In eight minutes I make the connection between marketing, information management and Big Data to position the real value of it and separate it from the hype.
Click here for the presentation.
Wishing you a great 2014, where you will make decisions based on facts and data be successful in all your endeavours.
Kind regards,
bert
Thoughts on business intelligence and customer relationship management as customer analytics need process based support for meaningful analysis.
dinsdag 31 december 2013
donderdag 19 december 2013
Download our Webinar slides from the ITMPI
We have posted the slides of two webinars on our Lingua Franca site for you to download:
How Business Analysis for Business Intelligence
Creates Strategic Value
How to Keep Business Intelligence in Sync with Your Strategic Priorities
Send us your feedback, we look forward to reading your comments.
donderdag 14 november 2013
Business Intelligence has become too big to allow failure.
Four
speakers between Ralph Kimball’s sessions, four topics and one unifying
thought: BI is getting to big to allow failure. The first Business Analytics
for All Insight session which took place in Brussels the 12th November gathered
over 250 attendees to hear Ralph Kimball’s insights on the data warehouse
design principles and how the Big Data phenomenon fits in this architecture. I
gladly refer to the Kimball Group’s website with articles like these for his vision on Big Data.
But between Ralph’s talks in the morning
and in the afternoon, four other topics were discussed which all lead to the same
conclusion: BI has become too big, too much of a strategic commitment to allow
for sloppy business analysis and project management.
Annelies Aquarius, European BI Project
manager from the Coca-Cola Company illustrated the anytime- anything-anywhere
aspects of mobile BI. Jelle Defraye from Laco made a case for self service BI. Jos Van Dongen from SAS taught us the basics
from data visualisation and Guy Van der Zande from USG ICT Professionals
explained why a well organised BI Competence Center (BICC) is essential to
manage technology trends and changing business requirements.
For a full description of their talks we refer to the website.
It is time for proper BI business analysis and project management
Let me explain my point. With the growth of
users, user types, data a lot of side effects have come into play since the
early days of DSS where you offloaded a few tables to make reports for the CFO.
Exabytes of data flowing in at incredibly
high speeds from a myriad of data sources in structured, semi-structured and
structured formats need to be exploited by more people in a faster decision
making cycle which is not limited to the strategic apex anymore. Thus the
feedback loops become more complicated as the one-to-many relationship of top
management and the workforce now becomes a many-to-many relationship between
more and more decision making actors in the organisation. Self service BI,
mobile BI and visualisation are all part of the solution and the problem if
your organisation has no duopolistic governance from IT and the business. because
both business processes and data management processes need to be mutually
adjusted to allow for maximum return on investment . The alternative is chaos.
So there you have the true value of a
well working BICC.
But to get there and to stay on that level,
only a thorough business analysis process and the proper BI project management
method will increase the success rate of business analytics. This success rate
worries me. Because after twenty years in this business I am still seeing
failure rates of 80% in BI. If we’d had the same rate of improvement in
medicine as in BI we would still be using leeches and bleeding our patients
regardless of the disease.
dinsdag 22 oktober 2013
Interview with a Business Intelligence User
Let’s call
him Eric. Because after the interview Eric decided he’d better remain anonymous.
Some of his answers could cause too much controversy in the organization, a
major European logistics company.
Eric is BI
manager in this company and when listening to his vision, his worries and his
concerns, it is like taking stock of the most common disconnects between IT and the
business.
Question:
What struck you the most when reading the book “Business Analysis for Business
Intelligence”?
Eric: I
think you have documented your book well and chose a useful starting point.
Most literature in Business Intelligence (BI) is divided in two
categories. On one side you have a
myriad of theoretical works on strategy and management, performance management and the inevitable scorecards and dashboards.
On the other side are plenty technical publications available discussing IT
performance and optimum data structures. What many of these books lack is a
vision of how business and IT should join hands to produce optimum BI results.
From my 20 years’ experience with BI, this is a serious problem.
Question:
What are the major impediments for your performance as a BI manager?
Eric: I see
three roadblocks: IT is either unaware or unwilling to admit that BI cannot be standardized.
But the business itself is not always capable of producing crisp and consistent
definitions to produce a coherent analytical frameworks changes its mind”. And
last but not least: the complexity of some analytics also causes a lot of
problems and is –of course- compounded by the two previous roadblocks.
Question:
Why would IT not be aware of the need for flexibility? Some IT guys we know say
stuff like “The business guys always change their mind”
Eric: No, it’s not about business changing its mind
because that can be prevented through thorough analysis as described in your
book. It is more about the prejudice that BI solutions are templates you can
use anywhere. IT people underestimate the uniqueness of each business process
and its context, culture and informal issues that make every business unique.
Management can shift its attention and rearrange its priority list in days and
weeks. If IT can’t follow, the users look for ad hoc (and often badly architected)
solutions.
Thank you
for sharing this with us, Eric.
To our
readers: don’t hesitate to share your experience with the gap between business
and IT in BI. We can all learn from this!
vrijdag 30 augustus 2013
Book Review Retail Analytics, The Secret Weapon by Emmett Cox
by Emmett Cox Wiley and SAS Business Series
2012
The book cover opens with promising references and
from Tom Davenport and all this on just 142 pages of 10 point Times new Roman
with double spaced interlines. Wow! This needs further reading I guess. Chapter
One gives a very high level introduction to the main process: sales and
replenishment and chapter two introduces the ins and outs of retail data
management. A diagram of the transaction log files could improve the didactic
impact of this chapter and the comparison between denormalised and normalised data
is at best superficial. But I guess the readers that, according to the editor
are helped with their critical management decisions don’t bother much about
technical mumbo jumbo, so let’s move on. Although this inspires me to publish
an in depth blog on the pros and cons of these datamodels that might interest
senior-level management. Because it is about money, time and quality and there
is no one size fits all choice to be made.
Inspiring introduction
Cox sums up a few interesting case studies ranging
from trade area modelling, site selection modelling, competitive threat
analysis, merchandise mix modelling, marketing effectiveness tracking, brand
analysis, clicks and mortar analysis, cross sell analysis to market basket
analysis. And he does so in plain English which at least will trigger the
interest of senior management. The schema on page 25 gives a quick and clear
view on how the analytical domains are part of a chain driving important retail
and merchandising decisions. While reading these case studies you are
confronted with a very mild and unobtrusive form of product placement with
sentences like “The statistical software company SAS has a strong set of
utilities within its SAS Enterprise Miner that we used heavily” (p. 33) Yet,
the case studies are very credible and well presented, even if they don’t go in
depth into the statistical details. I guess the target group of this book will
tell guys like me: “Find out how we can apply this method in our stores”.
Chapter three pays attention to the specifics of the apparel industry and does
so in a didactic manner, illustrating clearly the constraints apparel buyers
have to work with: long order lead times, optimum price elasticity management,
optimum sales promotion management,… Having worked in sales promotion for a
mail order company doing 80% of its sales in fashion I recognised the stressful
situations even the best retail analytics won’t attenuate. The fourth chapter
spends quite a few pages on explaining what a GIS tool can do and how it works
which is a bit redundant in the days of Google and Wikipedia, leaving only a
few pages for the application of GIS in retail. For example: the combination of
category management and GIS analytics can produce powerful results I am sure
the author could illustrate to his readers. It is as if Mr. Cox apologises for
this in the fifth chapter opening with “Chapter 4 was an extremely technical
illustration of the GIS tool.” Er.. well,… I wasn’t bothered with points,
vectors and surfaces of the GIS world, I just wanted to know more about the
applications….
A 101 on in store
promotion
In-store marketing and presentation get a lot
more attention with 31 of the 142 pages. Pricing strategies are explained very
well as they are the meat of retail analytics. But Cox’ emphasis on convenience
shopping hides its counterpart: fun in shopping which also needs tailor made
analytical insights to enhance the profitability potential. His reference to
DB2’s datablades brought me back to the heydays of Informix, once the leading
RDBMS before Oracle displayed a superior sales and marketing strategy. All in
all this chapter is a little too narrowly focused on the US situation. Phrases
like “Most retail chains are now open 24 hours a day…” (p. 98) will raise
eyebrows in Europe and sometimes the author gets lost in details like the
description on how floor graphics can be removed “without leaving any residue
behind”. I wonder who in the analytical community will bother about this. And
when I read stuff like “another form of subliminal messaging” (p. 101) related
to clearly detectable things like baking smells I get confused. “Subliminal”
means a stimulus the audience is not aware of. And literature and research are
not in unison about the effects of subliminal messages in stores. Chapter 6 handles
the store operations, an underestimated source of information opportunities to
be used; Think of workforce management where you optimise the match between
labour cost and service level based on POS data and longitudinal time series.
Think of differentiated messages to consumers, HVAC control, intrastore
communication, replenishment: Cox explains these operations and the link with
retail analytics in a clear and concise way.
Introduction to loyalty
marketing
Last but not at all least, the chapter on loyalty
marketing. Since Reichheld and Sasser’s publications on customer loyalty we are
aware that the link between excellence, customer satisfaction and loyalty is
not always obvious and in many cases it is not even existent! Measuring and
managing customer loyalty is a real pain in retail. To me, attracting customers
to loyalty programs is not equal to creating customer loyalty. It is just a
form of prolonged sales promotion that –if stopped- would cause an immediate
drop in sales. True loyalty is quite different. It took me one and a half page
to read about it: “(…) as long as the consumers do not just cherry-pick the
discounts and rewards offers.” Spot on Emmett! Weeding out the cherry pickers
is the true challenge for retail analytics. The author offers a checklist
before you begin a loyalty program.
To conclude
This book will certainly deliver value for novices
in retail marketing and analytics. If you're a CEO or CMO of a retail chain and
wonder whether you should invest heavily in an analytical environment, you are
probably too late as the competition will have obliterated your obsolete
business.That doesn't mean other C-level executives couldn't benefit from this
book. But to be really relevant for other profiles like a CIO or COO, it
should have related data and process management to analytics with diagrams and
models.
dinsdag 20 augustus 2013
A Short Memo for Big Data Sceptics
In an article in the NY Times from 17th August by James Glanz, a few Big Data sceptics are
quoted. Here is a literal quote: Robert J. Gordon, a professor of economics at Northwestern
University, said comparing Big Data to oil was promotional nonsense. “Gasoline
made from oil made possible a transportation revolution as cars replaced horses
and as commercial air transportation replaced railroads,” he said. “If anybody
thinks that personal data are comparable to real oil and real vehicles, they
don’t appreciate the realities of the last century.” I respectfully disagree with the learned scholar: the new oil
is a metaphor for how our lives have changed through the use of oil in
transportation. Cars and planes have influenced our social lives immensely but
why shouldn't Big Data do so in equal or even superior order? Let me name just
a few:
- Big Data reducing traffic jams (to stick close to the real oil world)
- Big Data improving the product-market match to the level of one to one, tailoring product specifications and promotions to individual preferences,
- Big Data improving diagnostics and treatments in health care combining the wisdom of millions of health care workers and logged events in diagnostics epidemiologic data, death certificates etc...
- Big Data and reduction of energy consumption via the smart grid and Internet of things to automate the match between production and consumption,
- Big Data in text mining to catch qualitative information on a quantitative scale improving the positioning of qualitative discriminants in fashion, music, interior decorating etc... and of course... politics Ask the campaign team from 44th President of the United States and they will tell you how Big Data oiled their campaign.
vrijdag 26 juli 2013
Time to muse over “holiday”
Greetings from a 30°C office, in the north of Belgium, Flanders. It’s late in the evening, a good time for some lighter philosophising before packing the suitcase.
The DATE dimension is a very simple flat table (although some designers and DBA’s prefer third normal form). It contains surrogate keys, the date, the name of the weekday, month, day number, week number, month number, quarter, trimester and semester number, IsLastDayOfMonthFlag and what have you. There will also be a IsAHolidayFlag somewhere and in case you only work in a few countries, you simply add a column per country.
In case you operate on a global scale you may want to snowflake a bit to support truly global analytics and compare apples with apples.
Example of a snowflaked Holiday model
Holidays can explain a lot
Imagine you are comparing last year’s June sales with June of this year. Last year, June had no holidays and 21 working days. This year it has 20 working days and one holiday, i.e. 19 working days. In case your revenue is directly linked to the number of working days, this explains for a revenue drop of no less than ten percent in June this year! And in case you sell holiday related products like ice cream or beer it may well run into the other direction explaining a sales increase of over twenty percent or more…
So, here’s my advice: enjoy your holiday but don’t forget to integrate it in your analysis.
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