Al enige tijd geleden (begin 2006) heeft T. Davenport zijn boek uitgebracht genaamd 'Competing on analytics". Een boek dat de ontwikkeling en hype rondom Business Intelligence zeker een zet heeft gegeven. Het boek bestaat uit 2 delen: het eerste beschrijft 'analytic organizations' en het tweede deel gaat in op de competenties die je nodig hebt om er een te worden. In het boek worden een aantal mooie casestudies beschreven. Het boek refereert ook aan 'Good to great' van Jim Collins. Vernieuwende inzet van 'Analytics' is wat deze organisaties laat excelleren.
Hieronder een samengevatte boekbespreking (via www.edmblog.com )
Kopen via: Harvard Business Review
Tom and Jeanne have written a new book (building on a paper
they wrote some time ago) about what they call "analytic competitors",
that is to say companies that use their analytic prowess not just to
enhance their operations but as their lead competitive differentiator.
The book discusses a number of these analytic competitors and gives an
overview of how analytics can be used in different areas of the
business and how
you can move up the analytic sophistication scale. So what are
analytics? Tom and Jeanne say
"the extensive use of data, statistical and quantitative analysis,
explanatory and predictive models, and fact-based management to drive
decisions and actions"
The book has two parts - one on the nature of analytical competition
and one on building an analytic competency. The first describes an
analytical competitor and how this approach can be used in both
internal and external processes. The second lays out a roadmap for
becoming an analytical competitor, how to manage analytical people, a
quick overview of a business intelligence architecture and some
predictions for the future.
They
define an analytical competitor as an organization that uses analytics
extensively and systematically to outthink and outexecute the
competition. The analytics are insupport of a strategic distinctive
competency and they argue, persuasively, that without a distinctive
capability you cannot be an analytic competitor. They also note that
analytical competitors need a primary focus but once created the
culture of test-analyze-learn spreads widely. They argue that to be
successful analysis has to become a broad
skill of the company not just the province of a few rocket scientists
and they repeat the famous Red Sox story about a manager who did not
believe the analytics and so lost the big game. [...]
The book outlines what they call four pillars of analytical
competition- a distinctiive capability, enterprise-wide analytics,
senior management commitment and large scale ambition. They lay out 5
stages of analytic competition from "analytically impaired" to"analytic
competitor" (something I saw Tom present at a Teradata conference).
The importance of experimentation is made clear (e.g. CapitalOne runs
300 experiments on
any given day) and the book repeatedly emphasizes the need for
companies and executives to be willing to run the business "by the
numbers"
The book is full of stories about how companies compete analytically. [...]
-
Capital One's focus on identifying and serving new market segments
before its peers can. They have a lovely concept of "deaveraging" -
breaking a segment into small segments for better targeting.
-
Marriott's total hotel optimization shows the importance of new
measures. They created one called "revenue opportunity" or what
percentage of the theoretical maximum revenue they actually made. Not
only did they get this to rise an amazing 8% but it shows the value of
getting your measures right.
-
Progressive Insurance is so certain that if someone else's rate is
better than theirs that they are taking on an unprofitable customer
that they are willing to tell you what their competitors rates are. I
have blogged about Progressive
before and the book pointed out that they are now so fast moving that
they can often target a new segment and win much of it before
competitors have managed to react!
-
The Veteran Administration's use of evidence-based medicine and
predictive analytics along with automated decisions for treatment
protocols is noted, as is the fact that perhaps only 25-30% of medical
decisions are scientifically-based!
-
Honda makes good use of text analytics to flag early problems in cars
by analyzing warranty claims calls by customers or dealers to
Headquarters etc. This was a nice example where automated analysis and
flagging was all that was needed to get value.
-
Toyota found that only 20% of possible users of yield management tools
could use it effectively- visualization tools helped but it seems to me
that this is one of the drivers for decision automation rather than
other approaches - embedding better decisions in existing processes
using decision services means less need for staff to learn to use the analytics.
-
Vertex, a pharmaceutical company, starts by identifying the right
metric to measure success and then drives into the data needed to
measure that. This is a great general approach - don't just collect
data but collect data with a purpose - "begin with the end in mind"
-
Harrah's focus on real-time analytics at the point of sale so that
action can be taken as it is being collected e.g. when a customer is
losing money recommend and promote the buffet, when crowding in one
area causes traffic jams, offer deals in slower parts of casino etc.
-
DnB NOR bank uses event triggers to prompt customer relationship offers, using analytics to trigger the right events.
-
O2 mobile phone company using personalized menus to maximize value of
limited phone real estate and uses predictive analytics to personalize
I loved this one as it is a great example of a hidden decision - the
decision to display a certain set of options to a mobile phone user is
often hidden as companies don't think of each new list as a decision -
they think of it as "the list".
-
CEMEX used analytics to move focus from the sale of a commodity
(cement) to the delivery window using analytics and GPS. They went from
3 hours for a change to 20 mins. Sometimes the power of analytics only
comes with a different view of the problem.
-
Netflix focusing on giving each customer a personalized website
experience based on recommentations, ratings, segmentation. Again,
another example of regarding every single visit to the website as
requiring a decision as to what to display. I call this extreme personalization.
Tom and Jeanne also referenced another book I liked, Good to Great,
about the power of "breakthrough results come about by a series of good
decisions, diligently executed and accumulated on top of another". This
is the mindset I think you need for Enterprise Decision Management -
this focus on improving lots of operational decisions not one big
strategic one. The authors also note that analytics are a way to
confront
the "brutal facts".
The book has a great list of questions to ask about a new initiative
- how will it make you more competitive, what data do you need, does
technology work etc but one was particularly important: "What
complementary changes need to be made in order to fake full advantage
of new capabilities". This resonate with me as I have seen companies
get in trouble by focusing only on one stage in their process. This is
why there is the "E" word in EDM. It is not because you must do
decision management at the enterprise level
to get value but because your must take a broader view of decision
management for maximum value.
They outline a number of ways to get a competitive advantage from
data - by collecting unique data, manipulating data better, using a
unique algorithm or embedding it in unique process. Regardless of the
competitive approach, the need for analytical executives to be willing
to act on the results of analyses was clear. Segmentating your
customers is not enough, you must differentiate your treatment of them
to make a difference. [...]
There is a lot in the book about data quality - a major focus on
getting a single version of the truth and clean, accurate data. Clearly
an analytic competitor will spend more time and effort on data quality
than others but is this cause or effect? My sense is that focusing on
getting the data right first, without a view of the kind of analysis
you are attempting, will get you in to trouble as well as delay the
benefits. Indeed I don't think you should try and collect and clean all
your data before doing analytics.
Instead I would say figure out what analytics you need and then see if
you have or can get the data you need and fix the problems with that
data. Tom and Jeanne seemed to imply that consistent, quality data
across the board was essential for analytic competition and I am not
sure I buy that.
Take one of their examples - a bank refusing o waive a $35 bounced
check fee for a customer who had a $100M trust fund. Does the data need
to be integrated to fix this problem? Well integrating the customer
data would be one way. But what about sharing the insight? The fact of
a $100M trust fund could lead the private banking group to identify the
person as an excellent customer and this fact could be shared. There is
still some integration - you must be able to identify that the customer
is the same person in
each case - but you don't necessarily have to integrate all the data.
[...]
The book ends with a great list of changes coming:
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