chicago, il real-time data requires real-time …...work for® list, protiviti is a wholly owned...

JPK

Gro

upBusiness Forecasting and Analytics Forum

September 19-20 • Chicago, IL

Real-Time Data RequiresReal-Time Governance

September 19, 2:30pm

Jeremy is the director of Data & Analytics for Protiviti in the Central & West

regions. He has 18 years of experience in all aspects of business intelligence,

advanced & predictive analytics, and data management solutions. This includes

both vertical and line-of-business expertise in Finance, Supply Chain,

Manufacturing, HR, Sales, Marketing, and Procurement functions. Prior to joining

Protiviti, he was a Senior Vice President at ZedVentures and a Solution Line

Director at NTTData, leading the Analytics service lines at both.

View presentation online at:

https://jpkgroupsummits.com/attendee5

Jeremy Stierwalt – Protiviti

Discover how governance is different in the real-time enterprise

September 2016

REAL-TIME DATA REQUIRES REAL-TIME DATA GOVERNANCE

APPROACH OVERVIEW, RELEVANT EXPERIENCES AND INSIGHTS

© 2015 Protiviti Inc.

CONFIDENTIAL: An Equal Opportunity Employer M/F/D/V. This document is for your company's internal use only and may not be copied nor distributed to another third party.

Let’s be strategic but tactical at the same time…

What would you like to get out of this discussion?

General Education?

Strategies for Data Governance?

Recommendations for technologies?

It was better than the other options at this time?

SURVEY SAYS?FOR DATA GOVERNANCE



AGENDA

Overview of Protiviti

Definition of Data Governance

Data Governance

Business Drivers & Approaches

Why Real-Time Data Governance?

Where do we begin?

Business/Use Case Example

Next Steps & Q/A



Protiviti (www.protiviti.com) is a global consulting firm that

helps companies solve problems in finance, technology,

operations, governance, risk and internal audit, and has

served more than 60 percent of Fortune 1000® and 35

percent of Fortune Global 500® companies. Protiviti and our

independently owned Member Firms serve clients through a

network of more than 70 locations in over 20 countries. We

also work with smaller, growing companies, including those

looking to go public, as well as with government agencies.

Ranked 57 on the 2016 Fortune 100 Best Companies to

Work For® list, Protiviti is a wholly owned subsidiary of

Robert Half (NYSE: RHI). Founded in 1948, Robert Half is a

member of the S&P 500 index.

4,200*professionals

Over 20 countriesin the Americas, Europe,

the Middle East and

Asia-Pacific

70+offices

Our revenue*:

$797 million in 2015

ABOUT PROTIVITI

*Inclusive of Protiviti’s Member Firm network

2

http://www.protiviti.com/

http://fortune.com/best-companies/protiviti-57/



OUR DEFINITIONFOR DATA GOVERNANCE

Planned: We must understand the full areas within scope for the governance, and carefully outline

the immediate actionable needs of the organization.

Holistic: We must build out carefully and only bite off what we can accomplish, but we must do

this always keeping in mind the needs for the complete organization and not building in silos.

Actionable: Data Governance activities must directly drive bottom line value, and have actionable

work steps, deliverables, and programs as opposed to just being overarching methodologies that

can never be realized.

Simple: Data Governance must only be applied where needed, and not ‘Governance for

Governance sake’. We must keep it simple and action oriented or risk losing the fundamental

benefits and risk failure.

Efficient: The underlying processes put in place must not hamper our overall ability to conduct day

to day business, and must be designed with efficiency in mind.

4

Data Governance is the set of fundamental controls – both technical and process driven – used by

organizations to manage and protect key information stored in systems and databases. These controls

cover the full Data Lifecycle with respect to the protection of data, retention & disposition, proper use, and

the management of data as a business asset.

DATA GOVERNANCE DEFINITION

CORE PRINCIPALS OF DATA GOVERNANCE



Document, Record & Content

Management

•Electronic Document Mgmt•Physical Record & File Mgmt•Information Content Mgmt

Data Warehousing & Business Intelligence

Management

•DW / BI Architecture•DW / Mart Implementation•BI Implementation•BI Training & Support•Monitoring & Tuning

Reference &Master Data Management

•Data Integration Architecture•Reference Data Management•Customer Data Integration•Product Data Integration•Dimension Management

Data Quality Management

•Quality Reqmt. Specification•Quality Profiling & Analysis•Data Quality Improvement•Quality Certification & Audit

Data Security Management

•Data Privacy Standards•Confidentiality Classification•Password Practices•User, Group & View Admin•User Authentication•Data Security Audit

Database Management

•DB Design •DB Implementation•Backup & Recovery•Performance & Tuning•Archival & Purging•Technology Mgmt

Data Architecture,Analysis & Design

•Enterprise Data Modeling•Value Chain Analysis•Related Data Architecture•Logical Modeling•Physical Modeling•Modeling Standards•Model Mgmt.

•Users & Needs•Architecture & Standards•Capture & Integration•Repository Admin•Query & Reporting•Distribution & Delivery

Data Governance

•Roles & Organizations•Data Strategy•Policies & Standards•Architecture•Compliance•Issue Management•Projects & Services•Data Asset Valuation•Communication

DATA GOVERNANCE FRAMEWORKSDATA GOVERNANCE GOALS AND KEY CHALLENGES

5

External Data Mgmt

• Mgmt of syndicated data

• Mgmt of Partner Data

• Acquisition / coordination

of external data

Mobile Platforms Data

• Policies for use

• Device / Platforms

• Data limitations

“Big Data”

• Collection / sourcing

controls

• Data quality requirements

• Infrastructure maintenance

• Query tools

Data Demand Management

• Requests for Reporting / Info

• Requests for new sources of

data

• Coordination and control of

master report library

Social Media

• Policies for use / control

• Usage and review

• Competitive analysis

Regulatory Coordination

• Auditable reporting sources

• External reporting coordination

• Ownership

Metadata Management

= DAMA-DMBOK

Functional Model

Brown= Additional Considerations

DAMA-DMBOK



What’s your current state of Data Governance?

Who currently has a data/information governance

program?

How are you interacting with that program?

Who is using a governance tool (i.e. technology)?

Which one? (i.e. SAP; Informatica; Oracle; Others)

Production/Development/Sandbox?

Who’s driving Data Governance?

IT vs. Business; mix?

Does your organization currently have a CDO, Chief-

Data-Officer?



CONFIDENTIAL: An Equal Opportunity Employer M/F/D/V. This document is for your company's internal use only and may not be copied nor distributed to another third party. 7

REAL-TIME DATA GOVERNANCE

BUSINESS/TECHNOLOGY DRIVERS & DATA

GOVERNANCE APPROACHES



BUSINESS DRIVERS GENERAL BUSINESS DRIVERS FOR DATA GOVERNANCE

8

Increased regulatory or compliance focus (and issued MRAs)

Increased linkage between AML, CCAR, DFAST, and BCBS239

Fragmented approach within key business processes; instituting a need for centralized oversight and monitoring.

A need to increase operating effectiveness and reduce administrative costs by defining clear roles and responsibilities for data management with agreed measures and metrics to improve efficiencies and avoid errors.

Data quality efforts lack developed measures, tracking and metrics which hinders quick and effective responses that address root causes rather than merely correcting errors.

Data error remediation process lacks efficiency and effectiveness.

External data sources are not properly utilized to improve the efficiency of data origination and maintenance of data (e.g., clear definition of golden record).

Difficulty meeting market demands for flexible, timely and relevant information.

The inability to efficiently and accurately deploy data for external use.

BUSINESS DRIVERS



2 DIFFERENT APPROACHESHOW DO YOU APPROACH DATA GOVERNANCE?

9

Many organizations are focusing on data governance and creating groups to specifically manage data across the enterprise.

This provides them with better control over data, reduces the costs of data management, improves consistency of data, and

enables their organizations with more complete information for decision making.

PROACTIVE

(Active)

REACTIVE

(Passive)

• Clear processes and procedures for managing data

• Clear communication of priorities

• Clear management and resolution of data issues

• Confidence in the reliability of data

• Clear ownership of data

• Clearly documented and controlled policies and procedures

• Everything is an emergency

• Different rules depending on who you talk to

• Recurring issues with quality, timeliness, and consistency

• Lack of accountability



• Unidentifiable Items

• Duplication

• Excess Inventory

• False Stock-Outs

• Equipment Downtime

• Increased Maverick Purchases

(Direct buys)

• In-efficient Part Searches

• Delayed Shipments Due to lack

of Master Data

Materials/Products

• Duplicate Invoice Payments

• Vendor Duplication

• Inconsistent Payment Terms

• Shorter cycles of DPO

• Legal Compliance (FEIN, W9)

• Lack of Global Account

Relationships

Suppliers/Vendors

• Adverse Cash Conversion Cycle

(>DSO)

• Missing Background Verification

• Global View of Customers

Managing Complex

Relationships

Customers

Many organizations are focusing on data governance and creating groups specifically to manage data

across the enterprise. This provides them with better control over data, reduces the costs of data

management, improves consistency of data, and enables their organizations with more complete

information for decision making.

ADDITIONAL BUSINESS DRIVERSWHY IS REAL-TIME/PROACTIVE DATA GOVERNANCE IMPORTANT?



TECHNOLOGY DRIVERS WHY IS REAL-TIME/PROACTIVE DATA GOVERNANCE IMPORTANT?

11

Technology Shift

In-Memory Databases are

driving the adoption of

combined activities; The

Real Time

Enterprise/Digital Core

consists of transactional,

operational, and analytical

data. Delivering clean,

consistent and timely

information to the business

is required. (see SAP

HANA, Oracle Exadata,

etc…)

Disparate Systems

Multiple, disconnected,

on-prem/cloud systems,

or an outdated application

infrastructure can

negatively impact the

business and increase

costs. The need to

automate traditional manual

data governance processes

is imperative, thereby

improving data quality &

business process

execution.

Reporting Needs

Flexible, integrated, real

time reporting is the need

of the hour. This requires

massive parallel processing

capabilities to help manage,

improve, and leverage

clean and accurate

information to drive

information so that the

business can react

accordingly.



What are your business drivers for Data Governance?

Challenges with Business Processes and quality of

information?

Technology changes?

Business Strategy changes?




WHERE DO YOU BEGIN?

BEST PRACTICES for DATA GOVERNANCE



Often times, for organizations starting out or “re-launching” their efforts, we would propose looking at these

initiatives from two angles – both from a “bottoms up” as well as a “top down” approach. The “bottoms up”

approach will serve to provide some of the key foundational elements required around the selected data

domains (e.g. for Vendor, define Metadata, data linage, data ownership, and data quality approaches) while

the “top down” approach would provide overall policy guidance, organizational structures, communications

management, and change management aspects. The “top down” model may be adopted more widely

throughout the rest of the organization.

Top Down focused on establishing:

- Executive buy-in and sponsorship;

- Creation of charter and scoping; (e.g. what will this function focus on –structured vs. unstructured data, data quality, data ownership, etc.)

- Identification of required resources;

- Drafted policies and procedures;

- Organizational DG staffing, executive model and structure;

- Change management;

- Training plans for DG; and

- Continuous monitoring of DG.

Bottoms up focused on driving standardization by:

- Definitions of key domains & data elements;

- Identification of business processes where data is leveraged; (e.g. the “context” to how the data is used and driving value for Bridgestone)

- Documenting system and process touch points for the data domains and elements;

- Establishing ownership and stewardship for the data; and

- Recommendations for Data Quality monitoring.

To

p D

ow

n A

pp

roach

Bo

tto

ms U

p A

pp

roach

DATA GOVERNANCE APPROACHESCHOOSING YOUR ANGLE

14



• Data Governance Policies,

Procedures

• Data Governance

Organization Structure with

roles and responsibilities

• Definition of Data

Stewardship andownership

across the organization for all

key processes and domains

Data Governance Strategy

• Define data profilingbusiness

rules to understand as-is

structure and quality of

the data

• Parse data through business

rules to identify data anomalies

and quantify the data quality

• Pre defined business rules

and use cases for accelerated

data profiling and key issues

finding

Data Profiling

• Establish master data

governance roadmap with

prioritized data domains for

implementation life cycle

• Develop technology and

infrastructure processes for

implementing real-time

solution

• Define one globalunique

process for master data

maintenance processes

• Develop a comprehensive

deployment strategy

organization wide for master

data governance processes

Master Data

Governance

• Define Metrics & Scorecards

from technology and business

perspectives to provide deep

insights of data quality

• Data quality metrics from

business perspectives

focus in measuring master

data compliance with

transaction data

• Leverage appropriate tools

across the organization for

corrective actions

Data Quality Metrics

Identify existing data quality issues and its impact on business but also provide strategic

direction for deploying real-time master data governance across the organization focusing

business value

DATA GOVERNANCE LEADING PRACTICEESTABLISH THE PROGRAM & PROCESS

15



As noted in scoping many times a Data Governance organization may have some core responsibilities, but

to build an effective organization you must also be able to leverage and partner with other existing functions.

This requires careful planning, agreement from all parties, and constant communication in order to properly

function. The example below represents an example Interaction model built out for one of our clients.

TMONew initiatives & projects

Data Issue Management & Resolution

Quarterly Updates, Proposed Initiatives

Oversight, Direction, Funding Resource Allocation, Goals, Decisions

Partners /

Affiliates

Executive Team /

Steering Committee

IT

Governance

Shared

Services

Governance

Regulatory

Governance

Risk

Management

Data Governance Core Team

Data

Demand /

Knowledge

Management

Data Policy /

Process

Improvement

Data Quality

Monitoring

Data Issue

Prioritization

Data

Correction /

Change

Management

IT / Data

Alignment

Data

Documentation

(Metadata)

Project

Reporting &

Tracking

IT

Enterprise

Architecture

Delivery

Assurance

Data

Owners

Data

Stewards

Technology

Stewards

LOB

RepresentativesWorking Groups

Priorities, Coordination, Support, Direction, Interaction, Tools & Training

SME Input, Fit for Purpose Requirements, SLA’s, Status

PDLC Integration

Production

Support

Metadata

Coordinate Governance Activities

Review and Coordinate PoliciesProject Status and Feedback

LOB’sShared

Services

MIT

Data Quality Metrics, Policies, Procedures, Tools &Training

SME Input, Feedback, Status

MA

Projects

Program

Management

DG Representation

DG Project Deliverables

DATA GOVERNANCE STRATEGYINTERACTION MODEL

16



DATA PROFILINGDIMENSIONS OF DATA QUALITY

The specific characteristics or dimensions of data that are analyzed in a Data Quality program differ from one business to

another based on the needs and priorities of that business. The following dimensions are commonly used:

Dimension Short Description Example

UniquenessA number or characteristic that identifies one and

only one entry in a data set

Every customer should have a Customer ID

and no two customers should have the

same Customer ID

AccuracyData fairly represents what it is intending to

representCorrect amount for Outstanding Balance

ConsistencyThe same data from two or more sources should

not conflict with each otherNAICS Code to Concentration Code

Completeness Data records have values where they are requiredEach record should have an Account

Number

TimelinessData is available and accessible when needed by

the business

Financial statements are received when

due

Currency The data is “up-to-date” Customer contact information

Conformance The data is stored in the correct formatPhone number has proper number of digits

and format

IntegrityThe relationships between data in different systems

is maintained

The relationship between account number

and Customer ID is not broken

Lineage Changes to data are recorded and identifiableAbility to audit changes made to Risk

Rating



DATA PROFILINGDIMENSIONS OF DATA QUALITY

The following is an example of a data quality mechanism leveraged to understand the dimensions related to the quality of

information in the source systems.



MASTER DATA MANAGEMENTHOW DOES IT FIT INTO A DATA GOVERNANCE PROGRAM?

From prior clients we have observed a ‘chicken or the egg’ situation, where the question is do I need an established Enterprise

Data Governance program prior to MDM? This really is very dependent on the organization, but often times if the organization

doesn’t already have Enterprise Data Governance we may recommend establishing some elements of Enterprise Governance

concurrently with the MDM establishment.

Top Down approach

focused on

establishing:

• Executive buy-in

and sponsorship;

• Creation of

Program charter;

• Establishing

program goals and

priorities;

• Identification of

required resources;

• Approving policies

and procedures;

• Communication;

• Change

management;

• Issue

management; and

• Training plans.

Policies

Bottoms Up

Approach focused

on driving

standardization:

• Definitions of key

domains & data

elements;

• Identification of

business

processes where

data is leveraged;

• Documenting

system and

process touch

points for the data

domains and

elements;

• Establishing

ownership and

stewardship for the

data; and

• Recommendations

for Data Quality

monitoring.

Processes

Organizational

Structure

Metrics

Methodologies

Systems & Data

Approach

Current state assessment - understand existing MDM

program and organization, and identify key gaps.

Establish baseline governance model, organizational

roles, policies, goals and priorities.

Prioritize MDM domains e.g. Item, Vendor, Customer

For each domain, an initiative will address:

• Data profiling – to establish a baseline for data quality

• Define subject matter experts and candidates to

become data stewards

• Document data life cycle – business processes

where data is created, changed, read, searched,

deleted (workflow)

• Define and document key data elements –

definitions, reference values

• Define data policies – security, archiving, purge

• Data cleanup

• Establish system edits and validation rules

• Design data synchronization across systems

• Establish data quality metrics

• Establish go-forward governance structure

On completion of each initiative, conduct review and

retrospective to validate overall governance model.

1

2

3

4

5

19



DATA QUALITY METRICSMEASURING TOLERANCES

Tolerances are the minimum acceptable Data Quality Scores for data elements. The tolerances may be

set to ensure that the data meets the banks reporting and regulatory needs.

The elements will be categorized into two groups as it

relates to tolerance levels:

– Customized tolerances will be applied to the

highest priority data elements.

– Standard tolerance levels will be applied to lower

priority elements.

Illustrative Customized Tolerance Level Analysis

Tolerance

Levels by Element

Illustrative Standard Tolerance Level Analysis

One Tolerance Level for

Many Elements



HOW BEST TO LEVERAGE BEST PRACTICES

WITHIN THE CONTEXT OF A “BUSINESS CASE”



Goal of effective DPO management is to look for opportunities to increase it as much aspossible

without straining supplier relationships

Days Payable

Outstanding

Average Payables

COGSX 365=

$

Driven By• Payment Terms

• Payment Triggers

• Payment Methods

• Invoice Processing Lead Times

Optimizing Supplier Credit Cycle

• Normalize payment terms by

benchmarking internally and externally

• Optimizing use of discountsby

introducing sliding discountscale

• Eliminating non-compliance with

payment terms

• Eliminating duplicates and over

payments by streamlining master data

Value of the creditor balance in terms of number of average days of purchases

4

BUSINESS CASE FOR DPOLARGE GLOBAL MANUFACTURING – OEM TO AUTO INDUSTRY



• Negotiate

better rates

• Educate

employees

on policies

DATA GOVERNANCE FROM BUSINESS PERSPECTIVE

Business rule driven vendor data scorecards provide deep analysis of payment data trends

impacting DPO conversion cycle

• Ranking

vendors

with highest

amount of

spend

• Vendor who

can be

targeted

first

• Identify

payment

term

normalization

opportunities

• Non

standard

payment

terms

• Invoice

processing

lead times

for early

and late

• Discounts

offered /

taken / lost

due to delay

• sliding

discount

scale

effectiveness

& offered

• Discount

offered Vs.

Taken

effectiveness

• Standardize

payment

terms

• Educate

employees

• Fix invoice

processing

issues and

restructure

terms

• Optimize

discount

structure and

push out std.

terms

What to do with

the insight

Top spend by

Vendor

Payment

Terms

Invoice

Processing

Discount

Optimization

Why use this

Metric

What to look for

FOCUS ON DPO IMPROVEMENTLARGE GLOBAL MANUFACTURING – OEM TO AUTO INDUSTRY



• Establish policies and

procedures for advance

payments across business

units

• Identify opportunities to

minimize advances without

interrupting business

Advance Payments

• Sliding discount scale to

take advantage of early

payments

• Streamline invoice receipt

process for timely payments

by eliminating master data

consistencies and

duplications

Early & Late Payments

• Standardize payment

methods and move to

electronic payments as

standard method of

payment for reduced

transaction costs

• Normalize payment terms

by profiling invoice data

of 18-24 months trend

analysis

Terms & Methods

Normalization

• Supplier categorization,

consolidation, negotiation

and stratification enabling

to establish discount

optimization structure for

sliding scale

Discount Optimization

Focus areas to identify optimization opportunities and increase days outstanding

DPO is quickest to influence working capital optimization compared to DSO and Inventory

outstanding ,and can provide great results in short span realizing time tovalue

5

FOCUS ON DPO IMPROVEMENTLARGE GLOBAL MANUFACTURING – OEM TO AUTO INDUSTRY



THE

RESULTAPPROACH

THE

CHALLENGE

25

Phase 1: Data Governance Strategy

10+Data

Entities & Metrics

5Key

master data domains

Regulatory

Enterprise Data

Standards

Business Data

Ownership

Uncertainty

Unknown

Enterprise Usage

and Definition of

Key Data.

Definition and

classification of key

Data Entities and

data quality metrics

Documented the

relationship and usage

of applications and data

within Business

Processes.Phase 2: Program Rollout

Phase 3: Data Profiling

1. Clear definition of policies, processes, roles

and responsibilities.

2. Implemented processes and supporting tools

to store business and data relationships and

maintain it through periodic certifications and

enterprise-wide publication of information.

Phase 4: MDG implementation

Phase 5: Global deployment schedules

Clear policy, strategy,

and approach for

identifying, documenting,

and validating key data.

Our approach focused on most troublesome areas of data governance and maintenance across the organization primarily in master

data domains where single version of truth is extremely critical for conducting day-to-day business operations efficiently. We focused

on developing master data policies, procedures, and processes articulating master data impact on business operations. During our

engagement we were able to successfully develop a business case, articulating how master data inconsistency is impacting cash

conversion cycle adversely in receivables, payables, and inventory management. Developed a multi year roadmap to standardize

master data processes and policies and in the journey of accomplishing end goal to improve cash conversion cycle.”

CLIENT CREDENTIALLARGE GLOBAL MANUFACTURING – OEM TO AUTO INDUSTRY

Business process

efficiency impacting

cash conversion

cycle



HOW DO YOU GET STARTED?



PROJECT APPROACHDATA GOVERNANCE PROGRAM DESIGN

27

Str

ate

gy

& G

oals

Business BenefitsVision & Mission Specific Objectives

Co

mp

lian

ce

StandardsPoliciesInternal Quality

Assurance

Technology

Wo

rkfl

ow

an

d C

on

tro

ls

Data

In

teg

rati

on

an

d

Syn

ch

ron

izati

on

BI / MDM

Process

Change

Control

Data

Quality

Data

Maintenance

Reporting /

Analytics

Data Identif ication

& Prioritization

People

Data Quality Executive

Commercial Bank Data Management Organization

Data Quality Analytics Manager

Data Integrity Manager

Data Accuracy Manager

Data Stewards

Data Custodians

Data Users

Cu

ltu

re

Man

ag

em

en

t Metric

s

Executive Sponsorship

Training and Awareness

Data Governance Framework

Data Dictionary & PrioritizationWhich data elements are most

important to Risk Management?

Metrics & ScorecardsWhat is our data quality and where are

our data gaps?

Governance & AssuranceWhat structure do we have in place to

monitor and address data quality gaps?

Authority MatrixWho can do what with the data and

how should it move throughout the

organization?

Data LineageHow does the data move through

the process?



PROGRAM EXECUTIONDATA GOVERNANCE PROGRAM DESIGN

28

Series of Computer Based Trainings, Recordings, Live Meetings

and Job Aids used to train employees on:

Upfront process through which prioritized data elements are subject to

enhanced monitoring via QC points. An example of this would be:

Back-end process to modify prioritized data elements after data has

changed:

• Ensure changes are consistently and accurately processed

• Update the authoritative system of record and other critical

business systems

• Facilitate the tracking and understanding of data error root causes

Automated quarterly reporting used to:

• Measure data quality across multiple dimensions

• Identify inaccurate data within authoritative source systems

• Drive batch data remediation

• Produce reports at the portfolio, LOB, Data Owner and data

element level to identify training and improvement opportunities

Data Governance Training Quality Control

Data Element ChangeScorecards and Data Remediation

Procedure

Document and Approval Requirements

Process Flow Forms

Guidance

Resources & Tools

• Data Governance

• Prioritized Data Elements

• The Data Change Process

• The Quality Control Process• Sales Process

• Underwriting process

• Approval process

• Booking process

Continuous Feedback

Loop



Questions & Answers

What other information do you need?

SURVEY SAYS ?FOR DATA GOVERNANCE



JEREMY STIERWALTDIRECTOR, CHICAGO

Contact Information

Direct: +1 312.931.8713

Mobile: +1 317.507.4101

E-mail: [email protected]

Areas of Expertise

• Business Intelligence

• Advanced Analytics

• Predictive Analytics

• Data Management

• Data Governance

• Big Data Solutions

• Data Warehousing

• Project Management

Industry Expertise

• Consumer Products

• Retail

• Manufacturing

• Wholesale Distribution

• Financial Services

• Sports & Entertainment

• Media

• Professional Services

Education

• B.A., Computer Science & English Writing,

DePauw University

Professional Memberships

& Certifications

• Americas SAP User Group, Executive

Exchange

Professional Experience

Jeremy is the director of Data & Analytics for Protiviti in the Central & West regions. He has 18 years of

experience in all aspects of business intelligence, advanced & predictive analytics, and data management

solutions. This includes both vertical and line-of-business expertise in Finance, Supply Chain, Manufacturing,

HR, Sales, Marketing, and Procurement functions. Prior to joining Protiviti, he was a Senior Vice President

at ZedVentures and a Solution Line Director at NTTData, leading the Analytics service lines at both.

He’s held various leadership positions including membership to the Executive Leadership Team, leading the

analytics & predictive service lines, leading the Business Intelligence service line, and building the

Professional Sports & Entertainment industry vertical. Jeremy is a frequent speaker at National & Regional

Conferences on topics related to data management, big data, and analytical solutions.

Representative examples of Jeremy’s engagements include:

• Led multiple complex engagements translating client’s analytical business requirements into specific

systems, applications or process designs with interlocked financial modeling for custom technical

solutions.

• Directed the implementation of technical enterprise-wide data and analytical solutions across various

industries, lines of business, and varying technology platforms.

• Oversees the completion of customer Analytics/BI solutions and strategies based on the analysis of the

customer’s business goals, objectives, needs, and general business environment while providing both

team and technical leadership.

• Led multiple engagements integrating and cleansing data from varying systems of record, including

traditional database management systems, streaming application data, varying operational and

transactional applications, and other proprietary and commercial big data platforms.

Jeremy Stierwalt101 North Wacker

Suite #1400

Chicago, IL 60606

Powerful Insights. Proven Delivery.®

Mobile +1 317-507-4101

[email protected]

mailto:[email protected]

chicago, il real-time data requires real-time …...work for® list, protiviti is a wholly owned...

Documents