The Concept: Moving from Data Analysis to Data Analytics

Transcription

1 The Concept: Moving from Data Analysis to Data Analytics May 19,

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3 Challenges: Addressing complex business demand with Data Analytics Solutions Business demands Business Analytics Data attributes Government Oversight Accountability Complex Business Needs o Government Oversight o Risk o Program & Service Delivery o Cost Complex solutions o Governance o Analytic Methods o Big Data Mgmt Volume Big Data with growth in absolute terms Velocity Real Time Decisions, predictive and prescriptive Liberal Platform Variety Structured (ie: relational DB), Unstructured (documents, audio, video etc.) Expenditure Management Standardization Behavioral change & performance People Outcomes and decision support Veracity Confidence, governance over internal and external data Insight Efficient & Effective Delivery of Programs and Services Process Technolog y Value What value? 3

4 Impact of Analytics 4

5 We are in the Midst of a Data-Enabled Shift Which is Transforming Our Clients and How we Serve Them We have transformed from a Digital Driven Economy to where the Economy is Digital 5

6 What is Big Data and What is Analytics? 6

7 Aligning D&A Capability to Value 7

8 Linking D&A Maturity and Business Value 8

9 Sample Tools and Techniques to Leverage Data Analytics (cont d) 9

10 Turning Data into Information Decisions Organizations face numerous challenges in harnessing the huge amounts of internal and external data available to drive true value within their businesses. 10

11 Practical Examples Defined 11

12 HISTORICAL MANUAL AUDIT THAT WAS PAPER-DRIVEN ANALYSIS SNAPSHOT OF A SMALL % OF SELECT TRANSACTIONS IN A DATA POPULATION SAMPLE ANALYSIS WITH LIMITED TRANSACTION COVERAGE CURRENT OVER 5 YEARS OF EXPERIENCE WITH DATA ANALYTICS eaudit DATA DRIVEN TECHNOLOGY DATA ANALYSIS 1 MILLION+ POPULATION SAMPLING ENHANCED QUALITY USING TECHNOLOGY + DATA ENABLES USE OF AUTOMATION FUTURE TECHNOLOGY INVESTMENTS ENABLES 100% ANALYSIS FULL POPULATIONS PATTERN ASSESSMENT DATA ANALYSIS OF OUTLIERS + ANOMALIES BENCHMARKING INTERNAL INDUSTRY PEER IDENTIFY PROCESS IMPROVEMENTS BUSINESS PERFORMANCE TRENDS 12

13 Other Examples and Discussion Points for Big Data.. Predicting revenue based on hurricane season forecasts Predicting revenue based on satellite imagery Using crowd sourcing to predict trends Open Data 13

14 Thank you Kevin Kolliniatis, CPA, CA, CISA Senior Manager, Public Sector KPMG LLP 2016 KPMG LLP, a Canadian limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International

15 Detail Heading Detail Heading Detail Heading Detail Heading kpmg.ca 2016 KPMG LLP, a Canadian limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. The KPMG name and logo are registered trademarks or trademarks of KPMG International. The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavour to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.