Preparing for Data Analytics

ESB GIA Staff Briefing Preparing for Data Analytics IIA Ireland Annual Conference 2016 21 April 2016 © 2016 Deloitte. All rights reserved. Introdu...
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ESB GIA Staff Briefing

Preparing for Data Analytics IIA Ireland Annual Conference 2016 21 April 2016

© 2016 Deloitte. All rights reserved.

Introduction

Conor Murphy

Manager, Data Risk Services [email protected]

© 2016 Deloitte. All rights reserved.

Agenda

• Overview of Data Analytics • Analytics Infrastructure • Beyond Assurance

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Data Analytics Overview

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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

Invoice No. Employee ID

Cost

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Cost

Quantity Quantity Data File

Duplicates

Inventory

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Various types of Data Analytics

Optimization algorithms

Pick up the early signals of an opportunity or threat

Foresight

Foresight

Simulation and modeling Predictive and Prescriptive

Advanced forecasting Insight

Insight Use data to understand the business’ current position

Role-based performance metrics Exceptions and alerts

Hindsight

Hindsight Conduct “rear-view mirror” assessments based on data generated by operations

Quantitative analyses

Descriptive

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Slice and dice queries and drill downs Management reporting

Enterprise data management

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Analytics Maturity Model

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Benefits of Data Analytics

Efficiency

Audit effectiveness

Cost effectiveness

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Consistency

Innovation

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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

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Analytics Infrastructure

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Infrastructure for Integrating Analytics

Strategy

Process

Technology

Data

People

Vision Focus Align

Activities Approach Flexible

Available Scalable Innovative

Scale Quality Efficiency

Relationships Stakeholders Learning

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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?

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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?

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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?

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00011000 01010101 01001011 01001010

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?

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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?

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People

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Beyond Assurance

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Updating our audit approach

Traditional audit steps Confirm audit objectives and scope

Identify potential analytics

Develop enhanced audit scope

Extract, transform and load data

Analyse data; compare, profile, visualise

Brainstorm with audit team and develop testing hypothesis

Audit commences

Test key hypothesis

Communicate results

Audit sampling, continue to support and iterate on hypothesis

Visualize and story board results

Integrated data analytic steps

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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

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Data analytics

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Thank You

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