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No Data is Better than Bad Data

Is your team making bad decisions based on bad data? Beyond the obvious danger of making misguided
operational decisions, the erosion of trust from even perceived small
discrepancies in data can damage executive buy-in and confidence in future
endeavors. Poor data quality is also
believed to cost organizations an average of $15 million per year in losses,
according to Gartner
Research. When the data isn't
trusted, individuals often seek their own paths for uncovering the "right
answers" either on their own or through the development of shadow data and
analytics operations. Those paths create
sprawl of data and inconsistent data patterns, almost ensuring multiple
versions of the data will exist and will result in different versions of the
truth.
So, if your data can't be trusted, it's better to not have
any data at all, right?
A lack of data often leads to more cautious decision making
- at least you know you don't have all the facts. With bad data, it is easy to have false
confidence in wrong decisions. The good
news is, there are straightforward things you can do to avoid the pitfalls of
the wrong data and build trust in your data and data process. The solution lies in the ability to meet
deliverables with predictable quality in an easy to interpret manner on a
reliable time schedule. To do this, I
recommend following four main tenants of your data process.
Ensure Data Consumers Are Part of the Process
It is critical to include the consumers of your data when developing the
vision and a pragmatic plan for what data you will be surfacing. Communicate what metrics are being delivered
and how they are formulated. Understand
what insights they need to be successful and allow them to be a part of
prioritization and the backlog. Customizing
the system to the needs of your users ensures relevancy, creates excitement, and
enables you to control expectations from the beginning. The result is a partnership
that strengthens trust in the data supported by more consistent and open
communication, a sense of ownership, and a feedback loop for future phases.
Don't Neglect Your Foundation
The foundation of your data is where bad data can start. If this is done wrong, or not at all, it continuously
creates challenges for accurate data. There
is no doubt that flashy data delivery products or the promise of an easy button
is attractive for data delivery but neglecting a proper plan and a foundation
of correct and repeatable data quality will negate even the coolest delivery
channels. This does not mean that a
cutting-edge tool or process is not for you, it means that is should be
considered along with the complete picture of your needs and data landscape. Balancing
a flashy delivery of data with a solid foundation meets two needs. However, it is important to build confidence
in the data itself, not the delivery tool.
It may take a little longer to initially start publicizing your data,
but when you do, it is on a foundation that you trust and is correct.
Be Honest About Your Team's Abilities and Core
Understanding of the Data
When embarking on a new data process or initiative, don't assume skills
that do not yet exist on your team will quickly exist or be easily learned -
whether that is using a specific technology or the ability to quickly assimilate
the true meaning of the data. It is important to not over commit on what can be
delivered based on current skills as this creates contention among the data
team and the business users. Instead,
create smaller deliverables up front, potentially with shorter time frames, to
analyze, wireframe, and document future desired capabilities. This is not a Waterfall approach, nor does it
negate an Agile approach; instead, it helps set the roadmap for an Agile
approach. Once your team's capabilities
and velocity are understood, you can augment the team with additional resources
or additional scope to balance ability with need. Failing to do this often results in data
being released prematurely and earning a reputation of bad data.
Clearly Communicate the Good, Bad, and Ugly about Your
Data
Consumers of data assume a level of validity and scope behind delivered
data. Once their assumptions are found
to be untrue, the data becomes suspect and it is a stigma that is often long
lived. Communication is key to avoiding
this trap. If data is limited to time,
organizational hierarchy, geography or other dimensions, you should simply include
an indicator that identifies the scope of the data within your report or
dashboard. To avoid data being
misinterpreted, metrics should have clearly outlined formulas, even if an
agreement has not yet been reached on the final formula. Finally, data that is not yet validated but
is included in delivery due to error, incomplete information, or prototyping should
be indicated as such. Color coding data at
a report level to show that it is known to be under review or certifying data
sets that are valid builds credibility with consumers.
These four tenants are not by any means an exhaustive list
of successful data processes. Starting
with these four, and having them work together, will help to build trust. Back to my statement about no data being
better than bad data - don't get me wrong, I want as much data as quickly as
possible, but I want that data to be valid, trusted, and actionable. Don't let bad data hurt your organization's ability
to make decisions or force your users to find other ways to get information
they need. Building a strong foundation
and emphasizing honest two-way communications not only builds trust but will allow
for growth and quickly pivoting to new initiatives as needed.