Troy Eversen | 19 May 2015 Data Integrity Workshop.

Slides:



Advertisements
Similar presentations
Testing Relational Database
Advertisements

Module 3: Business Information Systems
Test process essentials Riitta Viitamäki,
Access 2007 ® Use Databases How can Microsoft Access 2007 help you structure your database?
P3, M2,M3,M4.
Use Mobile Guidebook to Evaluate this Session – M1.5 Allowing Students to Update Their Program of Study Online.
Chapter 07: Lecture Notes (CSIT 104) 1111 Exploring Microsoft Office Excel 2007 Chapter 7 Data Consolidation, Links, and Formula Auditing.
Database Software Creation Process Arvin Meyer, MCP, MVP
QUT Payroll Services Sessional eForm Presented by Christine Delaney, QUT Payroll Manager with Technical Support from Edward Eacock, QUT Financial Systems.
3.1 Data and Information –The rapid development of technology exposes us to a lot of facts and figures every day. –Some of these facts are not very meaningful.
Objectives Overview Define the term, database, and explain how a database interacts with data and information Define the term, data integrity, and describe.
Data - Information - Knowledge
Today’s Goals Concepts  I want you to understand the difference between  Data  Information  Knowledge  Intelligence.
Your Interactive Guide to the Digital World Discovering Computers 2012 Chapter 10 Managing a Database.
Living in a Digital World Discovering Computers 2010.
Database Design Concepts Info 1408 Lecture 2 An Introduction to Data Storage.
Database Design Concepts INFO1408 Term 2 week 1 Data validation and Referential integrity.
Database Design Concepts Info 1408 Lecture 2 An Introduction to Data Storage.
Systems Analysis I Data Flow Diagrams
Chapter 1: The Database Environment
THE VU AGENDA EXCELLENT, ENGAGED AND ACCESSIBLE Victoria University Alesco Custom Business Rules.
Chapter 9 Database Management
McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 9 Processing the Data.
Topics Covered: Data preparation Data preparation Data capturing Data capturing Data verification and validation Data verification and validation Data.
SWIS Digital Inspections Project (SWIS DIP) Chris Allen, Information Management Branch California Integrated Waste Management Board November 5, 2008 The.
Data Warehousing Seminar Chapter 5. Data Warehouse Design Methodology Data Warehousing Lab. HyeYoung Cho.
DAY 15: ACCESS CHAPTER 2 Larry Reaves October 7,
Encoding, Validation and Verification Chapter 1. Introduction This presentation covers the following: – Data encoding – Data validation – Data verification.
Objectives Overview Define the term, database, and explain how a database interacts with data and information Define the term, data integrity, and describe.
MIS 385/MBA 664 Systems Implementation with DBMS/ Database Management Dave Salisbury ( )
Chris Wright Senior Systems Engineer, Lucity IMPORT & UPDATE.
 To explain the importance of software configuration management (CM)  To describe key CM activities namely CM planning, change management, version management.
Database Management.
The FIM Team User Group Proudly sponsored by November 2014.
© 2007 by Prentice Hall 1 Introduction to databases.
Data entry: Validation
Checking data Chapter 7 Prepared by:Sir Mazhar Javed.
Objectives of Control The objectives of control are:  To ensure that all data are processed  To preserve the integrity of maintained data  To detect,
Alesco User Group Cairns Conference 2015 Using API’s in Alesco.
I.Information Building & Retrieval Learning Objectives: the process of Information building the responsibilities and interaction of each data managing.
In-depth Integrated Settlement RA43 Breakout Session RAWG Member Name, Airline James Shannon, IATA.
Discovering Computers Fundamentals Fifth Edition Chapter 9 Database Management.
Introduction to Databases Trisha Cummings. What is a database? A database is a tool for collecting and organizing information. Databases can store information.
Current Projects in DTEI Presented By: Tracy Jordan.
Database Design and Management CPTG /23/2015Chapter 12 of 38 Functions of a Database Store data Store data School: student records, class schedules,
C6 Databases. 2 Traditional file environment Data Redundancy and Inconsistency: –Data redundancy: The presence of duplicate data in multiple data files.
© 2006 Prentice Hall Business Publishing Accounting Information Systems, 10/e Romney/Steinbart1 of 43 Every transaction cycle: –Relates to other cycles.
Databases. What is a database?  A database is used to store data. The word DATA is actually Latin for FACTS. A database is, therefore, a place, or thing.
SYSTEM TESTING AND DEPLOYMENT CHAPTER 8. Chapter 8: System Testing and Deployment 2 KNOWLEDGE CAPTURE (Creation) KNOWLEDGE TRANSFER KNOWLEDGE SHARING.
Improving Data Quality Tuscaloosa County School System STI Office/District, McAleer PR.
INFO1408 Database Design Concepts Week 15: Introduction to Database Management Systems.
Copyright © 2008 Pearson Prentice Hall. All rights reserved Copyright © 2008 Prentice-Hall. All rights reserved. Committed to Shaping the Next.
Lesson 4.  After a table has been created, you may need to modify it. You can make many changes to a table—or other database object—using its property.
Flat Files Relational Databases
Unit 17: SDLC. Systems Development Life Cycle Five Major Phases Plus Documentation throughout Plus Evaluation…
Electronic Design Change Process Paul Tobin Jr.- PKMJ Technical Services.
DATA MANAGEMENT AND DATABASES. Data Management Data management is the process of controlling the information generated during a research project or transaction.
Sequential Processing to Update a File Please use speaker notes for additional information!
SQL Triggers, Functions & Stored Procedures Programming Operations.
Advanced Higher Computing Science
Introduction To DBMS.
What’s New in ProMonitor 9
Prepared By: Bobby Wan Microsoft Access Prepared By: Bobby Wan
Training Documentation – Replacing GSPR with RFQ 2.0
Overview of MDM Site Hub
Why did you choose us? To address and provide a solution to the many problems associated with your current manual filing system -Problems include: -Lack.
Chapter Ten Managing a Database.
Overview of Business Processes
Data Quality By Suparna Kansakar.
Sample Assessment & Governance Results
Presentation transcript:

Troy Eversen | 19 May 2015 Data Integrity Workshop

2 DATA INTEGRITY dirty data accuracy completeness quality business decisions errors consistency reporting reliability mistakes punctuation incomplete inaccurate data cleanse out dated facts transpose automation analysis paper based results proof credibility checking time over payment manual entry

3 What is Data Integrity? »Data Integrity refers to the overall accuracy, consistency and completeness of data. »Data Integrity is enforced in part by rules and procedures within Alesco and Web Self Service (e.g. Preventing duplicate entries), however, there are other ways the accuracy of your data can be compromised. »Data Integrity is critical as data is frequently used for business decisions – Bad data can lead to bad decisions

4 What is Data Integrity? »When Data Integrity is compromised the data cannot be reliably used – Compromised data is dirty data. »Dirty data is data that contains errors such as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data »Dirty data can be difficult to indentify and sometimes difficult to correct if left unmanaged.

5

6 Understanding the source of “dirty data” Dirty data can come from any number of sources and for any number of reasons. These include but are not limited to: »Manual processing errors »Inaccurate interpretation of requirements »Inconsistent entry of “non-mandatory” information »External data sources (Custom Interfaces / API Bulk Loads)

7 Manual processing errors »Paper documents anywhere in the processing chain that require manual input into Alesco can add to the number of errors. »Paper forms add to delays in processing that can cause inconsistent or incomplete results. »Users transposing numbers or incorrectly entering data. »User interpretation of data and requirements. »Users skipping through non-mandatory fields or entering data to get to the next screen.

8 External data sources »Different systems have different inputs and outputs and are set up differently. Miscommunication can happen between systems just as easily as it does between people. »Miscommunication of changes across different applications and systems within the organisation. For example, if a general ledger account number is closed in a finance system but not in Alesco. »Incorrectly formatted or prepared load files.

9 Alesco OUT IN You only get out what you put in... OR

10 Unfortunately there is no magic button......so where do you start?

11 Maintenance of configuration Proper maintenance of configuration goes a long way to preventing “dirty data” »End date old codes to remove them from LOV’s. »If a code is no longer required don’t be tempted to re-label it and use it for another purpose. For Example: End-date old positions – don’t recycle position numbers for different roles (this can cause havoc with reporting). »Decide on a single source of truth where multiple systems contain similar information.

12 Maintenance of security »Control who has access to update the system and put in place a framework for new configuration (e.g. Naming conventions and structure for codes etc) »User security is a must! If a user does not need access to certain data for their role, why give them access? »Its easy to give a user access to the “MAS” security group but fixing errors is not. Create role specific security groups »If people move out of a role, update their access to reflect the change – The same applies for terminated staff.

13 Make fields fit your requirements with UI Configuration »If users are skipping key fields required for reporting etc., consider making them mandatory with UI Configuration. »Re-label fields to use naming that is more meaningful to your business so users know the purpose of the field. »Different UI Configuration labels can be set up for different groups of users (e.g. employees in countries).

14 Make fields fit your requirements with UI Configuration Also available in WSS

15 Prevention is better than cure... Education on the importance of data accuracy »Often one of the main reasons for data not being entered into Alesco is the lack of understanding of why the data is required. »Educating on what the data is used for, and issues caused by errors or missing data, will assist in user awareness and reduce error rates.

16 Incorporate user help into Alesco

17 Incorporate user help into Alesco User presses F1 or

18 Is your data open for interpretation?

19 User-defined Business Rules »Alesco offers powerful functionality to create custom business rules. »Business rules can be created for a variety of purposes and can be used to validate and prevent common data integrity issues around insert and update. »Business rules are core functionality maintained in Alesco via FG376 and FG377.

20 WARNING With great power comes great responsibility Business rules are complex functionality and require a strong understanding of sql, pl/sql and Alesco table structures. Business rules create triggers in the database that can disrupt key processes if not configured correctly. Always thoroughly test functionality before implementing in Production.

21 Example business rule »A new rule could be established to prevent the creation of codes with specific characters that are known to cause issues with processes or third party interfaces. The rule is set up with a list of restricted entries

22 The rule is attached to the Alesco table and a record trigger is created On insert / update of a record, the rule is checked and entry of data is rejected where the rule fails validation.

23 Some degree of errors are inevitable... »Timely feedback for correction of errors is important »One idea is to use notifications via FD067 to check data that is frequently misinterpreted, incorrectly entered or skipped. For example: ›Set up an notification to advise when a user has entered an old Company Level code. › an employee when they have not provided specific EEO information.

24 Timely feedback for correction of errors »If users and employees are made accountable for their own “dirty data” they might be less likely to repeat the same mistake again.