 Information
Management Part 1: Myths and Facts
By Lalitha Chikkatur, business intelligence consultant
As information
management is gaining acceptance, good governance is
becoming even more critical for the whole process of
retrieval, acquisition, organization and maintenance
of information. The crucial factor in information
and decision process analysis is an improved
design-thinking attitude. Only when decision-makers
use a good process and methodology for making
decisions under limited circumstances can people’s
information management needs and desires can be made
technologically feasible. Because all the
information ultimately is managed by individuals,
wherever there is human intervention conflicts
between facts and myths exist. I have come across
eight myths in information management. Here are the
first four:
Myth #1: Performance management is not a BI
solution.
For sure, performance management is the buzzword of
the year. Performance management is definitely
closely tied with BI. The fact is, any new
buzzword takes its own time and pattern to catch up
with the reality of implementation and utilization.
It’s like a new technology entering into the market
creating hype, promises and anxieties. Some
performance management vendors argue that they are
different from BI. For me this appears to be a
complete myth. To address it, let’s define business
performance management. The terminology could be
defined as a solution that enhances processes and
procedures by proactively identifying the risks and
problems by using specific methodology. It helps in
predicting and answering what-if scenarios with a
proactive approach rather than a reactive approach.
But before making it a strategic initiative,
companies have to do some homework in assessing the
true nature and purpose of the performance
management initiative, and if their environment is
conducive for the performance management tools. Any
performance management tool does well only if the
underlying system is stable and robust. And for
performance management, the underlying system is a
robust data warehousing and BI architecture and
infrastructure. There are lots of companies where
data warehouses still look like mirror images of
their operational systems. In such cases, you will
never receive any benefit from a performance
management investment. At most, the performance
management vendor will end up building a complete BI
and DW for you, which is not what a performance
management solution would typically offer.
Myth #2: Data warehousing and BI are technology
solutions.
It’s a myth to call a DW/BI solution a technology
solution because DW/BI is not any product, but a
combination of tools and technology put together
that enables a business to answer its
decision-making questions in a much faster and
efficient way. DW/BI is actually a business
solution. BI/DW terminology has been around for
a couple of decades, but the myth is still very
strong in the IT community that BI/DW is all about
delivering with tools, whether it’s ETL tools or
reporting tools. We always hear about the BI/DW in
terms of how many reports it delivered, how many
dashboards were created and the load time taken by
the batch. We hardly hear BI/DW solutions in terms
of “this initiative could address these business
issues.” One can find a number of write-ups on the
success criteria for a BI/DW (good design, best
tools, right staffing) and why BI/DW initiatives
fail (data quality, inadequate requirements
gathering). But there are hardly any pointers which
emphasize that it’s not important how much data you
bring into the DW, but its associated business
value. This essentially means that the BI/DW
solution has its ultimate value in generating
revenue and cost savings for the company. It’s
possible that many of us know this fact, but it’s
always good to get in touch periodically with
basics, particularly basics of data warehousing to
keep the investments, vision and directions right.
Myth #3: You cannot tangibly measure ROI on BI
investments.
Measuring the return on any investment is dependent
on various factors like your company, the person
owning these metrics, tangible and intangible
measurement types and direct and indirect returns.
All these things are true in BI/DW projects and
programs. You can’t manage what you can’t
measure. So, for sure, you can measure ROI. It’s as
simple as that. However, what’s not simple is
the way you measure it. The good news is now BI/DW
has reached a stable and matured state where
guidelines can be used to calculate ROI along with
the TCO (total cost of ownership) on the BI
investments. Some of the key factors used to capture
this are:
• Infrastructure costs
• Service costs (software vendors and service
vendors) and
• Staffing costs (both onshore and offshore).
Each of these factors can be expanded per the
requirement of the engagement, and you can derive
the ROI. All the measures coming from these factors
are numeric. In that case measurement should be
pretty much tangible. One can drill down to the
level where he/she can analyze various measurements,
like what’s the cost per user or what’s the cost per
terabyte volume, and derive information and further
plan for the forecasted data growth. Having said
that, ROI and TCO are still very subjective to the
individual person and business. The guidelines can
definitely trigger more detailed research and areas
of concentration, which will lead to specific
measurements on an ongoing perspective. Every detail
associated with the ROI and TCO is tangible. If
investment is a number and if cost is a number,
there is no way that ROI and TCO are non-numeric
measures.
Myth #4: Data Governance is a project.
A project has a definite start date and an end date.
But data governance is a process that involves
people, process, procedures and policies along with
technology to handle an organization’s data in a
secured and controlled way. Every BI/DW project will
have an essence of data governance which is inline
with the enterprise standards. There is no end data
for these programs. Data governance can never ever
be a project, but a through and through program. Its
life is as long as business is running and
technology is supported to run that business. Bottom
line, the data governance programs should be
systematic and a continuous investment in the
enterprise. This is very important because change is
the only constant in any business, and so the data
governance initiatives should be able to dynamically
adapt those changes coming from the business
initiatives and technology practices. Whether these
programs are top-down or bottom-up approaches is
left to the individual enterprise and the way they
want to implement it.
A data governance program might automatically start
addressing other key issues like data quality and
data security, which are other pillars where people
are skeptical about investing or, at least, they
don’t know how to measure any returns from those
investments. As far as I can understand, as the data
governance concept is fairly new from an investment
perspective, I see this as a change in the culture
and mind-set of people and organizations, which is
threatening for them. All of us resist change, which
is quite normal. But this barrier can be overcome by
educating people on these aspects and the importance
and returns that it brings to the organization.
Though data privacy and protection is a very old
concept, data governance is new to the industry and
addresses the process and procedure to achieve this
across the enterprise. Wherever there is human
intervention in existing process or protocols, there
should be some governance policies and control
around them.
Look for the last four myths and facts of
information management next month!
---Source: DM Review Special Report,
December 30, 2008. Lalitha Chikkatur is a business
intelligence consultant. She can be reached at
lchikkatur@gmail.com.
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