Tomorrow, March 9, at
10 am and 2 pm ET, APOS Systems will host a webinar on this topic:
If you can't attend, register
for the webinar anyway and we'll send you a link to the recorded webinar so you
can enjoy and share it at your leisure.
Chaos is never a neutral
term. It carries so much cultural baggage that it may not have been the most appropriate
naming convention for a scientific theory, and you'd think scientists might have
learned this lesson and not nicknamed the Higgs boson the "God particle."
I suppose we all have our blind spots.
Chaos Theory is the field of study within mathematics that
studies the behavior of dynamical systems that are highly sensitive to initial
conditions — a response popularly referred to as the butterfly effect. When we
say such systems are highly sensitive to initial conditions, we are saying that
the slightest variation in those conditions can cause drastic changes to
downstream events. Such systems are generally characterized by:
- High volume of inputs: the number of elements interacting within a chaotic system is so large that it is virtually impossible to account for them all without extreme computing power.
- Nonlinearity: complexity of inputs makes determination of cause and effect very difficult, if not impossible
- Dynamism: it's a moving target; before you can analyze the state of a chaotic system, the state has changed.
Chaos Theory is one of the most interdisciplinary of mathematical studies, because chaos is so prevalent in the world. In fact, the science of chaos started with an attempt to model and predict weather. Those studies started in the 1960s, with impressive, but limited, results. (You can usually get a pretty good idea of tomorrow's weather, but looking five days out, all bets are off.)
BI Chaos
A high volume of inputs,
nonlinearity and dynamism are characteristics of many of the BI systems we see.
BI Chaos is characterized by a high-volume,
nonlinear and dynamic state obscuring a clear understanding and deep knowledge
of the system, its dependencies and its future states, and creating a barrier
to proactive and preventive management and targeted administrative action.
Of course, we are
using chaos as a metaphor for the difficulties the BI team has in administering
the BI system, but if you've faced some of the challenges that BI
administrators face on a daily basis, I think you'll agree it is a very
appropriate metaphor.
A BI system may be
chaotic if it is difficult to form a clear understanding or a deep knowledge of
its component parts. For various reasons, such knowledge is obscured, making it
very difficult to manage the system proactively or preventively.
Chaos is less of a
problem if you have an unlimited pool of administrative resources, but for the
rest of us, this lack of predictability may frequently place the administration
team in a reactive mode. Chaos may prevent your team from entering into a proactive
mode and acting in a timely manner on evolving requirements and expectations.
BI Chaos - Feature or Bug?
BI Chaos happens
because your system is performing the function for which it was designed. In a
very real way, you are a victim of your own success. If you are not
experiencing chaos within your system, you have to ask yourself whether you are
realizing the full potential of the system.
If you want to reduce, prevent and manage the chaos in your BI deployment, you have to start by recognizing that chaos is the rule, not the exception. It's a feature, not a bug.
If you want to reduce, prevent and manage the chaos in your BI deployment, you have to start by recognizing that chaos is the rule, not the exception. It's a feature, not a bug.
