adv: solving the data visualization dilemma

Solving the data visualization dilemma

Contents 1 Introduction

2 Defining the problem

3 Methodologies to enable ADV

6 Functional capabilities

8 ADV gallery – Top 10 visualizations

14 Technical platform capabilities

15 Benefits realized

16 Risks and lessons learned


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Our brains are wired to love information, but when it comes to handling data, we quickly develop headaches. Advanced data visualization (ADV) is a rapidly emerging concept that is becoming pervasive in business and society. ADV has a lofty goal of transforming data into information. Merely noting how annual reports have changed over the past 10 years — with data displayed prominently in graphical formats — shows the impact of ADV. Three converging trends have brought data visualization to the forefront as a value driver. First is the prominence of big data as table stakes in any organization. The second has been the democratization of visualization tools, which allows access to users who do not have advanced technical skills to build visualizations. Finally, the pervasiveness of infographics in our daily lives has increased expectations for visual representations.

Introduction


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“ [Data] scientists will need visualization experts the way writers need editors.”

— Harvard Business Review, Visualizing Data, April 2013

Defining the problem

Despite solving for the fundamental capabilities of big data and providing easy-to-use tools for visualization, organizations are still struggling with the basics: graduating from static reporting to interactive, online presentation tools. The data visualization discipline needs to be seen as an analytic process, not a reporting outcome. This is the first barrier to overcome on the business intelligence (BI) maturity model.

The overarching pain points in achieving data visualization that are impediments to the goal are threefold.

1. Consumers want to easily recognize patterns in complex data sets.

2. Companies need to synthesize large amounts into a single palette. This is the “one-page thinking” principle.

3. The struggle of balancing breadth and depth of a complex data model turns most users away.

Another primary obstacle to achieving value in ADV is addressing the convergent skill sets needed: It is rare to find an expert in programming, design and statistics that can readily generate ADVs. Combining the right skills in a team seeking to build data visualizations starts with the ability to ask the right questions about data and toolsets. As we find answers, it becomes possible to align and deploy ADV solutions and capabilities.

81% of executives state they highly value data visualization, yet only 14% say they interact directly with data visualization tools and technology.— Based on research of more than 500 Grant Thornton

Technology Solutions engagements

Breadth of solution

Spee

d to

sol

utio

n

POC

Analytic Assessment Approach (A3) methodology

Reporting mockups &storyboards

Proof of concept (POC) – Developed and visualized directly in OBI based on known requirements and developed data models

Models rapidly develop conceptual designs and visuals using storyboards and wire-framing design tools. Used to validate key analytic paths

Comprehensive subject area design, including data source strategies and, measurement strategy, reporting requirements

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ADV solutions should contain seven primary capabilities that address these obstacles (see Figure 1). It is important that both functional and technical platform capabilities include each of these components, just as classic BI/data warehousing solutions strive to address reporting.

Methodologies to enable ADV

We hear two frequent questions across our BI and analytics projects.

1. How do I know what is possible when it comes to data visualization? (This deals with the classic conundrum of knowing what to ask for, and also seeking the “silver bullet” answer.)

2. How do I get started?

To answer these questions, Grant Thornton has developed tiered methodologies (see Figure 2) to comprehensively address initiating data visualization that take into account breadth of solution and speed to deliver.

7 primary capabilities1. Dynamic and immediate data

2. Visual interfaces with interactivity

3. Multidimensional analysis

4. Animation/use of motion

5. Personalization to end users

6. Actions/action frameworks

7. Proactive alerts

Figure 1: Fundamentals of ADV solutions

Figure 2: Tiered methodologies


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Storyboards and mock-upsUsing storyboards and mock-ups, we can rapidly develop conceptual designs and visuals. Prior to the storyboard process, we usually conduct a demonstration of BI application functionality to set the stage. Creating a conceptual design, the storyboard and mock-up process reduces development time and rework (see Figure 4). Additional activities include prioritization of content and reporting requirements, exploration of design options, and rapid prototyping in the actual storyboard sessions.

Analytic Assessment Approach (A3) The A3 methodology focuses on defining three key strategies or inputs (see Figure 3).

1. The measurement strategy that defines key metrics, hierarchies and calculations, which are important to the business. This is the precursor to key performance indicators (KPIs).

2. The reporting strategy, which focuses on the current and future-state delivery mechanisms for reporting.

3. Data strategy that assesses the target data sources, and how data will be extracted and transformed for analysis.

Key outcomes of the A3 methodology include: analytic roadmaps; detailed implementation and resource plans; business case and return on investment calculations; and technology selection and utilization plans.

Division heat map — portal into detail using key metrics and indicators

Division financial reporting (currently published monthly — period agnostic)

Project financialsummery

Project financialdetail

New business/CRM

AR/cashmanagement

DFO/cash management summery (initiative/KPI-based)

I.Measurement

Strategy

III.Data

Strategy

Inputs Outcomes

II.ReportingStrategy

Implimentation Plan

Create Prototype

Tool Utilization

Figure 3: Analytic Assessment Approach

Figure 4: Financial reporting review

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Prototyping and proof of concepts A proof of concept (POC) is a data visualization developed and visualized directly in the BI technology, based on known requirements and a sample data model. A POC relies on the storyboard conceptual vision — focusing on a primary subject area, detailed scenarios and aggregate presentation views. Often, this process leverages the storyboard and uses it as an interim landing or navigation page for new users in Oracle Business Intelligence. POCs are typically detailed, visualized analytic scenarios based on data models and reporting requirements.


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Modes of deliveryThe seven standard capabilities of ADV are delivered in three primary modes.

1. ADV customers engage with the toolset via visual analysis and discovery. Users interrogate the visualization — interact, drill, pivot and zoom — to answer questions and pose new analysis.

2. Users engage via a familiar display of snapshot or point-in-time reporting. The easiest way to relate to this mode is the classic balanced scorecard report. At the end of the day/month, the scorecard is the snapshot of reporting at that point in time, with further information on KPIs, etc.

Functional capabilities

3. Proactive alerts to end users — regardless of device or toolset, data visualizations can alert end users without the need to interrogate data visualizations to find answers to a predetermined question.

These modes of delivery combine with the ADV capabilities to frame the functional capabilities.

Figure 5: Graphical relationships

1a 2

3

1. Visual analysis2. Reactive snapshots3. Proactive reporting

1. Nominal comparisons2. Rankings3. Time series4. Part-to-whole5. Deviations6. Distributions7. Correlations

Datavisualization

gallery

1. Dynamic and immediate data2. Visual interfaces with interactivity 3. Multidimensional analysis4. Animation/use of motion5. Personalization to end users6. Actions/action frameworks7. Proactive alerts

Primary modes of delivery


Standard graphical relationships

Data visualizationgallery

1. Classic waterfall2. Strategy trees and wheels3. Geo-spatial/geoprompting4. Sparkline graphs 5. 80-20 relationships6. Comparative distributions7. Scatter cloud8. Boxplot and whisker9. Bubble chart10. Master/detail views



Standard graphical

relationships



Standard graphical relationships

Data visualizationgallery

Datavisualization

gallery



Standard graphical

relationships

Datavisualization

gallery



Standard graphical

relationships









Datavisualization

gallery



Standard graphical

relationships

1b

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1 Harvard Business Review. Visualizing Data, April 2013.2 Few, Stephen. “Selecting the Right Graph for Your Message,” Perceptual Edge, Sept. 18, 2004.

Understanding graphical relationshipsWith functional capabilities defined through general capabilities of ADV and the modes of delivery, it is also necessary to have a fundamental understanding of standard graphical relationships. Data scientists need designers like writers need editors1. Understanding the basic tools of graphical relationships and where they are used is a common cure for writer’s block when it comes to ADV.

There are seven classic forms of graphical relationships. The vast majority of quantitative depictions in business settings can be described as one or a combination of these seven graphical elements2. Understanding these fundamentals can drive value in selecting the right visualization concept.

1. Nominal comparisons are simple comparisons of the categories and subcategories of one or more components in any order.

2. Rankings simply list data points in a defined order by a dimensional value selected — commonly shown in descending or ascending order.

3. Time series relationships are a sequence of data points that are ordered in common time buckets and typically plotted for trending purposes.

4. Part-to-whole comparisons identify how subsets of a data population relate to the total population value — displaying ratios to the whole.

5. Deviations provide a comparative analysis of a standard deviation on a data point for a selected set of dimensions or values.

6. Distributions describe basic statistical discrete distribution views of a selected population or data set.

7. Correlations refer to any of a broad grouping of statistical relationships involving dependence between the different groups.


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Below are the top 10 visualizations based on Grant Thornton’s client projects and initiatives focusing on ADV and executive analytics3. Maintaining gallery visualizations are critical to answering, “What is possible?”

1. Classic waterfallWaterfall graphics show how an initial value is increased and decreased by a series of intermediate values. They are favorites of financial and accounting departments to show contributions and profitability.

ADV gallery – Top 10 visualizations

3 All gallery screen shots are from Oracle Business Intelligence Enterprise Edition samples.

Figure 6: Classic waterfall

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2. Strategy trees and wheels A strategy tree shows an objective and its supporting objectives and KPIs hierarchically. The contribution wheel consists of a center circle (or focus node) that represents the starting objective of the diagram.

3. Geospatial/geopromptingGeospatial reporting provides comparisons with a map backdrop or comparison of distances between. Geoprompting provides heat map alerts for users and prompts them to select areas and drill to greater detail.

Figure 7: Strategy trees and wheels

Figure 8: Geospatial/geoprompting


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4. Sparkline graphsA sparkline is a very small line chart, typically drawn without axes or coordinates. It presents the general shape of the variation — typically over time — in some measurement, such as temperature or stock market price, in a simple and highly condensed way.

Figure 9: Sparkline graphs

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5. 80-20 relationshipsThis report measures how the upper group of a specific population set contributes in descending order of value. Filters enable users to set a percentage limit of value for the top group, and the report renders the corresponding percentage of the population that makes up that value.

6. Comparative distributionsComparative distributions are representations of statistical distributions, by individuals, for a selected population. It allows users to see how a metric is distributed among different categories.

Figure 10: 80-20 relationships

Figure 11: Comparative distributions


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7. Scatter cloudThis report provides a graphical summary of a set of data. Individual values are represented by the position of the point in the chart space. It displays measures of central median, dispersion and skewness.

8. Boxplot and whiskerThis report displays a boxplot and whisker diagram comparing the spread of detailed data point values between individuals of a dimension. It depicts a set of values for each dimension individual through seven number summaries: smallest observation (bottom); lower decile (10% mark); lower quartile and upper quartile (IQR); median and average; upper decile (90% mark); and largest observation (top).

Figure 12: Scatter cloud

Figure 13: Boxplot and whisker

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9. Bubble chartBubble charts are used in scatter plot scenarios where more than two variables can be used. Data points are depicted by the location and size of round data markers (bubbles). Bubble graphs are used to show correlations among three types of values, especially when you have a number of data items and you want to see the general relationships. Bubble charts are useful to segment populations of data, apply quadrant labels and prompt users for further investigation.

10. Master/detail viewsThe master/detail linking allows you to establish a relationship between two or more views; one view is called the master and will drive changes in one or more views called detail views. You can think of a master/detail relationship in a manner similar to what you do when navigating from one report to another, but you do not lose sight of the master view.

Figure 14: Bubble chart

Figure 15: Master/detail views


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Technical platforms need to address many advanced requirements. We focus on three primary platform capabilities of note.

Engineered systemsAn engineered system simply refers to the “appliance concept” to deliver the function of BI, analytics and visualizations. Apart from the classic IT approach to technical platforms that often considers hardware and software separately, analytic technical platforms are increasingly thought of as an engineered system possessing all critical components — software applications, middleware, integration tools, hardware, etc. Perhaps the most popular engineered system to date is the Apple iPad. This solution-in-a-box thinking is a key requirement for ADV technical platforms.

Technical platform capabilities

In-memory processingIn-memory processing is a fairly simple, yet very powerful, innovation. Retrieving data from disk storage is the slowest part of data processing: The more data you need to work with, the slower the analytics process. The usual way of addressing this performance issue has been to preprocess data in some way (cubes, query sets, aggregate tables, etc.). In-memory processing makes it possible to see the data more actively and at a deeper level of detail, rather than in predefined high-level views. It allows data visualizations to be more like natural thoughts.

Advanced interaction via write backs Interactivity with data visualization is paramount, and often users of a visualization tool need to provide additional input to alter or enhance the analysis. From a BI standpoint, this is called a “write back” and has special complexities and implications. This goes beyond standard selection of parameters or prompting on predetermined values or filters. Certain BI tools handle write backs better than others; however, any ADV technical platform must address this critical requirement. Our clients most often use write backs to the underlying data model in what-if analyses, predictive models and interactive commentaries with the data set.

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CEOs are demanding faster insight from data on hand, which provides the platform for most business leaders and analysts. Data visualization allows data discovery and visual analysis and reduces time to insight.

As data visualization and BI tools drive interactivity with underlying data, you can apply the global positioning system (GPS) analogy. A strong ADV tells us where we are and where we are going. ADV should enable end users to create their own visualizations, providing a true democratization of analytics tools.You can reap these benefits from data visualization efforts, as well as the broader BI function:

Benefits realized

1. Improved operational efficiency

2. Alignment across organization and functional groups

3. Decreased time to insight

4. Faster response to changes

5. Ability to identify new business opportunities

6. Higher employee and partner productivity

7. Improved compliance with established standards


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The risks and lessons learned in executing data visualizations relate back to our three main problem areas: recognizing patterns in complex data, synthesizing data into a single point of view, and balancing breadth and depth. The following risks and lessons learned are common throughout ADV initiatives:

1. Data quality. Do not underestimate the importance of data quality. Master data management tools cleanse data at the integration level, and BI tools expose data issues to be addressed. Data visualizations can mask data issues and provide users with inaccuracies that will taint the analysis.

2. Content misrepresentation. Taking into account functional capabilities, it is possible to select inappropriate graphical representations and modes of delivery for data visualizations. This can cause a misrepresentation of the data and the information that the ADV is trying to convey.

3. Biases. Data visualizations can give power to the underlying biases of the developer, designer or statistician and contaminate the analysis of the end user.

Risks and lessons learned

4. Cluttered design. With all the functional capabilities for data visualization, it is possible to take things too far — especially in a single view. This can turn away the typical end user.

5. Data overload. Exposing too much data, without a logical progression, or using data that is not absolutely necessary for the intended purpose of the visualization, will overload the end user and limit the effectiveness of the tool.

6. Delivery device agnostic. With dozens of potential interface mechanisms, it is important to design the data visualization with the intent of being flexible regardless of device — online browser, laptop, tablet, smartphone, screen projection, etc.

7. Balance flash vs. function. Think simple and modern. Form must always follow function with ADV, making the purpose of the analysis the most important. Flashy graphics get “oohs” and “ahs” initially, but are often abandoned quickly for something else that works.

ConclusionAs organizations deal with exponentially increasing amounts of data, the patience of end users is decreasing. We see continued struggles in addressing data visualization and turning data into information. Perhaps the greatest sign of a successful data visualization or infographic is the degree to which it is used to solve problems. Data visuals must provide opportunities for comprehension, conveying knowledge and clarity in understanding. Finally, success can be measured in retention, or how well the visualization imparted meaningful knowledge. Using these fundamental factors for success, we can continuously improve our data visualizations and techniques.

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About the authorJohn Stilwell is a senior manager in Grant Thornton’s Business Advisory Services practice. He is currently a national lead in Grant Thornton’s Business Technology Solutions group with a focus on Oracle Business Intelligence. Stilwell has deep experience in the area of analytics and business transformation initiatives. He is a recognized national speaker and thought leader on the topics of foundation analytics, mobile analytics, scorecard and strategy management, and multidimensional reporting tools. Stilwell has more than 15 years of consulting and technology experience in a range of industries where he has provided clients with solutions, including analytics, enterprise performance management, strategic planning and strategic cost reduction.

John StilwellSenior Manager Business Advisory ServicesT 913.272.2721E [email protected]

Content in this publication is not intended to answer specific questions or suggest suitability of action in a particular case. For additional information on the issues discussed, consult a Grant Thornton LLP client service partner or another qualified professional.

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adv: solving the data visualization dilemma

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