Think about it. If
you are delivering a solution that is near 100% quality, what good does it do
if it is not the optimal solution?
Regardless of your definition of quality, If I create a product that
meets 100% of those levels, but does not solve the customer problem or advance
the ability to use the product, did I do any good? As an extreme example, if I write the highest
quality game application that could exist, but what the customer needed was
banking functionality, did I really add any value? Don’t get me wrong, I am a huge advocate of
quality in all products, and teach and coach software craftsmanship as a part
of my work with my customers. However,
we have to balance that out with the accuracy of the solution. Our attention to ‘Zero Defects’ has lead us
away from ‘Solved the Problem’
Solution Accuracy, on the other hand, has a strong focus on learning,
analyzing, measuring, validating and adjusting to deliver what our customers
need. Not what they tell us they need,
but what they really need. The adage
“the customer doesn’t know what they want, they only know what they don’t want
when they see it” is painfully true.
(Remember Henry Ford’s statement of “If I had asked customers what they
want, they would have said faster horses”).
Accuracy is about incrementally working towards an optimal solution by
applying the concepts of established Principle of Mission, iterating towards an
optimal solution, and applying leading indicators that help us course correct
or pivot as needed as we continue to study the impact on our customers of our
emerging capability(ies). From my years
of working with Fortune 100 companies I have experienced time and again the
lack of attention to accuracy and optimal solutions, instead following a
Crystal Ball plan of predicting/forecasting what the customer needs, and most
often arriving well short of the target.
Why is this a problem?
Assume Variability, Preserve Options
As an Enterprise Transformation coach and SAFe SPCT, I rely
heavily on two core aspects to raise awareness to the solution accuracy problem. The first is SAFe’s Principle #3 “Assume
Variability, Preserve options”. In
general, this principle shows us the benefit of not relying on predictive
‘Point Based’ solutions that rely on far too little knowledge and information
to state that we know what the customer needs/wants. The typical 3-9 month corporate project that
relies heavily on not only a stated outcome, but also a direct statement on how
to achieve that outcome, usually accompanied by a project plan with everything
detailed out to the low-level task. If
we were to honestly reflect on the results of this predictive planning we would
quickly see that these efforts rarely are on time and within budge, but even
worse, rarely actually solve the problem at hand.
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When we “Assume Variability” we understand that we don’t
have the full level of knowledge yet (that will be gained as we iterate towards
a solution) and that we should assume that our current assumptions are invalid
because of the lack of that yet to be gained knowledge. It is important to have assumptions of what
the best path forward is, but when we assume variability exists we accept that we are
most likely wrong (sometimes very wrong).
Assuming Variability says that we know we are going to learn more, so
let’s take advantage of that variability and iterate towards the solution with
a plan and process that inherently incorporates that new knowledge as we
progress.
To ‘Preserve Options’ means that we never lock ourselves
into a corner, never stating “this is the only way to solve this problem”,
until we have gathered the most knowledge we can. It’s great to have an assumed best path, but
we also need to acknowledge, and sometimes pursue, other options that may turn
out to be the better path. Yes, that
does mean sometimes you will pursue a solution that will be deprecated or
dropped based on knowledge that indicates it is no longer a viable path. If that sounds like wasted work, I would
gladly trade that level of ‘waste’ for the waste we find when we pursue a point
based solution and have the resulting adjustments, changes, and sometimes
project cancellations due to chasing the wrong path.
Tying these two together means we start out by
allowing and encouraging more than one possible solution direction, pursue one or
more to gain knowledge as fast as we can, and pivoting or dropping assumed
possible solutions when the knowledge shows we should. The end result is an optimal solution with
less effort because we were not forced into retrofitting the wrong solution
like a square peg in a round hole.
Example of “Assume Variability, Preserve Options”
I was working with a client on this concept, and as they
began to understand this principle they shared a story with me that is a great
example. This client is highly
regulated, and needs to process documents for regulatory and audit reasons on a
regular basis. When this new requirement
came to light, the business asked the IT group how they could solve it. Being the technology savvy group they were,
they stated they could create an automated system that would process the
documents as needed, at a cost of $8 million and about 6 months of effort. They completed the project a little over
budget and a little late, but since this was the norm they considered the
project a great success. Until they
turned it on.
The first month the system only processed 3 documents based
on the needed updates. The second
month? 2 documents. The third?
3 documents. The result after one
year was that each document cost about $67k to process. They then realized that if they had
“Preserved Options” they would have realized that an alternative option would
have been to hire a temp to come in one Saturday a month to process the
documents, at a cost of approximately $75 per document. Because they had not recognized the need to
“Assume Variability” in that they did not yet know the volume of documents,
they went to a point based solution that made sense. Bear in mind that the automated system was
still a viable option, but since the manual was such an easy approach it most
likely would have been the first option to pursue. The ‘quality’ of the effort was high based
on the low number of defects in production, but the accuracy was 180 degrees
from the optimum solution.
Focus on Solution Accuracy
Focusing on solution accuracy does not mean being predictive, it does
not mean having to know all the answers at the start. In fact, it assumes we don’t have all the
answers, and incorporates learning early and adjusting based on gained knowledge
throughout the effort. Another coach
just sent me an email with this quote on her signature line:
“Progress means getting
nearer to the place you want to be. And if you have taken a wrong turning, then
to go forward does not get you any nearer.If you are on the wrong road, progress means doing
an about-turn and walking back to the right road; and in that case the man who
turns back soonest is the most progressive man.”
C.S Lewis
Our goal in focusing on solution accuracy is trying to get to the
most valuable and informative pivot or pursue moments as soon and as often as
we can, and making those adjustments as needed in pursuit of the optimum
solution.
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