Magenta advisory: Data Driven Decision Making –Is Your Organization Ready For Big Data?

MAGENTA ADVISORY RESEARCH

Data Driven Decision Making – Is Your Organization Ready for Big Data?

Magenta aDvi sory publ ication

04 / 2013

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introduc t ion 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

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the BIg DAtA OppORtunItY

tYpIcAl chAllenges In leveRAgIng BIg DAtA

BuIlDIng DAtA DRIven OpeRAtIOns

heADlInes

the B ig data opportu nit 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, par tnerships and hosted solutions to keyword search as well as ad serving data.

Velocity We are shifting from static databases towards dynamic modeling of data. For example tesco 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.

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the B ig data opportu nit Y Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research05

grocerieS

online retailerS

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caSinoS

credit cardS

inSurance

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14

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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.

06 t Ypic al challengeS in le ver ag ing B ig 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?

01unclear 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.

02seeing 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.

07 t Ypic al challengeS in le ver ag ing B ig data Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research

cOMputeRs ARe useless. theY cAn OnlY gIve YOu AnsweRs.

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03underestimating 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.

04underestimating 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.

05starting 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.

08 Bu ild ing data dr iven oper at ionS 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 . v I s I O n A n D p l A n


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 A B I l I t Y n e e D s


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:

•Maptool requirementsfrombusinessperspective,basedontheend-usepurposeofdata.

•Assuresource data formatissupportedbythetool.

•Ensurecombining datafromdifferentsourcesismadepossible,includingthirdpartysourcedata.

•Investindata clean-upandeasymaintenance.

•Makesureuse-casesandoriginalbusinessquestionsaresupportedbythetool.

•Rememberrealistic budgetingrelatedtopeopleskills;amoresophisticatedtoolrequiresmorefrompeopleskills.

•Ensurefront line processesandways-of-workingaresupportedbythetool.

•Investin good usability,forexampleintuitiveuserinterfaceandsingle-sign-onincaseofseparatetools.

•Ensurewell-functioning interfacesbetweentools.

•Developingdata driven culturemustbestartedearlyinparallelwithacquiringatool.

11 c aSe StudY – g i lt grou pe 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 functiongilt 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.

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veRsIOns Of OuR MessAge thAt

gO Out tO custOMeRs.

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sources: McKinsey 2012, asterdata 2012,

apparel, 2012, techcrunch, 2012, google analytics,

Magenta advisory analysis.


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fIguRe 2 activitieS oF cuStoMer proFile BaSed MarKeting

BuIlDIng custOMeR pROfIleBuild insight | Create and manage business rules

Clean up and manage profile data

BusIness OBJectIvesgive guidance to consumer data work

gAtheRIng custOMeR DAtAGather data (internal, public, bought) | Gather marketing consent

Integrate and manage data

ActIOnABlecustOMeR

pROfIle

peRfORMAnce MAnAgeMentfeed into reporting | learn and develop continuously

InteRActIOn DelIveRYCreate and manage automated triggers

Manage customer interactions in multichannel

MARket cReAtIOnCreate content variants | Store content enabling re-use

MARket plAnnIngDesign marketing concepts and content

Develop and maintain channels

source: Magenta Advisory analysis


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

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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 D I n g c A p A B I 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


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

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On sen vAAtIMA AJAtuspROsessI.

catalin ciobanu, carlson wagonlit travel


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

hoW Magenta adv i SorY c an help Your organiz at ion? 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 CAPABILITYDEVELOPMENT

BUSINESSEXCELLENCE

VisionandmissiondefinitionStrategydefinitionTargetsettingStrategyroadmapcreationBusinessmodeldefinitionBusinesscasecreationCompetitoranalysis

BusinessconceptdefinitionCustomervaluepropositiondesignBestpracticebenchmarksBusinessrequirementsdefinitionDetailedbusinesscasecreationProgramplanning

VendorselectionProgrammanagementProgramauditsUsecasedefinitionOperatingprocesscreationOrganizationdesignandramp-upTraining&changemanagement

ManagementforhireKeyperformanceindicatordefinitionBusinessperformanceanalysisPerformancemanagementestablishmentDevelopmentroadmapping

CONCEPT CAPABILITIES

figure 4:

sample of Magenta

Advisory services

aBout the author S Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research18

lOttA kOpRAPartner

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.

[email protected]+358 50 444 6000

AnnI tupAMäkIConsultant

anni is a business development expert focusing on digital marketing and sales with industry experience covering consumer goods and telecom industries.

[email protected]

aBout the authorS

cOntAct InfORMAtIOn

Magenta [email protected] 6 a 1200120 helsinkiFinland

3magentaadvisory.com

Magenta advisory: Data Driven Decision Making –Is Your Organization Ready For Big Data?

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Transcript of Magenta advisory: Data Driven Decision Making –Is Your Organization Ready For Big Data?