From Data to Decisions Guidance

GUIDANCE

From Data to DecisionsFIVE STEPS TO EVIDENCE-BASED MANAGEMENTBernard Marr

What is the issue?Most organizations are drowning in data while thirsting for good fact-based insights that can support decision making.

Why is it important?At no point in history have organizations had access to more data, be it fi nancial or non-fi nancial. Today, an organization’s success depends on its ability to gain fact-based insights faster than the competition, and to turn those insights into good decision making.

What can be done?Evidence-based management provides a structured approach for turning data into critical management decisions.

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The material contained in this Guidance is designed to provide illustrative information of the subject matter covered. It does not establish standards or preferred practices. This material has not been considered or acted upon by any senior or technical committees or the board of directors of CPA Canada and does not represent an official opinion or position of CPA Canada.

1From Data to Decisions: GUIDANCE

A cursory glance into the operations of most organiza-tions shows a common challenge, irrespective of their size, industry or sector. Most struggle to turn the mass of data now available to them into the critical knowl-edge required to win in today’s fiercely competitive and highly unpredictable markets.

At the same time there are pioneering organizations throughout the world that are using an emerging disci-pline called evidence-based management (EBM) as a way to improve their competitive positions. Through EBM, organizations explicitly use the best and most appropriate data to guide the decision-making pro-cesses. Crucially, however, EBM involves much more than just the collection and storage of data and infor-mation in large quantities — it also requires building competitive 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, facing those facts, and acting on those facts — rather than doing what everyone else does, what you have always done, or what you thought was true.”

From data to decisions

2 From Data to Decisions: GUIDANCE

Data 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 used to determine the truth of an assertion.

Information includes a collection of words, numbers, sounds, or pictures that have meaning (e.g., on the 15th of March at 5 p.m., we were all at no. 68 Victoria St.).

Knowledge is acquired when we take in and understand information about a subject, which then allows us to form judgments to support decision making, and then act on the decision. We do this by using rules about how the world works based on information we have gained from past experience.

Business intelligence (BI) refers to technologies, applica-tions, 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) fact- based management to drive decision making.

Big data analytics refers to the analysis of data that come in vast volumes, is often fast-moving and usually varied in formats (structured and unstructured). Big data is too messy for traditional business intelligence and analytics approaches.

TEXT BOX 1 Defining some key terms The five-step EBM modelThere are five steps for the effective deployment of EBM (Figure 1). This begins with Step 1 — Defining objectives and information needs. During this step, these questions are asked: “What are our strategic aims?” and “Based on those aims, what do we need to know?” This vital first step ensures we clearly articulate the real information needs, and clarify who needs to know what, when, and why. Step 2 — Collecting data — calls for gathering and organizing the right data. The emphasis here is on meaningful and relevant data to meet the information needs identified in Step 1. Organizations need to (a) assess whether the needed data is already held somewhere in the organization, or (b) know the best way to collect the data. Step 3 — Analyzing data — focuses on turn-ing data into relevant insights. Data has to be analyzed and put into context to extract information. Step 4 — Presenting information — focuses on communicating the information and insights extracted in Step 3. The main focus here is to get the information, in its most appropriate form, to the decision makers.

Step 5 — Making evidence-based decisions — is con-cerned with turning information into knowledge and decisions. The emphasis here is on making sure the available evidence is used to make the best decisions. Here, it is important to create a knowledge-to-action culture and avoid the knowing-doing gap so prevalent in many organizations today.

In addition to the five steps there is a feedback loop between the last and the first step — after learning has taken place and decisions have been made, they in turn inform future informational needs.

As one can see from the framework, there is a sixth box — IT infrastructure and business intelligence (BI) applications as enablers. Even though it is not a step in its own right, IT and BI play a crucial role in evidence-based management. They are critical enablers of the data collection process, data analysis, and the presen-tation and dissemination of information.


FIGURE 1 EBM framework

What are our strategic aims?

Based on those aims, what do we

need to know?

Can we clearly articulate our

information needs?

Who needs to know what, when,

and why?

How can we turn the data into

relevant insights?

How can we put the data into

context and extract information?

DEFINING OBJECTIVES AND

INFORMATION NEEDS

ANALYZING DATA

IT INFRASTRUCTURE AND BUSINESS INTELLIGENCE APPLICATIONS AS ENABLERS

COLLECTING DATA

PRESENTING INFORMATION

MAKING EVIDENCE-BASED

DECISIONS

1 2 3 4 5

How do we ensure that the available evidence is used to make the best

decisions?

How do we create a knowledge-to-action culture?

How do we avoid the knowing–doing

gap?

Do we have or can we collect

meaningful and relevant data to meet our

information needs?

How do we best leverage our information technology infrastructure and our business intelligence applications

to support evidence-based decision making?

How can we best present and

communicate the insights and information to inform decision

makers?

FEEDBACK LOOP


Successfully negotiating Step 1 requires careful answering of one key question: “What do you need to know?” In most organizations, the use of BI and analytics is driven more by the information that is available than by the information needed to make essential organization decisions. This is clearly back-to-front. Effective EBM should be driven by the needs of the decision makers. In essence, identify the information needs first and apply BI and analytical capabilities accordingly.

STEP 1

Defining objectives and information needs



need to know?


information needs?


and why?


relevant insights?




INFORMATION NEEDS

ANALYZING DATA


COLLECTING DATA



DECISIONS

1 2 3 4 5


decisions?



gap?



information needs?





makers?

FEEDBACK LOOP


Identify the strategic objective/information needFirst, it is critical we link the data that organizations collect to the strategy and the key drivers of value and performance. By doing so, we ensure the analytics we generate (a) are relevant to the organization’s competi-tive positioning, (b) support its greatest information needs, and (c) are not wasted on irrelevant “interesting to know” issues. One very effective way to articulate a strategy is through the use of a strategy map, which allows organizations to express their strategy on a simple one-page document that can then be used to anchor any future data requirements.

Identify who has the information needThe second phase is to identify who needs the informa-tion. Here it is important to define the target audience (information customers). Information customers can be (a) groups of people such as the board of directors, senior managers, the HR department, the marketing managers, or (b) a single person. It is critical to clarify who requires the information, because different audi-ences have vastly different needs, even in relation to a single strategic objective.

Clarify what questions they want answeredNext you want to identify exactly what questions the target audience wants answered. Often, however, recipients of information don’t fully know their exact requirements. A powerful tool for guiding audiences to identifying their specific requirements is to for-mulate key analytics questions (KAQs). In essence, a KAQ makes sure we know what it is that we want to know — that we fully appreciate the exact performance issue we are grappling with.

KAQs should focus on the future. For example, ask “How effective are our attempts to increase our market share?” instead of “Has our market share increased?” By focusing on the future, we open up a dialogue that

allows us to “do” something about the future. We then look at data in a different light, trying to understand what the data and management information means for the future. This helps with data interpretation, and ensures we collect data that helps to inform our deci-sion making (see Text Box 2 for examples).

TEXT BOX 2 Examples of key analytics questions

• To what extent are we growing profitably?• To what extent are we retaining our most profitable

customers?• How well are we promoting our services?• How do our customers perceive our service?• How effective are we in managing our relationships

with key suppliers?• How well are we communicating within our

organization?• How well are we building our new competencies in X?• To what extent do people feel passionate about work-

ing for our organization?

Clarify what decisions need to be takenAlthough KAQs narrow the possible data that can be used, it still leaves many possible data sets to choose from. Another question can be used to narrow the range of possible indicators even further. This question seeks to clearly identify any important decisions the data would support (See Text Box 3 for examples). By articulating the question and the decisions performance data will possibly help to address, it is possible to reduce the potential number of indicators from an almost end-less number to a smaller and more focused set.

TEXT BOX 3 Examples 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 we

further optimize?


Once the strategic information needs are clear the right evidence (right data and data of the right quality) needs to be collected to support decision making.

STEP 2

Collecting data



need to know?


information needs?


and why?


relevant insights?




INFORMATION NEEDS

ANALYZING DATA


COLLECTING DATA



DECISIONS

1 2 3 4 5


decisions?



gap?



information needs?





makers?

FEEDBACK LOOP


In its broadest sense, evidence includes any data or information that might be used to determine the truth of an assertion. Building evidence requires the careful collection of the right data. And yet our understanding of the word “data” is confused. People often wrongly believe the word “data” has a narrow numeric defini-tion. This is incorrect. Data comes in myriad forms — sounds, text, graphics, and pictures are as much data as are numbers.

Consequently, it is important to become familiar with the available data collection methodologies. These approaches are usually described as either quantita-tive (being concerned with the collection of numerical data) or qualitative (concerned with the collection of non-numerical data). Both approaches have dif-ferent purposes, and each has identifiable strengths and weaknesses. What is important is that we get the balance right between the quantitative and qualitative data. This can be achieved, for example, by conducting a survey that asked customers to score their likelihood to recommend your company on a scale from one to ten (quantitative) and asking additional open ques-tions such as “What do you really like about this com-pany?” and “What could we do better?” (qualitative).

Qualitative vs. quantitative data collection methodsThe aim of quantitative data collection methodologies is to classify features, count them, and then construct statistical models in an attempt to explain what is observed. Quantitative data is usually collected automatically from operations, or through structured questionnaires that incorporate mainly closed ques-tions, with specified answer choices.

Collecting evidence and dataBy collecting both quantitative and qualitative data, we are 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., customer

feedback questionnaire), focus groups (e.g., employee feedback sessions), interviews, observations, assess-ments, etc.

When collecting data we have to ensure it is reliable and valid. Reliability and validity can be substantially heightened through applying the idea of “triangula-tion” — 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 compare the information gathered from use of the different techniques. The rationale behind this is the more information we have from as many possible sources, the greater the likelihood that it is reliable.

Planning data collectionWhen planning your data collection the following steps are recommended:

• Decide on the data collection method: Before deciding how to collect the data, it is important to establish whether or not existing data can be used. It is important, though, to make sure the existing data is of the appropriate quality. If appropriate data is not available or needs to be supplemented with more evidence, new data has to be collected.

• Decide on the source of the data: At this stage, it is crucially important to think about access to data and answer questions such as: “Is the data readily available?” “Is it feasible to collect it?” “Will the data collection method, for example interviews with senior managers, provide honest informa-tion?” If not, it might be appropriate to combine various data collection methods.

• Decide when the data will be collected, and in what sequence and frequency: Here, one needs to determine when and how often the data for that indicator should be collected. Some data sets are collected continuously, others hourly, daily,


monthly, or even annually. It is important to decide what frequency provides sufficient data to answer the key performance questions and helps to support decision making.

• Decide on who is responsible for collecting the data: Here we identify the person, function, or external agency responsible for data collection and data updates. The person responsible for measur-ing could be an internal person or function within your organization or, increasingly, it can be exter-nal agencies, since many organizations outsource the collection of specific data such as customer or employee surveys.


After ensuring we are collecting the right data, we need to turn this data into insights and information. In the context of EBM, data analysis is the application of analytical tools (e.g., statistical analysis or qualitative text analysis) to gain organizational insights. Data analysis is a core requirement in creating evidence used for decision making. Yet repeated research shows most organizations are still more focused on simply collecting and distributing data than they are in doing any meaningful analysis.

STEP 3

Analyzing data



need to know?


information needs?


and why?


relevant insights?




INFORMATION NEEDS

ANALYZING DATA


COLLECTING DATA



DECISIONS

1 2 3 4 5


decisions?



gap?



information needs?





makers?

FEEDBACK LOOP


Practical examples of data analysisThere are too many data analysis techniques to provide an appropriate overview; however, here are some examples of common types of analysis to illustrate usage:

• Financial analytics: The right analytics allow organizations to produce sophisticated algorithms for much more precise forecasts and better predic-tions of future revenues, profits, cash flows, etc. Also, having the right available data and appropri-ate analytics capabilities can enable organizations to consolidate their information and automate their reporting.

• Value driver modelling: One type of analytics that is becoming increasingly popular is value-driver modelling. Organizations have realized over the years that financial indicators such as earnings per share, net income, or economic return on invested capital are not reliable predictors of future perfor-mance (e.g., market value of a company). Many organizations have started to create more compre-hensive models of value creation to include some of the more intangible drivers of future performance. They use approaches such as multiple regression analysis to understand how various elements such as brand reputation, customer service, or staff engagement might drive future performance.

• Marketing and sales analytics: Here, organiza-tions are using analytics to better understand their markets and their customers, their changing consumer trends and their buying patterns. This can be done by analyzing sales data or by analyz-ing website usage, Internet search trends or even social media conversations.

• Quality analysis: Since the awakening of the total quality movement in the 1980s, quality analysis has been an important component in most firms. Based on data from mass manufacturing, tools such as Six Sigma are now also used in service organizations. It basically uses statistics to understand variations in performance levels. This then allows organiza-tions to set much more precise quality targets,

and understand (a) what levels are acceptable, and (b) normal fluctuations in quality. Tools such as statistical process control, Six Sigma, and other quality analytics are not just used to monitor an organization’s own performance, but also to design and measure performance contracts with suppliers using, for example, service level agreements.

Big data analyticsMany predict the future success of companies — big or small — will depend on their ability to capture, analyze and gain insights from so called “big data.” The idea is basically that companies use larger and more complex sets of data to inform their decision making. What makes “big data” big is the fact traditional data ware-house set-ups and BI applications can’t handle it because it is so vast in volume, so fast-moving and varied in formats.

Big data is not really clearly defined yet but it generally refers to the more messy types of data we can’t easily put into rows and columns and are too big to store and analyze in our traditional data systems. Examples of big data include the vast and ever changing amounts of data generated in (a) social networks where we have conversations with each other, (b) video or photo data, (c) Internet search and browser logs, as well as (d) the ever-growing amount of data generated by the sensors and chips in our smartphones and tablets. Using big data would, for example, allow companies to better understand customer or employee behaviors by using their own transactional data and weblogs and combin-ing this with video analysis, social media postings and search engine data to get much richer insights.

The analysis of big data has become possible because of the recent advances in technology, which now enable us to analyze and use larger and less structured volumes of data. So, in essence, big data refers to lever-aging data that was previously seen as unmanageable, which is now possible because of new technologies and approaches to data processing.


It is crucial, when analyzing data, to keep the target audiences and their specific needs in mind. After all, organizations gain competitive advantage when the right information is delivered to the right people at the right time.

STEP 4

Presenting information



need to know?


information needs?


and why?


relevant insights?




INFORMATION NEEDS

ANALYZING DATA


COLLECTING DATA



DECISIONS

1 2 3 4 5


decisions?



gap?



information needs?





makers?

FEEDBACK LOOP


But it is crucial the information presented is relevant and meaningful to that audience. Earlier, we outlined the importance of the right reporting frequency; a great indicator is of little use if that information gets to its audience too late for timely decision making. The designer of the indicator must also consider reporting channels — that is, what outlets or reports will be used to communicate the data. An indicator can, for example, be included in the monthly perfor-mance report to the executive management commit-tee, or included in the quarterly performance report to the board. It might be required for the weekly per-formance reports to heads of service, reported on the organizational Intranet, or made available to external stakeholders through external reports or the website. The designers of data reports must also consider reporting formats, thus deciding how best to present the data. As examples, data can be shown as a number, a narrative, a table, a graph, or a chart.

Charting data Let’s look in more detail at presentation formats. We will stress that, in engaging the minds of the target audience, it is crucial the visual presentation tools are clear, informative and compelling.

In displaying information, we would recommend always starting with the KAQ the data/information sets out to answer. This provides context to what will follow. It should also ensure the report is focused squarely on meeting a critical information need of the target audience, thus avoiding any inclination to focus on “interesting” rather than “valuable” information.

The KAQ should be followed by meaningful graphs and charts. Graphs are the most widely used visual display tools in organizations. Many different types of 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 therefore be used appropriately.

Graphs provide many benefits for conveying informa-tion. They are quick and direct, highlight the most important facts, facilitate an easy understanding of the data, and can be easily remembered. Here are some more generic tips for producing graphs:• Keep them simple and focus on the message the

user needs to receive;• Try to avoid three-dimensional graphs — they are

harder to read;• Rarely use emphasis colours (e.g., bright red,

yellow, orange, or green), and only where you want to highlight specific issues;

• Don’t use too many different varieties of graphs, because an analysis across different graphs is difficult; and

• Try to avoid any unnecessary decorations, back-ground colours, etc. Any additional and unneces-sary elements just distract us and make it harder to extract the insights.

Placing a graph directly after a KAQ is a great of way of quickly showing progress in answering that question.

TEXT BOX 4 When to use which graph

Bar graphs, which can display multiple instances, provide for easy comparison between adjacent values. They are particularly good for nominal or ordinal scales.

Line graphs can best display time series data, e.g., the share price or quality fluctuations over a given period. However, a line graph is not suitable for data in nominal or ordinal scales. What it does do well is show trends, fluctuations, cycles, rates of change, and comparing two data sets over time.

Pie charts highlight various data as a percentage of the total data, each segment representing a particular category. They are generally not suitable for more than six components, or when the values of each component are similar, because it makes it too difficult to distinguish between the values.

Scatter plots are useful for depicting the correlation between two sets of data and showing the strength and direction of that relationship.


Headlines and narratives Flowing down from the graphs, a good report (or dashboard) should then use narratives and “head-lines.” A headline summarizes the main finding from the data, whereas the narrative provides context and meaning. Using graphs and narrative together enable the telling of the story, which neither can fully do in isolation. For instance, a graph containing past per-formance is extremely useful for analyzing trends over time, but a narrative can put the graphical information into context — explaining why the trend is as it is.

TEXT BOX 5 Information dashboard design mistakes

In his book, Information Dashboard Design, Steven Few outlines the following 13 common dashboard design 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.


This final step looks at how to turn the information into (a) knowledge, and (b) better decisions to act on.

STEP 5

Making evidence-based decisions



need to know?


information needs?


and why?


relevant insights?




INFORMATION NEEDS

ANALYZING DATA


COLLECTING DATA



DECISIONS

1 2 3 4 5


decisions?



gap?



information needs?





makers?

FEEDBACK LOOP


Yet, just as we caution care in how organizations analyze data sources (for example, that they should not be overly concerned with quantitative data only), we also suggest a certain circumspection in the use of information for decision making. We often find a predisposition to make important decisions based on a very narrow information set. Consequently, errone-ous decisions can be made that have damaging, and sometimes catastrophic, consequences. Often, manag-ers are in such a rush to gain performance advantages from “proven” approaches they fail to ensure consider-ation of other information when making decisions.

Knowledge must be drawn from the best available information, which will likely come from many sources. But amassing knowledge, however insightful or compelling in and of itself, is of little value unless it is turned into action. Put in stark terms, if knowl-edge is not turned into action, then the entire effort expended in sequencing through the previous steps in the EBM framework would have been a pointless exercise and a waste of resources. Decisions have to be made and acted upon.

The knowing–doing gapThe book The Knowing–Doing Gap — How Smart Companies Turn Knowledge into Action, explains why many organizations that possess plentiful knowledge fail to turn that knowledge into action. The authors argue the knowing–doing gap (where knowledge is not implemented) is the most menacing phenomenon most organizations face today. This phenomenon, they rightly claim, costs organizations billions of dollars and leads to a wide array of failures in strategic imple-mentation and other failures.

The most destructive aspect of the knowing–doing gap, the authors argue, is what they call the “smart talk trap,” where talk becomes a substitution for action, and where myriad members of the organizations make decisions that change nothing. Other reasons for the

gap are (a) entrenched and outdated culture, (b) fear of change, (c) internal competition, and (d) measure-ments that lead nowhere.

Consequently, closing this knowledge–doing gap often requires a wholesale reworking of the process for turning knowledge into action — a reworking that has cultural as well as process, structural, and techno-logical components.

For creating a culture that is conducive to transforming knowledge into action, we recommend organizations follow the following seven steps:

1. Have passion for learning and improvement. The most important ingredient, which is why it is the first on the list, is to create an organization-wide passion for learning and improvement.

2. Ensure leadership buy-in. To make EBM a reality, senior level buy-in and support is important. Tom Davenport and Jeanne Harris argue in their book Competing on Analytics: “If the CEO or a signifi-cant faction of the senior executive team doesn’t understand or appreciate at least the outputs of quantitative analysis or the process of fact-based decision making, analysts are going to be relegated to the back office, and competition will be based on guesswork and gut feel, not analytics.”

3. Develop widespread analytical capabilities through-out the organization. Without the competencies and skills to turn data into insights EBM won’t work. Most organizations have a big training need in business analytics and EBM.

4. Use judgment. In making analytics work, employ-ees (at all levels) must balance facts and judgment.

5. Share information. For EBM to be effective, the message has to get out, loud and clear, that infor-mation belongs to the organization, and that all employees should be focused not on its ownership


but on working together to create the richness of different perspectives that can turn this informa-tion into golden nuggets of actionable knowledge.

6. Reward and recognize fact-based decision-making. In planning their strategy for implementing the EBM framework, organizations should look to weaving in some form of supporting reward strategy, as it is important to recognize and reward EBM attempts. This will show that organizations take the approach seriously, and value those trying to make it a practi-cal reality. This can start with a simple thank you and sharing of success stories.

7. Build appropriate IT infrastructure. You can have a wealth of analytical intentions and skills, but you also need the tools to put them into practice. Organizations need the right IT Infrastructure. Essentially, this comprises (a) databases, data warehouses, data marts, etc. to store the data; (b) networks and connections to share the informa-tion and to make it accessible; and (c) the software to analyze and share the data.

What makes organizations succeed in today’s com-petitive and unpredictable world is the ability to learn faster than the competition, and the ability to identify and act on facts faster than the competition. This Guidance outlines how EBM can enable organiza-tions to do exactly that. Any organization can boost its competitive position by aligning the data collection to the strategic value drivers, and collecting the best available evidence, by using this evidence to extract valuable insights and by communicating the infor-mation in a way that allows acting on those insights. The tips, tools, and templates presented as part of the five-part EBM model should enable organizations to become more evidence-based in their decision mak-ing, and avoid the traps of making decisions based on anecdotal data or dangerous half-truths. ☐

This publication is one in a series on From Data to Decisions. An Overview and Case Studies are also available on our website. For additional information, please contact Carol Raven, Principal, Strategic Management Accounting & Finance at 416-204-3489 or email [email protected]


Alexander, Jack. Performance Dashboards and Analysis for Value Creation. Hoboken, NJ: John Wiley & Sons, 2007.

Ayres, Ian. Super Crunchers. New York: Bantam Dell, 2007.

Bensoussan, Babette E., and Fleisher, Craig. Analysis Without Paralysis: 10 Tools to Make Better Strategic Decisions. Saddle River, NJ: FT Prentice Hall, 2008.

Few, Stephen. Information Dashboard Design: The Effective Visual Communication of Data. Sebastopol, CA: O’Reilly Media, 2006.

Few, Stephen. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press, 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 to Success with Evidence-Based Management. Chichester, UK: Wiley 2010.

Marr, Bernard. Managing and Delivering Performance. Oxford, UK: Elsevier Ltd., 2008.

Additional sources of information

Marr, Bernard. Strategic Performance Management: Leveraging and Measuring your Intangible Value Drivers. Oxford, UK: Elsevier Ltd., 2006.

Redman, Thomas C. Data Driven: Profiting From Your Most Important Business Assets. Boston: Harvard Business School Press, 2008.

About the authorBernard Marr is the founder and CEO of the Advanced Performance Institute. He is acknowl-edged as a leading global authority and best-selling author on organizational performance and organization success. In this capacity he regularly advises leading companies, organizations and gov-ernments across the globe, and he is an acclaimed and award-winning keynote speaker, researcher, consultant and teacher. His latest books include: The Intelligent Company: Five Steps To Success With Evidence-Based Management and Key Performance Indicators: The 75+ Measures Every Manager Needs To Know. For more information visit www.ap-institute.com or contact Bernard at [email protected].

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From Data to Decisions Guidance