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Business Management Issue
The
'Balanced Scorecard' has been a popular seminar attraction and hot topic
in corporate boardrooms for years. Why all the buzz? Quite simply as Tom
Peters puts it,' what gets measured gets done.'
The Balanced Scorecard, according to Harvard Business School professor
Robert Kaplan, translates a company's vision and strategy into a coherent
set of performance measures. The four perspectives of the scorecard -
financial measures, customer knowledge, internal business processes, and
learning and growth - offer a balance between short-term and long-term
objectives, between outcomes desired and performance drivers of those
outcomes, and between hard objective measures and softer, more subjective
measures.
Yet
many Balanced Scorecard initiatives never become practical or usable.
Our analyses reveal both management and IT scorecard deployment issues
that defeat its purpose, which are documented in Figure 1, and we define
as subversion issues:
Figure 1: Balanced Scorecard Deployment Challenges
|
Management
Subversion
|
IT
Subversion
|
| Allow
bottoms-up initiatives that lack a sense of urgency and enterprise
perspective |
Don't
question initiatives that are bottom-up or lack enterprise perspective |
| Take
too much to time to (re)align measures with strategy |
Don't
align IT strategy to business change |
| Avoid
accountability issues |
Build
systems to avoid data ownership issues |
| Refusal
to allow performance measurement to be interdependent |
IT
avoids linking scorecard measures to transaction processing systems |
| Use
pervasive suspicion of data (beyond the ledger) as an excuse for not
fully deploying balanced scorecards |
Present
data as facts and not business measures. |
Business
Implications
The
Balanced Scorecard is intended to bring attention to enterprise issues.
It depends on a hierarchy of accountability to deliver high performance
processes that create measurable business value. A Balanced Scorecard
will not fix management and accountability problems related to gaming
the goals. For example, having the right set of delivery measures will
not prevent managers from taking action counterproductive to the core
process strategy. It will however make such actions (or inaction) more
visible. To assure enterprise value, measures must be built from the top
down. They must be relevant at each level; causing companies, divisions,
department, and work teams to become interdependent. More than likely,
this will put a strain on functional leaders not experienced in solving
cross-functional problems.
Failure
to address the IT issues related to the quality and availability of non-financial
data, forces companies to rely only on the data they have and/or trust
(hint: the financials). Lead indicators of performance such as customer,
organization, and employee metrics take the back burner or are designed
in an ad hoc fashion. Business are left with a myriad of measures that
are either out of alignment with business strategies or irrelevant to
strategy. Far worse is the likelihood that some existing measures are
counterproductive and likely to defeat or delay strategic achievement.
IT
Implications
Traditionally,
large scale Balanced Scorecard programs are enabled with focused Executive
Information Systems (EIS) with connections to financial and operational
application systems. As a result, Balanced Scorecard initiatives are often
not considered information systems projects and therefore, IT gets into
the game late. As a result, IT departments often find themselves in the
position of stalling the effort or committing resources that have already
been committed elsewhere. Even when resources are committed to assist
with implementation and data provisioning, IT frequently finds itself
delivering data from "the most accessible" systems and not necessary the
"most appropriate" one.
Unavailable resources are assigned to implement an unknown system that
has executive steam behind it and provision it with data drawn from the
wrong places. Can it get worse? Yes, it can! Data quality just might not
be sufficient to satisfy the needs of the measurement system. We all know
that it is only when data is used to measure people that it will become
clean. We also know that data stewardship is a controversial issue. Balanced
Scorecards enabled through Executive Guidance Systems force the issue
through rigorous attention to operational definitions and challenges to
them.
Architecture
Impacts
IT
professionals can put the 'balance' back in scorecard efforts by providing
detailed architecture guidelines. These guidelines are the characteristics
that the various components of enterprise systems should demonstrate to
ensure synchronized/synergistic delivery of data to performance measurement
systems. These guidelines must address:
- Operational definitions of data
- Data edit and quality assurance rules
- Definition of Systems of Record
- Data extraction and transformation rules
- Data summarization rules
- Data provisioning systems architecture
- Flags on inconsistent data
- Ability to audit data from inquiry or report back to system of
record
Business
Management Response
- Identify and fix your leadership issues before you begin.
- Be clear about your operational strategy before building measures.
- Involve information technology (data access professionals) people
in the Balanced Scorecard business case development process.
- Identify operational data that will be used to populate the Balanced
Scorecard as early in the process as possible.
- Be aware that measures will change over time. This will demand access
to new data items. Often, focused implementations of Executive Guidance
Systems do not leave behind the skills to make such changes nor are
such changes supported by productive tools.
- Do not count on ERP (Enterprise Resource Planning) systems to fix
data quality or access problems.
Information
Technology Management Response
- Establish a Data Management architecture and governance systems early
and ensure application.
- Design data provisioning systems into all applications paying particular
attention to Systems of Record, Replicated Sources and Reporting Systems
(Data Warehouse) assuring that data is extracted and interpreted consistently.