From Data to Decisions - FIVE STEPS TO EVIDENCE BASED MANAGEMENT

From Datato DecisionsFIVE STEPS TO EVIDENCE-BASED MANAGEMENTBernard MarrWhat is the issue?Most organizations are drowning in data whilethirsting for good fact-based insights that cansupport decision making.What can be done?Evidence-based management provides astructured approach for turning data intocritical management decisions.Why is it important?At no point in history have organizations hadaccess to more data, be it financial or non-financial.Today, an organization’s success depends on itsability to gain fact-based insights faster than thecompetition, and to turn those insights into gooddecision making.GUIDANCE

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From Data to Decisions: GUIDANCE1From data to decisionsA cursory glance into the operations of most organizationsshows a common challenge, irrespective of theirsize, industry or sector. Most struggle to turn the massof data now available to them into the critical knowledgerequired to win in today’s fiercely competitiveand highly unpredictable markets.At the same time there are pioneering organizationsthroughout the world that are using an emerging disciplinecalled evidence-based management (EBM) as away to improve their competitive positions. ThroughEBM, organizations explicitly use the best and mostappropriate data to guide the decision-making processes.Crucially, however, EBM involves much morethan just the collection and storage of data and informationin large quantities — it also requires buildingcompetitive strategies around data-driven insights(see Text Box 1 for an overview of key terms).Robert Sutton, a professor at Stanford University,argues: “Evidence-based management is a simple idea.It just means finding the best evidence you can, facingthose facts, and acting on those facts — rather thandoing what everyone else does, what you have alwaysdone, or what you thought was true.”

2 From Data to Decisions: GUIDANCETEXT BOX 1 Defining some key terms The five-step EBM modelData comes in a myriad of forms. It includes numbers,words, sounds, or pictures, but without context (e.g.,15/3, 5, 68).Evidence is any data or information that might be usedto determine the truth of an assertion.Information includes a collection of words, numbers,sounds, or pictures that have meaning (e.g., on the 15thof March at 5 p.m., we were all at no. 68 Victoria St.).Knowledge is acquired when we take in and understandinformation about a subject, which then allows us to formjudgments to support decision making, and then act onthe decision. We do this by using rules about how theworld works based on information we have gained frompast experience.Business intelligence (BI) refers to technologies, applications,and practices for collecting, integrating, analyzing,and presenting business information.Analytics refers to the use of (a) data and evidence,(b) statistical, quantitative, and qualitative analysis,(c) explanatory and predictive models, and (d) factbasedmanagement to drive decision making.Big data analytics refers to the analysis of data thatcome in vast volumes, is often fast-moving and usuallyvaried in formats (structured and unstructured). Big datais too messy for traditional business intelligence andanalytics approaches.There are five steps for the effective deployment ofEBM (Figure 1). This begins with Step 1 — Definingobjectives and information needs. During this step,these questions are asked: “What are our strategicaims?” and “Based on those aims, what do we needto know?” This vital first step ensures we clearlyarticulate the real information needs, and clarifywho needs to know what, when, and why. Step 2 —Collecting data — calls for gathering and organizingthe right data. The emphasis here is on meaningfuland relevant data to meet the information needsidentified in Step 1. Organizations need to (a) assesswhether the needed data is already held somewherein the organization, or (b) know the best way to collectthe data. Step 3 — Analyzing data — focuses on turningdata into relevant insights. Data has to be analyzedand put into context to extract information. Step 4 —Presenting information — focuses on communicatingthe information and insights extracted in Step 3. Themain focus here is to get the information, in its mostappropriate form, to the decision makers.Step 5 — Making evidence-based decisions — is concernedwith turning information into knowledge anddecisions. The emphasis here is on making sure theavailable evidence is used to make the best decisions.Here, it is important to create a knowledge-to-actionculture and avoid the knowing-doing gap so prevalentin many organizations today.In addition to the five steps there is a feedback loopbetween the last and the first step — after learninghas taken place and decisions have been made, theyin turn inform future informational needs.As one can see from the framework, there is a sixthbox — IT infrastructure and business intelligence (BI)applications as enablers. Even though it is not a step inits own right, IT and BI play a crucial role in evidencebasedmanagement. They are critical enablers of thedata collection process, data analysis, and the presentationand dissemination of information.

From Data to Decisions: GUIDANCE3FIGURE 1EBM framework1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Who needs toknow what, when,and why?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

4 From Data to Decisions: GUIDANCESTEP 1Defining objectives andinformation needsSuccessfully negotiating Step 1 requires careful answering of one key question:“What do you need to know?” In most organizations, the use of BI and analyticsis driven more by the information that is available than by the information neededto make essential organization decisions. This is clearly back-to-front. EffectiveEBM should be driven by the needs of the decision makers. In essence, identify theinformation needs first and apply BI and analytical capabilities accordingly.1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Who needs toknow what, when,and why?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

From Data to Decisions: GUIDANCE5Identify the strategic objective/information needFirst, it is critical we link the data that organizationscollect to the strategy and the key drivers of value andperformance. By doing so, we ensure the analytics wegenerate (a) are relevant to the organization’s competitivepositioning, (b) support its greatest informationneeds, and (c) are not wasted on irrelevant “interestingto know” issues. One very effective way to articulatea strategy is through the use of a strategy map, whichallows organizations to express their strategy ona simple one-page document that can then be usedto anchor any future data requirements.Identify who has the information needThe second phase is to identify who needs the information.Here it is important to define the target audience(information customers). Information customers canbe (a) groups of people such as the board of directors,senior managers, the HR department, the marketingmanagers, or (b) a single person. It is critical to clarifywho requires the information, because different audienceshave vastly different needs, even in relation toa single strategic objective.Clarify what questions they wantansweredNext you want to identify exactly what questionsthe target audience wants answered. Often, however,recipients of information don’t fully know their exactrequirements. A powerful tool for guiding audiencesto identifying their specific requirements is to formulatekey analytics questions (KAQs). In essence, aKAQ makes sure we know what it is that we want toknow — that we fully appreciate the exact performanceissue we are grappling with.allows us to “do” something about the future. We thenlook at data in a different light, trying to understandwhat the data and management information meansfor the future. This helps with data interpretation, andensures we collect data that helps to inform our decisionmaking (see Text Box 2 for examples).TEXT BOX 2Examples of key analytics questions• To what extent are we growing profitably?• To what extent are we retaining our most profitablecustomers?• How well are we promoting our services?• How do our customers perceive our service?• How effective are we in managing our relationshipswith key suppliers?• How well are we communicating within ourorganization?• How well are we building our new competencies in X?• To what extent do people feel passionate about workingfor our organization?Clarify what decisions need to be takenAlthough KAQs narrow the possible data that can beused, it still leaves many possible data sets to choosefrom. Another question can be used to narrow therange of possible indicators even further. This questionseeks to clearly identify any important decisions thedata would support (See Text Box 3 for examples). Byarticulating the question and the decisions performancedata will possibly help to address, it is possible to reducethe potential number of indicators from an almost endlessnumber to a smaller and more focused set.TEXT BOX 3Examples of possible questions• Which customers to target.• How best to redesign our website.• The best route for our delivery trucks.• In which part of our branding should we invest?• How best to package our service offerings.• Which people should we recruit?• Which part of our production process should wefurther optimize?KAQs should focus on the future. For example, ask“How effective are our attempts to increase our marketshare?” instead of “Has our market share increased?”By focusing on the future, we open up a dialogue that

6 From Data to Decisions: GUIDANCESTEP 2Collecting dataOnce the strategic information needs are clear the right evidence (right dataand data of the right quality) needs to be collected to support decision making.1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Who needs toknow what, when,and why?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

From Data to Decisions: GUIDANCE7In its broadest sense, evidence includes any data orinformation that might be used to determine the truthof an assertion. Building evidence requires the carefulcollection of the right data. And yet our understandingof the word “data” is confused. People often wronglybelieve the word “data” has a narrow numeric definition.This is incorrect. Data comes in myriad forms —sounds, text, graphics, and pictures are as much dataas are numbers.Consequently, it is important to become familiar withthe available data collection methodologies. Theseapproaches are usually described as either quantitative(being concerned with the collection of numericaldata) or qualitative (concerned with the collectionof non-numerical data). Both approaches have differentpurposes, and each has identifiable strengthsand weaknesses. What is important is that we get thebalance right between the quantitative and qualitativedata. This can be achieved, for example, by conductinga survey that asked customers to score their likelihoodto recommend your company on a scale from one toten (quantitative) and asking additional open questionssuch as “What do you really like about this company?”and “What could we do better?” (qualitative).Qualitative vs. quantitative datacollection methodsThe aim of quantitative data collection methodologiesis to classify features, count them, and then constructstatistical models in an attempt to explain whatis observed. Quantitative data is usually collectedautomatically from operations, or through structuredquestionnaires that incorporate mainly closed questions,with specified answer choices.Collecting evidence and dataBy collecting both quantitative and qualitative data, weare then able to begin assigning meaning to the data.Data can be collected automatically (e.g., web logs,sensor data, etc.) or through surveys (e.g., customerfeedback questionnaire), focus groups (e.g., employeefeedback sessions), interviews, observations, assessments,etc.When collecting data we have to ensure it is reliableand valid. Reliability and validity can be substantiallyheightened through applying the idea of “triangulation”— collecting data using various techniques (e.g.,interviews with board members, middle managers,and front-line workers) and methodologies (e.g.,survey 70% of your suppliers and interview 30%).This allows organizations to contrast and comparethe information gathered from use of the differenttechniques. The rationale behind this is the moreinformation we have from as many possible sources,the greater the likelihood that it is reliable.Planning data collectionWhen planning your data collection the followingsteps are recommended:• Decide on the data collection method: Beforedeciding how to collect the data, it is important toestablish whether or not existing data can be used.It is important, though, to make sure the existingdata is of the appropriate quality. If appropriatedata is not available or needs to be supplementedwith more evidence, new data has to be collected.• Decide on the source of the data: At this stage, itis crucially important to think about access to dataand answer questions such as: “Is the data readilyavailable?” “Is it feasible to collect it?” “Will thedata collection method, for example interviewswith senior managers, provide honest information?”If not, it might be appropriate to combinevarious data collection methods.• Decide when the data will be collected, and inwhat sequence and frequency: Here, one needsto determine when and how often the data forthat indicator should be collected. Some data setsare collected continuously, others hourly, daily,

8 From Data to Decisions: GUIDANCEmonthly, or even annually. It is important todecide what frequency provides sufficient datato answer the key performance questions andhelps to support decision making.• Decide on who is responsible for collecting thedata: Here we identify the person, function, orexternal agency responsible for data collection anddata updates. The person responsible for measuringcould be an internal person or function withinyour organization or, increasingly, it can be externalagencies, since many organizations outsourcethe collection of specific data such as customer oremployee surveys.

From Data to Decisions: GUIDANCE9STEP 3Analyzing dataAfter ensuring we are collecting the right data, we need to turn this data intoinsights and information. In the context of EBM, data analysis is the applicationof analytical tools (e.g., statistical analysis or qualitative text analysis) to gainorganizational insights. Data analysis is a core requirement in creating evidenceused for decision making. Yet repeated research shows most organizations are stillmore focused on simply collecting and distributing data than they are in doing anymeaningful analysis.1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Who needs toknow what, when,and why?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

From Data to Decisions: GUIDANCE11STEP 4Presenting informationIt is crucial, when analyzing data, to keep the target audiences and their specificneeds in mind. After all, organizations gain competitive advantage when the rightinformation is delivered to the right people at the right time.1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?Who needs toknow what, when,and why?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

12 From Data to Decisions: GUIDANCEBut it is crucial the information presented is relevantand meaningful to that audience. Earlier, we outlinedthe importance of the right reporting frequency; agreat indicator is of little use if that information getsto its audience too late for timely decision making.The designer of the indicator must also considerreporting channels — that is, what outlets or reportswill be used to communicate the data. An indicatorcan, for example, be included in the monthly performancereport to the executive management committee,or included in the quarterly performance reportto the board. It might be required for the weekly performancereports to heads of service, reported on theorganizational Intranet, or made available to externalstakeholders through external reports or the website.The designers of data reports must also considerreporting formats, thus deciding how best to presentthe data. As examples, data can be shown as a number,a narrative, a table, a graph, or a chart.Charting dataLet’s look in more detail at presentation formats. Wewill stress that, in engaging the minds of the targetaudience, it is crucial the visual presentation toolsare clear, informative and compelling.In displaying information, we would recommendalways starting with the KAQ the data/informationsets out to answer. This provides context to whatwill follow. It should also ensure the report is focusedsquarely on meeting a critical information need of thetarget audience, thus avoiding any inclination to focuson “interesting” rather than “valuable” information.The KAQ should be followed by meaningful graphsand charts. Graphs are the most widely used visualdisplay tools in organizations. Many different typesof graphs can be deployed to convey information.These include, for example, pictographs, tally charts,bar graphs, histograms, scatter plots, line graphs,and pie charts.Each chart has a different purpose, and should thereforebe used appropriately.Graphs provide many benefits for conveying information.They are quick and direct, highlight the mostimportant facts, facilitate an easy understanding ofthe data, and can be easily remembered. Here aresome more generic tips for producing graphs:• Keep them simple and focus on the message theuser needs to receive;• Try to avoid three-dimensional graphs — they areharder to read;• Rarely use emphasis colours (e.g., bright red,yellow, orange, or green), and only where youwant to highlight specific issues;• Don’t use too many different varieties of graphs,because an analysis across different graphs isdifficult; and• Try to avoid any unnecessary decorations, backgroundcolours, etc. Any additional and unnecessaryelements just distract us and make it harderto extract the insights.Placing a graph directly after a KAQ is a great of wayof quickly showing progress in answering that question.TEXT BOX 4When to use which graphBar graphs, which can display multiple instances, providefor easy comparison between adjacent values. They areparticularly good for nominal or ordinal scales.Line graphs can best display time series data, e.g., theshare price or quality fluctuations over a given period.However, a line graph is not suitable for data in nominalor ordinal scales. What it does do well is show trends,fluctuations, cycles, rates of change, and comparing twodata sets over time.Pie charts highlight various data as a percentage ofthe total data, each segment representing a particularcategory. They are generally not suitable for more thansix components, or when the values of each componentare similar, because it makes it too difficult to distinguishbetween the values.Scatter plots are useful for depicting the correlationbetween two sets of data and showing the strengthand direction of that relationship.

From Data to Decisions: GUIDANCE13Headlines and narrativesFlowing down from the graphs, a good report (ordashboard) should then use narratives and “headlines.”A headline summarizes the main finding fromthe data, whereas the narrative provides context andmeaning. Using graphs and narrative together enablethe telling of the story, which neither can fully do inisolation. For instance, a graph containing past performanceis extremely useful for analyzing trends overtime, but a narrative can put the graphical informationinto context — explaining why the trend is as it is.TEXT BOX 5Information dashboard design mistakesIn his book, Information Dashboard Design, StevenFew outlines the following 13 common dashboarddesign mistakes:1. Exceeding the boundaries of a single screen;2. Supplying inadequate context for the data;3. Displaying excessive detail or precision;4. Choosing a deficient measure;5. Choosing inappropriate display media;6. Introducing meaningless variety;7. Using poorly designed display media;8. Encoding quantitative data inaccurately;9. Arranging the data poorly;10. Highlighting important data ineffectively or not at all;11. Cluttering the display with useless decorations;12. Misusing or overusing colour; and13. Designing an unattractive visual display.

14 From Data to Decisions: GUIDANCESTEP 5Making evidence-based decisionsThis final step looks at how to turn the information into (a) knowledge, and(b) better decisions to act on.1 2 3 4 5DEFININGOBJECTIVES ANDINFORMATIONNEEDSCOLLECTINGDATAANALYZINGDATAPRESENTINGINFORMATIONMAKINGEVIDENCE-BASEDDECISIONSWhat are ourstrategic aims?Based on thoseaims, what do weneed to know?Can we clearlyarticulate ourinformation needs?Who needs toknow what, when,and why?Do we have orcan we collectmeaningful andrelevant datato meet ourinformation needs?How can we turnthe data intorelevant insights?How can we putthe data intocontext and extractinformation?How can webest present andcommunicatethe insights andinformation toinform decisionmakers?IT INFRASTRUCTURE AND BUSINESS INTELLIGENCEAPPLICATIONS AS ENABLERSHow do we best leverage our information technologyinfrastructure and our business intelligence applicationsto support evidence-based decision making?How do we ensurethat the availableevidence is usedto make the bestdecisions?How do we createa knowledge-toactionculture?How do we avoidthe knowing–doinggap?FEEDBACK LOOP

From Data to Decisions: GUIDANCE15Yet, just as we caution care in how organizationsanalyze data sources (for example, that they shouldnot be overly concerned with quantitative data only),we also suggest a certain circumspection in the useof information for decision making. We often find apredisposition to make important decisions based ona very narrow information set. Consequently, erroneousdecisions can be made that have damaging, andsometimes catastrophic, consequences. Often, managersare in such a rush to gain performance advantagesfrom “proven” approaches they fail to ensure considerationof other information when making decisions.Knowledge must be drawn from the best availableinformation, which will likely come from manysources. But amassing knowledge, however insightfulor compelling in and of itself, is of little value unlessit is turned into action. Put in stark terms, if knowledgeis not turned into action, then the entire effortexpended in sequencing through the previous stepsin the EBM framework would have been a pointlessexercise and a waste of resources. Decisions have tobe made and acted upon.The knowing–doing gapThe book The Knowing–Doing Gap — How SmartCompanies Turn Knowledge into Action, explains whymany organizations that possess plentiful knowledgefail to turn that knowledge into action. The authorsargue the knowing–doing gap (where knowledge isnot implemented) is the most menacing phenomenonmost organizations face today. This phenomenon, theyrightly claim, costs organizations billions of dollarsand leads to a wide array of failures in strategic implementationand other failures.The most destructive aspect of the knowing–doinggap, the authors argue, is what they call the “smart talktrap,” where talk becomes a substitution for action,and where myriad members of the organizations makedecisions that change nothing. Other reasons for thegap are (a) entrenched and outdated culture, (b) fearof change, (c) internal competition, and (d) measurementsthat lead nowhere.Consequently, closing this knowledge–doing gapoften requires a wholesale reworking of the processfor turning knowledge into action — a reworking thathas cultural as well as process, structural, and technologicalcomponents.For creating a culture that is conducive to transformingknowledge into action, we recommend organizationsfollow the following seven steps:1. Have passion for learning and improvement. Themost important ingredient, which is why it is thefirst on the list, is to create an organization-widepassion for learning and improvement.2. Ensure leadership buy-in. To make EBM a reality,senior level buy-in and support is important. TomDavenport and Jeanne Harris argue in their bookCompeting on Analytics: “If the CEO or a significantfaction of the senior executive team doesn’tunderstand or appreciate at least the outputs ofquantitative analysis or the process of fact-baseddecision making, analysts are going to be relegatedto the back office, and competition will be based onguesswork and gut feel, not analytics.”3. Develop widespread analytical capabilities throughoutthe organization. Without the competenciesand skills to turn data into insights EBM won’twork. Most organizations have a big training needin business analytics and EBM.4. Use judgment. In making analytics work, employees(at all levels) must balance facts and judgment.5. Share information. For EBM to be effective, themessage has to get out, loud and clear, that informationbelongs to the organization, and that allemployees should be focused not on its ownership

16 From Data to Decisions: GUIDANCEbut on working together to create the richness ofdifferent perspectives that can turn this informationinto golden nuggets of actionable knowledge.6. Reward and recognize fact-based decision-making.In planning their strategy for implementing the EBMframework, organizations should look to weavingin some form of supporting reward strategy, as it isimportant to recognize and reward EBM attempts.This will show that organizations take the approachseriously, and value those trying to make it a practicalreality. This can start with a simple thank you andsharing of success stories.7. Build appropriate IT infrastructure. You can havea wealth of analytical intentions and skills, butyou also need the tools to put them into practice.Organizations need the right IT Infrastructure.Essentially, this comprises (a) databases, datawarehouses, data marts, etc. to store the data;(b) networks and connections to share the informationand to make it accessible; and (c) the softwareto analyze and share the data.What makes organizations succeed in today’s competitiveand unpredictable world is the ability to learnfaster than the competition, and the ability to identifyand act on facts faster than the competition. ThisGuidance outlines how EBM can enable organizationsto do exactly that. Any organization can boostits competitive position by aligning the data collectionto the strategic value drivers, and collecting the bestavailable evidence, by using this evidence to extractvaluable insights and by communicating the informationin a way that allows acting on those insights.The tips, tools, and templates presented as part of thefive-part EBM model should enable organizations tobecome more evidence-based in their decision making,and avoid the traps of making decisions basedon anecdotal data or dangerous half-truths. ☐This publication is one in a series on From Data to Decisions. An Overview and Case Studies arealso available on our website. For additional information, please contact Carol Raven, Principal,Strategic Management Accounting & Finance at 416-204-3489 or email craven@cpacanada.ca

From Data to Decisions: GUIDANCE17Additional sources of informationAlexander, Jack. Performance Dashboards andAnalysis for Value Creation. Hoboken, NJ: JohnWiley & Sons, 2007.Ayres, Ian. Super Crunchers. New York: BantamDell, 2007.Bensoussan, Babette E., and Fleisher, Craig. AnalysisWithout Paralysis: 10 Tools to Make Better StrategicDecisions. Saddle River, NJ: FT Prentice Hall, 2008.Few, Stephen. Information Dashboard Design: TheEffective Visual Communication of Data. Sebastopol,CA: O’Reilly Media, 2006.Few, Stephen. Show Me the Numbers: Designing Tablesand Graphs to Enlighten. Oakland, CA: AnalyticsPress, 2004.Marr, Bernard. Key Performance Indicators: The 75+Measures Every Manager Needs to Know. Harlow, CM:Financial Times Prentice Hall, 2012.Marr, Bernard. The Intelligent Company: Five steps toSuccess with Evidence-Based Management. Chichester,UK: Wiley 2010.Marr, Bernard. Strategic Performance Management:Leveraging and Measuring your Intangible ValueDrivers. Oxford, UK: Elsevier Ltd., 2006.Redman, Thomas C. Data Driven: Profiting FromYour Most Important Business Assets. Boston: HarvardBusiness School Press, 2008.About the authorBernard Marr is the founder and CEO of theAdvanced Performance Institute. He is acknowledgedas a leading global authority and bestsellingauthor on organizational performance andorganization success. In this capacity he regularlyadvises leading companies, organizations and governmentsacross the globe, and he is an acclaimedand award-winning keynote speaker, researcher,consultant and teacher. His latest books include:The Intelligent Company: Five Steps To SuccessWith Evidence-Based Management and KeyPerformance Indicators: The 75+ Measures EveryManager Needs To Know. For more informationvisit www.ap-institute.com or contact Bernard atbernard.marr@ap-institute.com.Marr, Bernard. Managing and Delivering Performance.Oxford, UK: Elsevier Ltd., 2008.

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