suspended Y/N </li></ul><ul><li>(b) override the not-null condition Y/N </li></ul><ul><li>(c) fail Y/N </li></ul>
  60. 60. Server Security <ul><li>PRIVILEGE MANAGEMENT </li></ul><ul><li>Difficult to manage large numbers of users with individual privileges </li></ul><ul><li>In real life many users have the same privileges </li></ul><ul><li>Common privilege users are normally associated with GROUPS (as with Unix, VMS) </li></ul><ul><li>A Group Privilege change affects all members of the group </li></ul>
  61. 61. Server Security <ul><li>ROLE Privileges </li></ul><ul><li>Privileges dynamically available to users of a database system during the running of an application </li></ul><ul><li>When the system ends, or the user quits the application, the privileges assigned to the user(s) are disabled. </li></ul>
  62. 62. Server Security <ul><li>RESOURCE MANAGEMENT </li></ul><ul><li>Generally associated with CPU processing time </li></ul><ul><li>per statement (transaction), disk I/O’s per statement </li></ul><ul><li>(transaction), and disk space per user. </li></ul>
  63. 63. Server Security <ul><li>AUDITING USERS </li></ul><ul><li>Some server software supports the audit and analysis of individual users (Student Network system at Monash) </li></ul><ul><li>This facility will ‘finger’ a user who: </li></ul><ul><li> is deleting (or attempting to) rows from a table </li></ul><ul><li> requesting delete table functions </li></ul><ul><li> altering table names .... etc ..... </li></ul>
  64. 64. Oracle Security <ul><li>Security Manager </li></ul><ul><ul><li>Menu Options: </li></ul></ul><ul><ul><li>- Create (a new user) </li></ul></ul><ul><ul><li>- Create Like (an existing user) </li></ul></ul><ul><ul><li>- Remove </li></ul></ul><ul><ul><li>- Revoke Privilege (remove a selected privilege) </li></ul></ul><ul><ul><li>- Add Privilege to user </li></ul></ul><ul><ul><li>- Change Account Status (enable/disable access) </li></ul></ul>
  65. 65. Oracle Security <ul><li>Role </li></ul><ul><ul><li>- Create (create a role) </li></ul></ul><ul><ul><li>- Create Like (an existing role) </li></ul></ul><ul><ul><li>- Remove (delete nominated role) </li></ul></ul><ul><ul><li>- Revoke Privilege </li></ul></ul><ul><ul><li>- Add Privilege </li></ul></ul>
  66. 66. And Microsoft Access ? There are a number of privileges available to the System Administrator. They are similar in application to the Security features of Oracle, but are more limited. Access in Network mode offers more security features. And if you have time you could research the Security aspects of SQLServer
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