Delta Corporation has developed a new clothing line that is designed for young people. In order to create a suitable image for this new product line

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CHAPTER 11

Data Visualization and Geographic Information Systems

C H A P T E R O U T L I N E

Case 11.1 Opening Case: Safeway and PepsiCo Apply Data Visualization to Supply Chain

11.1 Data Visualization and Learning

11.2 Enterprise Data Mashups

11.3 Digital Dashboards

11.4 Geospatial Data and Geographic Information Systems

Case 11.2 Visualization Case: Are You Ready for Football?

Case 11.3 Video Case: The Beauty of Data Visualization—Data Detective

L E A R N I N G O B J E C T I V E S

11.1 Describe how data visualization applications and interactive reports support learning and business functions.

11.2 Explain how data mashup applications streamline the process of integrating diverse data sources and information feeds to support data needs that cannot be anticipated.

11.3 Describe how companies optimize operations with the help of dashboards. Explain how enterprise dashboards are built and how they leverage real-time data and people’s natural ability to think visually.

11.4 Assess the business applications and benefits of geospatial data and geographic information systems.

Introduction The concept of using pictures or graphics to understand data has been around for centuries— from seventeenth century maps and graphs to the invention of the pie chart in the early 1800s. In recent years, technology has brought the art and science of data visualization to forefront, and it is changing the corporate landscape.

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Historically, data analytics was performed by statisticians, programmers, and data scien- tists who rarely interact directly with the business. However, easier-to-use data visualization, dashboard, and mashup technologies have changed this “experts-only” approach to data anal- ysis and presentation. Data analytics are being pushed out into the business by advances that make it possible for employees at most levels of the organization to analyze data in a mean- ingful way. Vendors of enterprise-level analytics are also upgrading their visualization and reporting platforms previously designed for use by the statistical experts.

In Chapter 3, you learned about big data analytics, data mining, and business intelligence (BI) and how they are being used to enhance performance, productivity, and competitive advantage in organizations around the globe. In this chapter, we expand on these topics to introduce you to the latest in data visualization, visual discovery, dashboards, mashups, and geographic information systems (GISs). We also introduce you to another important concept— geospatial data and how companies are incorporating geospatial data and GISs into their cus- tomer relationship management (CRM), supply chain management (SCM), BI, and other related enterprise activities

Several tools discussed in this chapter enable you to be self-sufficient. Drag-and-drop, automation, “show me” wizards, and easy-to-use dashboards enable you to develop your own interactive data visualization apps and dashboards. Reducing dependency on IT staff has a long history. For example, at one time, managers did not analyze data with spreadsheets, but now Excel expertise is expected. Vendors offer academic alliances to enable universities to teach their software in MBA and undergraduate business courses. Tableau Desktop, QlikView, TIBCO Spotfire, and IBM’s SPSS Analytic Catalyst enable business users to perform the kind of advanced analysis that could only have been performed by expert users of statistical software a few years ago.

Geospatial data is data that has an explicitly geographic component, ranging from vector and raster data to tabular data with site locations.

Case 11.1 Opening Case

Safeway and PepsiCo Collaborate to Reduce Stock Outages using Data Visualization If there’s one activity that is central to retail operations, it’s inventory management. Striking just the right balance between enough and not too much stock puzzles even those retailers who are regarded as inven- tory management experts. So, when PepsiCo suggested to Safeway that they try using data visualization software to improve forecasting and inventory management, Safeway leaders jumped at the chance!

Enhancing Supply Chain Visibility In an effort to improve awareness and sharing of POS data and data about product orders, inventory levels, demand forecasts, transpor- tation, and logistics, Safeway implemented data-sharing programs with PepsiCo and other key vendors using data visualization tech- niques (Figure 11.1 and Table 11.1). This type of improved data vis- ibility can result in increased sales and millions of dollars in reduced

costs along the entire supply chain—from raw material to delivery to end customer.

Safeway’s Data Visibility program was already forward thinking, so when they partnered with PepsiCo’s 360¤ Retail execution program, Safeway’s teams were equipped to improve an already lean supply chain. But to further improve their supply chain, Safeway needed an altogether different way to view the data. So, when Deloitte Consulting offered to partner with PepsiCo and Safeway to provide an effective way to inter- pret massive amounts of data at its Highly Immersive Visual Environment (HIVE), they were very interested.

Excel-based analytics In the past, when Safeway wanted answers about stockouts, managers used spreadsheets to gather and compile inventory data and see how stockouts trended across the company. With spreadsheets, managers could discover general trends over time, but they could not identify trends across a specific brand or universal product code (UPC). Trends

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Introduction 333

PepsiCo worked with major customers to improve demand forecasts in order to minimize inventory on-hand.

Deloitte Consulting partnered with PepsiCo and Safeway to help them visually analyze POS data at the Deloitte Analytics HIVE, short for Highly Immersive Visual Environment.

PepsiCo and Safeway

The goal of Safeway’s Data Visibility program is to improve supply chain visibility with key vendors, such as PepsiCo.

FIGURE 11.1 Deloitte Consulting partnered with PepsiCo and Safeway to help them analyze massive amounts of point-of-sale (POS) data at its state-of-the-art visualization center called HIVE.

TA B L E 1 1 . 1 Opening Case Overview

Business Safeway, headquartered in Pleasanton, CA. has 197,000 employees and 1,368 stores in the United States and Canada. Safeway, Inc., reported revenue of $36.3 billion in 2015. PepsiCo, Headquartered in Purchase, NY. has 263,000 employees across operations in over 200 countries and territories in Europe, Sub-Saharan Africa; in Asia, Middle East, and North Africa. PepsiCo reported a net revenue of $63 billion in 2015.

Products Lines PepsiCo—food, snacks and beverages Safeway—food and drug retailers

Business challenges Inventory management is critical in retail operations—and a challenge throughout the supply chain.

Digital Technology HIVE—a physical environment where people can examine the latest analytics approaches themselves using their own data offered by Deloitte Consulting

Taglines PepsiCo—“You Got the Right One Baby” Safeway—“Ingredients for Life”

about each brand required more data than could be represented in rows and columns of a spreadsheet. These spreadsheet limitations ulti- mately led the company to try data visualization. To initialize the pro- ject, representatives from Safeway and PepsiCo traveled to Deloitte’s HIVE in Washington, DC, for a day-long design session to analyze many terabytes of data.

HIVE Deloitte’s HIVE is a research lab that measures and studies the inter- actions between business analytics technologies and real-world data. The applications used at the HIVE to develop real-world business solu- tions are translated into portfolios that are intuitive to understand. Deloitte hosts business leaders who want to understand business analytics better in sessions tailored to address their specific business

challenges (Curtis, 2013). At the HIVE, executives get help with analytics tools using their own data.

The HIVE gathers together a wide range of the latest analytics tech- nologies from all over the world. In a very short amount of time, execu- tives can learn what might otherwise have taken months of meetings, demonstrations, and business pitches. You can find out more about the HIVE in the video “Deloitte Analytics HIVE”.

Data Visualization at the HIVE PepsiCo and Safeway participants collaborated to understand how to reduce the “number of days of supply” from their supply chain while maintaining service levels—a project that would save PepsiCo and Safeway millions of dollars each year! During their HIVE session, they built data visualizations to explore questions about stockouts.

334 C H A P T E R 1 1 Data Visualization and Geographic Information Systems

11.1 Data Visualization and Learning Data visualization harnesses the power of data analytics and adds a visual display to capital- ize on the way our brains work. You’ve probably heard the saying “A picture is worth a thousand words”—interactive displays, charts with drill down capability, and geospatial data analysis do just that and are some of the many ways companies can present data to enhance decision- making. For example, maps can tell a much more compelling story than words or numbers, as shown in Figure  11.2, by effective use of visual cues. Organizational decision-makers rely on visual cues to grasp and process huge amounts of information.

Visualizing data can save a business money, help communicate important points, and hold customer attention. Data visualization is important because of the way the human brain processes information. Using pie charts, histograms, or bar graphs to visualize large amounts of complex data is much easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner—and you can experiment with dif- ferent scenarios by making slight adjustments.

Data visualization software can be extremely powerful and complex, similarly to Deloitte’s HIVE platform. At the other continuum are tools with simple, point-and-click interfaces that do not require any particular coding knowledge or significant training. Most non-data-scientist-friendly tools have interactive elements and can pull data from Google

Data visualization is the presentation of data in a graphical format to make it easier for decision-makers to grasp difficult concepts or identify new patterns in the data.

Drill down is searching for something on a computer moving from general information to more detailed information by focusing on something of interest, for example, quarterly sales—monthly sales—daily sales.

The data included brands, UPC barcodes, costs, districts, store numbers, out-of-stock scans, and out-of-stock reason codes. After Safeway and PepsiCo decided on the visualization technique that best represented their supply chain, they designed three processes to operationalize it. The three processes they chose to design were as follows:

1. How to feed the huge data sets into the visualization software 2. The best ways to display the data visually 3. How to gather feedback

Within 40 days after their session at HIVE, PepsiCo and Safeway were able to implement their initial data visualization with dash- boards and drill-down capabilities, then spent another 20 days refin- ing it. Employing these data visualization techniques led to greatly improved performance and reduced the frequency of stockouts at Safeway. In some areas, managers were able to increase accu- racy by 35%!

What PepsiCo and Safeway Learned from Data Visualization and Dashboards Safeway identified the stores experiencing the most stockouts and their root causes. For example, it learned a disproportionate number of stockouts were occurring at a store on Catalina Island. The store is in a resort area where the tourist traffic causes uneven demand. Safeway adjusted its supply chain strategy to address uneven demand patterns.

Safeway also discovered that they were sending multiple and con- flicting forecasts to their vendors from various departments. Safeway changed the way the company creates and communicates forecasts with its suppliers.

Two significant operational improvements at Safeway from discov- eries made through data visualization are as follows:

1. Improved forecast accuracy by 35% 2. Reduced on-hand warehouse inventory, which cut inventory

carrying costs significantly

PepsiCo also benefited because now it has incredible, near real- time access to the movement of every PepsiCo item, at every Safeway store, every day. Moreover, Pepsi recognizes that communicating data in an effective manner is important as Generation Z is increasingly becom- ing a large proportion of the customer base and workforce. The new players in the workforce need visuals that abbreviate information but still provide thorough analysis to make quicker decisions. Pepsi’s experi- ence at Deloitte have allowed it to develop a mobile app for cross-team collaboration and data publication, derive consistent information from customer surveys, and more accurately segment and attract different consumer markets.

Questions 1. What is a potential benefit of supply chain visibility? 2. What was the limitation of Excel-based data analytics at Safeway? 3. What makes Deloitte’s HIVE unique in its approach to data analysis? 4. What steps did Safeway and PepsiCo undertake to arrive at their

data visualization solution?

5. What were the two operational improvements at Safeway? 6. Name one way in which PepsiCo benefited from the partnership

with Safeway?

Sources: Compiled from Deloitte (2016), Pathak (2015), pepsico.com (2017), safeway.com/ShopStores/Our-Story.page (2017).

Data Visualization and Learning 335

Docs, Excel spreadsheets, Access databases, and other sources that most business people work with already. Some useful business applications for data visualization include the following:

• Identifying areas that need attention or improvement • Clarifying which factors influence customer behavior • Helping understand which products to place where • Predicting sales volumes

First, we’ll explore different technologies that fall into the data analytics category, as shown in Figure 11.3. Vendor packages usually offer tools in more than one category. In general, reporting tools generate BI that shows what has already happened in a business. Analytical tools show what might or could happen in the future. Later sections discuss information delivery and data integration.

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FIGURE 11.2 U.S. Census Bureau map shows easily identifiable changes in county population density. Different colors are used to indicate areas that gained and lost population. Intensity of color indicates extent of gain/loss.

Information Delivery Data IntegrationData Analytics

• Data visualization • Data discovery • Geospatial & GIS

• Dashboards • Interactive reports

• Data mashups • GIS

FIGURE 11.3 Tools and technologies in this chapter fall into three related categories.

336 C H A P T E R 1 1 Data Visualization and Geographic Information Systems

Learning, Exploration, and Discovery with Visualization Data visualization enables learning that is the basis for continuous improvement. When com- panies, political parties, sports teams, or fund-raising agencies invest in marketing programs, campaigns, promotions, special events, or other projects, they use visualization to learn some- thing from them. Visualization is also used as a data explorer and data discovery tool. Com- panies, such as Safeway and PepsiCo, are discovering new relationships and learning how to improve performance using data visualization in all types of industries and governmental agen- cies. Enterprise visualization apps for Androids, Apple iPads, and Surface tablets are replacing static business reports with real-time data, analytics, and interactive reporting tools.

Examples of Visuals Examples of visualizations include dials, charts, graphs, time- lines, geospatial maps, and heat maps. The tricolor heat map in Figure  11.4 instantly alerts the viewer to critical areas most in need of attention. Visual displays make it easier for individ- uals to understand data and identify patterns that offer answers to business questions such as “Which product lines have the highest and lowest profit margins in each region?” Interactivity and drill-down capabilities are standard features that make visualization even more valuable. Two other types of heat maps, created in Tableau Desktop, are shown in Figure  11.5; both heat maps are based on the same data set. Notice that the way in which the data are visually displayed depends on what you want to learn or convey.

Human expertise is an essential component of data visualization (see Figure  11.6). A common mistake organizations make is to invest in the analytics foundation—tools, quality data, data integration, touch screens—but overlook the most crucial component, which is the users’ ability to interpret the visual reports and analyze them correctly.

Data Discovery Market Separates from the BI Market According to Gartner Research, the data analytics market has split into two segments: the tradi- tional BI market and the newer data discovery market. Data discovery software had previously been viewed as a supplement to traditional BI platforms. Now it is a stand-alone alternative to BI. This split occurred because today’s data discovery technologies provide greater data explo- ration and ease of use to help users find answers to “why” and “what if” questions through self- service analytic apps. The split is another example of pushing analytics onto the computers of business workers. IT at Work 11.1 describes the trend at IBM.

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FIGURE 11.4 This heat map uses three colors to convey information at a glance. The heat map is like a spreadsheet whose cells are formatted with colors instead of numbers.

Data Visualization and Learning 337

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Office Supplies Appliances

Office Supplies Paper

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Office Supplies Binders and Binder Accessories

FIGURE 11.5 These heat maps represent the same data set using different colors (usually red and green) and color intensity to show the profitability of three product categories and their subcategories. In (a), data labels show detailed profit, while in (b), the area of each segment is used to make comparisons.

338 C H A P T E R 1 1 Data Visualization and Geographic Information Systems

Analytics/Visualization Vendors Respond to Demand Smaller data visu- alization vendors are competing head-on with BI megavendors IBM, Oracle, and SAS. For example, vendors DOMO, QlikView, Birst, Tableau, Sisense, and others are adding enterprise features with each new release. SAS is one of the leaders in the data visualization space. SAS® Visual Analytics uses intelligent autocharting to help business analysts and nontechnical users create the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis. The SAS® LASR™ Analytic Server feature executes and accelerates analytic computations through in-memory processing. The combination of high-performance analytics and an easy-to-use data explora- tion interface enables different types of users to create and interact with graphs and charts to better understand and derive more value from their data faster than ever.

Data Visualization TechniquesHuman

Expertise

High-Quality Data

Patterns, trends, and relationships Context to understand what numbers represent

& how to interpret them Action to be taken

FIGURE 11.6 Data visualization, human expertise, and high-quality data are needed to obtain actionable information.

IT at Work 11.1

IBM Tackles Big Data Discovery As one of the world’s leading technologies corporations, IBM con- sistently takes advantage of opportunities to increase its market share in the computing and data analytics realm. In addition to hosting data storage platforms, IBM produces ways for customers to analyze data more effectively. Its most recent development is a service package of application programming interfaces (APIs) called the Watson Discovery Service. Watson is intended to decrease the amount of time analysts have to spend organizing and cleaning data and allow them to focus on making data-driven decisions.

The most prevalent issue in data analytics is the struggle to standardize and organize data in a way that makes information usable. Steve Lohr of the New York Times claims that analysts spend 80% of their time cleaning and organizing data for use (Lohr, 2014). The Watson Discovery Service solves this problem by standardizing

and categorizing data and making it available for query by the user. The most impressive aspect of the new service is its ability to accu- rately analyze text sources on a “massive scale” (Forrest,  2016). This allows employees of any level to gather the most important information from numerous sources without having to manually research each source individually.

IT at Work Questions 1. How is IBM’s approach to big data unique? 2. Why is a data organization service so vital to data analysis? 3. What makes the Watson Discovery Service attractive to

companies? 4. Do you think the service will make data analysis more

accurate?

Sources: Lohr (2014) and Forrest (2016).

Data Visualization and Learning 339

Others, such as Qlik, are integrating inference engines to replace the query-based approach, which divorces data from its context. Using an inference engine, users can input as much information as they have, and the software not only will search for the information provided but also will make associations with all other data that is related to the information provided.

These vendors continue to focus on business users of all levels and backgrounds. For example, Jeff Strauss, BI architect at Allstate Insurance Company, explained that Allstate invested in Tableau data discovery tools, so users throughout the organization could do their own analysis rather than rely on the IT department. Tableau has built a large following with its easy-to-access dashboards.

Data Discovery Offers Speed and Flexibility Data discovery is expected to take on a greater role in corporate decision making. Companies are investing in the latest data dis- covery solutions largely because of their speed and flexibility. Experts and novices can collect data quickly from disparate sources and then explore the data set with easy-to-use interactive visualizations and search interfaces (Figure 11.7). Drill-down paths are not predefined, which gives users more flexibility in how they view detailed data.

FIGURE 11.7 Data discovery tools allow users to interact with multiple corporate data sources.

A powerful feature of data discovery systems is their ability to integrate data from multiple data stores and identify data types and roles. See Tech Note 11.1. While data are being loaded into the program, the software automatically extracts and organizes them by data type. Soft- ware may also extract and organize terms from unstructured content, such as texts, e-mail, and PDFs, and create tag clouds. Figure 11.8 shows an example of a Word cloud that give users a quick way to evaluate the most aspects of SCM and start to make discoveries.

Big Data Visualization Challenges The speed, size, and diversity of big data brings new challenges to visualization. One challenge is how to display the results of data discovery in a meaningful way that is not overwhelming. For example, you may need to collapse and con- dense the results to display graphs and charts in a way that decision makers are accustomed to viewing. Results may also need to be available quickly on mobile devices, and users may want to be able to easily explore the data on their own in real time.

340 C H A P T E R 1 1 Data Visualization and Geographic Information Systems

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Tech Note 11.1

Understanding Data Types and Roles Data types and roles are fundamental components that affect how visualizations behave. Each field in any data source has an associ- ated data type. For example, a field that contains customer names has a string (text) data type, and a field that contains price informa- tion usually has a numeric data type. To visualize data QlikView, Tableau—or in any analytics or BI tool, for that matter—you need to understand dimensions and measures.

• Dimensions Dimensions contain discrete or categorical data, such as a region (e.g., Northeast, Southwest), product category, product subcategory, product name, supplier, size,

date, and zip code. Dimensions often become labels in the data visualization.

• Measures A measure is a calculation based on numeric data, such as profit, margin, quantity sold, speed, and miles. The calculation always returns one single value that summarizes all relevant records. The calculation is called an aggregation. As in spreadsheets, there are several aggregation functions: Sum(), Count(), Average(), Min(), Max(), and so on. Key performance indicators (KPIs) of interest might include monthly revenue, number of orders, quantity on hand, and total cost. A measure is always based on an aggregation.

Another issue associated with big data is the speed within which traditional architec- tures and software can process the data. If the data are not processed in a timely manner, the data may not be accurate or useful, for example, stock market data. For example, IBM SPSS integrates three visualization tools to handle big data—Netezza, InfoSphere BigInsights, and InfoSphere Streams—to provide comprehensive analytics capabilities in the big data platform. Netezza is a high-performance data warehouse whose data can be used for model building, scoring, and model refresh; InfoSphere BigInsights is an enterprise-ready distribution of Hadoop.

How Is Data Visualization Used in Business? The ultimate goal of data analytics is to drive profits, and often that depends on learning how to manage assets, such as inventory, or engage customers in a smarter way. Collecting data is relatively easy. Making sense of that data is not. Here are examples of how companies and/or entire industries are using data visualization and interactivity to improve decision speed and performance often with mobile displays.

The latest data visualization software addresses issues associated with processing big data by speeding up data discovery and returns the visualization within an appropriate time- frame, in an easy-to-understand format. BI and data visualization vendors are working to assist business analysts and nontechnical users in determining how best to display these massive amounts of data.

Data Visualization and Learning 341

Quick Detection and Decisions in Stock Markets Wall Street firms, traders, wealth managers, risk analysts, and regulators rely on their ability to process and capitalize on market anomalies in real time. Because of the demanding pace of their decisions, capital market professionals use visualization for risk analysis, pretrade and posttrade checks, compli- ance monitoring, fraud detection, client profitability analysis, research and sales, and portfolio performance. Vendor Aqumin provides real-time visual interpretation solutions for the finan- cial services industry. Aqumin’s OptionVision enables traders, risk managers, and market par- ticipants

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