WH Y P ROJ E C T S FA I L
data, they should have a clear idea of what
they want to do with it with from a business
sense. Here’s what you need to consider:
Turn over part or all of big data
solution delivery to business leaders.
Project management and ownership
from business (not IT) in big data solutions is the key for success. In the meantime, make sure to have clear alignment
between business and IT.
Partner with business peers to
identify opportunities and solutions.
If we talk about big data, the impact of
these projects should also be “big.” Create a cross-organization team and involve all stakeholders early in the game.
Value co-creation of value with
customers. Overall business objective
should always be about customers. If
one of the initiatives is about big marketing outcome, than it should be about how
to set up customer-centric marketing,
how to provide targeted dynamic advertisement, how to engage customers and
how to manage personalized shopping.
Start small – with an eye to scale
quickly. While big data solutions may
be quite advanced, everything else surrounding it – best practices, methodologies, org structures, etc. – is nascent.
No one has all the answers, at least
not yet. Understand why traditional
business intelligence and data warehousing projects can’t solve a problem.
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Small, simple and scalable. When
launching big data initiatives, avoid 1) getting too complicated too fast, and 2) not
being prepared to scale once a solution
catches on. Big data solutions can quickly
grow out of control since discovering value from data prompts wanting more data.
Identify what part of the business
would benefit from quick wins. Look
for opportunities that will show quick
wins within no more than three months.
Success brings more people to the table.
This is not a one-time implementation. Understand that this is a living and
evolving organism that will grow exponentially very fast. It is a culture change
in the company with the way that you
collect and use data, and the way you
make outcome-based decisions.
Develop a minimal set of big data
governance directives upfront. Big
data governance is a chicken-and-egg
problem – you can’t govern or secure
what you haven’t explored. However,
exploring vast data sets without governance and security introduces risk.
New processes to manage open
source risks. Most big data solutions
are being built on open source software,
but open source has both legal and skill
implications as firms are: 1) exposed to
risk due to intellectual property issues
and complex licensing agreements; 2)
concerned about liability if systems built
W W W. I N F O R M S . O R G