Governance Track – Audit PanelNovember 7, 2018

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Agenda9:30am-10:30am

Brief Introductions (5 minutes)

Panel Presentations (35 minutes)

Discussion, Q & A (20 minutes)

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Panelists

•Kathleen Thomason, Purdue University

Senior Director, Comptroller, Comptroller’s Office

•Abby Snodgrass, Purdue University

Strategic Data Manager, College of Agriculture

• Jennifer Littlefield, Purdue University

Financial Operations Accountant, Accounting Services

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Data Auditing… is it important?

• Bad data is expensive

• IBM estimates that poor data quality cost $3.1 trillion annually in the US

• Human Error

• CIO Insight Survey – 56% said that Human error accounts for one of the

top causes of inaccurate data

• Access to data has grown

• Accuracy and understanding is more important than ever

• Distrust - “The data is wrong”

• 50% — the amount of time that knowledge workers waste hunting for data,

finding and correcting errors, and searching for confirmatory sources for data

they don’t trust

A data audit is the auditing/reviewing of data to assess its

quality or utility for a specific purpose.

Data-Driven Organizations

•Knowledge/information sharing

•Collaboration

•Transparency

•Strong focus on data collection

•Data governance

•Data driven decisions

•Invest in data resources

Big Data & Analytics has more focus

than ever before

Data Silos•What is a data silo?• Isolated groups of data

• Raw data that hasn’t yet been processed or analyzed

• Data held by different business units in a large organization

• Common causes of data silos

• Structure of our organizations, large organizations split into specialized teams in order to streamline work processes and take advantage of particular skill sets

• Different Priorities and Roles

o Central offices

o Colleges/departments

o IT

Why are Data Silos a Big Deal?

• Impact every part of an organization, finance, IT, HR,

colleges

• Slows down the organization

• Limits communication and collaboration

• Reduces efficiency and eats storage space

• Decreases quality and credibility of data

Becoming More Data-Driven

• Develop effective instruments to validate information and to promote accountability

• Resources for documentation and maintenance.

• Design sufficiently robust systems for central monitoring and dissemination of information

• Standardize information collection to put data into a common format that allows for collaboration, analytics, and application of various tools tools

• Communicate, communicate, and communicate

• Validate, audit, and identify training opportunities

Wisdom

Knowledge

Information

DataStudent

Financial

Donor/Gift

Using Data

Audit

AAlign goals, key metrics and key users

U Understand the current implications

DDocument data gaps and opportunities to improve

IIdentify and prioritize fixes and enhancements

T Tell the story and share findings

An audit is an investigation of existing systems, reports, or

entities, and come in many different types (Steven Bragg)

What is a Data Audit?1. Quality Assurance

2. Baseline

3. Access

4. Usage

5. Security

6. Information Gaps

7. Risks

8. Compliance

9. Privacy

10.Source System

11.Action Plans

12.Shared Language

13.Business implications

A data audit refers to the auditing of

data to assess its quality or utility for a

specific purpose. Auditing data, unlike

auditing finances, involves looking at key

metrics, other than quantity, to create

conclusions about the properties of a

data set. (Techopedia)

1. Approach to managing data does not ensure consistency in quality and application

2. Inadequate oversight of data policy development and implementation increasing the risk of poorly informed decision-making

3. Lack of a clear understanding of the data used and its impact.

4. Vulnerabilities which may create gaps in ownership and control

5. Errors, omissions and inaccuracies in the data that undermines the information integrity and management decision making

6. Unreliable IT environment, technology or tools compromising the quality and integrity of the data and its processing

1. Ensure that data quality is maintained

throughout the process

2. Sets the tone and provides appropriate

oversight necessary for sound decision

making

3. Ensure appropriate and timely reporting

to support required governance

procedure, management decision

making process and timely detection of

issues

4. Ensure that data used, its impact and

vulnerabilities have been clearly

identified and maintained

5. Ensure that data quality (complete,

accurate, appropriate, and

timely/current) is continuously

maintained

6. Ensure that the quality of data and its

processing for use is maintained

RisksControl Objectives

It doesn’t have to be complicated• Start small, tackle those hurdles

•One or two audits

•Write definitions, review across silos, transparency

• Purdue uses Data Cookbook

• Definitions

• Report and dashboard specs

•Official Seal

• Available to all with a Purdue Career Account

•Manage scope, limit it to material issues

•Track successes and failures

•Continuous and regular review

•Automate when possible

Examples

Organizational Readiness

Improved

Value

Operational Efficiency

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Group Discussion, Q & A

-- Everyone --

•Kathleen Thomason, Purdue University

Senior Director, Comptroller, Comptroller’s Office

•Abby Snodgrass, Purdue University

Strategic Data Manager, College of Agriculture

• Jennifer Littlefield, Purdue University

Financial Operations Accountant, Accounting Services