1. MAGENTA ADVISORY RESEARCH
Data Driven Decision Making
– Is Your Organization Ready for Big Data?
Magenta advisory publication
04 / 2013

2. Partner in your digital transformation
Magenta Advisory helps clients create competitive
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in their digital transformation.

3. introduc tion
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research03
the amount of data available is growing tremen-
dously fast. The appearance of a new kind of data
will drastically change the whole dna of the com-
pany.Where earlier only a bunch of people were re-
sponsible for corporate data,now also top managers
need to start taking data seriously.
There is debate ongoing whether a fact-based cor-
porate culture dominates innovation and creativity.
We see that these two will live side by side. A bet-
ter factual understanding on business drivers can
significantly boost the decision-making process.At
the same time, there might be creative processes
ongoing where data acts as a generator for new
ideas.
Data is a good servant but a bad master. It is just
as valuable as the decisions we make on the basis
of it. In this paper we dig into the core topics to
which the management should pay attention while
planning and managing business in the data-laden
world.
It doesn’t matter if you get
the best data scientists in a room;
if no one across the organization knows
what to do with the data,
the insight they come up with doesn’t matter.
Data is a business issue, not an I T issue
micheline casey,
chief data officer for the state of colorado
2012
Magenta Advisory Research
Data Driven Decision Making –
Is Your Organization Ready for Big Data?
I
II
III
The big data
opportunity
Typical challenges in
leveraging big data
Building data driven
operations
HEADLINES

4. the big data opportunit y
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research04
What is big data?
The nature of big data can be characterized with
the traditional classification of three V’s: Volume,
Variation and Velocity.
Volume According to IBM (2012), due to the vast pro-
liferation of data sources, 90 % of the data in the world
today has been created during the last two years.This
small fact highlights the magnitude of change we are
talking about.
The definition of big data refers to the collection
of large and complex data sets that are challenging to
process with traditional data processing tools. Instead,
these terabytes of data call for de-centralized and often
cloud-based data management and processing.
Variation We all remember the days when corporate
data was stored on a company server and consisted of
data mainly from internal sources, all structured along
the customer base, company offering and so on.Today,
the data we utilize can be very versatile and also come
from third party sources. Sources for third party data
can vary from public and bought data, partnerships
and hosted solutions to keyword search as well as ad
serving data.
VelocityWe are shifting from static databases towards
dynamic modeling of data. For exampleTesco integrates
weather data in their purchase optimization modeling.
Automatic algorithms also allow companies to react to
customer behavior and optimize real-time offering and
messaging to the clients.
Modeling the ideal target audience for a product
based on tweets, likes and shares differs greatly from
analyzing traditionally structured data from the com-
pany server. Future competitive advantage is created by
integrating data from different sources, interpreting its
meaning and making it actionable.
Why does big data matter for business?
According to a mit research “How does data-driven
decisionmaking affect firm performance” (2012), com-
panies applying big data and analytics in their busi-
ness show 5–6 % higher profitability rates than their
competition.
When McKinsey studied the performance of big
data driven companies against competition it found
similar results; leading big data companies in se-
lected industries have outperformed their competi-
tion by 7.8 % of revenue and 13.8 % of ebitda.
The big data opportunity
Global data is growing explosively.Thanks to massive trails of data, today’s
companies know more about their clients than ever before. However, we
don’t know yet all the possibilities and benefits of big data, as only a tiny
fraction of it has been analyzed and utilized.
I

5. the big data opportunit y
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research05
Groceries
online retailers
big box retailers
casinos
credit cards
insurance
12 11
14
12
3
2
1
11
14
6
5
5
-1
-1
-15
24 22
9 10
9
5
9
9
8
Figure 1 Performance of big data-driven companies versus competition
Percent, 10-year CAGR (1999 – 2009)
Big data leader Competition
Revenue ebitda
Source: McKinsey Global Institute, 2011.
A key field for applying big data is Customer
Relationship Management (crm). Before digital
breakthrough, the most effective sales method was
footwork. Being one of the few ways to reach the
customer in the first place, it allowed real-time tai-
loring of customer messages. Today, big data has
drastically changed all this by providing a cost-
effective, multi-channeled way to targeted mass
messaging.
The term crm refers to the operations with which
a company manages its interactions with customers
and prospects. The aim of crm is to maximize the
customer lifecycle value throughout the stages of
that lifecycle: awareness, engagement, acquisition
and retention. This is done by providing right mes-
sages at right times in right channels.
Customer data is the fuel for customer relation-
ship management. It drives the decisions to differ-
entiate the offering, sales, marketing and customer
care based on customer preferences and potential.It
also drives the investment decisions to ensure that
sales,marketing and customer care budgets are allo-
cated to activities with the biggest expected returns.
Marketing automation is one of the key ways
to utilize big data. Marketing automation refers to
the process where prospects are scored based on an
integrated understanding on them, and marketing
communication is customized according to their
interests and potential throughout their lifecycle.
Key benefits of marketing automation are increased
conversion rates,better-aligned sales and marketing
departments as well as increased marketing effec-
tiveness through greater relevancy.
Alongside direct monetary value, customers also
create reference value for a company by recom-
mending a product or service to other customers
and prospects, or sharing product development
ideas with the company. Reference value is rapidly
gaining importance as a quite accurate driver for
future monetary value.

6. 06 Typical challenges in lever aging big data
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
II Typical challenges
in leveraging big data
picasso once said, “Computers are useless. They can
only give you answers”. Indeed, computers and ap-
plications never solve real life challenges on their
own. What differentiates big data success stories
from failures is often on the soft side; that is, in the
company’s ability to organize its people and opera-
tions and to successfully create capabilities.
When working with our clients on this topic, we
have identified the following key challenges in lev-
eraging the advantages of big data.
Technology has evolved rapidly, and there are great applications available
today to reap the advantages of big data.Then, why is every organization
not able to benefit big data?
01
Unclear big data vision
and approach
One of the most common challenges organizations
face when trying to exploit the benefits of big data,
is lacking a shared understanding of the company’s
vision and targets, as well as the role of data in reach-
ing those targets.There might be conflicting views on
which end-goals the data initiative should drive, what
are the required investments, and who should be
responsible for what.This kind of misalignment within
an organization tends to make big data initiatives scat-
tered and ineffective.
02
Seeing the transformation purely
as a technical challenge
When approaching big data, companies way too often
put most emphasis on building data collection and stor-
age capabilities while focusing too little on data analy-
sis and communication tools.This has also been identi-
fied by eConsultancy (2013) as the biggest challenge in
building a data-driven culture.That said, having the tools
for data analysis and communication is far from enough
as almost in all cases, human involvement is required
for conducting the analysis and turning it into action.

7. 07 Typical challenges in lever aging big data
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
Computers are useless.
They can only give you answers.
pablo picasso
03
Underestimating resource
and competence needs
According to a study by Forrester (2013),
only 17 % of ebusiness companies said they
have proper staffing in place to achieve
their goals.This is even exaggerated as
far as data analysts and scientists – the
specialists who should not only know how
to deal with large quantities of information
but also have a sound business understand-
ing – are concerned. Unfortunately, people
with this rare skill combination are not only
expensive but also scarcely available.
Thus, there are three common mistakes
that companies tend to make when
staffing up for big data:
· Prioritizing industry experience over data
and analytics experience when choosing
among candidates for the role.
· Assuming that introducing more
advanced technology will reduce the
competence needs.This is rarely
the case, as the more sophisticated
a tool is, the more sophisticated a user
it tends to require.
· Having insufficient understanding
on needed skillset for the role.
04
Underestimating
operational implications
A transformation into a data-centric company is a comprehensive
change that requires adjusting operational processes to realize the
benefits of the data. Unfortunately, all too often operational impli-
cations are underestimated, and the extent of the transformation
is not properly understood.This can easily result in the company
not being efficiently able to act on the insights it is capturing –
even if the right tools and people are already in place.Too many
times, this inability to demonstrate the value delivered has resulted
in lost management buy-in and scope decreases or cancellations of
otherwise viable big data initiatives.
05
Starting too big
As we have seen above, there are several potential pitfalls in
introducing a big data initiative. Not surprisingly, companies that
are overly aggressive in their ambitions tend fall flat when trying
to implement their approach.While there is nothing wrong with
dreaming big, starting too big often results in companies missing
important lessons on what the largest value drivers are, and how
those can actually be reached. Organizational learning takes time,
and trying to take too big steps too quickly easily leads the whole
initiative astray.

8. 08 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
Building data driven
operations
III
On the basis of our experience, we have identified useful patterns for
building and leading big data driven operations and summarized them in
the following three-step structure,Vision and plan, Defining capability needs,
and Building capabilities successfully.
A critical success factor for introducing big data
capabilities is the management’s ability to align
all key stakeholders to work as one team towards
a shared direction. The managerial tools available
for this include defining a clear vision for big data,
setting measurable goals, and having the necessary
commitment and empowerment in place.
Shared vision On the path to exploiting big data
and analytics, a common trap is to start with exist-
ing capabilities. Companies often start analyzing
what data they have and what they can do with it,
instead of being really clear on the desired outcome
and impact. For example, is the purpose to leverage
big data for improving customer service or foster-
ing consumer dialogue? Or should it just produce
sales leads?
A clearly formulated vision answers all these
questions and crystallizes the company’s big data
ambitions. When communicated efficiently to all
relevant employees, it provides a strong foundation
for aligning the whole organization to efficient de-
ployment of big data.
Measurable goals Once the vision is clear, the
management should set measurable goals for the
big data initiative that will help steer it to the right
direction and enable a follow-up on the outcomes.
What is crucial here, is that these goals should fo-
cus on the kpi value the initiative delivers (such
as increased profit or client satisfaction), and not
merely on how the implementation project was
run – for instance, whether it was on time and
budget.This will help everyone working on the ini-
tiative keep their eye on the ball and look for solu-
tions that drive the highest Return On Investment
for the company.
Management sponsorship and empowerment
Only once the vision and related goals are clear, the
management should communicate it to the em-
ployees and commit them to it, as well as allocate
resources and ensure empowerment of key stake-
holders. As the transformation into a true big data
company always takes time, it is absolutely crucial
for the management to ensure long-term sponsor-
ship for the initiative.
1 . Vi s io n a n d p l a n

9. 09 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
In our work we see that managers sometimes think
once the technology is launched, the big data oper-
ations will run themselves. Big data operations are
only as strong as their weakest link.In the following,
we present considerations on how to build a bal-
anced combination of people, processes and tools
for successful data driven operations.
People
When assessing the organization to run data driven
operations, one needs to consider both skills and
scale of people,and thirdly organizational structure.
Skills Defining necessary skills should start with
defining the role one is staffing and map needed
skillset for the role. In practice, this means looking
for talents who speak ‘business’ and understand
how the value of the company is generated, while
also having a proven track record in the field of ana-
lytics.The need for experience is emphasized when
hiring for a company which is in the early stage of
building up its operations.
Reserving enough time for recruiting necessary
talents is key. Digital business is one of the fast-
est growing areas of business, so finding the right
people is not easy. If appropriate people cannot be
found right away, one can always use temporary tal-
ents. Especially in building up new operations, it
often creates superior value to use external support –
may it be support in defining a new operational
area or a temporary ramp-up manager until the
permanent team is found and trained. This helps
the company avoid typical pitfalls and accelerates
the ramp-up of new operations.
Headcount Companies often underestimate the
number of employees necessary for successful ex-
ploiting of big data. Thus, defining the headcount
should be based on the desired scale of operations
as well as other targets and ways of acting, such as
the degree of automation. In the field of marketing,
for instance, this could mean the amount and com-
plexity of tailoring as well as degree of automation
in customer interaction.
When taking advantage of big data, the need for
headcount arises across the organization, not only
in directly data related teams. One of the areas is
self-evidently the area of gathering, modeling and
analyzing of data and in their support functions,
such as infrastructure and privacy teams. Another
area is on the business side,where operative persons
for running daily optimization and channel devel-
opment are needed.
Many of our clients who are new to targeted
marketing, (more advanced tailoring of customer
offering based on large volume and variation of
data), have been surprised by the heavy increase
in content creation work. As an example, our case
company Gilt sends out 3 000 different versions of
its daily marketing letter. As content creation is
often partially or fully outsourced, it can become
very expensive to create tens of versions, of visual
and textual content.These things should be bear in
mind when budgeting for long term, even if pro-
ceeding with baby steps.
Organization There is no single right way to or-
ganize around big data. When we define digital or-
ganizations for our clients, we look at a number of
topics including the nature of the business they are
in, targets both in short and long term and strategy
that defines the path to reach the targets. We also
examine the maturity of operations, current organi-
zation with its strengths and weaknesses as well as
company culture.
Classical options for organizing a function are:
firstly to centralize it into one know-how hub;
secondly to de-centralize under business units; or
thirdly, to organize the functional know-how as a
matrix with the business. Generally, of course, it
benefits to be near the ‘business’, but operating big
data often requires such a specific expertise that it is
better operated and developed as one entity.This is
particularly the case when the expertise area is new
for the company.
2 . D e f i n i n g c a p abi l i t y n e e d s

10. 10 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
Tools
We divide tools into three groups according to their
purpose of use: firstly, data collection and storing;
secondly, analytics; and finally, decision support.
Data collection and storing tools is typically
the most straight forward category of tools. The
market has already started to mature, and there
are plenty of ready and custom-made tools avail-
able whose features and total costs are fairly easy
to compare.
Analytics tools The term ‘analytics’ refers to the
use of statistical and machine-learning techniques
to support decision-making and offering personali-
zation. Acquiring or developing a suitable analytics
tool is currently on the agenda of many companies.
Often the buyer is not clear on all its needs and re-
quirements when buying. Typical challenge area is
the data format requirements regarding both ends,
source data, as well as ready-to-use insights.
Decision support tools Actionability of data
defines the value the data provides to us. The need
for decision support tools is often underestimated.
According to our experience, this is the major tool
gap in companies where big data is operated. Most
companies would benefit largely from investing in
easy-to-use communication and decision support
tools as well as organizing training around these
tools for needed people. The users of decision sup-
port tools are often not the data analysts but experts
of other areas as well as the management. Hence it
should be assured that the tools support the com-
pany’s current processes and ways of working.
When acquiring new technology,
pay attention at least
to the following:
• Map tool requirements from
business perspective, based on the
end-use purpose of data.
• Assure source data format
is supported by the tool.
• Ensure combining data from
different sources is made possible,
including third party source data.
• Invest in data clean-up and easy
maintenance.
• Make sure use-cases and original
business questions are supported
by the tool.
• Remember realistic budgeting
related to people skills; a more
sophisticated tool requires more
from people skills.
• Ensure front line processes
and ways-of-working are supported
by the tool.
• Invest in good usability, for example
intuitive user interface and single-
sign-on in case of separate tools.
• Ensure well-functioning interfaces
between tools.
• Developing data driven culture
must be started early in parallel
with acquiring a tool.

11. 11 case study – gilt groupe
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
The Gilt Groupe was born this way. Gilt,
flash sales retailer founded in 2007, is a
success story of a new era retailer whose
business is based on big data. Gilt has a
compelling offering: designer goods at
prices up to 70 % off retail. An important
part of the growth and earnings logic is also
to reward customers for inviting friends.
Superior analytics function
Gilt has an advanced way of tailoring cus-
tomer offering and communication based
on large volume and variation of data.The
key is to collect data from multiple sources
real time, for example behavioral, demo-
graphic and warehouse data. In addition
Gilt makes a consumer sentimental analysis
over tweets.The analysis is communicated
on a dashboard where the employees can
break the results down to customer pro-
files. Other example of active social media
usage is Facebook. Gilt responds proactively
to customers making comments or com-
plaints in social media and continues the
dialogue offline.
Gilt also has an active data driven channel
development function and is today in the
forefront of mobile channel development in
the retail branch.Today over 35 % of its rev-
enue comes from mobile and iPad devices.
Employees empowered
Gilt is a true born data driven company.To
make decisions based on data is in its DNA.
Gilt employees are empowered to make
decisions and take action based on data.
People in all departments and on all levels
of the organization are armed with function
specific dashboards with data that updates
every four hours. In addition Gilt produces
hundreds of ad-hoc reports each week.
Factors behind Gilt’s success:
• Great variety of data sources
• Freshness of data
• Massive cloud based analytics tool
• Precise on demand business driven
insights building
• Customer profile based analytics
• Real time activities made based on data
• Employees empowered to make decisions
• Investments in data based multichannel
development
Case STUDY
It’s about tailored one-to-one
communication with the customer.
Within a single minute
at noon every day,there are over 3 000
versions of our message that
go out to customers.
alexis maybank
gilt groupe
D ata dri v e n d e c i s io n ma k i n g
Sources: McKinsey 2012,Asterdata 2012,
Apparel, 2012,Techcrunch, 2012, Google Analytics,
Magenta Advisory analysis.

12. 12 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
buildingtheprofile
leveragingtheprofile
FIGURE 2 Activities of customer profile based marketing
building customer profile
Build insight | Create and manage business rules
Clean up and manage profile data
BUSINESS OBJECTIVES
Give guidance to consumer data work
Gathering customer data
Gather data (internal, public, bought) | Gather marketing consent
Integrate and manage data
actionable
customer
profile
performance management
Feed into reporting | Learn and develop continuously
interaction delivery
Create and manage automated triggers
Manage customer interactions in multichannel
Market creation
Create content variants | Store content enabling re-use
market planning
Design marketing concepts and content
Develop and maintain channels
Source: Magenta Advisory analysis

13. 13 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
Processes
In general, operating model refers to the collec-
tion of activities and processes to represent how an
organization operates across organization and tech-
nology domains in order to accomplish its function.
Big data driven operating model can mean a variety
of things. As an example, we describe here the pos-
sible implications of turning marketing communi-
cations into automated and customer profile based.
Customer profile based automated marketing is
the target state for many companies. The needed
transformation from traditional content driven
marketing departments is vast.
The starting point for operations is always the
business objectives. First, good quality data and
marketing consents are being collected. Keep in
mind the end use purpose of data, and strive for
consistent and up-to-date data. Also, pay attention
to data integrity, the level to which multiple data-
sets can be correctly joined together.
The end product,customer profile is important to
be kept up-to-date with systematic cleanup process.
The business rules give meaning and structure for
data – possibility to act on it. Marketing plans and
creates content variants to be utilized in different
channels according to customer needs. Customer
interaction is triggered by customer behavior based
on a structure of business rules. Customer interac-
tion is measured and developed in a multichannel
environment.
In practice, this described set of activities is
overwhelming in complexity. When defining big
data related operating model, it is important to
start with the big picture. Focus on doing the right
things rather than doing things right. Avoid the
trap of completely defining and implementing one
area before moving to next one.
When talking about operative model, it is good to
also keep in mind the unofficial mechanisms of in-
fluence. Especially the decision-making culture, clock
speed of operations, and culture of testing and failure
should be in special focus for companies when
moving towards data driven operations.
Decision-making culture It is nice to have loads
of data.Nevertheless,many managers start to sweat
when it comes to genuinely fact-based decision-
making.As obvious,the benefits of data can only be
gained if the decision-making mechanisms utilize
the data. Unfortunately, only 27 % of respondents
in an eConsultancy (2013) study among start-ups
believe data is crucial for decision-making.
Clock speed of operations Also, data-driven
operations require a whole different clock speed of
operations from what many companies are running
today. We might call it agile eCommerce or real time
customer care, whatever the name, to embed the cul-
ture of testing and continuous improvement,which
often requires a major transformation of a company.
Culture of testing and failure Once fashion-
able terms culture of testing and culture of failure are
slightly worn-out, but they still carry an important
message. When a company takes off with big data
transformation initiative, learning will never be
linear. Those who put themselves on the line and
are willing to try new and question old as well as
build up teams of differently backgrounded people,
will find and adapt fastest the new way of working.
Hence, we recommend leading the development
of corporate culture as systematically as the official
part of the transformation.
Building and establishing a right kind of a cor-
porate culture is at the very core in a data-driven
organization. We recommend starting early with
the transformation towards a data-driven culture
and letting it gradually develop in parallel with ca-
pability building.
data-driven
operations require
a whole different
clock speed of
operations from what
many companies are
running today

14. 14 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
1
Invest heavily in data collection
Company’s IT department drives an
initiative to acquire new technology for
collecting and storing data.
2
Start building insight
Company realizes that data is not
actionable and makes a tehnology
investment to analyze data.
3
Launch activities to leverage data
Finally the company launches activities
based on the data but soon realizes that
the insights are not always actionable.
Also it has gaps in people skills and on
the process side.
Traditional capability
building approach
The traditional capability building approach has
evolved through the ages within different indus-
tries.This traditional approach, known for example
from the construction industry,is often applied still
today in business development initiatives, such as
big data transformations.
Slightly exaggerated, a typical path in a big data
project could go as illustrated in figure 2:
First,all focus is put in data collection and storage,
and hence analysis capabilities are underweighted.
Typically in this phase, a large-scale investment is
made to collect a large variety of raw data,involving
mainly the it department of the company.
In phase two, management realizes they cannot
do much with collected raw data after which they
attempt to correct these shortcomings with heavy
investments in analytics capabilities, however still
lacking the actionability of data.
Finally, the company starts planning and im-
plementing activities based on insights. This turns
out to be challenging as insights are not all action-
able for business, people lack necessary capabilities,
there is no shared view on ways-of-working, and
decision support tools neither exist nor are used
correctly.
As a result, the company fails to achieve measur-
able results. Management buy-in weakens and the
whole project becomes a washout.
When starting too big, the company misses out
important lections on where the largest benefits are,
and how they can be actually reached.Most compa-
nies underestimate the time organizational learning
takes.
3 . B u i l di n g c a p abi l i t i e s s u c c e s s f u l ly
Challenges of
the traditional approach
- Large initial investment, high CAPEX
- Slow time-to-result due to low
activity in leveraging data
- Not all collected data add value
- Building of sufficient people and
process capabilities is late
- Inaquate communication
and usage tools
- Contradiction between data
and business teams
- Project not measured on business
measures but on IT measures
which weakens the transparency
of challenge areas
FIGURE 3a Capability building gone wrong

15. 15 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
1
Create more actions to
leverage current data
Company launches new activities to
leverage current data and develops
understanding on needed data, skill, and
tool needs from business’ perspective.
2
learn to create better,
actionable insights
Company develops new analytics
capabilities based on business’
real time needs.
3
increase amount of data collected
& scale big data activities
Company has gradually developed its
people, process and tool capabilities,
and is now ready to scale up the
operations and volume of data.
Best practice approach
On the basis of our experience we see that the fol-
lowing, almost inverse approach, is the most pro-
ductive when developing big data capabilities.
The method will start from raising the activity
level for utilizing currently existing data for busi-
ness purposes. The company identifies quick wins,
activities with high expected return with low risk
and cost level. These ‘low-hanging fruits’ are imple-
mented by involving relevant people from different
parts of the organization. Company starts to build
its center of excellence and ways-of-working as
real business activities are conducted with data.We
recommend that in this phase the company starts
defining its target state of big data activities - how
does big data contribute to reaching the company’s
objectives?
Phase 2 consists of building on top of the pre-
vious one and upgrading analytic skills to create
better insights from current data, and learn to op-
timize based on insights. A more systematic build-
ing of the new way-of-working, and culture of test-
ing and continuous optimization, is started, as the
company’s know-how in this area will be further
reinforced.
The previously defined target state description
shall now be validated. Future development areas
FIGURE 3B Capability building best practice
Tärkein osatekijä
datan hyödyntämisessä
on sen vaatima
ajatusprosessi.
catalin ciobanu,
carlson wagonlit travel

16. 16 Building data driven oper ations
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research
are defined for data collection,analytics capabilities,
team sizes, expertise, processes and so on.
In phase 3, already running big data operations
will be scaled up. When a company has a small but
well-performing analytics team working together
with business and other stakeholders and deliver-
ing measurable results, it is fairly easy to scale up
operations. Only at this point of time we recom-
mend making needed large scale investments for
collecting, analyzing and utilizing data.
Transforming the way-of-working within the
organization is well under way but will demand
plenty of time.Anticipate that the learning will not
be linear but can experience delays.Those can be re-
lated to expanding activities or unexpected changes
of core people, just to name some.
An important factor of success is also the effi-
cient transfer of knowledge and the empowering of
the cross-functional teams as ambassadors of the
new data driven decision making culture.
In this context we would like to emphasize that
in real life the best practice capability building ap-
proach is not a three-phase linear process. Instead,
it is an iterative path of learning and developing,
where big iteration cycles contain many small cy-
cles of experimenting and learning.
Benefits of best practice
capability building approach:
+ Fast time to first results, quick wins
+ Better certainly on technical
investments being on spot
+ Long investment horizon visible
but with decision gates on the way
+ People skills, processes, and decision
making culture maturing over time
+ Utilizing the full potential
of existing data
+ Only meaningful data is
collected and stored
+ Sufficient focus in
decision support tools
+ Data and business teams working
together from start
+ Project possible to be measured
based on business value

17. How Magenta Advisory can help your organiz ation?
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research17
Magenta Advisory helps both top management and operative teams create
competitive advantage digitally. Be it strategy creation or continuous optimization
of digital business operations, we commit to helping our clients become digital
leaders. Magenta Advisory can help your company in its’ digital transformation to
outperform the competition.
In Figure 4 we have selected a sample of services we offer across a clients
digital transformation.
We believe in building long-term relationships with our clients to work with
them throughout their digital transformation.Our clients choose us and stay with
us because:
• We are leading experts in digital and multichannel business
• We understand digital channels should be business and facts driven
• We have a track record of helping our clients succeed
• We understand both multinational and local brands
• We deliver results
How can
Magenta Advisory help?
STRATEGY CONCEPT
CAPABILITY
DEVELOPMENT
BUSINESS
EXCELLENCE
Vision and mission
definition
Strategy definition
Target setting
Strategy roadmap
creation
Business model
definition
Business case creation
Competitor analysis
Business concept
definition
Customer value
proposition design
Best practice
benchmarks
Business requirements
definition
Detailed business case
creation
Program planning
Vendor selection
Program management
Program audits
Use case definition
Operating process
creation
Organization design
and ramp-up
Training & change
management
Management for hire
Key performance
indicator definition
Business performance
analysis
Performance
management
establishment
Development
roadmapping
CONCEPT CAPABILITIES
Figure 4:
Sample of Magenta
Advisory services

18. about the authors
Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research18
Lotta Kopra
Partner
Lotta is a leading expert in digital customer
relationship management, online sales
and marketing. Lotta has broad industry
expertise from e.g. telecommunications,
media, retail, pulp and paper, electronics and
oil industries.
lotta.kopra@magentaadvisory.com
+358 50 444 6000
Anni Tupamäki
Consultant
Anni is a business development expert
focusing on digital marketing and sales with
industry experience covering consumer
goods and telecom industries.
anni.tupamaki@magentaadvisory.com
About the authors
Contact information
Magenta Advisory
info@magentaadvisory.com
Bulevardi 6 A 12
00120 Helsinki
Finland

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