AUTHOR'S NOTE: Escape velocity (Ve) is the speed at which the kinetic energy + the gravitational
force on an object is zero. Any speed above that will allow the object to break
free from the gravitational pull of the larger mass. Ve is a combination of G= gravitational pull; M=mass of the larger object; and r = distance from the center of
gravity. Ve is used to launch
rockets free from the gravitational constraints of planets, and it can also be used
to leverage the gravitational mass as a slingshot - where the gravitational pull of large bodies
can actually be used as a slingshot for acceleration and escape. This is
written as 
In our context the global desire is to escape from the
dark pull of failure. Let’s call this the black-hole of failure.
Ve= Escape Velocity in
BI projects (including SAP HANA BI) can thus be defined as
Where
F is the pull towards failure as a constant (where the rate
of failure is constantly balanced by the rate of success thus a point of
accepted tolerance by business stakeholders and users). F can be more clearly understood where if a team delivers 5
successful reports (defined as reports that meet business expectations) and at
the same time deliver 5 reports that do not meet business expectations- but are
not punished by the system, governance or the business owners as a failure team
the 0.5 is the balancing constant. Now if the team produces 4 successful and 6
not successful deliverables which begins a gravitational pull that is negative,
or a pull that can crash either the developer of the team towards the black-hole
of failure then a corrective action has to be taken at all points below 0.5. In
our example if the team delivers 10 out of 10 reports that do not meet business
expectations it assumes the developer or team reaches a 0 velocity and falls
straight into the black-hole of failure never to emerge again. This is a
perpendicular fall to oblivion.
P represents the
propensity towards failure. This represents the negative mass of your
black-hole, i.e. the pull of failure. P represents the place you don’t want to
go. P also represents the failure probability with specific standards,
processes and methodologies. P is often predictable when viewed through a scientific
lens. For example if you have standards and processes that constantly deliver a
50% failure rate, then your P factor has to be large whichever way
you look at it.
n represents the center of negative gravity of the dark planet and the lowest point of no
return. At this point the trajectory of the developer and/or team has no recourse
to getting additional budgets, additional chances or any recourse for another
try. At this point managers are fired, partners are fired and individuals are
fired. Of course in our example it is not the end of existence as the teams are
simply replaced by another partner, manager and team – mostly in desperation,
at far lower budgets, in very unplanned projects and with a lot of (n) negative
pressure.
I
tend to be hyper-attracted to two Gartner findings on global BI.
Gartner
in 2003
– “50% of BI projects will not meet
business expectations”
Translation: There is a
very high probability that 50% of reports in your production environment are
either not being used or will never be used by your business users.
Your
takeaway: As you rush ahead at full speed understand
that what got you here (i.e. a design that constantly delivers a 50% failure
rate) should not be used to take you to the strategic HANA future
Your decision:
1.
Continue with the current standards and
processes
2.
Continue with your current architects and
modelers
3.
Continue with your current System Integrators
.. and reach the same point of failure where
you currently exist only after deploying a net new acceleration technology
Gartner
in 2013
– “70% of BI projects will not meet business expectations”
Translation: In the
2012-14 period more and more customer are treating new technologies as
Technical installations. While global spend on BI has gone up their propensity
to failure has also increased. The current rate of failure is over 70%, from a
50% in the 2003-09 periods.
Your takeaway:
[1]
HANA is a business solution and not a technology installation, what is the
benefit of accelerating a 70 second query to 1 second if business will never
use it
[2]
There are many scientific ways to reduce the cost of your HANA migration
anywhere from 20 to 60%
Gartner’s
2013 advice for big data projects “This
is a time of accelerating change,
where your current IT architecture will be rendered obsolete,”
Mr. Sondergaard, Gartner Sr. Analyst, said.
“You must lead through this
change, selectively destroy low impact systems, and aggressively
change your IT cost structure. This
is the New World of the (big-data), the next age of computing.”
Your decision:
1. Find out how to
leverage the scientific options of the IQDCT HANA methodology. Increase
Quality, Decrease Cost and Time
2. Build net-new global
HANA standards and processes and then seriously consider moving greenfield to
BoH (BW-on-HANA)
3. Build net-new global
HANA FEDW (Gartner architecture) and leverage automated HANA modeling
4. Consider changing your
current System architects, design planners and even your SI, i.e. there is
adequate data that proves that existing folks will only follow existing /
familiar paths. If you leverage existing resources then they will take you only
on a predictable path, i.e. 70% failure. If this were not true then they should
have taken you on a path of Escape Velocity way before you read this paper.
5. Consider merging your
many BW system on a global FEDW architecture. Benefit 100% central governance,
with 100% local independence. Ability to detach a business unit for this
environment over a weekend without disrupting any other central or local
business unit.
..
and reach the same point of failure where you currently exist only after
deploying a net new acceleration technology
Leveling the playing field: It has been
clearly established that successful
BI projects experience a higher acceleration that is directly related
to the degree of success. Acceleration here is measured by satisfied business
users, lower spend per project, fewer unplanned projects, happier stakeholders
that view BI as an investment, easier budgetary allocations and a high degree
of trust – competency of the IT team to deliver results.
On the other hand
it is similarly established that failed
BI projects experience a drastic slowdown proportional to their degree
of failure. Deceleration here is measured by more dissatisfied users, higher
budgetary allocations due to trying to fix failed deliveries, higher number of
unplanned projects due to lack of meeting business expectations, disappointed
stakeholders that start to view BI as an expense, difficulty in getting new
budgets approved due to a total lack of confidence in the team to deliver any
business results – lack of business confidence in the delivery team.
Examples of F in existing BI environments are
totally measurable. Take an inventory of every report in your production BI
environment. Drop them to an excel sheet. Separate them by Business function,
i.e. FI, OTC, P2P, SCM, etc. Place all the FI reports into one worksheet, OTC
into another and so on. Create three columns [1] Can use as is- do not change;
[2] Can use but needs change; [3] cannot use please delete. For each report
that can be used as-is score a +1; for each report that needs change score 0;
for each report that cannot be used score a -1. Then come and post your score
on this URL for a global BI Score of F. Enter your email if you want results
shared. No company or individual details will ever be share and your input will
be in strict confidence.
Examples of F in new BI projects, i.e. not yet
live, are also measurable. Take an inventory of every report that your SI
partner plans to deliver. Apply the same rules as above and get your users to
fill the three columns as above. In this case most of your users will not be
able to accurately score the individual reports but you will still get a trend
that will be highly beneficial. You will find out quite early what your
probable F factor is. Once you go live conduct the same exercise around 30-60
days after go live, as by this time your users will understand the usability of
each report and its direct business benefit or lack of it thereof. Compare the
two and we once again get very interesting results.
Examples of P in existing projects is scientifically predictable. Your
partner’s P score determines the speed at which your partner will pull your HANA
BI project towards failure. The pull is a a gravitational pull based on the
mass of defective procedures and methodologies that come with a partner. It is
the combined score of their recommendations and methodologies. If your partner
recommends a HANA installation as a technical installation then score them -3, and
so on and so forth. If your partner simply delivers what the client asks them
without any scientific inputs or forecasts then socre them -4, i.e. when a
pharmaceutical company asked a leading SI to simply replicate 100% their 1998 Cognos
reports on to the new HANA plus BusinessObjects 4.0 platform. This was as much
the fault of the customer in firstly demanding legacy reports and for the
partner in simply accepting a P
factor with a very high mass, i.e. the probability of absolute failure was
extremely large. In order to get your own propensity score, i.e. the propensity
of your current SI, or their recommendations, request your P questionnaire and
get your own propensity mass of your black-hole. The bigger the propensity mass
the faster this black-hole will pull your strategic success towards failure,
the faster you need to travel towards success as your escape velocity.
Examples of n in existing projects is scientifically predictable. In
our above example of Cognos the SI ended up providing over 6 months of services
in a fixed bid contract – total loss. Even after 6 months the customer had to
cancel the project as the very foundations of the project were erroneous. The
pull of the large mass of failure was disproportionally large and at a certain
point all parties know that Ve could not be achieved. Could this have been predicted –
absolutely right before the project had even started.
In yet another
example a SI went and delivered over 500 reports. Within 20-30 days the
business users realized that the deliverables were not going to meet their
expectations, by day 40 businesses reverted to their old reports and stopped
using the new BI environment. By day 50 our team was invited with a single
statement ‘How can you help get business confidence back in BI’. Upon analysis
we found that the methodology, standards, processes and resources were all
wrong- so the next project started with a total replacement and rebuild of the
BI environment but one-step-at-a-time. Success went from 12%, i.e. 88% failure,
to 92% user satisfaction in a matter of 4 months.
So understand Ve, work towards it
and measure if your trajectory can release you from the mass of failure and
launch you to your goal of success.
In this example
the impact of, or resistance caused by, bad decisions of managers and/or
inexperienced resources is not taken into consideration even though it has a profound
impact on the total Ve of BI
projects.
In addition Escape
Velocity is a misnomer as it is not any particular velocity but a relative
speed that is dependent on the distance from n, or the negative mass of
failure. The farther away from P
that we are the lesser speed we need to apply to attain Ve, at a certain
distance from failure one can remain perpetually free from the negative
gravitational pull of P.
Quantifying the HANA BI Escape Velocity
By simple scientific rules
n: is the point of damnation and the center of the mass
of failure. It is also the absolute point of no return. No amount of budgets or
efforts can make the project escape failure.
The Red circle is the point of escape. The lower your
project flies the more energy (budgets, resources and efforts) the project will
require to simply remain afloat. The degree of failure (Gartner says this is
50% to 70% right now) determines the point of location on the E1 axis.
The Green circle is the point of survival or equilibrium, at which
point the gains are fully sustained with minimal efforts. This is the point launch to Ve. The degree of survival is determined by the point of location
between the surface and the green circle represented by E2
E3 is the point of escape velocity, where with a minimal
effort BI projects can soar to high degree of success, very low wastage and
minimal efforts to attain high gains.
What’s on your roadmap… can you see past the horizon or
do you never look beyond your feet..
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