Data Governance Business Value Metrics

1© 2018 IDERA, Inc. All rights reserved.
BUSINESS VALUE METRICS FOR DATA GOVERNANCE
NOVEMBER 27, 2018
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2018 IDERA, Inc. All rights reserved.
AGENDA
 This is NOT a technical session
• It is business focused
 Background
• Information capability & data maturity
 Data Governance
 What is business value?
• Quantifying it
 Frame of reference
• Vision, mission, objectives
• Key performance indicators (KPI)
 How do I communicate the message?
 Example: HotShot Manufacturing
 Final Thoughts

INFORMATION CAPABILITY STUDY - FINDINGS
 Very few organizations utilize information to its full potential
 Deficiencies in technical capability, skills, lacking data culture
 Lack of investment in value-driven information strategies
 Very few understand how to derive maximum value from information
• This will erode corporate value if not corrected
* Based on 2015 PwC/Iron Mountain study: Seizing the Information Advantage

INFORMATION MANAGEMENT DISPARITY
 Misguided Majority – 76%
• Informed but constrained
• Uninformed and ill-equipped
 Data seen as a byproduct, or taken
for granted
• Low comprehension of commercial
benefits that can be gained
 Constrained by legacy approaches,
regulations
 Weak analytic capability, or
• strong analytic capability, lacking
value focus
• Low analytical capacity
 Can be overwhelmed by data volume
 Data is domain of data architects
 IT led rather than business led
 “Spreadsheet hell”
 Information Elite – 4%
 Proactive Action
• Diversify business models
• Improve operating efficiency
• Identify / implement new market
opportunities
 Tangible data value
• Linked to organizational KPIs
 Exploit data for competitive advantage
 Balanced approach between security
and value extraction
 Holistic approach
• Governance is part of normal business
 Well defined information strategy
• Reflects business objectives
 Often in following sectors
• Healthcare, Manufacturing & Engineering

Technology &
Infrastructure
Information &
Strategic Business
Enablement
HIGH LOW
LOW HIGHValue Generation
Primary IT Focus
Risk
Level 0 1 2 3 4 5
Description None Initial Managed Standardized Advanced Optimized
Data Governance None Project Level Program Level Division Level Cross Divisional Enterprise Wide
Master Data Management
no formal master
data clasification
Non-integrated
master data
Integrated, shared
master data
repository
Data Management Services
Master data stewards
established
Data stewardship
council
Data Integration
ad-hoc, point to
point
Reactive, point-to-
point interfaces,
some common tools,
lack of standards
common integration
platform, design
patterns
Middleware utilization:
service bus, canonical
model, business rules,
repository
Data Excellence
Centre (education
and training)
Data Excellence
embedded in
corporate culture
Data Quality
Silos, scattered data,
inconsistencies
accepted
Recognition of
inconsistecies but no
management plan to
address
Data cleansing at
consumption in
order to attempt
data quality
improvement
Data Quality KPI's and
conformance visibility,
some cleansing at source.
Prevention approach
to data quality
Full data quality
management
practice
Behaviour
Unaware /
Denial
Chaotic Reactive Stable Proactive Predictive
Data Maturity
Introduction Expansion Transformation

ACHIEVING DATA MATURITY
 Information governance oversight body comprised of all key functional areas
• Supported by senior leadership
• Owned by the business – NOT owned by IT
 Culture of evidence based decision making
• Information is a valuable asset
 Protect sensitive and valuable information
• Secure access to those who need it
 Fit for purpose data analysis, interpretation, visualization
 Sound data architecture & enterprise architecture
• Data modeling – understanding the data
• Business process modeling – how data is created and used

GOVERNANCE CONSIDERATIONS
 Data Classification
• Master, Reference, Transaction
• Prioritize
• Divide and conquer
 Data Quality
• Data characteristics
• Critical data elements
 Regulations
• Security
• Privacy
Data
Governance
Data
Architecture
Data Modeling
& Design
Data Storage
& Operations
Data Security
Data
Integration &
Interoperability
Documents &
Content
Reference &
Master Data
Data
Warehousing
& Business
Intelligence
MetaData
Data Quality

CLASSIFICATION

QUALITY - DEFINITIONS
 Quality Assurance
• The entire system of policies, procedures and guidelines established by an
organization in order to achieve and maintain quality.
 Quality Control
• A series of planned measurements that are designed to determine if quality
standards are being met.
 Quality Engineering
• Includes quality considerations in design and to predict possible quality problems
prior to production.
 Quality Assurance is not Quality control alone
• A fundamental principle of quality is that quality cannot be inspected into a product;
it MUST be built in.
• The responsibility for quality belongs to EVERYONE in the organization.

DATA QUALITY
 Data Quality
• The degree to which data is accurate, complete, timely, consistent with all
requirements and business rules, and relevant for a given use.
 Information Quality
• The degree to which information consistently meets the requirements and
expectations of knowledge workers in performing their jobs.
• In the context of a specific use, the degree to which information is meet the
requirements and expectations for that use.

DATA QUALITY
 Accuracy
 Timeliness
 Completeness
 Consistency
 Relevance
 Fitness For Use

DATA QUALITY IMPROVEMENT OPPORTUNITIES
 Poor quality data from source. Accounting staff spend 25% of their time
reconciling submitted invoices due to inconsistencies
 Inconsistent pricing of products across regions, resulting in lost revenue of $1
million in Europe.
 Inaccurate bills of material, resulting in inventory outages and production delays
 Multiple ID’s for the same product in different systems, leading to incorrect
orders and quotes
 Multiple instances of the same customer across systems, resulting in incorrect
credit checks
 Inaccurate patient prescription recording, which could result in dangerous or
fatal drug interactions
 Incomplete safety credentials tracking, resulting in uncertified disaster rescue
teams
 Incorrect engineering data that could result in uncontained explosions due to
incorrect manufacturing specifications

POOR DATA QUALITY IMPLICATIONS
 Costs a typical company the equivalent of 15% to 20% of revenue
• Estimated by US Insurance Data Management Association
 Low Quality = Low Efficiency
 It is insidious – most data quality issues are hidden in day to day work
 From time to time, a small amount of bad data leads to a disaster of epic
proportions

WHEN DATA FLAWS HAPPEN…
Space Shuttle Challenger QueCreek Mine Flooding

BUSINESS VALUE
 Business value: The net benefit that will be realized from an initiative,
measured in monetary and/or non-monetary terms.
 3 classic approaches
• Revenue enhancement
• Cost savings
• Cost avoidance
 What about competitive advantage?
• Typically realized in 1 or more of the above categories.
 Quantifying
• Return on Investment (ROI)
• Net Present Value
• Payback

PRODUCTIVITY IMPROVEMENTS
 Improve Efficiency
• Lower total operating costs
• Savings in labor time
• Savings in machine time
• Reduce waste
 Improve Effectiveness
• Better decision making
• Better communication
 Achieve Higher Performance
• Increase Quality
• Reduce accidents, lost time
• Minimize equipment breakdowns
 Better Organizational Health
• Improve morale
• Improve satisfaction
• Improve cooperation
Productivity measures in service organizations / functional areas is more difficult to measure than processes with physical
inputs & outputs. They are often stated in terms of benefit/cost ratios.

COST AVOIDANCE
 Not the same as cost savings
 A way of decreasing costs by lowering a potential increase in
expenses through specific preemptive actions.
 Assuring that specific types of costs are never incurred.
 Sometimes known as “soft savings”
 Examples:
• Compliance to avoid regulatory penalties and fines
• Negotiated reduced price increase from vendors
• Elimination of headcount increase due to process improvements
• Reschedule maintenance of critical equipment to avoid work
stoppage

Actionable: easy to understand. It is clear when chart your
performance over time which direction is good and which direction is
bad, so that one knows when to take action.
Measurable: Need to be able to collect data that is accurate and
complete.
Specific: Metrics must be specific and target the area that is
being measured.
Relevant: There is a common trap of trying to measure everything.
Only measure what is relevant. Ignore the noise from irrelevant data.
Timely: Need to be able to get data when it is needed (as near to
real time as possible). If data is received too late, it may no longer be
actionable.

BUSINESS VALUE MEASUREMENT
 Return On Investment (ROI)
 Net Present Value (NPV)
 Payback

RETURN ON INVESTMENT (ROI)
 Financial ratio used to calculate benefit in relation to investment cost
 ROI
• = Income / Cost of investment
• = Cost savings / Cost of investment
 Example:
• Assumptions:
• Initial investment to begin (1st year staffing + tools) = 300K
• Estimated cost savings (increased efficiency) 1st year = 800K
• ROI = (800K – 300K) / 300K
• = 1.67
• = 167%

NET PRESENT VALUE - FUNDAMENTALS
 Present Value (PV)
• A dollar today is worth more than a dollar tomorrow
• The dollar today can be invested and start earning interest immediately
• The present value of a future payoff is determined by multiplying the
payoff by a discount factor which is < 1.
 Discount Factor
• The reciprocal of 1 + rate of return: 1 / (1 + r)
• Discount future payoffs by the rate of return that can be obtained by
investment alternatives
• Rate of return is also known as
• Discount rate
• Hurdle rate
• Opportunity cost of capital

NET PRESENT VALUE (NPV)
 C0 is cash flow in period 0 [The initial investment]
• Cash outflow, expressed as negative value
 C1 is cash flow amount yielded by the investment (initiative)
 NPV = Present Value of the return (C1) – Required Investment (C0)
 NPV = C0 + C1 / (1 + r)

NPV EXAMPLE
 Assumptions:
• Initial investment to begin (1st year staffing + tools) = 300K (C0)
• Estimated cost savings (increased efficiency) 1st year = 800K (C1)
• Discount (r) = 15% (0.15)
 NPV
• = -300,000 + (800,000/1.15)
• = -300,000 + 695,652
• = 395,652
 Same principles apply when dealing with streams of costs and savings.
• Calculate the NPV for the (savings – costs) in each period.
• NOTE: Discount rate may vary as well (such as compound interest)
 Decision rules:
• Accept investments that have positive NPV
• Accept investments that offer rate of return > cost of capital

PAYBACK
 Your organization may require that initial investment on any initiative should be
recoverable within a specified cutoff period.
 Payback is determined by counting #periods it takes before cumulative cash
forecast equals the initial investment
 Discounted payback discount the cash flows before determining payback
• Payback based on cumulative NPV
 Arbitrarily setting cutoff period too soon has the risk of disqualifying viable
investments.

STATING THE BUSINESS CASE
 Know your business/organization
• What’s your corporate mission
statement?
• What are the key performance
indicators?
 Know your audience
• Their interests?
• Their concerns?
• Look at it from their perspective
 How do you want them to react?
• Think?
• Feel?
• Do?
 Elevator pitch
• Clear
• Concise
• Effective
 Align the benefits with business
objectives
• Corporate mission
• State benefit measurement to show
achievement/improvement of
corporate KPI’s
 What would be the mission statement
of the initiative you are trying to sell?

ELEVATOR PITCH TECHNIQUES
 Align your message with corporate objectives
 Know your audience
 Give examples
• Show the positive result of taking action
 Draw on metaphors to help people understand
 Ask rhetorical questions
• Caveat: Make sure you know what their answer would be
 Don’t try to communicate everything
• Just the important points
• Open the door for further discussion
• Don’t expect instant approval
 Do your homework!

VISION VS. MISSION
 Vision Statement
• The dream: how does your organization wish to change the world?
• “Some day …”
• Should be big, exciting, compelling
 Mission Statement
• What are you going to do to accomplish the dream?
• WHAT you do!
• WHO benefits from it?
• HOW you do it!
• “Every Day !”
• Should NOT be stated in financial terms
 Supporting Goals & Objectives
• Quantifiable
• Measurable
“Mission statements that express the
purpose of the enterprise in financial
terms fail inevitably to create the
cohesion, the dedication, the vision of the
people who have to do the work so as to
realize the enterprise’s goal.”
“The mission statement has to express
the contribution the enterprise plans to
make to society, to economy, to the
customer.”
Peter Drucker

MISSION STATEMENTS
 Patagonia
• Build the best product, cause no unnecessary harm, use business to inspire and
implement solutions to the environmental crisis.
 Google
• To organize the world's information and make it universally accessible and useful.
 Coca Cola
• To refresh the world...To inspire moments of optimism and happiness...To create
value and make a difference.
 Ikea
• To create a better everyday life for the many people.
 Nike
• Bring inspiration and innovation to every athlete in the world.* If you have a body,
you are an athlete.

EXAMPLE: HOTSHOT MANUFACTURING
 Mission:
• To be the world leader in the industrial heating industry, providing the highest
quality, safest technology for hazardous environments, combined with the best
delivery and customer service in the industry.
 Some supporting objectives:
• Increase market growth and share of at least 10% annually
• Continuous improvement in manufacturing and supply chain efficiency
• Inventory is viewed as a liability, not an asset:
• Evolve to just-in-time manufacturing with no pre-stock of finished goods
• Improve inventory turnover to 6 turns/year

HOTSHOT MANUFACTURING: DATA GOVERNANCE PRIORITIES
 Order lead time from suppliers
• Improve order policy models
• Time period supply, part period balancing, balance order vs. carrying costs
 Work center cycle times and setup to improve efficiency
• Reduce downtime
• Improve product flow
 Improve product data
• Bill of material accuracy
 Improved forecast rationalization
• Improved forecast/component delivery from suppliers
 Minimize/eliminate safety stock
 Initial Targets
• Decrease inventory levels 5% annually
• Improve general efficiency 1% annually

HOTSHOT MANUFACTURING: BASELINE
Annual Sales 100,000,000
Gross Margin 30%
Cost of Goods Sold % 70%
Yearly Growth 10%
Inventory Carrying Cost 25%
Current Turns/Yr 3.50
Hurdle Rate 10%
Current Forecast without implementing governance program
Year 0 1 2 3 4 5 Totals
Annual Sales 100,000,000 110,000,000 121,000,000 133,100,000 146,410,000 610,510,000
Cost of Goods Sold 70,000,000 77,000,000 84,700,000 93,170,000 102,487,000 427,357,000
Gross Margin 30,000,000 33,000,000 36,300,000 39,930,000 43,923,000 183,153,000
Inventory Level 20,000,000 22,000,000 24,200,000 26,620,000 29,282,000

HOTSHOT MANUFACTURING: QUANTIFYING COSTS/BENEFITS

HOTSHOT MANUFACTURING: THE PITCH
 There are inconsistencies in our product, supply chain and manufacturing data
that are restricting us from attaining our corporate mission and objectives.
 We would like to institute a formal data governance program to address this.
• With an investment equivalent to only 0.55% of sales, we can significantly reduce
inventory carrying costs, increasing turns from the current level of 3.5 to 4.6 over the
next few years
• General efficiency will also improve dramatically. An improvement of only 1% will
drive bottom line improvements exceeding $19 million over 5 years.
 Net Present Value of the program would exceed $14 million over 5 years, but
accrues returns immediately with NPV > 600K after year 1.
 ROI is forecast > 600%.
 We would like to start immediately, as this will be the cornerstone for similar
improvements in other areas as well.

FINAL THOUGHTS
 Data governance is business
 Run the governance program like a business
• Governance vision statement
• Governance mission statement
• Governance goals, objectives
• Aligned with corporate vision, mission, goals
 SMART METRICS
 Classify and Prioritize
• Based on business value & impact
• Competitive advantage
 Governance is a journey, and is hard work
• There is no easy button !
• Start small and grow - pilot project(s) to demonstrate value
 Celebrate success!

THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator

Data Governance Business Value Metrics

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