Implementation is THE Challenge!
1. IT Project Success vs. Cost & Size
2. Data – A Perennial Problem
3. Gaps in MIS Data: Kraft Foods Case Example
4. Data Management Issues: Nestle Case Example
5. Data Cleansing vs. Data Hygiene
6. Other Big Hurdle: People Issues
7. How to Manage Organizational Resistance
8. What Works? What Does Not Work?
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IT Project Cost vs. Success
“Once you hit $10 million, the chances of coming in on time
and on budget are statistically zero” - Jim Johnson (Standish Group)
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More Recent Statistics ….
In Budget, On Schedule and Met Expectations – 28%
Over Budget, Over Schedule and with Less
Functionality
– 49%
Cancelled or Failed Prior to Completion
– 23%
- Source: Standish Group, 2000
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IT Project size, fucntion points (FP)
IT Project Size vs. Outcome
Probable Outcome (% )
100
81
62
1,000
10,000
100,000
28
14
12
18
24
20
48
21
On time or early
7
65
Delayed
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Stopped
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IT Project Size vs. Duration
IT Project size, function points
(FP)
Average duration of IT project (months)
100
1,000
9
1
22
10,000
100,000
Expected
8
36
14
48
26
Deviation from expected duration at completion
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IT Projects & Bridge Building
Bridges are normally built:
 On-time...
 On-budget...
 And don’t fall down
Software
 Never on-time...
 Never on-budget...
 Always break down
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Why are Bridges Successful?
Bridges:
 Extreme detail of design
 Design is frozen at beginning
 Contractor has little flexibility on changing the
specifications
Hard model to use in today’s fast
moving business environment
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Other Differences
When a bridge falls down:
 It is investigated
 A report is written on the cause of failure
When a IT project fails:
 Covered up
 Ignored
 Rationalized
We don’t seem to learn from our mistakes!
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Implementation of Enterprise Systems
- Two Big “Non-IT” Hurdles:
1. Data Problems
– Cleansing the “dirty” data in legacy systems
– Integrating the data from the silos
2. Organizational Resistance to Change
– Getting Buy-in:
Unfreezing
Moving
Refreezing
(Lewin-Schein Model of Change)
– Performance Measurement and Reward Systems
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Murphy’s Law for Data
The Data You HAVE
Is NOT
The Data You WANT
Is NOT
The Data You NEED
Data problems are more difficult to solve
than hardware and software problems.
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The Data Problem for Analytical Systems
 Needed data is not collected
 Useful data, e.g., time spent by sales
people, are not captured in the computer
 Available data is not usable because it is
poorly organized, not timely, poorly
analyzed, difficult to access and internally
inconsistent
 The focus of information systems has
been on hard-data-oriented
applications
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Gaps in MIS Data - An Old Problem
Citibank
“We found … that management … didn’t even have a
good profile of its market and customers. It didn’t really
know in summary form what (its position was) with
respect to discrete market segments … There was very
little account profitability and not even market segment
profitability information.”
General Electric
“Information on orders, sales and margins … are of
maximum value when tied to … meaningful market
segments. And segment-based data are of limited use to
finance, hence the common misalignment problem
between finance and marketing.”
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A More Serious Problem . . .
Data That is NOT Available
“Soft” Data relating to ...
 Customer’s Buying Process
 Reasons for Infrequent Purchase
 Reasons for Defection
 Quality of Customer Support
 Who Should Collect This Data ?
 People at the Customer’s Touch-Points
. . . Field Sales, Telesales, Service,
Call Centers, Storefront, . . .
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Kraft Foods:
Missing Data for a BI System
BI Objective: To evaluate sales impact of
trade promotions
Available data on promotions: How much
was spent? When the bills were paid?
Missing key data: When were the
promotions run?
...to correlate with sales data
 Data problem is solvable in principle
But... Is it worth the effort and cost?
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How to Assess Cost-Effectiveness of
Missing Data - A Pragmatic Approach
Design a Prototype scaled to the barest minimum
Collect data for the Prototype
- Lowest data cost
Develop Prototype using real data
Users evaluate benefits of system
Stop
“No Go”
Value vs
Cost?
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“Go”
Full-blown
System
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The Low-Cost Prototype
- To Assess Value of Data
Model limited to the core variables
sales, promotion expenditures and dates,
margins
Detailed data still needed for useful analysis
by packs for each brand and by markets
weekly data for capturing sales fluctuations
two years of data to compare pre- with post-deal
sales levels
Cost of data
Manual effort to extract dates of promotions
from logbooks
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Quick & Dirty Prototype
- The Barest-Minimum
 Limited the System to:
- 2 brands, a major brand and a new brand
- 8 markets (out of 50),
3 large, 3 medium and 2 small
 User saw value of collecting the missing data
 Led to the development of a promotion-event
calendar system
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The Data Integration Problem
Required data resides in different incompatible systems
Compounded by discrepancies in data definitions
and coding schemes
Building an Integrated Data Base
Takes Significant Time and Cost
BUT, Data for Data’s Sake is a Worthless Luxury
-- Users should get value from the Data
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Data Management Issues
 Develop Relevant and Clear Data Definitions
and Coding Standards
 Streamline Procedures for Data Collection and Flow :
 Eliminate unnecessary paperwork
 Ensure timeliness of data
 Assign responsibility and authority to a specific
individual: The Data Administrator
 A demanding job
for which appropriate rewards must be given
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A Common Source of Data Problems
- Autonomous Divisions: Nestle Case
 Before 1991:
A collection of independently operating brands such as
Carnation and Stouffer’s, owned by the Swiss-based parent
 After 1991:
Brands were unified and reorganised into Nestle USA
 Still, Not a Single Entity:
New company continued to function more like a holding
corporation
 Autonomous Operation: An Insurmountable Hurdle
Divisions located in dispersed headquarters offices made their
own business decisions
 Bottom-Line:
– 9 Different Ledgers, 28 Points of Customer Entry
– Multiple Purchasing Systems: No clue of total business with a
particular vendor
– Every factory had set up their own vendor masters and
purchased on their own.
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Lack of Common Data Standards
- 29 Brands of Vanilla
 1997: Took stock of various systems across the company
– Found troubling redundancies
 An Example: 29 Brands of Vanilla !
– Different brands were paying 29 different prices for
Vanilla, a synonym for bland
– To the SAME VENDOR !
“Every plant would buy vanilla from the vendor, and the vendor would
just get whatever it thought it could get. And the reason we couldn’t
even check is because every division and every factory got to name
vanilla whatever they wanted to. So you could call it 1234, and it might
have a whole specification behind it, and I might call it 7778. We had no
way of comparing.”
“My team could name the standards but the implementation roll-out
was at the whim of the business.” CIO of Nestle USA
Source: CIO Magazine, May 15, 2002
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LOCKHEED EXAMPLE
What is “Sales”?
“The importance of standard definitions can be illustrated by the use
of the word “sign-up.” In general, the term refers to a customer’s
agreement to buy an aircraft. However, prior to the establishment of a
standard definition, it was used differently by various organizational
units. To marketing people, a sign-up was when a letter of intent to
buy was received. Legal services considered it to be when a contract
was received. Finance interpreted it as when a down payment was
made. The standard definition of a sign-up now used is a signed
contract with a non-refundable down payment.”
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Get the Dirt on Your Data
– Dirty Data: Incorrect Data; Missing Data; Misplaced Data
… It’s Everywhere !
2006 Poll of 1,160 knowledge workers by Harris Interactive Inc.
… 75% of respondents “made critical decisions based on faulty
data”
– A Simple Check: Look at Trends
… “If you’re looking at data that seems too good, too bad, or
just too strange to be true, it probably is.”
- Financial reporting systems manager at ABB Inc.
Source: Computerworld, Sept. 11, 2006
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“Cleaning House” – An Action Plan
1. Determine which types of information must be
captured
Form a data mapping committee – but keep it small
or risk never reaching agreement
2. Find mapping software that can harvest data from
different sources such as legacy applications, PC
files, HTML files, unstructured data sources and
enterprise-wide systems (ERP)
3. Start with a high payoff project inside a business unit
that is a big revenue generator for the company
- you will get the cost justification for the buy-in from
the top
4. Create an ongoing process for data hygiene - to keep
the data clean.
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Process Change Is Another Hurdle
Employees, especially touch-point personnel, have
to change the way they work
 how they collect data from customers
 quality of the data collected
 how to customize the products and services
offered to the customers
Big roadblock for CRM implementation
 Why should a sales rep record details of his
customer contacts because it doesn’t help him
sell more? He sees it as filling out forms just for
the sake of filling out forms.
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No. 1 Implementation Problem:
Resistance to Change
 Change in Process Typically Results in:
 Changes in peoples’ jobs
 Changes in required skills
 Most Important:
 Must consider what people think, what they
believe is important and what motivates them
 Align these with the new processes
 May require changes in measurement and
reward systems
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A Reengineering Failure
- Ignored People Issues
Levi Strauss
Time to fill orders too long
 Embarked on BPR project
 Reduced time from 3 weeks to 3 days
BUT….
 Created extreme turmoil by demanding that 4,000
workers re-apply for their jobs as part of a
reorganization into process groups
 BPR project timetable stretched to 2-years
 Had to make repeated promises for “no layoffs”
 Spent an extra $14 million for a 2- year “education”
effort to “calm” employees
Wall Street Journal, Nov.26,1996
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Hammer* Acknowledges….
• ...Reengineering forgot about people.
I wasn’t smart enough about that. I was reflecting my
engineering background and was insufficiently
appreciative of the human dimension. I’ve learned this
is critical.
Expanded BPR three-day “basic
training class” to five
 Two more days for “people issues”
* Michael Hammer, the Reengineering Guru
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One Key to Sell Change
Get key people involved
Buy-in from opinion leaders would
persuade others
Better to get criticism from the inside,
than resistance from the outside
Let them take some ownership of the
project
Participation creates a feeling of control
 Instead of “them” doing it to “you”,
“we” are all doing it together
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Managing Organizational Resistance
1. People whose data the BI requires
- Include the data owners in the project team
- Incorporate capabilities in BI to benefit data feeder
2. People who do not have access to the BI
- BI is becoming Everyone's Information System
- Access to data pertaining to knowledge worker's
responsibility
3. People who fear too much visibility into their operations
- One solution: vertical security
- BI is for Everyone
4. People who see no need to change things
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- Educate and persuade resisters
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A "Good" BI Sponsor
1. AUTHORITY and INFLUENCE
- Because an BI system has far-reaching
organizational impacts.
2. VISION and COMMITMENT
- To position the BI where it has maximum leverage
- To provide adequate support during implementation.
3. "RIGHT" ATTITUDE to COMPUTERS
- As a tool for improving managerial efficiency
and effectiveness.
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Problems With The BI Sponsor
 No Time to work with BI team
 System drifts along steered by MIS people
Solution: Designate Operating Sponsor
- Time and knowledge to direct the BI
- Attend to the political hurdles
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The BIG Implementation Traps
1. Data Quality Matters
“During the next two years, more and more global business will be
conducted via automated decisions and automated processes – all relying
on the data residing in corporate databases. But, given the poor state of
data quality today, that’s a scary thought, according to a study of PWC in
New York…
Study found that 75% of the 599 companies surveyed experienced
financial pain from defective data.”
2. People, Process & Politics
-
More daunting than the technical challenges of building BI system
Quaker Chemical CIO: “It was 9 to 10-year process for implementing
BI… not a simple matter of installing the applications and giving
workers access to them… because BI required employees to
collaborate and share information on a global basis, the company
decided to tie employee’s pay to their level of cooperation on the
project.”
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Technology is NOT the Solution
 Place more emphasis on :
Business Process Execution vs.
IT Infrastructure
 Do Your Homework !
 Identify High Value-add Applications
 Pilot Test Analytical System
. . . Measure returns
. . . Proof-of-concept
. . . Top Management buy-in
 Pay Heed to Non-IT Hurdles
 Getting Right Data
 Organizational Barriers
 Performance Measurement & Reward Systems
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Connect All the Dots…
1. Identify the business problem
2. Set specific goals to address the problem
3. Develop the strategy to achieve these goals,
incorporating all relevant groups and processes
4. Implement in a series of small steps to get value
We derive more long-term value by trying to hit
singles instead of home-runs.
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Low Risk Approach To Build Analytical System
- Works Especially When IT Budgets Get Slashed
 Break it into smaller modules
with a clearly defined benefit for each
 Pick the first module with significance
for proof-of-concept and top management buy-in
 Phased implementation will harvest benefits
to provide incentives for continuing the BI project
Best Way To Build a Big System
- Not to Build It…..Let It Evolve
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Serious BI Mistakes
1. Doesn’t do the Job
2. Time and Cost Overruns
3. Can’t Trust the Data or Outputs
4. Users Hate It – Won’t Use It
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1. BI Doesn’t Do The Job!
WHY?
 The project-scope was not clearly defined.
 The software lacked the functions or was forced to fit the job.
 Software modifications were not fully tested.
 Users were not properly trained on how to use the new tools in
their jobs to replace existing tools.
ACTION ITEMS:
 Define the project requirements with the help of key users who are
hungry for a BI to do their jobs better.
 Train the trainers, the early champions, who can train, and provide
ongoing support, to the other users
 Training is needed not only to navigate the BIS but, more
important, on how to use it in their daily tasks
 Pilot test of BI BEFORE rollout is a MUST!
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2. Users Hate It – Won’t Use It!
WHY?
 System is not user-friendly
- The system interface is unfamiliar and not easy to user
 Technical considerations took precedence over users
 Users are not motivated to use the system due to lack of attention
to change management
ACTION ITEMS:
 BI projects should NOT be led IT
 Prototype the system with hungry users
- They can “test-drive” it and provide constructive feedback to
improve to improve the usability and usefulness of the system.
 Critical to get “buy-in” from user group by making them recognize
the need for change and demonstrating “What’s in it for me”
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3. Can’t Trust the Data or Outputs!
WHY?
 Data in legacy systems is not clean.
- problems created when data is transferred “as-is” to HRIS
 Not enough validation edits in the data entry screens to stop entry of
patently incorrect data
 No incentive for data feeders to enter correct data
 System tested with only dummy data before rollout
ACTION ITEMS:
 Must include a Data Quality Plan for which significant time and money
should be budgeted in the BI project
 Test the quality of legacy data and cleanse it BEFORE moving it to BIS
 Implement an ongoing process for data hygiene - to keep the data
clean
 Data feeders must benefit from the system
 System test with REAL data is a MUST before rollout
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4. Time and Cost Overruns
WHY?
 Unrealistic estimates for both time and cost
 Hidden costs in project implementation
 Changes in project scope
ACTION ITEMS:
 SBUs must take ownership of the BI project to get payoff from IT
investment
 Project oversight must be at TWO levels
- Executive Steering Committee, including top management of
vendors, for monitoring progress and approving any changes in
project scope and the cost/budget implications
- Project Management Office with SBU and IT managers jointly
overseeing the project work
- Must budget specifically for hidden costs in: Training, Integration
of BI with other systems, Conversion of Legacy Data & Data
Analysis
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