|
|
- Merryl Baker
- 6 years ago
- Views:
Transcription
1
2
3
4
5
6 Format of Session 1. Forensic Accounting then and now 2. Overview of Data Analytics 3. Fraud Analytics Basics 4. Advanced Fraud Analytics 5. Data Visualization 6. Wrap-up Question are welcome and encouraged! 6
7
8 How Things Used to Be Paper More on-site data collection Volume Types of information Data compilation Fraud focus being more reactive 8
9 How Things are Evolving Move to digital Data in the Cloud More varied sources and types of information Need for different skill sets to deal with the volume and nature of information Proactive exploratory analysis 9
10
11
12 Fighting Fire with Fire 99% of both our problems and solutions come from technology. - Kevin Kelly, Senior Maverick, Wired Magazine 12
13 What is Data Analytics? The science of examining large volumes of data, with the purpose of drawing conclusions about the information. 13
14
15 Challenges for Internal Audit Some of the challenging expectation faced by internal audit include: Harnessing and managing the power of data and technology to be more efficient Broadening focus to include Risk Management Delivering more robust and actionable insights Driving change within the business 15
16 What Data Analytics can Do for You Many companies use data analytics in order to: make better business decisions; gather business intelligence; understand industry trends. Data analytics uses sophisticated software tools in order to identify or uncover patterns/relationships and anomalous transactions. 16
17
18
19 Rules-Based Analytics in Internal Audit Help focus audit effort on the key issues Examples: Identify unauthorized payments Identify suspicious journals Identify unauthorized creation, modification, access of an account Identify invalid information Identify duplicate/near-duplicate information Identify extreme deviations from the norm 19
20 Start Your Journey with Rules-Based Analytics Based on your audit objectives, risk assessment, or hypotheses: Identify the data that you have Create rules that are testable against your objectives or hypotheses Run the rules on the data to identify exceptions to investigate 20
21 Simple Rules-Based Analytics Example 1 Risk False vendors Conflicts of interest Rule Identify all vendors with the same address or phone number as an employee. 21
22 Simple Rules-Based Analytics Example 2 Risk Theft of proprietary information Accessing information beyond job requirements Rule Identify all system administrator logins between 9PM and 6AM. 22
23 Intermediate Rules-Based Analytics Example 1 Risk Double-billing by vendors Over-payment to vendors Rule Identify fuzzy duplicates on invoice numbers. Assess whether same vendor 23
24
25 Intermediate Rules-Based Analytics Example 2 Risk Inappropriate use of purchase cards Rule Identify purchase card transactions that are outside of what would be considered business use. 25
26 Applied Data Visualization Example 26
27 Basic Does Not Equal Simple Rules-Based Analysis can be used to get sophisticated results Combine more data sets (internal and external) Unstructured data sets Larger data sets 27
28 Skills You Need to do Basic Analytics Data Extraction Data Wrangling Getting the raw data you extracted into a format that you can perform your analysis on 80% of the work Technical Skills Databases Understanding of the Business Communication Skills 28
29 Tools You Need to do Analytics Start with what you have! Excel Data Wrangling Text-to-x conversion (e.g., DATE) Text string manipulation (e.g., CONCATENATE, LEFT, MID, RIGHT, LEN, SEARCH, etc.) Analysis VLOOKUP/HLOOKUP/MATCH/etc. Pivot Tables 29
30 Limitations of Excel Data is easily mutable Can introduce errors in the analysis Data size limitations Summarizing and combining/joining data is cumbersome Hard to control and document your analysis e.g., rolling back analysis if a mistake or data issue is discovered 30
31 Excel Alternatives Microsoft Access Possibly also included on your computer SQL Server or other database software Your IT department may have a license and instance that could be used Open-source analysis software or programming languages R, Python, etc. Proprietary analysis software or programming languages SAS, SPSS, IDEA, ACL 31
32
33 What does Advanced Mean? For the purpose of this presentation: Using external or non-traditional data sources with internal data sources Using statistical methods Using data mining methods 33
34 External or Non-Traditional Data Sources GPS Data Fleet Tracking Data Social Media Feeds Wearables Network log data 34
35 Use of Geospatial information Example 1 Mandate Client asked us to review exceptions to policies related to hotel stays and transportation expenses Interesting result: Identified employees that were staying in hotels less than 40km from their own residence. 35
36 Use of Geospatial information Example 2 36
37 Use of Geospatial Information Example 3 Source: 37
38 Use of Non-Traditional Data Source Example Mandate Client asked us to help them analyze whether there was evidence of employees booking time when they were not working Data Sources used: Payroll data Fleet tracking data (GPS + engine data) Car log data (paper) 38
39 Using Statistical Methods Benford s Law Benford s Law is about the frequency distribution of first/leading digits in a natural population of numbers. Natural populations are not bound by minimums or maximums. According to Benford s Law, in a natural population, the first digit is more often 1 than 2, more often 2 than 3, and so on, up to 9. 39
40 Benford s Law 40
41 Using Benford s Law to Detect Bid-Collusion Application to Public Bid Contracts Bid amounts are generally limited by the nature of the work (presence of a maximum value), i.e., Benford s Law does not apply. BUT, the differences between the amounts submitted by the winner of the tender and the 2nd, 3rd, etc. bidder represents a natural population of numbers where Benford s Law is applicable. We compare these differences with Benford's predictions and then refine the analysis by segments (products, years, users, types of services, etc.) 41
42 Bid-Collusion Example 42
43 What is Data Mining? Using computing power to help you find patterns which you might not be able to detect by just looking at the data manually. 43
44 Data Mining Testing Example Examined a database using data mining software Identified if-then rules that applied to the database at least 95% of the time Extracted the exceptions or deviations from these rules such that they could be further reviewed. Results 14 rules were identified. E.g. If NAME is XXXX then COMM_PCT is
45
46 What is Data Visualization? the presentation of data in a visual format (e.g. pictorial, graphical), enabling users to grasp difficult analytical concepts, identify new trends or patterns, and gain a better understanding of the underlying data. 46
47 Why is Data Visualization Important? 70% 30% Visualization has been scientifically shown to make it easier to solve problems and make better decisions 47
48 The Risks of Poor Data Visualization Keep it Simple, Stupid! It seems that perfection is reached not when there is nothing to add, but when there is nothing left to take away. -Antoine de Saint Exupéry 48
49 The Risks of Poor Data Visualization 49
50 Poor Data Visualization Truncated Axes Truncating axes on a chart can exaggerate differences in trends, resulting in a misleading representation of the data. 50
51 Poor Data Visualization Dual Axes By adjusting the scaling of each individual axis, data can be shown to correlate when in reality no such correlation exists. Source: Spurious Correlations 51
52 Poor Data Visualization Narrowed Scopes 1 Omitting Data By narrowing the scope of the data presented, visualizations can mislead by failing to provide context and points of reference for interpretation. A common example of this is limited time scope. 52
53 Poor Data Visualization Narrowed Scopes 53
54 Poor Data Visualization Inaccurate Scaling Single-Dimension Scaling on Multiple Dimensions By limiting the scaling of graphical representations to a single dimension, data can be shown with much greater variance than is factually present. This is a frequent error made when using size to denote a specific measure. Source: 54
55 Poor Data Visualization Extraneous Complexity Irrelevant Decoration Adding additional elements to data visualizations (e.g. images, icons, textures) often serve to detract from the impact of the visualization rather than add to it. 3D Effects Adding an additional dimension to charts rarely adds value to viewers of the visualization, and can make the original chart more difficult to interpret. Poor Organization Although standalone visualizations may be well designed, the impact may be impeded by poor presentation or organization. 55
56 Illustrative Examples: Pie Charts 56
57 Illustrative Examples: Effective Use of Colour 1 57
58 Illustrative Examples: Effective Use of Colour 2 Sequential Colours are ordered from Low to High typically to illustrate concentration or strength of a selected measure. 58
59 Illustrative Examples: Effective Use of Colour 3 Diverging Two sequential schemes are arranged to extend from a neutral mid-point, frequently used to illustrate deviations specific ranges (e.g. budget, forecasts). 59
60 Illustrative Examples: Effective Use of Colour 4 Categorical Contrasted colours used to denote different categories of data. Should usually be limited to a maximum of six. 60
61 Illustrative Examples: Effective Use of Colour 5 61
62 Illustrative Examples: Effective Use of Colour 6 62
63 Additional Guiding Principles 63
64
65
66
67
68
69
70
71
72 Internal Audit & Data Analytics Access to all parts of the organization Big picture view of the organization Can see the connections between all the data in the organization Inherently analytic mind-set Business Knowledge Access to senior management and governance 72
73
74
75
WHITE PAPER. The General Data Protection Regulation: What Title It Means and How SAS Data Management Can Help
WHITE PAPER The General Data Protection Regulation: What Title It Means and How SAS Data Management Can Help ii Contents Personal Data Defined... 1 Why the GDPR Is Such a Big Deal... 2 Are You Ready?...
More informationVERSION EIGHT PRODUCT PROFILE. Be a better auditor. You have the knowledge. We have the tools.
VERSION EIGHT PRODUCT PROFILE Be a better auditor. You have the knowledge. We have the tools. Improve your audit results and extend your capabilities with IDEA's powerful functionality. With IDEA, you
More informationOutlier Detection With SQL And R. Kevin Feasel, Engineering Manager, ChannelAdvisor Moderated By: Satya Jayanty
Outlier Detection With SQL And R Kevin Feasel, Engineering Manager, ChannelAdvisor Moderated By: Satya Jayanty Technical Assistance If you require assistance during the session, type your inquiry into
More informationData Management Glossary
Data Management Glossary A Access path: The route through a system by which data is found, accessed and retrieved Agile methodology: An approach to software development which takes incremental, iterative
More informationFunctionality Guide. for CaseWare IDEA Data Analysis
Functionality Guide for CaseWare IDEA Data Analysis CaseWare IDEA Quick Access Functionality Crib Sheet A quick guide to the major functionality you will use within IDEA. FILE TAB: Passport The single
More informationIntroduction to Data Mining and Data Analytics
1/28/2016 MIST.7060 Data Analytics 1 Introduction to Data Mining and Data Analytics What Are Data Mining and Data Analytics? Data mining is the process of discovering hidden patterns in data, where Patterns
More informationData Analyst Nanodegree Syllabus
Data Analyst Nanodegree Syllabus Discover Insights from Data with Python, R, SQL, and Tableau Before You Start Prerequisites : In order to succeed in this program, we recommend having experience working
More informationNot your Father s SIEM
Not your Father s SIEM Getting Better Insights & Results Bill Thorn Director, Security Operations Apollo Education Group Agenda Why use a SIEM? What is a SIEM? Benefits of Using a SIEM Considerations Before
More informationData Analyst Nanodegree Syllabus
Data Analyst Nanodegree Syllabus Discover Insights from Data with Python, R, SQL, and Tableau Before You Start Prerequisites : In order to succeed in this program, we recommend having experience working
More informationMicroStrategy Desktop Quick Start Guide
MicroStrategy Desktop Quick Start Guide Version: 10.4 10.4, June 2017 Copyright 2017 by MicroStrategy Incorporated. All rights reserved. If you have not executed a written or electronic agreement with
More informationManagement summary. From people in business to people doing business
Product overview Management summary Xeelo is a software platform which will increase your existing business performance exponentially. Whether you need to refine your existing ERP, better integrate many
More informationCreating a Basic Chart in Excel 2007
Creating a Basic Chart in Excel 2007 A chart is a pictorial representation of the data you enter in a worksheet. Often, a chart can be a more descriptive way of representing your data. As a result, those
More informationTHE CORPORATE CON: INTERNAL FRAUD AND THE AUDITOR
THE CORPORATE CON: INTERNAL FRAUD AND THE AUDITOR GLOBAL HEADQUARTERS THE GREGOR BUILDING 716 WEST AVE AUSTIN, TX 78701-2727 USA TABLE OF CONTENTS I. INTRODUCTION Video Supplement... 1 Course Objectives
More informationTHE SIX ESSENTIAL CAPABILITIES OF AN ANALYTICS-DRIVEN SIEM
THE SIX ESSENTIAL CAPABILITIES OF AN ANALYTICS-DRIVEN SIEM Modern threats demand analytics-driven security and continuous monitoring Legacy SIEMs are Stuck in the Past Finding a mechanism to collect, store
More informationCITADEL INFORMATION GROUP, INC.
CITADEL INFORMATION GROUP, INC. The Role of the Information Security Assessment in a SAS 99 Audit Stan Stahl, Ph.D. President Citadel Information Group, Inc. The auditor has a responsibility to plan and
More informationICBA Summary of FFIEC Cybersecurity Assessment Tool (May 2017 Update)
ICBA Summary of FFIEC Cybersecurity Assessment Tool (May 2017 Update) June 2017 INSERT YEAR HERE Contact Information: Jeremy Dalpiaz AVP, Cyber and Data Security Policy Jeremy.Dalpiaz@icba.org ICBA Summary
More informationDetect Fraud & Financial Crime
IBM i2 Intelligence Analysis Detect Fraud & Financial Crime Acquire Discover Action! Urs Christen Security Sales Government urs.christen@ch.ibm.com 1 IBM Security 2014 IBM Corporation Build an integrated
More informationLies, Damned Lies and Statistics Using Data Mining Techniques to Find the True Facts.
Lies, Damned Lies and Statistics Using Data Mining Techniques to Find the True Facts. BY SCOTT A. BARNES, CPA, CFF, CGMA The adversarial nature of the American legal system creates a natural conflict between
More informationData-Driven Policing Summit
Reduce Crime and Manage Risk in Policing with Data Analysis Data-Driven Policing Summit Using Data Analytics and Predictive Modeling to Mitigate Risk and Reduce Crime September 18-19, 2017 Washington,
More informationManagement Information Systems Review Questions. Chapter 6 Foundations of Business Intelligence: Databases and Information Management
Management Information Systems Review Questions Chapter 6 Foundations of Business Intelligence: Databases and Information Management 1) The traditional file environment does not typically have a problem
More informationMicroStrategy Desktop Quick Start Guide
MicroStrategy Desktop Quick Start Guide Version: 10.4 10.4, December 2017 Copyright 2017 by MicroStrategy Incorporated. All rights reserved. Trademark Information The following are either trademarks or
More information1. The narratives, diagrams, charts, and other written materials that explain how a system works are collectively called
CH 3 MULTIPLE CHOICE 1. The narratives, diagrams, charts, and other written materials that explain how a system works are collectively called a) documentation. b) data flows. c) flowcharts. d) schema.
More informationA Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA)
International Journal of Innovation and Scientific Research ISSN 2351-8014 Vol. 12 No. 1 Nov. 2014, pp. 217-222 2014 Innovative Space of Scientific Research Journals http://www.ijisr.issr-journals.org/
More informationCOPYRIGHTED MATERIAL PART I. LESSON 1: Introducing VBA. LESSON 2: Getting Started with Macros. LESSON 3: Introducing the Visual Basic Editor
PART I LESSON 1: Introducing VBA LESSON 2: Getting Started with Macros LESSON 3: Introducing the Visual Basic Editor LESSON 4: Working in the VBE COPYRIGHTED MATERIAL 1 Welcome to your first lesson in
More informationIntroduction to Data Science
UNIT I INTRODUCTION TO DATA SCIENCE Syllabus Introduction of Data Science Basic Data Analytics using R R Graphical User Interfaces Data Import and Export Attribute and Data Types Descriptive Statistics
More informationTrustwave Managed Security Testing
Trustwave Managed Security Testing SOLUTION OVERVIEW Trustwave Managed Security Testing (MST) gives you visibility and insight into vulnerabilities and security weaknesses that need to be addressed to
More informationAnalytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset.
Glossary of data mining terms: Accuracy Accuracy is an important factor in assessing the success of data mining. When applied to data, accuracy refers to the rate of correct values in the data. When applied
More informationBPS Suite and the OCEG Capability Model. Mapping the OCEG Capability Model to the BPS Suite s product capability.
BPS Suite and the OCEG Capability Model Mapping the OCEG Capability Model to the BPS Suite s product capability. BPS Contents Introduction... 2 GRC activities... 2 BPS and the Capability Model for GRC...
More informationPART 5: INFORMATION TECHNOLOGY RECORDS
PART 5: INFORMATION TECHNOLOGY RECORDS SECTION 5 1: RECORDS OF AUTOMATED APPLICATIONS GR5800 01 AUDIT TRAIL RECORDS Files needed for electronic data audits such as files or reports showing transactions
More informationNow, Data Mining Is Within Your Reach
Clementine Desktop Specifications Now, Data Mining Is Within Your Reach Data mining delivers significant, measurable value. By uncovering previously unknown patterns and connections in data, data mining
More informationConstruction and Real Estate. Improve system performance and data security with SQL Server
Construction and Real Estate Improve system performance and data security with SQL Server Sage Impact 2 3 Improve system performance and data security with SQL Server 3 What is Microsoft SQL Server? 3
More informationSection 1: Definition of Fraud / Fraud Analysis Coderre Chapters 1 6, 8, & 9
Page 1 of 13 Section 1: Definition of Fraud / Fraud Analysis Coderre Chapters 1 6, 8, & 9 1. True or false, is ALL theft fraud? a. True b. False 2. True or false, are ALL deceptive statements examples
More informationQuickBooks Online Certification Bootcamp: May 23 &
QuickBooks Online Certification Bootcamp: May 23 & 24 2018 Eastern Time Wednesday, May 23, 2018 Track 1 Eastern Time Wednesday, May 23, 2018 Track 2 10:45 AM 11:00 AM 11:00 AM 11:30 AM 11:30 AM 11:45 AM
More informationMay 4, :00 3:00pm ET
The Plague of Spreadsheet Fraud and How to Address It May 4, 2010 2:00 3:00pm ET Technical Support# 1-800-xxx-xxxx Welcome Agenda Speaker Ralph Baxter, Founder and CEO, ClusterSeven Agenda 1. Why is spreadsheet
More informationThis demonstration is aimed at anyone with lots of text, unstructured or multiformat data to analyse.
1 2 This demonstration is aimed at anyone with lots of text, unstructured or multiformat data to analyse. This could be lots of Word, PDF and text file formats or in various databases or spreadsheets,
More informationSecurity analytics: From data to action Visual and analytical approaches to detecting modern adversaries
Security analytics: From data to action Visual and analytical approaches to detecting modern adversaries Chris Calvert, CISSP, CISM Director of Solutions Innovation Copyright 2013 Hewlett-Packard Development
More informationChapter 6 VIDEO CASES
Chapter 6 Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:
More informationEXAM PREPARATION GUIDE
When Recognition Matters EXAM PREPARATION GUIDE PECB Certified ISO 9001 Lead Auditor www.pecb.com The objective of the PECB Certified ISO 9001 Lead Auditor examination is to ensure that the candidate possesses
More informationCell and PDAs Policy
Cell and PDAs Policy CHAPTER: 13 Information Services Department SECTION: 13 SUBJECT: Cell Phones and PDAs POLICY #: 13.13.00 Revised OFFICE/DEPARTMENT: Information Services EFFECTIVE DATE: October 1,
More informationSAS/STAT 13.1 User s Guide. The NESTED Procedure
SAS/STAT 13.1 User s Guide The NESTED Procedure This document is an individual chapter from SAS/STAT 13.1 User s Guide. The correct bibliographic citation for the complete manual is as follows: SAS Institute
More informationSOLUTION BRIEF RSA ARCHER IT & SECURITY RISK MANAGEMENT
RSA ARCHER IT & SECURITY RISK MANAGEMENT INTRODUCTION Organizations battle growing security challenges by building layer upon layer of defenses: firewalls, antivirus, intrusion prevention systems, intrusion
More informationCOUNTY OF GOGEBIC REQUEST FOR PROPOSAL FOR WEBSITE and LOGO REDESIGN and CONTENT MANAGEMENT SERVICES
COUNTY OF GOGEBIC REQUEST FOR PROPOSAL FOR WEBSITE and LOGO REDESIGN and CONTENT MANAGEMENT SERVICES Notice is hereby given that Gogebic County will receive proposals for the county s website and logo
More informationRSA INCIDENT RESPONSE SERVICES
RSA INCIDENT RESPONSE SERVICES Enabling early detection and rapid response EXECUTIVE SUMMARY Technical forensic analysis services RSA Incident Response services are for organizations that need rapid access
More informationReduce fraud losses and improve operational efficiency with advanced fraud detection technology
Technical White Paper Reduce fraud losses and improve operational efficiency with advanced fraud detection technology Risk Solutions Most institutions know identity fraud exists and many already have identity
More informationSecurity Intelligence and Next Steps
CHAPTER 7 Security Intelligence and Next Steps INFORMATION IN THIS CHAPTER: n Overview (17 pages) n Security Intelligence n Basic Security Intelligence Analysis n Business Extension of Security Intelligence
More informationMcAfee Total Protection for Data Loss Prevention
McAfee Total Protection for Data Loss Prevention Protect data leaks. Stay ahead of threats. Manage with ease. Key Advantages As regulations and corporate standards place increasing demands on IT to ensure
More informationMicroStrategy Desktop
MicroStrategy Desktop Quick Start Guide MicroStrategy Desktop is designed to enable business professionals like you to explore data, simply and without needing direct support from IT. 1 Import data from
More informationRun the business. Not the risks.
Run the business. Not the risks. RISK-RESILIENCE FOR THE DIGITAL BUSINESS Cyber-attacks are a known risk to business. Today, with enterprises becoming pervasively digital, these risks have grown multifold.
More informationPrivacy Statement. Your privacy and trust are important to us and this Privacy Statement ( Statement ) provides important information
Privacy Statement Introduction Your privacy and trust are important to us and this Privacy Statement ( Statement ) provides important information about how IT Support (UK) Ltd handle personal information.
More informationThe Definitive Guide to Preparing Your Data for Tableau
The Definitive Guide to Preparing Your Data for Tableau Speed Your Time to Visualization If you re like most data analysts today, creating rich visualizations of your data is a critical step in the analytic
More informationEXAM PREPARATION GUIDE
When Recognition Matters EXAM PREPARATION GUIDE PECB Certified ISO/IEC 20000 Lead Auditor www.pecb.com The objective of the Certified ISO/IEC 20000 Lead Auditor examination is to ensure that the candidate
More informationLearning Objectives for Data Concept and Visualization
Learning Objectives for Data Concept and Visualization Assignment 1: Data Quality Concept and Impact of Data Quality Summarize concepts of data quality. Understand and describe the impact of data on actuarial
More informationUsing Threat Analytics to Protect Privileged Access and Prevent Breaches
Using Threat Analytics to Protect Privileged Access and Prevent Breaches Under Attack Protecting privileged access and preventing breaches remains an urgent concern for companies of all sizes. Attackers
More informationArticle II - Standards Section V - Continuing Education Requirements
Article II - Standards Section V - Continuing Education Requirements 2.5.1 CONTINUING PROFESSIONAL EDUCATION Internal auditors are responsible for maintaining their knowledge and skills. They should update
More informationUnit 1 Lesson 4 Representing Data. Copyright Houghton Mifflin Harcourt Publishing Company
Florida Benchmarks SC.6.N.1.1 Define a problem from the sixth grade curriculum, use appropriate reference materials to support scientific understanding, plan and carry out scientific investigation of various
More informationSix Core Data Wrangling Activities. An introductory guide to data wrangling with Trifacta
Six Core Data Wrangling Activities An introductory guide to data wrangling with Trifacta Today s Data Driven Culture Are you inundated with data? Today, most organizations are collecting as much data in
More informationPlanning for Information Network
Planning for Information Network Lecture 2: The network design methodology Assistant Teacher Samraa Adnan Al-Asadi 1 Contents The PPDIOO network lifecycle. Benefits of the lifecycle approach to network
More informationIJESRT. (I2OR), Publication Impact Factor: (ISRA), Impact Factor: 2.114
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY EVALUATING ISO STANDARDS APPLICATION OF SECURITY REQUIREMENTS OF E- BANKING IN SUDAN Inshirah M. O. Elmaghrabi*, Hoida A. Abdelgadir,
More informationLesson 1. Exploring QuickBooks INTRODUCTION OBJECTIVES
Exploring QuickBooks INTRODUCTION This first lesson is an introduction to the QuickBooks software program and the chart of accounts, and it contains two reading assignments. Assignment 1 takes you on a
More informationDr. Michael Curry. Oregon. The Big Picture: SQL Overview and Getting the Most from SQL Saturday
Dr. Michael Curry michael.curry@wsu.edu Oregon The Big Picture: SQL Overview and Getting the Most from SQL Saturday Academic Data Management E-Commerce Entrepreneurship Dr. Michael Curry /michaellcurry/
More informationData Analytics for Auditors. ALGA Regional Training Panella County, FL April 10 th, 2018
Data Analytics for Auditors ALGA Regional Training Panella County, FL April 10 th, 2018 1 Analytics and Audit What we have Massive quantities of data and analytical tools What we need Analytical skills,
More informationAdobe Target Analyst Adobe Certified Expert Exam Guide
Adobe Target Analyst Adobe Certified Expert Exam Guide Exam number: 9A0-399 Note: To become certified as an Adobe Target Analyst requires passing this exam and exam 9A0-398 Adobe Target Business Practitioner.
More informationBIG DATA ANALYTICS IN FORENSIC AUDIT. Presented in Mombasa. Uphold public interest
BIG DATA ANALYTICS IN FORENSIC AUDIT Presented in Mombasa Uphold public interest Nasumba Kwatukha Kizito CPA,CIA,CISA,CISI,CRMA,CISM,CISSP,CFE,IIK Internal Audit, Risk and Compliance Strathmore University
More informationISC2 EXAM - SSCP. Systems Security Certified Practitioner. Buy Full Product.
ISC2 EXAM - SSCP Systems Security Certified Practitioner Buy Full Product http://www.examskey.com/sscp.html Examskey ISC2 SSCP exam demo product is here for you to test the quality of the product. This
More informationProtecting Against Modern Attacks. Protection Against Modern Attack Vectors
Protecting Against Modern Attacks Protection Against Modern Attack Vectors CYBER SECURITY IS A CEO ISSUE. - M C K I N S E Y $4.0M 81% >300K 87% is the average cost of a data breach per incident. of breaches
More informationImplementing ITIL v3 Service Lifecycle
Implementing ITIL v3 Lifecycle WHITE PAPER introduction GSS INFOTECH IT services have become an integral means for conducting business for all sizes of businesses, private and public organizations, educational
More informationIvy s Business Analytics Foundation Certification Details (Module I + II+ III + IV + V)
Ivy s Business Analytics Foundation Certification Details (Module I + II+ III + IV + V) Based on Industry Cases, Live Exercises, & Industry Executed Projects Module (I) Analytics Essentials 81 hrs 1. Statistics
More informationProgress DataDirect For Business Intelligence And Analytics Vendors
Progress DataDirect For Business Intelligence And Analytics Vendors DATA SHEET FEATURES: Direction connection to a variety of SaaS and on-premises data sources via Progress DataDirect Hybrid Data Pipeline
More informationAnd the benefits are immediate minimal changes to the interface allow you and your teams to access these
Find Out What s New >> With nearly 50 enhancements that increase functionality and ease-of-use, Minitab 15 has something for everyone. And the benefits are immediate minimal changes to the interface allow
More informationManagement Information Systems
Foundations of Business Intelligence: Databases and Information Management Lecturer: Richard Boateng, PhD. Lecturer in Information Systems, University of Ghana Business School Executive Director, PearlRichards
More informationDatameer for Data Preparation:
Datameer for Data Preparation: Explore, Profile, Blend, Cleanse, Enrich, Share, Operationalize DATAMEER FOR DATA PREPARATION: EXPLORE, PROFILE, BLEND, CLEANSE, ENRICH, SHARE, OPERATIONALIZE Datameer Datameer
More informationDeploying, Managing and Reusing R Models in an Enterprise Environment
Deploying, Managing and Reusing R Models in an Enterprise Environment Making Data Science Accessible to a Wider Audience Lou Bajuk-Yorgan, Sr. Director, Product Management Streaming and Advanced Analytics
More informationRippleMatch Privacy Policy
RippleMatch Privacy Policy This Privacy Policy describes the policies and procedures of RippleMatch Inc. ( we, our or us ) on the collection, use and disclosure of your information on https://www.ripplematch.com/
More informationThe University of Iowa Intelligent Systems Laboratory The University of Iowa Intelligent Systems Laboratory
Warehousing Outline Andrew Kusiak 2139 Seamans Center Iowa City, IA 52242-1527 andrew-kusiak@uiowa.edu http://www.icaen.uiowa.edu/~ankusiak Tel. 319-335 5934 Introduction warehousing concepts Relationship
More informationHP Automation Insight
HP Automation Insight For the Red Hat Enterprise Linux and SUSE Enterprise Linux operating systems AI SA Compliance User Guide Document Release Date: July 2014 Software Release Date: July 2014 Legal Notices
More informationSentryWire Next generation packet capture and network security.
Next generation packet capture and network security. 1 The data landscape 5 big cyber security trends for 2018 More data, more danger. Data proliferation brings many new opportunities but also many downsides:
More informationSentryWire Next generation packet capture and network security.
Next generation packet capture and network security. 1 The data landscape More data, more danger. Data proliferation brings many new opportunities but also many downsides: more data breaches, more sophisticated
More informationAUDITING (PART-18) (UNIT-III) INTERNAL CONTROL (PART 4)
1. INTRODUCTION AUDITING (PART-18) (UNIT-III) INTERNAL CONTROL (PART 4) Hello students welcome to the lecture series of auditing. Today we shall be taking up unit 3 rd and under unit 3 rd we shall continue
More informationEXAM PREPARATION GUIDE
When Recognition Matters EXAM PREPARATION GUIDE PECB Certified ISO 22000 Lead Auditor www.pecb.com The objective of the Certified ISO 22000 Lead Auditor examination is to ensure that the candidate has
More informationTHINGS YOU NEED TO KNOW BEFORE DELVING INTO THE WORLD OF DIGITAL EVIDENCE. Roland Bastin Partner Risk Advisory Deloitte
Inside magazine issue 16 Part 03 - From a risk and cyber perspective perspective Roland Bastin Partner Risk Advisory Deloitte Gunnar Mortier Senior Manager Risk Advisory Deloitte THINGS YOU NEED TO KNOW
More informationPlay with Python: An intro to Data Science
Play with Python: An intro to Data Science Ignacio Larrú Instituto de Empresa Who am I? Passionate about Technology From Iphone apps to algorithmic programming I love innovative technology Former Entrepreneur:
More informationData Analysis Utilizing Excel - Part 2 By Palani Murugappan
Data Analysis Utilizing Excel - Part 2 By Palani Murugappan Understanding data analysis In the last article, the process of data analysis in terms of selecting the lowest two quotations from various vendors;
More informationVisualization? Information Visualization. Information Visualization? Ceci n est pas une visualization! So why two disciplines? So why two disciplines?
Visualization? New Oxford Dictionary of English, 1999 Information Visualization Matt Cooper visualize - verb [with obj.] 1. form a mental image of; imagine: it is not easy to visualize the future. 2. make
More informationIn-Memory Analytics with EXASOL and KNIME //
Watch our predictions come true! In-Memory Analytics with EXASOL and KNIME // Dr. Marcus Dill Analytics 2020 The volume and complexity of data today and in the future poses great challenges for IT systems.
More informationBig Data Analytics: What is Big Data? Stony Brook University CSE545, Fall 2016 the inaugural edition
Big Data Analytics: What is Big Data? Stony Brook University CSE545, Fall 2016 the inaugural edition What s the BIG deal?! 2011 2011 2008 2010 2012 What s the BIG deal?! (Gartner Hype Cycle) What s the
More informationUnit title: IT in Business: Advanced Databases (SCQF level 8)
Higher National Unit Specification General information Unit code: F848 35 Superclass: CD Publication date: January 2017 Source: Scottish Qualifications Authority Version: 02 Unit purpose This unit is designed
More informationIDEA Official Winter UK Release. James Loughlin Head of Training & Consultancy AuditWare Systems Ltd
IDEA 10.3 Official Winter UK Release James Loughlin Head of Training & Consultancy AuditWare Systems Ltd Agenda Welcome IDEA 10.3 UK Release New Features and Enhancements SmartAnalyzer Apps & Tutorial
More informationTHE BASICS. 2. Changes
Thank you for using Aubrey Allen or visiting one of our websites. This policy explains the what, how, and why of the information we collect when you visit one of our websites, or when you use our Services.
More informationPRIVACY POLICY QUICK GUIDE TO CONTENTS
PRIVACY POLICY This privacy policy describes the policies and practices of Comodo Security Solutions, Inc. and Comodo Security Solutions Ltd. (collectively and individually referred to herein as "Comodo"),
More informationChapter 3. Foundations of Business Intelligence: Databases and Information Management
Chapter 3 Foundations of Business Intelligence: Databases and Information Management THE DATA HIERARCHY TRADITIONAL FILE PROCESSING Organizing Data in a Traditional File Environment Problems with the traditional
More informationPROVIDING INVESTIGATIVE SOLUTIONS
PROVIDING INVESTIGATIVE SOLUTIONS Experienced Professionals Northeast Intelligence Group, Inc. (NEIG) has been helping clients meet challenges for more than twenty years. By providing meaningful and timely
More informationPolicy 24 Identity Theft Prevention Program IDENTITY THEFT PREVENTION PROGRAM OF WEBB CREEK UTILITY DISTRICT
Policy 24 Identity Theft Prevention Program IDENTITY THEFT PREVENTION PROGRAM OF WEBB CREEK UTILITY DISTRICT The Utility maintains accounts for its customers to pay for utility service where bills are
More informationSAS Visual Analytics 8.2: Working with Report Content
SAS Visual Analytics 8.2: Working with Report Content About Objects After selecting your data source and data items, add one or more objects to display the results. SAS Visual Analytics provides objects
More informationAdvanced Security Tester Course Outline
Advanced Security Tester Course Outline General Description This course provides test engineers with advanced skills in security test analysis, design, and execution. In a hands-on, interactive fashion,
More informationDOWNLOAD PDF LEARN TO USE MICROSOFT ACCESS
Chapter 1 : Microsoft Online IT Training Microsoft Learning Each video is between 15 to 20 minutes long. The first one covers the key concepts and principles that make Microsoft Access what it is, and
More informationExpense Management for Microsoft Dynamics NAV
Expense Management for Microsoft Dynamics NAV Tables and Fields Documentation - Version 2.60 Expense Management - Tables and Fields Documentation - Version 2.50 Page 1 / 67 TABLE OF CONTENTS INTRODUCTION...
More informationThe chances are excellent that your company will
Set Up Chart of Accounts and Start Dates The chances are excellent that your company will have been operating, if only for a short time, prior to the time you start using QuickBooks. To produce accurate
More informationData Visualization Techniques
Data Visualization Techniques From Basics to Big Data with SAS Visual Analytics WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Generating the Best Visualizations for Your Data... 2 The
More informationCopyright 2013 EMC Corporation. All rights reserved. BIG DATA AND SECURITY JOINING FORCES
1 BIG DATA AND SECURITY JOINING FORCES 2 Agenda Security for Big Data Big Data for Security Conclusions Structured + Unstructured Data = Big Telemetry, Location-Based, etc. Structured in Relational Databases
More informationBusiness Data Analytics
MTAT.03.319 Business Data Analytics Lecture 9 The slides are available under creative common license. The original owner of these slides is the University of Tartu Fraud Detection Wrongful act for financial
More information