Transcript of "Magenta advisory: Data Driven Decision Making –Is Your Organization Ready For Big Data?"
MAGENTA ADVISORY RESEARCHData Driven Decision Making– Is Your Organization Ready for Big Data?Magenta advisory publication04 / 2013
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introduc tionData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research03the amount of data available is growing tremen-dously fast. The appearance of a new kind of datawill 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 managersneed 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 cansignificantly boost the decision-making process.Atthe same time, there might be creative processesongoing where data acts as a generator for newideas.Data is a good servant but a bad master. It is justas valuable as the decisions we make on the basisof it. In this paper we dig into the core topics towhich the management should pay attention whileplanning and managing business in the data-ladenworld.It doesn’t matter if you getthe best data scientists in a room;if no one across the organization knowswhat to do with the data,the insight they come up with doesn’t matter.Data is a business issue, not an I T issuemicheline casey,chief data officer for the state of colorado2012Magenta Advisory ResearchData Driven Decision Making –Is Your Organization Ready for Big Data?IIIIIIThe big dataopportunityTypical challenges inleveraging big dataBuilding data drivenoperationsHEADLINES
the big data opportunit yData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research04What is big data?The nature of big data can be characterized withthe 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 worldtoday has been created during the last two years.Thissmall fact highlights the magnitude of change we aretalking about.The definition of big data refers to the collectionof large and complex data sets that are challenging toprocess with traditional data processing tools. Instead,these terabytes of data call for de-centralized and oftencloud-based data management and processing.Variation We all remember the days when corporatedata was stored on a company server and consisted ofdata mainly from internal sources, all structured alongthe customer base, company offering and so on.Today,the data we utilize can be very versatile and also comefrom third party sources. Sources for third party datacan vary from public and bought data, partnershipsand hosted solutions to keyword search as well as adserving data.VelocityWe are shifting from static databases towardsdynamic modeling of data. For exampleTesco integratesweather data in their purchase optimization modeling.Automatic algorithms also allow companies to react tocustomer behavior and optimize real-time offering andmessaging to the clients.Modeling the ideal target audience for a productbased on tweets, likes and shares differs greatly fromanalyzing traditionally structured data from the com-pany server. Future competitive advantage is created byintegrating data from different sources, interpreting itsmeaning and making it actionable.Why does big data matter for business?According to a mit research “How does data-drivendecisionmaking affect firm performance” (2012), com-panies applying big data and analytics in their busi-ness show 5–6 % higher profitability rates than theircompetition.When McKinsey studied the performance of bigdata driven companies against competition it foundsimilar 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 opportunityGlobal data is growing explosively.Thanks to massive trails of data, today’scompanies know more about their clients than ever before. However, wedon’t know yet all the possibilities and benefits of big data, as only a tinyfraction of it has been analyzed and utilized.I
the big data opportunit yData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research05Groceriesonline retailersbig box retailerscasinoscredit cardsinsurance12 1114123211114655-1-1-1524 229 1095998Figure 1 Performance of big data-driven companies versus competitionPercent, 10-year CAGR (1999 – 2009)Big data leader CompetitionRevenue ebitdaSource: McKinsey Global Institute, 2011.A key field for applying big data is CustomerRelationship Management (crm). Before digitalbreakthrough, the most effective sales method wasfootwork. Being one of the few ways to reach thecustomer in the first place, it allowed real-time tai-loring of customer messages. Today, big data hasdrastically changed all this by providing a cost-effective, multi-channeled way to targeted massmessaging.The term crm refers to the operations with whicha company manages its interactions with customersand prospects. The aim of crm is to maximize thecustomer lifecycle value throughout the stages ofthat lifecycle: awareness, engagement, acquisitionand 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 customercare based on customer preferences and potential.Italso drives the investment decisions to ensure thatsales,marketing and customer care budgets are allo-cated to activities with the biggest expected returns.Marketing automation is one of the key waysto utilize big data. Marketing automation refers tothe process where prospects are scored based on anintegrated understanding on them, and marketingcommunication is customized according to theirinterests and potential throughout their lifecycle.Key benefits of marketing automation are increasedconversion rates,better-aligned sales and marketingdepartments as well as increased marketing effec-tiveness through greater relevancy.Alongside direct monetary value, customers alsocreate reference value for a company by recom-mending a product or service to other customersand prospects, or sharing product developmentideas with the company. Reference value is rapidlygaining importance as a quite accurate driver forfuture monetary value.
06 Typical challenges in lever aging big dataData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchII Typical challengesin leveraging big datapicasso once said, “Computers are useless. They canonly give you answers”. Indeed, computers and ap-plications never solve real life challenges on theirown. What differentiates big data success storiesfrom failures is often on the soft side; that is, in thecompany’s ability to organize its people and opera-tions and to successfully create capabilities.When working with our clients on this topic, wehave identified the following key challenges in lev-eraging the advantages of big data.Technology has evolved rapidly, and there are great applications availabletoday to reap the advantages of big data.Then, why is every organizationnot able to benefit big data?01Unclear big data visionand approachOne of the most common challenges organizationsface when trying to exploit the benefits of big data,is lacking a shared understanding of the company’svision and targets, as well as the role of data in reach-ing those targets.There might be conflicting views onwhich end-goals the data initiative should drive, whatare the required investments, and who should beresponsible for what.This kind of misalignment withinan organization tends to make big data initiatives scat-tered and ineffective.02Seeing the transformation purelyas a technical challengeWhen approaching big data, companies way too oftenput 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 inbuilding a data-driven culture.That said, having the toolsfor data analysis and communication is far from enoughas almost in all cases, human involvement is requiredfor conducting the analysis and turning it into action.
07 Typical challenges in lever aging big dataData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchComputers are useless.They can only give you answers.pablo picasso03Underestimating resourceand competence needsAccording to a study by Forrester (2013),only 17 % of ebusiness companies said theyhave proper staffing in place to achievetheir goals.This is even exaggerated asfar as data analysts and scientists – thespecialists who should not only know howto deal with large quantities of informationbut also have a sound business understand-ing – are concerned. Unfortunately, peoplewith this rare skill combination are not onlyexpensive but also scarcely available.Thus, there are three common mistakesthat companies tend to make whenstaffing 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.04Underestimatingoperational implicationsA transformation into a data-centric company is a comprehensivechange that requires adjusting operational processes to realize thebenefits of the data. Unfortunately, all too often operational impli-cations are underestimated, and the extent of the transformationis not properly understood.This can easily result in the companynot being efficiently able to act on the insights it is capturing –even if the right tools and people are already in place.Too manytimes, this inability to demonstrate the value delivered has resultedin lost management buy-in and scope decreases or cancellations ofotherwise viable big data initiatives.05Starting too bigAs we have seen above, there are several potential pitfalls inintroducing a big data initiative. Not surprisingly, companies thatare overly aggressive in their ambitions tend fall flat when tryingto implement their approach.While there is nothing wrong withdreaming big, starting too big often results in companies missingimportant lessons on what the largest value drivers are, and howthose can actually be reached. Organizational learning takes time,and trying to take too big steps too quickly easily leads the wholeinitiative astray.
08 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchBuilding data drivenoperationsIIIOn the basis of our experience, we have identified useful patterns forbuilding and leading big data driven operations and summarized them inthe following three-step structure,Vision and plan, Defining capability needs,and Building capabilities successfully.A critical success factor for introducing big datacapabilities is the management’s ability to alignall key stakeholders to work as one team towardsa shared direction. The managerial tools availablefor this include defining a clear vision for big data,setting measurable goals, and having the necessarycommitment and empowerment in place.Shared vision On the path to exploiting big dataand analytics, a common trap is to start with exist-ing capabilities. Companies often start analyzingwhat data they have and what they can do with it,instead of being really clear on the desired outcomeand impact. For example, is the purpose to leveragebig data for improving customer service or foster-ing consumer dialogue? Or should it just producesales leads?A clearly formulated vision answers all thesequestions and crystallizes the company’s big dataambitions. When communicated efficiently to allrelevant employees, it provides a strong foundationfor aligning the whole organization to efficient de-ployment of big data.Measurable goals Once the vision is clear, themanagement should set measurable goals for thebig data initiative that will help steer it to the rightdirection 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 (suchas increased profit or client satisfaction), and notmerely on how the implementation project wasrun – for instance, whether it was on time andbudget.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 Investmentfor the company.Management sponsorship and empowermentOnly once the vision and related goals are clear, themanagement should communicate it to the em-ployees and commit them to it, as well as allocateresources and ensure empowerment of key stake-holders. As the transformation into a true big datacompany always takes time, it is absolutely crucialfor the management to ensure long-term sponsor-ship for the initiative.1 . Vi s io n a n d p l a n
09 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchIn our work we see that managers sometimes thinkonce the technology is launched, the big data oper-ations will run themselves. Big data operations areonly as strong as their weakest link.In the following,we present considerations on how to build a bal-anced combination of people, processes and toolsfor successful data driven operations.PeopleWhen assessing the organization to run data drivenoperations, one needs to consider both skills andscale of people,and thirdly organizational structure.Skills Defining necessary skills should start withdefining the role one is staffing and map neededskillset for the role. In practice, this means lookingfor talents who speak ‘business’ and understandhow the value of the company is generated, whilealso having a proven track record in the field of ana-lytics.The need for experience is emphasized whenhiring for a company which is in the early stage ofbuilding up its operations.Reserving enough time for recruiting necessarytalents is key. Digital business is one of the fast-est growing areas of business, so finding the rightpeople is not easy. If appropriate people cannot befound right away, one can always use temporary tal-ents. Especially in building up new operations, itoften creates superior value to use external support –may it be support in defining a new operationalarea or a temporary ramp-up manager until thepermanent team is found and trained. This helpsthe company avoid typical pitfalls and acceleratesthe ramp-up of new operations.Headcount Companies often underestimate thenumber of employees necessary for successful ex-ploiting of big data. Thus, defining the headcountshould be based on the desired scale of operationsas well as other targets and ways of acting, such asthe 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 automationin customer interaction.When taking advantage of big data, the need forheadcount arises across the organization, not onlyin directly data related teams. One of the areas isself-evidently the area of gathering, modeling andanalyzing of data and in their support functions,such as infrastructure and privacy teams. Anotherarea is on the business side,where operative personsfor running daily optimization and channel devel-opment are needed.Many of our clients who are new to targetedmarketing, (more advanced tailoring of customeroffering based on large volume and variation ofdata), have been surprised by the heavy increasein content creation work. As an example, our casecompany Gilt sends out 3 000 different versions ofits daily marketing letter. As content creation isoften partially or fully outsourced, it can becomevery expensive to create tens of versions, of visualand textual content.These things should be bear inmind 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 oftopics including the nature of the business they arein, targets both in short and long term and strategythat defines the path to reach the targets. We alsoexamine the maturity of operations, current organi-zation with its strengths and weaknesses as well ascompany culture.Classical options for organizing a function are:firstly to centralize it into one know-how hub;secondly to de-centralize under business units; orthirdly, to organize the functional know-how as amatrix with the business. Generally, of course, itbenefits to be near the ‘business’, but operating bigdata often requires such a specific expertise that it isbetter operated and developed as one entity.This isparticularly the case when the expertise area is newfor 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 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchToolsWe divide tools into three groups according to theirpurpose of use: firstly, data collection and storing;secondly, analytics; and finally, decision support.Data collection and storing tools is typicallythe most straight forward category of tools. Themarket has already started to mature, and thereare plenty of ready and custom-made tools avail-able whose features and total costs are fairly easyto compare.Analytics tools The term ‘analytics’ refers to theuse of statistical and machine-learning techniquesto support decision-making and offering personali-zation. Acquiring or developing a suitable analyticstool 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 isthe data format requirements regarding both ends,source data, as well as ready-to-use insights.Decision support tools Actionability of datadefines the value the data provides to us. The needfor decision support tools is often underestimated.According to our experience, this is the major toolgap in companies where big data is operated. Mostcompanies would benefit largely from investing ineasy-to-use communication and decision supporttools as well as organizing training around thesetools for needed people. The users of decision sup-port tools are often not the data analysts but expertsof other areas as well as the management. Hence itshould be assured that the tools support the com-pany’s current processes and ways of working.When acquiring new technology,pay attention at leastto the following:• Map tool requirements frombusiness perspective, based on theend-use purpose of data.• Assure source data format is supported by the tool.• Ensure combining data fromdifferent sources is made possible,including third party source data.• Invest in data clean-up and easymaintenance.• Make sure use-cases and originalbusiness questions are supported by the tool.• Remember realistic budgetingrelated to people skills; a moresophisticated tool requires morefrom people skills.• Ensure front line processes and ways-of-working are supportedby the tool.• Invest in good usability, for exampleintuitive user interface and single-sign-on in case of separate tools.• Ensure well-functioning interfacesbetween tools.• Developing data driven culturemust be started early in parallel with acquiring a tool.
11 case study – gilt groupeData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchThe Gilt Groupe was born this way. Gilt,flash sales retailer founded in 2007, is asuccess story of a new era retailer whosebusiness is based on big data. Gilt has acompelling offering: designer goods atprices up to 70 % off retail. An importantpart of the growth and earnings logic is alsoto reward customers for inviting friends.Superior analytics functionGilt has an advanced way of tailoring cus-tomer offering and communication basedon large volume and variation of data.Thekey is to collect data from multiple sourcesreal time, for example behavioral, demo-graphic and warehouse data. In additionGilt makes a consumer sentimental analysisover tweets.The analysis is communicatedon a dashboard where the employees canbreak the results down to customer pro-files. Other example of active social mediausage is Facebook. Gilt responds proactivelyto customers making comments or com-plaints in social media and continues thedialogue offline.Gilt also has an active data driven channeldevelopment function and is today in theforefront of mobile channel development inthe retail branch.Today over 35 % of its rev-enue comes from mobile and iPad devices.Employees empoweredGilt is a true born data driven company.Tomake decisions based on data is in its DNA.Gilt employees are empowered to makedecisions and take action based on data.People in all departments and on all levelsof the organization are armed with functionspecific dashboards with data that updatesevery four hours. In addition Gilt produceshundreds 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 driveninsights building• Customer profile based analytics• Real time activities made based on data• Employees empowered to make decisions• Investments in data based multichanneldevelopmentCase STUDYIt’s about tailored one-to-onecommunication with the customer.Within a single minuteat noon every day,there are over 3 000versions of our message thatgo out to customers.alexis maybankgilt groupeD ata dri v e n d e c i s io n ma k i n gSources: McKinsey 2012,Asterdata 2012,Apparel, 2012,Techcrunch, 2012, Google Analytics,Magenta Advisory analysis.
12 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchbuildingtheprofileleveragingtheprofileFIGURE 2 Activities of customer profile based marketingbuilding customer profileBuild insight | Create and manage business rulesClean up and manage profile dataBUSINESS OBJECTIVESGive guidance to consumer data workGathering customer dataGather data (internal, public, bought) | Gather marketing consentIntegrate and manage dataactionablecustomerprofileperformance managementFeed into reporting | Learn and develop continuouslyinteraction deliveryCreate and manage automated triggersManage customer interactions in multichannelMarket creationCreate content variants | Store content enabling re-usemarket planningDesign marketing concepts and contentDevelop and maintain channelsSource: Magenta Advisory analysis
13 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory ResearchProcessesIn general, operating model refers to the collec-tion of activities and processes to represent how anorganization operates across organization and tech-nology domains in order to accomplish its function.Big data driven operating model can mean a varietyof 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 isthe target state for many companies. The neededtransformation from traditional content drivenmarketing departments is vast.The starting point for operations is always thebusiness objectives. First, good quality data andmarketing consents are being collected. Keep inmind the end use purpose of data, and strive forconsistent and up-to-date data. Also, pay attentionto data integrity, the level to which multiple data-sets can be correctly joined together.The end product,customer profile is important tobe kept up-to-date with systematic cleanup process.The business rules give meaning and structure fordata – possibility to act on it. Marketing plans andcreates content variants to be utilized in differentchannels according to customer needs. Customerinteraction is triggered by customer behavior basedon a structure of business rules. Customer interac-tion is measured and developed in a multichannelenvironment.In practice, this described set of activities isoverwhelming in complexity. When defining bigdata related operating model, it is important tostart with the big picture. Focus on doing the rightthings rather than doing things right. Avoid thetrap of completely defining and implementing onearea before moving to next one.When talking about operative model, it is good toalso keep in mind the unofficial mechanisms of in-fluence. Especially the decision-making culture, clockspeed of operations, and culture of testing and failureshould be in special focus for companies whenmoving towards data driven operations.Decision-making culture It is nice to have loadsof data.Nevertheless,many managers start to sweatwhen it comes to genuinely fact-based decision-making.As obvious,the benefits of data can only begained if the decision-making mechanisms utilizethe data. Unfortunately, only 27 % of respondentsin an eConsultancy (2013) study among start-upsbelieve data is crucial for decision-making.Clock speed of operations Also, data-drivenoperations require a whole different clock speed ofoperations from what many companies are runningtoday. We might call it agile eCommerce or real timecustomer care, whatever the name, to embed the cul-ture of testing and continuous improvement,whichoften requires a major transformation of a company.Culture of testing and failure Once fashion-able terms culture of testing and culture of failure areslightly worn-out, but they still carry an importantmessage. When a company takes off with big datatransformation initiative, learning will never belinear. Those who put themselves on the line andare willing to try new and question old as well asbuild up teams of differently backgrounded people,will find and adapt fastest the new way of working.Hence, we recommend leading the developmentof corporate culture as systematically as the officialpart of the transformation.Building and establishing a right kind of a cor-porate culture is at the very core in a data-drivenorganization. We recommend starting early withthe transformation towards a data-driven cultureand letting it gradually develop in parallel with ca-pability building.data-drivenoperations requirea whole differentclock speed ofoperations from whatmany companies arerunning today
14 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research1Invest heavily in data collectionCompany’s IT department drives aninitiative to acquire new technology forcollecting and storing data.2Start building insightCompany realizes that data is notactionable and makes a tehnologyinvestment to analyze data.3Launch activities to leverage dataFinally the company launches activitiesbased on the data but soon realizes thatthe insights are not always actionable.Also it has gaps in people skills and onthe process side.Traditional capabilitybuilding approachThe traditional capability building approach hasevolved through the ages within different indus-tries.This traditional approach, known for examplefrom the construction industry,is often applied stilltoday in business development initiatives, such asbig data transformations.Slightly exaggerated, a typical path in a big dataproject 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 ismade to collect a large variety of raw data,involvingmainly the it department of the company.In phase two, management realizes they cannotdo much with collected raw data after which theyattempt to correct these shortcomings with heavyinvestments in analytics capabilities, however stilllacking the actionability of data.Finally, the company starts planning and im-plementing activities based on insights. This turnsout 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, anddecision support tools neither exist nor are usedcorrectly.As a result, the company fails to achieve measur-able results. Management buy-in weakens and thewhole project becomes a washout.When starting too big, the company misses outimportant lections on where the largest benefits are,and how they can be actually reached.Most compa-nies underestimate the time organizational learningtakes.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 lyChallenges ofthe 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 andprocess 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 areasFIGURE 3a Capability building gone wrong
15 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research1Create more actions toleverage current dataCompany launches new activities toleverage current data and developsunderstanding on needed data, skill, andtool needs from business’ perspective.2learn to create better,actionable insightsCompany develops new analyticscapabilities based on business’real time needs.3increase amount of data collected& scale big data activitiesCompany has gradually developed itspeople, process and tool capabilities,and is now ready to scale up theoperations and volume of data.Best practice approachOn 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 activitylevel for utilizing currently existing data for busi-ness purposes. The company identifies quick wins,activities with high expected return with low riskand cost level. These ‘low-hanging fruits’ are imple-mented by involving relevant people from differentparts of the organization. Company starts to buildits center of excellence and ways-of-working asreal business activities are conducted with data.Werecommend that in this phase the company startsdefining its target state of big data activities - howdoes big data contribute to reaching the company’sobjectives?Phase 2 consists of building on top of the pre-vious one and upgrading analytic skills to createbetter 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 thecompany’s know-how in this area will be furtherreinforced.The previously defined target state descriptionshall now be validated. Future development areasFIGURE 3B Capability building best practiceTärkein osatekijädatan hyödyntämisessäon sen vaatimaajatusprosessi.catalin ciobanu,carlson wagonlit travel
16 Building data driven oper ationsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Researchare defined for data collection,analytics capabilities,team sizes, expertise, processes and so on.In phase 3, already running big data operationswill be scaled up. When a company has a small butwell-performing analytics team working togetherwith business and other stakeholders and deliver-ing measurable results, it is fairly easy to scale upoperations. Only at this point of time we recom-mend making needed large scale investments forcollecting, analyzing and utilizing data.Transforming the way-of-working within theorganization is well under way but will demandplenty of time.Anticipate that the learning will notbe linear but can experience delays.Those can be re-lated to expanding activities or unexpected changesof core people, just to name some.An important factor of success is also the effi-cient transfer of knowledge and the empowering ofthe cross-functional teams as ambassadors of thenew data driven decision making culture.In this context we would like to emphasize thatin 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 practicecapability 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 Advisory can help your organiz ation?Data Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research17Magenta Advisory helps both top management and operative teams createcompetitive advantage digitally. Be it strategy creation or continuous optimizationof digital business operations, we commit to helping our clients become digitalleaders. Magenta Advisory can help your company in its’ digital transformation tooutperform the competition.In Figure 4 we have selected a sample of services we offer across a clientsdigital transformation.We believe in building long-term relationships with our clients to work withthem throughout their digital transformation.Our clients choose us and stay withus 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 resultsHow canMagenta Advisory help?STRATEGY CONCEPTCAPABILITY DEVELOPMENTBUSINESS EXCELLENCEVision and mission deﬁnitionStrategy deﬁnitionTarget settingStrategy roadmapcreationBusiness model deﬁnitionBusiness case creationCompetitor analysisBusiness concept deﬁnitionCustomer valueproposition designBest practice benchmarksBusiness requirements deﬁnitionDetailed business case creationProgram planningVendor selectionProgram managementProgram auditsUse case deﬁnitionOperating process creationOrganization design and ramp-upTraining & change managementManagement for hireKey performance indicator deﬁnitionBusiness performance analysisPerformance management establishmentDevelopment roadmappingCONCEPT CAPABILITIESFigure 4:Sample of MagentaAdvisory services
about the authorsData Driven Decision Making – Is Your Organization Ready for Big Data? | Magenta Advisory Research18Lotta KopraPartnerLotta is a leading expert in digital customerrelationship management, online salesand marketing. Lotta has broad industryexpertise from e.g. telecommunications,media, retail, pulp and paper, electronics andoil email@example.com+358 50 444 6000Anni TupamäkiConsultantAnni is a business development expertfocusing on digital marketing and sales withindustry experience covering consumergoods and telecom firstname.lastname@example.orgAbout the authorsContact informationMagenta Advisoryinfo@magentaadvisory.comBulevardi 6 A 1200120 HelsinkiFinland