April 28, 2015 Virginia Tech. Data Analytics “Analytics is the combustion engine of business, and it will be necessary for organizations that want to.

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April 28, 2015 Virginia Tech

Data Analytics “Analytics is the combustion engine of business, and it will be necessary for organizations that want to grow, innovate and optimize efficiency. Given its far-reaching impact, it’s one of the few software markets that thrive even in adversity” Rita Sallam, BI Analyst, Gartner What is Data Analytics? How would you define it

Data Analytics Maturity Model

Tools, Technology and Techniques

PwC Draft VisualizationDashboarding Trending & comparisons Data Financial, Operational Structured, Unstructured, Internal, External Analytics; discovery and communication of meaningful patterns in data Big data; collection of large and complex data sets Speed and portability; available anytime, anywhere Characteristics of analytics today

PwC Draft Analytics Data Management Monitoring Visualization Core Technological Components and Key Vendors To have an effective analytics program for a complex organization, there is no “one size fits all” for technology. An analytics toolbox is necessary to bring the highest value PwC SpotfireQlikviewTableau Lavastorm What tools are being used? Major vendors within the analytics space

PwC Comparison Tools Capacity Ease of use Availability Analytic Capabilities Documentation Microsoft Excel Microsoft Access ACL 65,536 rows by 256 columns (pre ’07 versions) 255 chars per field 2 GB database 255 fields (columns) Unlimited Standard, easy to use office application Training is required Requires basic training Menu based Standard installation on PwC laptop Standard installation on PwC laptop Standard installation on PwC laptop license Data analysis toolkit Built-in functions Built in functions Great for joining tables Complete set of preprogrammed analysis To be done manually by user To be done manually by user Log file is documents every step continuously Excellent SatisfactoryPoor Very Poor Good Tool choice should depend on user expertise, data to analyze and analysis to be performed

PwC ACL  Audit Command Language  Point-and-click based software with ability to create executable scripts  Commonly used for data analysis related to fraud detection among Big Four firms ACL can be applied to assist with:  Journal Entry Testing  Internal Audit Testing  Record Sampling

PwC Industry Focus Media Entertainment Retail Financial Services Insurance Pharmaceutical Healthcare Technology Industrials Consumer Products Data Auditing is “Industry Agnostic”

PwC Business Analysis Audit Support SAS99 Journal Entry Testing Testing of Unrecorded Liabilities AR Liquidation analysis Revenue Recognition Re-performance Vendor and Accounts Payable Analysis Fixed Asset Analysis Inventory Testing Analysis Non-Audit Support Travel and Expense Investigation Vendor Master Data Analysis Inventory Analysis Database Development Process Automation Data Migration Validations

PwC Draft Analytics Methodology - AVAD Acquisition Acquisition: Gather data from client systems; collect system business rules through interviews and available documentation Delivery Delivery and Documentation: Reports, transformed data for target systems, graphic representation, models, etc. Validation Validation: Assess data; ensure data adheres to business rules; reconcile Analysis: Analyze data to address business issues (fraud, errors, buying patterns, target model support, etc.) Analysis Acquisition Validation Delivery Analysis

PwC Draft IA / BasePaks Technology 12 Client Data Raw Data BasePak Simplified Data Model Standardized Tests, Analytics Standardized Results Standardized Analysis Standardized Reporting Standardized Dashboards Why IA / BasePaks  Repeatability, Sustainability  Efficiency  Reliability Foundational Data Analytics (Demo)

“The transformational change occurring in organizations today is dependent upon adopting insight from data... Everyone is now an analyst. ” Data Science Central “The transformational change occurring in organizations today is dependent upon adopting insight from data... Everyone is now an analyst. ” Data Science Central “Last year people stored enough data to fill 60,000 Libraries of Congress...” The Economist “90% of the world’s data was created in the past two years ” IBM “…Data volume will double over the next two years” Gartner Drivers of Data Auditing Common Data Challenges Types of Data Embedding Analytics throughout the Lifecycle Data Analytic Tools and Visualization Benefits of Data Auditing AICPA’s Audit Data Standards How Can Analytics Help?