The document discusses the challenges and essential components of successful digital transformation in analytics, emphasizing the need for accessible, actionable, auditable, and accepted analytics frameworks. It highlights the pitfalls organizations face, such as data mismanagement, lack of trust in analytics, and resistance to change among stakeholders, while proposing solutions to overcome these issues. Ultimately, fostering a genuinely data-driven culture requires C-suite leadership and continuous adaptation of analytics strategies to meet evolving expectations and technologies.

1. Acceptance, Accessible,
Actionable and Auditable
A model for the digital transformation and excellence in analytics
Alban Gérôme
@albangerome
Data Festival London
15 June 2018

3. "Le doute, morne oiseau, nous
frappe de son aile...
Et l'horizon s'enfuit d'une fuite
éternelle !..."
"Doubt, dismal bird, beat us
down with its wing...
- And the horizon rushes away in
endless flight!..."
Arthur Rimbaud, Sun and Flesh
(Credo in Unam)@albangerome

5. Endemic misestimating
• “More than half of senior executives experienced a backlog of at least
two years on critical new analytics applications.”
• 4 in 5 of Fortune 1000 companies claim that their big data
investments are successful
• 1 in 4 claim they have started seeing signs of a data driven culture
@albangerome

7. Cheap data storage
• “You cannot improve what you cannot manage” has lead to an ever
increasing appetite for data, driving the cost of data storage down
• Intelligence agencies notorious for collecting data with little
discrimination
• Historical data is good, useless historical data costs little so let’s
collect data without discrimination
@albangerome

9. Losing support
• Projects need key supporters from the start to protect resources
allocation from demands from other competing projects
• Complex, multi-disciplinary projects get split into smaller parts and
the projects moves only as fast the lowest prioritised part
• A new project comes along with better prospects of success and
steals a key supporter and then three, ten
@albangerome

11. IT cannot implement analytics
• Analytics tagging is mostly Javascript, IT has Javascript developers but
IT is not competent to deliver an analytics implementation. You will
need one or several implementation consultants
• The vendor is only too happy to sell you premium consultancy
services. Independent consultants are cheaper but this still causes
delays and budget overruns
• The implementation is never “done”, it evolves because customers
expectations and technology evolve
@albangerome

13. Multiple versions of the truth
• 2 managers find discrepancies in analytics data and end up blaming
the tool
• Senior management recognises that the organisation needs a single
version of the truth, i.e. a dedicated analytics team
• Building a team from only internal or only external has advantages
and disadvantages
@albangerome

15. First reporting outage
• IT developers pushed code changes live and broke analytics reporting
in spite of following a rigorous testing procedure
• Broken analytics tends not to produce errors, it pushes garbage data
in the reports or even no data instead
• IT believes that breaking analytics is no big deal but the analytics
team should be the guardian of the data quality
@albangerome

17. Rebuilding trust
• The analytics department is expected to monitor a large and
increasing number of metrics
• The stakeholders beginning to ask for a lot of analytics data, a sign of
regained trust
• The stakeholders are no longer asking for actionable insight. The
analytics team should welcome this with a sigh of relief, this is a bad
sign
@albangerome

19. First data analyst leaves
• The analytics team is treated like a cost-centre. This means a small
team without prospects of promotions or opportunities to develop
team leadership skills
• Each day will consist of monitoring hundreds of metrics, producing
daily, weekly, monthly reports and huge data extracts that the
stakeholders barely exploit
• Head of analysts without analytics experience only counting the days
until their next rotation, never read of a book on analytics, complete
unknown in the analytics community
@albangerome

21. Data cherry-picking
• Pick only the data that confirms prior beliefs, discard the rest on
grounds of potential implementation issues
• Blaming how the data was captured is much easier than questioning
one’s beliefs when the data contradicts them
• Data cherry-picking makes them look data-driven but they are really
data-justified, biased and they get away with it because the CXOs are
either naïve or complacent and too few of them lead this by example
@albangerome

23. Unwanted conservatorship
• Spending ones resources for the benefits of another team at the
expense of one’s own ideas and projects can only meet resistance
• The incumbent teams want to retain autonomy of decision-making,
claim credit and use the analytics purely as a support team
• Implementing the analytics’ team ideas undermines their relevance
and if given free rein, could usher something resembling holacracy
@albangerome

25. Apple juice anyone?
• If making apple juice is harder than expected, perhaps something is
blocking at the bottom of the press
• Collecting ever more data and fancy data visualisations may not be
the solution
• People make decisions based on emotions and then justify them with
data but we are supposed to have a data-driven decision process
@albangerome

27. Inspiration in strange places
To create a fire, you will need three elements
• Oxygen
• Fuel
• Heat
You will not achieve self-combustion until you reach a certain heat
threshold. Remove one of these three items and a fire will stop
@albangerome

29. Accessible
• John Gall wrote “Some complex systems actually work but building a
complex one from scratch never works. You have to start over,
beginning with a working simple system.”
@albangerome

30. Accessible
• John Gall wrote “Some complex data capture implementations
actually work but building a complex one from scratch never works.
You have to start over, beginning with a working simple
implementation.”
• With too much data captured, one faces an increased noise to signal
ratio. Beware analysis paralysis
• Gradually increase the number of data points you collect data for and
only give access to that data to people who need that data. This
allows people to become more data literate at their pace
@albangerome

32. Actionable
• The CXOs must lead the digital transformation by example and stop
tolerating data-justified recommendations. They will strive to become
a data-informed organisation rather than just a data-driven one
• You need an analytics centre of excellence with a steer from the other
teams. The analytics experts will focus on complex finding actionable
insight and rotate between teams to gain operational experience
• The incumbent teams will need “moles” or “canaries”, i.e. junior data
analysts doing the reporting and metrics monitoring. Data quality is
every teams’ concern
@albangerome

34. Auditable
• Although a lack trust can be a very handy excuse for not
implementing recommendations, trust in analytics data is key
• Implement alerts, introduce tests in the developers test suites to
check the integrity of the analytics tracking, ask your “moles” and
“canaries” to report any issues they spot
• All recommendations must document why you are tracking these
data points, the data sources, the date ranges, how the data was
cleaned and processed to ensure reproducibility
@albangerome

36. Acceptance
• Dr Elisabeth Kübler-Ross wrote: “Any natural, normal human being,
when faced with any kind of loss, will go from shock all the way
through acceptance.”
• An analytics project will inflict a shock to any organisation and this
will come with a feeling of loss for many. Data-informed organisations
still need years of experience and domain knowledge
• An analytics programme will take an organisation on a journey, from
doubt until finally reaching acceptance
@albangerome

38. You need all four
• The digital transformation must be accessible, actionable, auditable
and achieve acceptance
• Having all four will not usher the digital transformation until there is
widespread acceptance all the way to the C-suite
• If you lose one of the four A’s, you can wave your digital
transformation goodbye
@albangerome

40. “The most beautiful people we
have known are those who have
known defeat, known suffering,
known struggle, known loss, and
have found their way out of the
depths. These persons have an
appreciation, a sensitivity, and an
understanding of life that fills them
with compassion, gentleness, and a
deep loving concern. Beautiful
people do not just happen.”
Dr Elisabeth Kübler-Ross
@albangerome

41. The data-informed
stakeholder does
not just happen
@albangerome

42. Thank you!
http://www.albangerome.com
@albangerome

43. AAAA – Cheat Sheet
Problem Cause Answers Net Benefits
1
Vendors give the illusion that analytics is
easy and downplay that implementing and
operating their tools often requires experts
To achieve the most sales for product
licences, consulting services and premium
support
Address the expectations set by the vendors
and explain that actionable insight requires
effort and often starts with nothing more
than an hypothesis which will then require
A/B testing to verify
A better alignment between expectations
and capabilities and a better understanding
of the resources required such as
implementation experts and a web analytics
team
2
The organisations decide to track everything
and spend months capturing the
requirements from the stakeholders
The organisations do not know what to track
but the cost of data storage keeps
decreasing. Intelligence agencies are
notoriously tracking with little discrimination
and set a bad example. The organisations
decide to track everything
At first, focus on tracking very little data,
demonstrate the value of a data-driven
approach early, support a small number of
stakeholders and then expand gradually by
tracking more and supporting more
stakeholders
Traction, an earlier and faster
implementation. Once implemented, the
stakeholders will learn analytics at their own
pace. Restraint in your tracking appetite will
help your organisation comply with
regulators
3
The implementation drags on and the initial
key stakeholders are withdrawing their
support and resources to support other
projects with better chances of success
The IT team may believe they can implement
an analytics tool but they lack the skills in the
analytics vendor proprietary code
Hire an implementation expert. Large
projects may require a team of
implementation experts
Traction, ensuring the continued support
from the key stakeholders and their
resources
4
At best, the first reports contain little
actionable insight. At worse, the data is just
garbage or contradicts the stakeholders'
beliefs
The IT team's lack of implementation
expertise leads to collecting garbage. When
the data is correct, it may paint a picture at
odds with the stakeholders' beliefs
Expect to hire implementation experts more
than once as an implementation is never
"done"
Continued trust in the analytics data and an
implementation that evolves with the
organisation's needs
© Alban Gérôme @albangerome

44. AAAA – Cheat Sheet
Problem Cause Answers Net Benefits
5
Two stakeholders extract data that should
match but it does not and by a wide margin
too. The organisation does not know who to
trust anymore.
The stakeholders lack experience with the
analytics tool and data literacy too
You will also need a web analytics
department
A single version of the truth
6
The stakeholders hide correct but
unflattering data. The organisation suspects
that their stakeholders lack objectivity
The stakeholders leverage the perception
that analytics is scientific and therefore the
whole and accurate truth
The head of the analytics department should
report to the C-suite directly, the COO or the
CEO preferably
Reporting impartiality
7
Who should be in the analytics department?
Internal staff or external experts?
Internal staff have operational knowledge
often struggle with analytics and make
mistakes when extracting data. External
experts can use data but often do not know
which data could have a commercial impact
Create a hub and spoke model, the
incumbent teams who need analytics data
will nominate a junior web analyst. Rotate
the junior analysts between teams if
possible. In the analytics team, you need
external talent who will start spending time
in other teams to gain operational
experience
The actionable insight is better aligned with
the business objectives and strategy. The
incumbent teams will stop become
responsible for their own reporting and free
the hub to focus on analysis
© Alban Gérôme @albangerome

45. AAAA – Cheat Sheet
Problem Cause Answers Net Benefits
8
The IT developers keep breaking the
analytics code and the stakeholders blame
the analytics department
The IT developers still lack implementation
knowledge and do not know what to test for
when pushing changes live and end up
breaking the analytics code. Tracking garbage
data or no data at all causes no errors
Get your implementation expert to speak
with the developers to include automated
tests to check for the integrity of the
analytics implementation in their testing
suites
Serenity through knowing that reporting
outages due to IT changes will become less
and less frequent and true partnership
between IT and the analytics team will
emerge
9
The analytics dedicate an increasing number
of resources just to monitor metrics at the
expense of finding actionable insight
The analytics team is now responsible for the
data quality
With a hub and spoke model, delegate all
reporting and monitoring to the junior
analysts
The junior web analysts will look at a smaller
dataset and will be your canaries in the coal
mine when a reporting outage occurs and the
advocates for analytics in their team and the
organisation when the hub members are not
available
10
The analytics experts are expensive, hard to
find and hard to retain
When an analyst spends his or her time
monitoring hundreds of metrics and
reporting instead of searching for actionable
insight and runnning tests, the analyst will
have no trouble finding another job and give
your organisation a bad reputation in the
analytics community
Analytics should be about increasing the
company ROI by improving the customer
experience, not about avoiding looking bad.
Monitoring and reporting will only turn the
analytics team into a cost centre and your
organisation will earn a bad reputation in the
analytics community
Recruitment is faster, cheaper and retaining
is easier when your analysts leverage their
personal networks. Talented web analysts
want to leave the companies where they do
little else than reporting and monitoring
data, watching for the outage caused by IT
and analyse data instead
© Alban Gérôme @albangerome

46. AAAA – Cheat Sheet
Problem Cause Answers Net Benefits
11
The stakeholders keep making ever
increasing demands rather than keeping
their promise and implementing any
actionable insight
The stakeholders can borrow an idea from an
external thought-leader, tweak it enough to
claim credit and ownership. Looking data-
driven, i.e. data-justified, is much easier than
being genuinely data-driven and the C-suite
either cannot tell the difference or tolerate it
By growing your implementation rather than
trying to track everything from the
beginning, your stakeholders will have to
make do with the data available and stop
cherry-picking data
The stakeholders can develop their
experience with the analytics tools, their
data literacy too
12
Many biases are standing in the way of buy-
in such as belief persistence, confirmation
bias and selective attention bias
The stakeholders are trying to address the
gap between their beliefs and what the data
is telling
The analytics team must review the
stakeholder recommendations, protect and
educate the organisation about theses
biases, their causes and effects
The organisation becomes wiser to the
biases and protect them from potentially
disatrous ideas
13
Making actionable insight easier to
understand, memorable and other
persuasion and influence techniques do not
work
Asking stakeholders to implement actionable
insight without question is tantamount to
conservatorship, puts their careers at risk and
generates a strong but slient resistance
The analytics team can also find interesting
trends without potential commercial impact.
They need a steer from the stakeholders.
This guidance will provide the stakeholders
with opportunities to claim some of the
credit and the ownership for the actionable
insight
The seasoned stakeholders can regain pride
in their experience, feel valued for guiding
the analytics team and help them find real
actionable insight
© Alban Gérôme @albangerome

47. AAAA – Cheat Sheet
Problem Cause Answers Net Benefits
14
The C-suite executives asks their
stakeholders to be data-driven but they their
organisation is data-justified instead and the
digital transformation sees very little traction
The C-suite executives do not lead by
example and either fail to recognise the
difference between data-driven and data-
justified ideas or tolerate it. They also
believe that the risk of disruption for their
organisation is largely exaggerated
Embrace data-driven decision-making, start
with a business questions, collect data, find
your answers by analysing that data
Proactive organisations can gain compettive
advantage by becoming genuinely data-
driven, become more robust and keep
potential disruptors in check. What could be
scarier than your 30-year competitor
suddenly becoming data-driven and you are
not ready?
© Alban Gérôme @albangerome