Post on 17-Apr-2022
Governance Track – Audit PanelNovember 7, 2018
2
Agenda9:30am-10:30am
Brief Introductions (5 minutes)
Panel Presentations (35 minutes)
Discussion, Q & A (20 minutes)
3
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
4
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
16
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