Preparing for Data Analytics

Transcription

1 ESB GIA Staff Briefing Preparing for Data Analytics IIA Ireland Annual Conference April 2016

2 Introduction Conor Murphy Manager, Data Risk Services

3 Agenda Overview of Data Analytics Analytics Infrastructure Beyond Assurance

4 Data Analytics Overview

5 What is Data Analytics? Analytics not a technology; but a concept. Use of certain technologies, skill sets, and processes for the exploration, evaluation, and investigation of business operations. Makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modelling, and fact-based management to drive decision making. Unusual Items Quantity Employee ID Invoice No. Quantity Data File Cost Duplicates Cost Inventory

6 Various types of Data Analytics Foresight Pick up the early signals of an opportunity or threat Insight Use data to understand the business current position Hindsight Conduct rear-view mirror assessments based on data generated by operations Hindsight Insight Foresight Predictive and Prescriptive Descriptive Optimization algorithms Simulation and modeling Quantitative analyses Advanced forecasting Role-based performance metrics Exceptions and alerts Slice and dice queries and drill downs Management reporting Enterprise data management

7 Analytics Maturity Model

8 Benefits of Data Analytics Efficiency Audit effectiveness Consistency Cost effectiveness Innovation

9 Common challenges Analytics is manually intensive We do what we have to do to get the information we need We have people in our business unit whose primary role is to collect, consolidate, cleanse and present information they need to spend their time analyzing the data, not manipulating it We don t know where to go for what We spend considerable time asking for information. Where to go for what is based on comfort level with a data source Resources with a combination of domain expertise and deep analytics experience are hard to find in the market Analytics results are inconsistent There are few common KPI s or standard definitions across the organization We are lacking a unified view of the data that comes from the integration of multiple applications Developing a system is difficult and time consuming Building user adoption and creating a culture of analytics is difficult Technical pieces, such as data models, dashboards, and KPI libraries are hard to build, and will leave insights hidden if they are wrong We have a system, but it s difficult to manage Even if we get it all right at once, it s very hard to repeat and roll out across our business Managing an enterprise-wide data projects is complex, with multiple implementation risks, and we don t have the required experience

10 Analytics Infrastructure

11 Infrastructure for Integrating Analytics Strategy Process Technology Data People Vision Focus Align Activities Approach Flexible Available Scalable Innovative Scale Quality Efficiency Relationships Stakeholders Learning

12 Analytics Strategy Key considerations Develop an upfront vision, define objectives and strategic direction Determine where to focus Fraud Inefficiencies Compliance Risk Align with broader business strategy Key questions What do you want the IA department to look like in two to three years from now? How can we use analytics to be more strategic? Does executive leadership understand the importance and benefits of embedding analytics into the IA function? What types of audit testing are you performing and how frequently? What are the biggest challenges you currently face in implementing data analysis?

13 Process Key considerations Define a high-level process Inputs Activities Outputs Defined process regardless of attrition or other changes Reporting process Key questions When is the right time to identify analytics projects and on which are the best projects to focus our efforts? What are the steps we need to take to ensure that these projects are a success? How will analytics change the approach of our current audits and what is the impact of this change? What are the steps we should be taking to extract and load data timely? How will we measure our progress and capture lessons learned?

14 Technology Key considerations Engage and build relationships with IT Understand system interfaces Promote automated where practical Know what tools are available Focus on efficiency over time rather than initial investment Key questions What technologies do we need not only to process the data but also to present the results in a meaningful way? Are these technologies already licensed by the business? Are these tools scalable and are they capable of supporting our long-term vision? How can we most effectively collaborate with IT? What kind of technical support is available? How will we document and map the data landscape to support our long-term vision? What internal or external systems are available to provide data to support internal audit? How receptive are IT to dealing with data requests?

15 Data Key considerations Validating data integrity and completeness Extraction Cleansing Manipulation Data governance, protection and management Challenges in acquiring data Key questions What specific data do we need to answer the important questions? From where is it sourced (i.e., internal, external, licensed, open, etc.)? How do we bring it together and what are the challenges in transforming, linking and publishing it? What about quality and accuracy? For audits that employ or are proposed to employ data analysis, what is the typical size of the source data required?

16 People Key considerations Executive buy-in and support Stakeholders Champions Recipients Organisational structure Identify and develop competencies Key questions Who is the accountable IA owner for Data Analytics? What organizational structure do we need to put in place to support our analytics strategy? Do we need new skill sets, such as statistical know-how, data-management expertise, and visualization and presentation skills? Who do we need to engage in other departments as well as our own? How will we train our staff?

17 People

18 Beyond Assurance

19 Updating our audit approach Traditional audit steps Confirm audit objectives and scope Develop enhanced audit scope Audit commences Test key hypothesis Communicate results Identify potential analytics Extract, transform and load data Analyse data; compare, profile, visualise Brainstorm with audit team and develop testing hypothesis Audit sampling, continue to support and iterate on hypothesis Visualize and story board results Integrated data analytic steps

20 Sample roadmap 1 Assessment: Analyse current analytics capabilities both within IA and across the business and rapidly develop proof of concepts to identify challenges and opportunities. 2 Roadmap: Create a long-term strategy and vision for analytics; scope and prioritize projects to achieve this. 3 Deliver & Monitor: Initiate the program, deliver the roadmap, and monitor your implementation successes against key performance indicators. Stage 3: Deliver & monitor Subject matter specialists Stage 2: Roadmap Integrated approach Stage 1: Assessment Core internal audit Data analytics

21 Thank You