For some organizations, deriving or even communicating the
value from these programs is hard to articulate effectively, and
these implementations are often at risk of failure or cancelation.
Other firms seem to have good management support and recognize the
value and opportunity resulting from MDM implementations. So what
drives this success, and what kinds of value are these
organizations realizing?
Let's grant the obvious: Successful MDM or data consolidation work
requires executive management sponsorship, good cross-business
support, and governance capabilities led by business stakeholders.
Let's even assume a level playing field in data governance and data
management capabilities (a topic in and of itself). What then
drives success? Simply put, a well-articulated destination, bounded
by clear near-term tactics that can demonstrate quick value. Also,
incrementally built capabilities and measurements that initially
help direct activities and behaviors while communicating value, but
that also help sponsors see benefits and stay engaged.
For banks, as with most organizations, core target
benefits are:
- Forward looking. They allow for
cross-and-up-sell opportunities and improved customer service.
- Foundational. Data quality/consistency is
improved, enabling better and faster insights, improved service
offerings, and both the modernization and rationalization of
IT system (costs, simplification, optimization, etc.)
- Operational. Time to market for new products
is improved and complaints are reduced.
- Compliance and privacy. They provide more
accurate and complete data to support compliance reporting (BASEL
II, SOX, etc.) and consolidated mechanisms to consistently support
privacy opt-in/out etc. across all business lines
The key is to be clear on what your targets are, how you will
measure success, and most important, how that success will be
achieved incrementally. Big bang, top-down-only, or single, large
program solutions almost never work. As an example, we often look
to MDM solutions to help improve business intelligence
time-to-answer (TTA) metrics. Complex organizations often have very
high TTA for important processes and situations. One firm sought to
reduce its direct marketing campaign time from around three months
to a day or less. With a clear target in mind, and the realization
that that could not be achieved in one step, they established a
plan that realized business improvements in TTA, while building out
the technological and business capabilities required to support
their needs.
In the end, over a multiyear period, they transitioned from a TTA
of 45 days to one day. Now, for targeted interactions, they can do
highly automated self-learning with a TTA of 200 milliseconds. In
addition, as they made progress they sought to build a flexible and
sustainable solution designed to continue to evolve. The solutions
were a dramatic improvement by any measure and they did it by
keeping the goal in mind, but driving to incremental goals to
demonstrate success and value; and by communicating the impact to
both business and IT. In this case, they were able to show a
correlation between business capability investment, and sustainable
platform improvements and costs.
In another example, a firm sought to improve its ability to sell
across very siloed lines of business. Opportunities for
cross-selling existed, but each business head saw the problem
differently. Each also saw major impediments, but engaged in
heroics to make it work since the CEO was the sponsor and it was
something they needed to do.
After three years of working to establish basic capabilities and
drive consistency in a warehouse platform, they had little in the
way of good metrics to demonstrate the impact of the work and they
still struggled with achievable goals. On the surface they looked
like they were doing the right things, but they didn't take them
deep enough or establish good, achievable rally points for the
different teams. They had large measures like total numbers for
cross-sells, but few milestones that reflected the reality on the
ground, which was that they had 30-year-old platforms with
different data meanings and contexts across different lines of
business with different priorities, customer privacy, and legal
entity concerns. So, where it was difficult or unclear on how to
agree on definitions, measures, opportunities, or even basic
ownership or stewardship, they simply avoided it, leaving big holes
in their capabilities and causing growing executive frustration
that the expected benefits were still unrealized.
Once the issues were put on the table, the business recognized the
need to change its approach. When it began to take actions in this
direction, it started to see even more executive support across
lines of business and better front-line engagement in their
efforts.
What changed? (1) The basic data management capabilities were
maturing during this time; (2) The workers realized the need to
rally resources around just one tactical solution; (3) They created
real accountabilities with clear, top-to-bottom measures to move
the effort forward (percent compliance with consolidated data
standards, definitions, delivery to platform, etc.); and (4) They
collected measurements. While the four components are critical, it
is clear that simple, aligned metrics at the larger program level,
as well as at the frontline worker level, helped to alter biases
and behaviors blocking success.
The bottom-line is that as you look at your existing MDM solutions
or consider altering them to better align with changing compliance,
regulatory, competitive, or operational needs, you must lay out a
simple executable plan with near-term benefits and milestones, with
business measures that incrementally mature with your
capabilities.