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Sas visual-analytics-startup-guide

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SAS Visual Analytics is a high-performance, in-memory solution for
exploring massive amounts of data very quickly. It enables you to spot
patterns, identify opportunities for further analysis and convey visual
results via Web reports or a mobile platform such as iPad® or Androidbased
tablets.This presentation is a very brief overview of the many features and
capabilities of SAS Visual Analytics. It is meant to get you started
quickly, with a relatively modest data set example (only 1.4 million
rows).Insight Toy Company is an organization that produces and sells toys to
resellers (“vendors”). The data is made up of 34 years of Sales information,
covering 128 cities across the world.
 For each row of data (transaction) we have:
 Information on the items sold (product brand, line, make, style, SKU)
 The sale value (“order total”) and various related costs (distribution, marketing, product)
 Information on the sales representative (rating, sales target, actual to date, etc.)
 Geographic information (on the vendors as well as the selling facility)
 Information on the vendors (rating, satisfaction, distance to nearest facility)
Text Notes taken at the moment of the order taking, based on conversation with the vendor.

Published in: Data & Analytics
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Sas visual-analytics-startup-guide

  1. 1. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS AN OVERVIEW OF POWERFUL DISCOVERY, ANALYSIS AND REPORTING
  2. 2. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. WELCOME TO SAS® VISUAL ANALYTICS SAS Visual Analytics is a high-performance, in-memory solution for exploring massive amounts of data very quickly. It enables you to spot patterns, identify opportunities for further analysis and convey visual results via Web reports or a mobile platform such as iPad® or Android- based tablets. This presentation is a very brief overview of the many features and capabilities of SAS Visual Analytics. It is meant to get you started quickly, with a relatively modest data set example (only 1.4 million rows). For more in-depth information, you can consult the online User Guide, available via the Help menu, or go to the website at http://www.sas.com/technologies/bi/visual-analytics.html.
  3. 3. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CONTENT • Overview of the Data • Getting Started • Bar Charts • Hierarchies • Filters • Calculations • Geographies • Cross Tabs • Forecasting and Simulation • Decision Trees • Treemaps • Heatmaps • Correlations & Regressions • Network Diagrams • Box Plots & Statistics • Bubble Plots With Time Animation • Text Analytics • Sankey Diagrams • Report Design & Distribution • Data Dictionary
  4. 4. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. OVERVIEW OF THE DATA  Insight Toy Company is an organization that produces and sells toys to resellers (“vendors”). The data is made up of 34 years of Sales information, covering 128 cities across the world.  For each row of data (transaction) we have:  Information on the items sold (product brand, line, make, style, SKU)  The sale value (“order total”) and various related costs (distribution, marketing, product)  Information on the sales representative (rating, sales target, actual to date, etc.)  Geographic information (on the vendors as well as the selling facility)  Information on the vendors (rating, satisfaction, distance to nearest facility)  Text Notes taken at the moment of the order taking, based on conversation with the vendor.  The data source is made up of 1.4 million rows and 60+ columns NOTE: a complete data dictionary is available at the end of this document
  5. 5. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. GETTING STARTED IN SAS® VISUAL ANALYTICS – THE ESSENTIALS For more information, please consult the SAS Visual Analytics User Guide, available under the Help menu. The toolbar enables you to select your visual explorations and visualizations. The data pane lists the available categories and measures in your selected data. The right pane contains tabs that enable you to change the properties of your visualizations, filter the data and create and view comments. The workspace displays your visualizations To start a new visualization on a blank workspace, simply minimize the current visualization.
  6. 6. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BAR CHART – THE BASICS STEP 1: starting with a blank workspace, select Bar Chart from the toolbar. STEP 2: Drag and drop Facility Continents, Gross Margin and Order Total on the workspace. Note: you can select multiple items by holding the CTRL key. In order to explore this in more detail, we will need to create a hierarchy that will allow us to dig deeper and focus our analysis.
  7. 7. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BAR CHART – ADD A HIERARCHY STEP 1: From the Data tab, select New Hierarchy. STEP 2: Name it Facility Hierarchy. Select Facility Continents, Facility Country/Region, Facility City and Facility (in that order). Click OK. Let’s add more “investigative” functionality to this bar chart by creating a drill-down path.
  8. 8. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. USING A HIERARCHY Drag the new Facility Hierarchy from the Data Pane and drop it on Facility Continents. The new hierarchy has replaced the single-level Facility Continent element . Notice that the appearance of the chart does not change, but a “breadcrumb trail” appears in the upper left of the chart. Double-click on North America (‘NA’) to explore further. You can also click on the label “NA” at the bottom of the chart.
  9. 9. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. EXPLORING A HIERARCHY From this graph, it is clear that the US has a much higher Order Total and Gross Margin. Notice the breadcrumb trail highlighted in the upper left. You can continue to drill-down and gain even more insight. Double-click on US to get to city level details.
  10. 10. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CREATING A HIERARCHY - RECAP The Hierarchy was created on the fly, without the need to ask our IT department to create a special data structure, and is IMMEDIATELY applicable to all your data, usable for explorations and reports. Further, you can easily edit the hierarchy to drill down, from Facilities, to Products, Units, Sales Representatives, etc. Return to Table of Content
  11. 11. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FILTERS Filters are an easy way to subset the data, allowing you to focus on a specific area. There are two types of filters: Visualization filters affect only the current visualization, while Data Source filters affect all visualizations using this data source. Let’s create a simple visualization to practice Filters: STEP 1: starting with a blank workspace, select Bar Chart from the toolbar. STEP 2: Drag and drop Product Hierarchy and Order Total on the workspace.
  12. 12. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FILTERS - BASICS Let’s focus on 2012 and 2013 only. STEP 1: Drag and drop the Transaction Year field to the filter tab. Put it in the Visualization section. STEP 2: simply drag the left arrow to subset the data to 2012 – 2013 only. Notice how SAS Visual Analytics automatically lets us know the general distribution of the data?
  13. 13. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FILTERS – SELECT AND INCLUDE Let’s look at all various Product Make in our Game Product line, except Puzzle 3d and Card Games. STEP 1: Drill into Toy and then Game. You can select specific items in your visualization (holding the CTRL key), or you can use the mouse to lasso around multiple items. Let’s CTRL-select Puzzle 3d and Card Game. STEP 2: Now right-click and select Exclude Selection. A new filter is added
  14. 14. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FILTERS - ADVANCED Filters behave differently depending if they are for a numerical measure or a category. Every time you apply filters, SAS Visual Analytics tells you how much of the data you are looking at. Options allow you to edit a filter, to refine it even more with various conditions and operators, and to make it as sophisticated as you need. You can also copy filters to other visualizations. Return to Table of Content
  15. 15. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CALCULATIONS SAS Visual Analytics allows you to create on-the-fly calculations of all your data. There are two types of calculations: 1. Calculated Items are applied to every row of data. The results will be aggregated like any other data. 2. Aggregated Measures will be calculated after all data has been aggregated for any visualization. This is usually best for ratios.
  16. 16. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CALCULATIONS - CREATING This is the name of the Calculated Item or Aggregated Measure. It will appear as a data item in the data pane, available for visualizations and reports. These are the various data elements that you can use in a calculation. These are the various functions and operators that can be used to create your formula. You can specify how the results of your calculation will be formatted. To create (or edit) a calculation, simply: STEP 1: build the structure of your calculation: drag and drop the operator(s) you want on the main window. STEP 2: drag and drop the data element inside the calculation structure. You can switch between “visual” (drag and drop) and “text” mode to edit the calculation.
  17. 17. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CALCULATIONS – EDITING A few calculations have already been created for you in the Insight Toy First Exploration. Among them, Gross Margin, which is a row-level Calculated Item, and Gross Margin Ratio, which is a post-aggregation Measure. To view how these calculations were created or modify them, simply select one, right-click and select “Edit…”. Note that it is possible to use the result of a Calculated Item in another calculation, but it is not possible to use the result of a an Aggregated Measure calculation in a Calculated Item or another Aggregated Measure.
  18. 18. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. QUICK CALCULATIONS – DERIVED ITEMS A quick way to create calculations is to right-click on the measure and create a derived item. Derived data items are aggregated measures that perform calculations for your data.  You can create Distinct Counts on Category Items (for example, to display the number of cities where each product line is sold).  You can create a Percent of Total, or multiple types of time-derived calculations such as Year-To- Date, Difference From Previous Period, etc.  As with any calculations, you can view and edit these calculations after they are created. Return to Table of Content
  19. 19. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. GEOGRAPHIES In SAS Visual Analytics, any category field can be designated as a Geography. You might assign a geography to a pre- defined geographic role (such as country name), or create a customized geography data item to identify geographic information that is specific to your organization (for example: sales regions, warehouse locations, oil platforms, and so on). For the purpose of Insight Toy Company, we have already designated the following fields as Geographic locations. Notice they now have a different icons to identify them as geographies. Further, we have already created a few hierarchies to explore data from continent to country, region and city.
  20. 20. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. ANALYZING GEOGRAPHIES If you have any visualization opened, minimize it and start from a blank workspace. Select Geo Hierarchy and hold the CTRL key to select Order Total. Simply drag and drop them on the workspace. Because the category selected has been pre-defined as a geography item, SAS Visual Analytics will automatically offer us a Map visual.
  21. 21. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. ANALYZING GEOGRAPHIES Since you used a hierarchy, you can drill down on it: double-click on the NA bubble and then on the US bubble. You can also add another measure. In this case here, by dragging and dropping Gross Margin Ratio, you are able to see revenue of each region (the size of the bubbles) as well as their relative Gross Margin contribution (the color of the bubbles). Insight: Notice that most revenue (bubble size) seems to be coming more from the East than West side of the country. Gross margin (bubble color) seems to be concentrated in the mid South and North East of the country.
  22. 22. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. A DIFFERENT WAY TO LOOK AT GEOGRAPHIES If your geographic category has been determined based on pre-defined names, instead of latitudes and longitudes, you can choose to display the result using Regions instead of Bubbles. You can also select Coordinates. Coordinates are very useful to represent MANY points. Return to Table of Content
  23. 23. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CROSSTAB – GETTING STARTED This creates a blank Crosstab palette. Notice the Roles tab on the right. Each element can have multiple categories or measures. A crosstab shows the intersections of category values and measure values. STEP 1: starting with a blank workspace, select Crosstab from the toolbar.
  24. 24. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CROSSTAB – GETTING STARTED From the Data tab, select Facility Country/Region and drop it on the Columns box. Select Product Line and drop it on the Rows box. Select Order Sales Cost and drop it on the Measures box. Now you have a Crosstab that shows aggregated Order Sales Cost for each Product Line by Facility Country/Region.
  25. 25. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CROSSTAB – ADDING DEPTH Lets add more depth to the Crosstab. Select Product Make and drop it on the Rows box. Select Order Total and Gross Margin Ratio (which is an aggregated measure) and drop them on the Measures box. Now you have multiple rows and columns of aggregated product financial data, in seconds. It is easy to add or replace elements in this manner.
  26. 26. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CROSSTAB – DRILL DOWN WITH HIERARCHIES Lets create a new crosstab. Simply minimize the one you were working with, and start a new one. This time, select Geo Hierarchy as your Columns, and Product Hierarchy as your Rows. Select Order Total as your Measure. Notice how, because we are now using Hierarchies, we can drill down or expand on any row or column? You can read more about hierarchies here.
  27. 27. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CROSSTAB – CREATING TOTALS Select the Properties tab on the right pane. You can turn on Column subtotals and totals You can turn on Row subtotals and totals. You place these before or after the corresponding data item. You can also rename the Visualization to something more meaningful than the default “Visualization 1”. Return to Table of Content
  28. 28. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – GETTING STARTED STEP 1: starting with a blank workspace, select Line Chart from the toolbar. STEP 2: From the Data tab, select Transaction Month and Year and Sales Rep Actual and drag them over the workspace. IMPORTANT: your line chart needs to be based on a valid TIME SERIES, otherwise you will not be able to use it for a forecast. A valid time series can be identified by these icons:
  29. 29. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – GETTING STARTED STEP 3: Now that we have a time-series-based line chart, all we have to do is “turn on” forecasting to display a forecast. The sophisticated forecasting algorithm in SAS Visual Analytics will run multiple forecasting models and pick the ‘winner’ – the forecast model that best fits the particular pattern of data. You can get more information on this forecast model by clicking on the  icon.
  30. 30. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – INCREASING FORECAST PERIODS You can increase the periods forecasted by changing the duration in the Properties tab. Change it to 18 months and click on the checkbox.
  31. 31. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – REFINING AND ADDING SIMULATION We can improve the accuracy of our forecast by adding measures that we believe should have an influence on the revenue. Select Order Marketing Cost, Sales Rep Orders, Sales Rep Rating, Sales Rep Target, and Vendor Distance and drag them over in the “Underlying Factors” box.
  32. 32. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – REFINING AND ADDING SCENARIO ANALYSIS & GOAL SEEKING By adding underlying factors to our forecast, many things have happened: The solution has evaluated and determined if the additional variable were having an influence on the forecast results. Notice how “Vendor Distance” is greyed out and does not influence the forecast. The forecast itself has been recalculated and is now much more precise, as evidenced by the much tighter confidence interval.       A new feature, “Scenario Analysis” has been added, allowing you to perform simulations and goal seeking.
  33. 33. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – SCENARIO ANALYSIS If you click on the “Scenario Analysis” button in the Roles tab, you can now change the future values of the Underlying Factors and see the impact on the Forecast. Use the mouse to change a value, or right-click and use the dialog box to change multiple values. Remember to click on Apply to save your changes.
  34. 34. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – GOAL SEEKING With Goal Seeking, you change the value of the Target, and Visual Analytics will optimize the values of the underlying factors to arrive at your desired target. You can decide to work with all the underlying factors, or just some of them, and also you can set boundaries, so your underlying factors will make sense from a business point of view (for example to avoid ending up with negative headcounts). STEP 2: Right-click on your target variable, and set the desired goal. Remember to click on OK then Apply. STEP 1: Select which underlying factors will be affected in your goal seeking exercise, and set boundaries for them.
  35. 35. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – SCENARIO ANALYSIS & GOAL SEEKING RESULTS If you click the Table View button, you will see the values that have changed (in bold) as a result of your scenario simulation or goal seeking exercise.
  36. 36. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. FORECAST – SCENARIO ANALYSIS & GOAL SEEKING RESULTS If you hover over the upper right corner of your visualization, you will see you can “Show Details”. If you select the Results tab, you can see the underlying data supporting this forecast. You can right-click and select “Export Data” to obtain a csv file of the current data supporting this forecast. If you select the Analysis tab, more explanation will be given on forecasting, as well as the algorithm that was used. Return to Table of Content
  37. 37. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. DECISION TREES – GETTING STARTED STEP 1: starting with a blank workspace, select Decision Tree from the toolbar. Decision Trees, also known as classification or regression trees, can be used as a prediction process, to explain the behavior of, or as a grouping/segmentation method for your data. STEP 2: For this exercise, we want to better understand what drives our Vendor Satisfaction. Select it from the Data tab and drag it on the Workspace.
  38. 38. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. DECISION TREES – GETTING STARTED Immediately, the solution returns a histogram representing the distribution of Vendor Satisfaction. Notice that the average vendor satisfaction for Insight Toy is only 57%. If we had started our analysis with a Category instead of a numerical measure, we’d have a bar chart to represent our population. STEP 3: Select a few variables that you believe should have an impact on Vendor Satisfaction. Select the Vendor Loyalty Program category, and the measures Vendor Distance, Market Penetration, and Sales Rep Rating and drag them on the workspace. Note: you can select multiple items by holding the CTRL key.
  39. 39. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. DECISION TREES – NAVIGATING AND EXPLORING Hovering in the upper right corner of the visualization, you can select Show Overview to open a special Overview window that will allow you to zoom on sections of the Tree. Our Tree here indicates that Sales Rep Rating seems to be the best explanatory variable to explain Vendor Satisfaction. Not only that, but it tells us the best breaking point, when our sales representatives are rated 60% or better.
  40. 40. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. DECISION TREES – FURTHER EXPLORATIONS Return to Table of Content You can select any segment of the tree and right- click to Create Visualization From Node. A new visualization will be open with a filter that subsets your data accordingly. There is an add-on to SAS Visual Analytics that allows you to generate numerous segmented models in parallel and on–the-fly. Visit www.sas.com/visualstatistics to try it now!
  41. 41. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TREEMAPS – GETTING STARTED STEP 1: starting with a blank workspace, select Treemap from the toolbar. STEP 2: Drag and drop Product Hierarchy on the Treemap workspace.
  42. 42. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TREEMAPS – GETTING STARTED This gives you a Treemap of the highest level of the hierarchy (Product Brands) and defaults to the frequency (i.e. how many rows of data) for each product brand. In order to get a more insightful chart, you need to add measures from the Data pane. For this exercise, drag and drop Order Total (for Size) and Gross Margin Ratio (for Color).
  43. 43. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. EXPLORING TREEMAPS Since you started with a Hierarchy, you can simply drill- down on any rectangles by double-clicking on it. Notice as well the breadcrumb at the top that indicates where you currently are exploring in the hierarchy tree. Now you can quickly see which product groups have the best relative gross margin (in blue) compared to other groups that have the same level of revenues (size of the rectangle).
  44. 44. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TREEMAPS – MORE EXPLORATION Using the mouse as a lasso to highlight multiple boxes, or by holding the CTRL key, you can select multiple boxes, and, using a right-click menu, include (or exclude) them from your selection, in effect creating a filter. Return to Table of Content
  45. 45. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – GETTING STARTED STEP 1: starting with a blank workspace, select Heat Map from the toolbar. STEP 2: Select Facility Country/Region, Product Line and Market Penetration from the Data tab with CTRL-click. Drag and drop them onto the Heat Map workspace.
  46. 46. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – GETTING STARTED You now have a Heat Map of Market Penetration by Facility Country/Region and Product Line. It seems that market penetration varies widely by product line (y-axis), but is fairly consistent by country (x-axis). Lets change the presentation somewhat to get a better sense of this. If things are not exactly displayed as you would like them, you can always re-organize them in the Roles tab.
  47. 47. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – ANOTHER PERSPECTIVE Lets get a different view of this relationship. On the Roles tab, drag Product Line from the Y axis element and drop it on the X axis element. In effect, swapping Product Line and Facility Country/Region. Notice how the map has drastically changed? Now we see the same information, but from a different, potentially more meaningful perspective. We can see much consistency in terms of market penetration for each country.
  48. 48. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – IDENTIFYING PROBLEM AREAS Let’s dig a bit deeper. Select Product Make from the Data pane and drag and drop it over Product Line. Now we have the same data, but at the Product Make level.
  49. 49. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – IDENTIFYING PROBLEM AREAS Working with the Y axis scroll bar, you can ‘zoom out’, and identify each Product Make individually.
  50. 50. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. HEAT MAPS – DRILLING DEEPER You can either click and hold the CTRL key or “Lasso” these areas with the mouse by holding the button and selecting the areas to be explored. Then, press the right mouse button to get the menu and select Include Only Selection. Notice the new filter in the Filters tab? We are now using only a fraction of the original data. We can single out areas with the lowest Market Penetration. Return to Table of Content
  51. 51. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CORRELATIONS – GETTING STARTED STEP 2: Click the first measure (Gross Margin), hold the SHIFT key and select the last measure. This should select all measure as shown in the screenshot. Drag them on the Correlation workspace. STEP 1: starting with a blank workspace, select Correlation Matrix from the toolbar.
  52. 52. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CORRELATION MATRIX Notice the intersection Vendor Satisfaction and Sales Rep Rating. It has a strong relationship of 0.973 (“1” being the strongest possible correlation). This implies they share a strong relationship. Double Click this intersection to explore it further.
  53. 53. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CORRELATION & REGRESSION There is a strong relationship between Vendor Satisfaction and Sales Rep Rating. The color is represented as transaction frequency. Next, drag Gross Margin from the data pane to the Visual pane. This will overlay Gross Margin as the color instead of frequency.
  54. 54. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CORRELATION WITH GROSS MARGIN As Vendor Satisfaction is increasing, so is Sales Rep Rating. This makes sense and is good information. Notice however that some unsatisfied vendors, serviced by some of our lesser rated sales representatives (bottom left), are also making us money (Gross Margins in blue). Interesting as well. We’ll need to investigate.
  55. 55. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CORRELATIONS - INVESTIGATING Using the mouse as a lasso, highlight the blue bins in the bottom left, as shown in the screenshot. Right click on the highlighted transactions and select Include Only Selection. This will create an instant filter on our data.
  56. 56. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SATISFIED AND PROFITABLE CUSTOMERS… BUY WHICH PRODUCT? It looks like the highest gross margins come from sales reps with a rating right around 40%. Let’s find out which products are associated with these gross margins to see if those have an effect as well. Drag Product Line into the pane and replace Sales Rep Rating with Product Line.
  57. 57. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. GROSS MARGIN RATIO – STAR PRODUCTS Click the Bar Chart icon in the toolbar. The visualization will change to a Bar Chart. From this map, it is very clear that our highest gross margins come from the Figure and Game product lines. Drag Order Total over Vendor Satisfaction. The Figure and Game product lines not only have the highest gross margins, but the highest sales as well. Good to know. Return to Table of Content
  58. 58. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. NETWORK DIAGRAMS – GETTING STARTED Network Diagrams are about showing relationships and their structure. Flow can be shown with a direction (arrow). Most business operations have some form of network: Supply chains, Import/export, Debt and loans, Twitter influencer analysis, transportation routes, etc. In fact, any hierarchy can be represented as a network. SAS Visual Analytics supports two types of Networks: hierarchy-based, and ungrouped. Ungrouped networks require two data items: a Source and a Target. The target has to be a subset of the Source. Examples of this type of Network are:  Employees and their managers (because all managers are also employees)  Intercompany transactions (subsidiary to subsidiary)  Transportation origin and destination cities  Sports team playing against each other In this Insight Toy example, we will be working with a hierarchical network.
  59. 59. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. NETWORK DIAGRAMS – GETTING STARTED STEP 1: starting with a blank workspace, select Network Diagram from the toolbar. STEP 2: From the Roles tab, select the Hierarchical Network Type. STEP 3: From the Data Pane, select and drag Product Hierarchy onto the workspace.
  60. 60. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. NETWORK DIAGRAMS – REFINING THE VISUAL STEP 4: Select: Order Total the Node size; Order Count for the node color; Gross margin for the Link width; and Gross Margin link color for the Link color. From here you can drill down and explore on any level. You can also modify many things on the Properties tab. STEP 5: On the Properties tab, select “Show labels” and vary the Node Spacing to your liking.
  61. 61. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. NETWORK DIAGRAMS – THE ART OF THE POSSIBLE Network Diagrams are highly dependent on the type of data available. Here are some additional examples of network diagrams based on other sources of data: Airline source and target destinations Twitter feed Analysis Transportation Route Manufacturing Supply Chain Return to Table of Content
  62. 62. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BOX PLOTS Box Plots are a very powerful way to derive multiple statistics about your data. A box plot represents the distribution of data values by using rectangular box and lines called whiskers. Outliers Maximum Value Q3 (75th percentile) Median Mean Q1 (25th percentile) Minimum Value Basically, this means that half of your data ends up in this range for the particular measure you are looking at.
  63. 63. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BOX PLOTS – GETTING STARTED Step 2: Drag and drop one measure and one category over the workspace. In this example, we have selected to look at the distribution of the Vendor Distance (measure), per Facility City (geography). Notice how we can immediately see the average distance that vendors are from our stores. The majority seem fairly close to our stores, but there are definitely a few far away. Step 1: Minimize any open visualizations, to start with a blank workspace. Select Box Plot from the toolbar.
  64. 64. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BOX PLOTS - REFINEMENT Here is another example: You can immediately see the Order Marketing Cost by Product Line. You can see that the “Plush” and “Thrift” products have the lowest marketing costs, and that the “Promo” products have the widest distribution of marketing costs. By further refining this visualization with filters, you will be able to quickly focus on any region, facility or sale representative in order to investigate at a more detailed level. Return to Table of Content
  65. 65. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – GETTING STARTED STEP 1: starting with a blank workspace, select Bubble Plot from the toolbar. Please note that the Bubble Plot visualization is one of the more complex ones, as evidenced by the various field roles available.
  66. 66. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – GETTING STARTED STEP 2: Assign a GROUP. The Group is important because it dictates how many elements can be displayed on the screen at one time. Here, we will drag and drop Product Hierarchy as our group.
  67. 67. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – GETTING STARTED STEP 3: Now you can assign the rest of your data elements. For this visualization, you will assign: Sales Rep Rating for the X Axis Vendor Satisfaction for the Y Axis Order Total for the Bubble Size.
  68. 68. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – EXPLORING Since you chose a Hierarchy as your Group, you will be able to drill-down to further explore your visualization by double-clicking on the bubbles. Notice the bread crumb trail that expands indicating your drill-down path. Also note that clearly, the better a sales representative is rated, the more his/her vendors seem satisfied. However, this seems independent of the actual sales revenue (the size of the bubble).
  69. 69. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – REFINING Using your mouse as a lasso, you can select a few bubbles, Right- Click and select Include Only Selection, to create a filter on-the- fly, and focus on just those items. See the Filters section for more info on filters.
  70. 70. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. BUBBLE PLOTS – TIME ANIMATION Now, wouldn’t it be interesting to see how that visualization evolves over time? You can simply select Transaction Year and drag and drop it in the Animation role. Now you will be able to play the bubble plot, and see how things evolve over time. Return to Table of Content
  71. 71. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – SIMPLE AND COMPLEX WORD CLOUDS SAS Visual Analytics can do two types of word clouds:  Simple word clouds using a category value; or  More sophisticated word clouds that leverage SAS Text Analytics. Word clouds that use text analytics analyze each value in a data item as a text document that can contain multiple words. Words that often appear together in the document collection are identified as topics. SAS Text Analytics also allows you to perform Sentiment Analysis on the text.
  72. 72. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – GETTING STARTED For this exercise, we will change exploration and with it the underlying data. This new data actually contains free form text that we will explore using SAS Text Analytics. STEP 1: From the Explorer interface, select File > Open. STEP 2: If you are asked to save your exploration, select “Don’t’ Save”
  73. 73. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – GETTING STARTED / ACCESSING NEW DATA STEP 3: In the OPEN dialog window, navigate to SAS Folders > Shared data > DemoData > Insight Toy Trial > Exploration and Reports And then select Insight Toy Text Exploration and click OPEN. This will open a pre-defined exploration, and will also point you to another data set. This new data contains specific customer comments for the Bead product line, from January to October of 2013.
  74. 74. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – SIMPLE WORD CLOUDS STEP 1: starting with a blank workspace, select Word Cloud from the toolbar. STEP 2: From the Roles tab, select Using Category Values. STEP 3: Drag and Drop a category item. Here we selected Product Style.
  75. 75. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – SIMPLE WORD CLOUDS The result shows the relative size of each Product Style, representing the frequency of our transactions. For example, we see we sell much more Orange Mixed than we do Gold 8mm. STEP 4: Select and Drag and Drop Gross Margin and Order Total on the workspace. The Visualization is now very different: the size of the Font representing Gross Margin contribution of each Product make, while the color represents the Revenues generated.
  76. 76. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – LEVERAGING ADVANCED SAS® TEXT ANALYTICS STEP 1: starting with a blank workspace, select Word Cloud from the toolbar. STEP 2: From the Roles tab, select Using Text Analytics. To leverage text analytics, we have pre-defined two settings in the data: 1. A data item (Order) has been identified as the “unique row identifier”; and 2. A category (Order Note) has been identified as a “document collection”. This is the data item that will be analyzed. STEP 3: Back in the Roles tab, click on the little down arrow for Document Collection, and pick the only choice available: Order Note. Visual Analytics will analyze the content of that data, identify topics (words that often appear together), and represent the most relevant terms for each topic by varying the size of the terms.
  77. 77. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – LEVERAGING ADVANCED SAS® TEXT ANALYTICS Terms that we identify as “red flags” (eg. “unhappy”) can be focused on by simply clicking on them, which will display at the bottom all the Documents that have these terms. Many sets of topics have been identified in this data. You can explore the analysis by selecting various topics.
  78. 78. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. TEXT ANALYTICS – LEVERAGING ADVANCED SAS® TEXT ANALYTICS Return to Table of Content In the Properties tab, you can select “Analyze document sentiment”. SAS Visual Analytics will evaluate the sentiment of the content, for each topic as well as each individual entry.
  79. 79. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – PATH ANALYSIS Sankey diagrams are a very powerful visualization technique to analyze various data paths. They can be leveraged to analyze web visits (our example in the next pages), or to explore customer journey or resource consumption (for example, “where did my budget go?”, or “which vehicles consume the most gas?”). In order to support this kind of analysis, a data source must be structured in a very particular way: each row must contain an event descriptor, a sequence order (usually a timestamp or date, but can be any number) and a transaction identifier.
  80. 80. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – GETTING STARTED For this exercise, we will change exploration and with it the underlying data. Data for a Sankey diagram has to be structured in a very specific format. STEP 2: If you are asked to save your exploration, select “Don’t’ Save” STEP 1: From the Explorer interface, select File > Open.
  81. 81. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – GETTING STARTED / ACCESSING NEW DATA STEP 3: In the OPEN dialog window, navigate to SAS Folders > Shared data > DemoData > Insight Toy Trial > Exploration and Reports And then select Web Site Analysis and click OPEN. This will open a pre-defined exploration, and will also point you to another data set. This new data contains a very simple example of web site visitors.
  82. 82. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – WEB PATH ANALYSIS In this visualization, we see on the left that we have 4 entry points to our website. And the most popular one obviously is the “Welcome” page, followed by our “Search” page. Notice that one third, 5 out of 15 visitors, immediately drop off and do not go further on our website. This might mean we did a very good job with our Welcome page, and people found the info they were looking for right then and there, or it might mean we need to create more incentive for them to stick around…
  83. 83. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – WEB PATH ANALYSIS Out of the 10 web visitors that do stick around, half of them immediately go to our “Deals” page. Which is great! It has a good ‘draw’. But then, 4 out of 5 drop off… and 1 out of 5 buys something. By navigating the diagram, we can find the various paths that our visitors follow to finally buy something from our website. This will in turn help us fine tune and enhance our web experience, to eventually drive more sales.
  84. 84. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SANKEY DIAGRAMS – WEB PATH ANALYSIS – GOING FURTHER… From this point, you can select any paths, or any node, right-click, and create a new visualization that contains only the data specific to the characteristics you are looking for. In our case here, if we had more information in this data set, we could find the list of users associated with a selected path or event, and then use SAS Visual Analytics to find what they have in common – say, most are males browsing the web during work hours. Return to Table of Content
  85. 85. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – GETTING STARTED SAS Visual Analytics comes with a complete Report Designer and Mobile Report Distribution capabilities. To access the Report Designer, you will first need to go back to Visual Analytics’ Home page. To do so, simply click on the Home button. Please note that in this trial, you will not be allowed to save your current work.
  86. 86. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – THE HOME PAGE This is your Home page. From here, you can access Explorations, Reports, Prepare your data, manage analytical models in SAS Visual Statistics and manage your servers. Note that in this trial, you can only access Explorations and Reports. Links can be customized for your organization’s needs. Any recent content you have been working on, or Favorites you would like to have, would show up here.
  87. 87. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – OPEN SAMPLE REPORT To open the Sample report for insight Toy, from the home page, click on the open folder on the upper left corner. Navigate to SAS Folders > Shared Data > DemoData > Insight Toy Trial > Explorations and Reports and select the Insight Toy Sample Report. Then click on EDIT to open the report Designer.
  88. 88. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – THE ESSENTIALS For more information, please consult the SAS Visual Analytics User Guide, available under the Help menu. The interface for creating, editing and publishing reports is very similar to the Data Exploration interface, with a few differences… The right pane contains tabs that enable you to change the properties of your visualizations, filter the data and apply display rules. The workspace allows for interaction between different objects. The Objects tab lists the various types of Tables, Graphs, Gauges, Controls and Containers for your report. As before, the Data tab lists the available categories and measures in your selected data sources.
  89. 89. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – CREATING A NEW SECTION Let’s create a new section by clicking on the + sign at the top. For the purpose of this exercise, we’ll show you how to recreate the Sales Overview page. Reports in SAS Visual Analytics are made of sections (see the three tabs at the top) and each section is made up of tiles. In this section, we have 3 tiles: a pie chart, a line plot, and a treemap. On the top right, we also have two pull- downs that allow the user to subset the data (in this case, we’re looking at 2011 and at Europe).
  90. 90. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – EDITING A NEW SECTION STEP 1: From the Objects tab, drag and Drop a Drop-Down list control in the top part to create a section prompt. Note that your report already has a report prompt for the year (see the shaded area at the very top)… so this will affect your new section as well. STEP 2: From the Data tab, drag and Drop Facility Continents on the section Drop-Down list.
  91. 91. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – ADDING A PIE CHART STEP 1: From the Objects tab, drag and Drop a Pie Chart graph object on the report section. STEP 2: From the Data tab, drag and Drop Order Total on the pie chart and assign it as a Measure, and then drag and drop Facility Country/Region as your category.
  92. 92. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – LIVE ON BIG DATA Before we keep going, let’s review what we just did. What you see below, that literally took us seconds to create, is a LIVE report, on big data, ready to be consumed. There is no need to “run” the report, to pre- summarize it, etc. You could save this now and it would be accessible to anyone who has authorization on your system to view reports, or through mobile tablets as well. No matter if it is on ‘just’ a few thousands rows of data, or hundreds of millions! Now lets keep going…
  93. 93. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – ADDING A LINE CHART STEP 1: From the Objects tab, drag and Drop a Time Series Plot graph object on the RIGHT side of the report section. STEP 2: From the Data tab, drag and Drop Order Total and Gross Margin to the Time Series Plot. Assign both as a New Measure.
  94. 94. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – ADDING A LINE CHART STEP 3: From the Data tab, drag and Drop Transaction Month and Year as the Time Axis over the Time Series Plot. STEP 4: Make sure the Time Series plot is selected, and from the Properties tab, set the Binning Interval to “Use Format”, so only monthly data gets displayed.
  95. 95. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – ADDING A TREEMAP STEP 1: From the Objects tab, drag and Drop a Treemap Graph object at the bottom of this report section. Note: you can always re-arrange the tiles afterwards if you prefer. STEP 2: From the Data tab, drag and Drop the category Product Line over the Treemap. Then, Drag and Drop the measure Order Total as the New Size, and the Aggregated Measure Gross Margin Ratio as the New Color.
  96. 96. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – CREATING INTERACTION STEP 1: To create an interaction between your various Tiles, click on the View menu and select Show Interactions. STEP 2: From the Pie Chart, Drag and Drop to the Time Series Plot and then to the Treemap tiles. When you’re done, go back to the layout view by clicking on Close button.
  97. 97. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – WORKING WITH THE REPORT You’re almost done! Your report section is now fully functional. Try changing the pull down values, and also click on one section of the pie chart. See what happens. Now all that’s left to do is to refine the look of the Report Section...
  98. 98. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. REPORTING IN SAS® VISUAL ANALYTICS – FINALIZING THE REPORT The Properties and Styles tab on the right allows you to change many parameters to improve the look of the various tiles. The Display Rules tab is used to assign intervals when working with KPI gauges, or to assign a specific color to a data segment (such as product line ‘xyz’). These can also be leveraged in the Alerts tab.
  99. 99. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. Here are two more sections of the same sample report. You can have a ‘Button Bar’ Across the top. The user can simply pick a Product line, and the report automatically adjusts. Return to Table of Content REPORTING IN SAS® VISUAL ANALYTICS – VIEW REPORTS The midsection is a “stack container” which is actually 4 different objects… users can pick the visualization they prefer. Here we are looking at our average vendors satisfaction & rating score per region effectiveness by regions. We also see the sales representative targets for each country.
  100. 100. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. www.SAS.com EXPLORE!
  101. 101. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. INSIGHT TOY COMPANY – DATA DICTIONARY PAGE 1 OF 3 Field Type Description Facility Country/ Region Code Category 2-letter unique code for each country Facility Date Closed Category/Date If a facility were ever to be closed. None are in this dataset. Facility Date Opened Category/Date Date the manufacturing facility was opened. Varies from 1980 to 2010. Manufacturing Batch Category Manufacturing batch corresponding to each transaction. All unique – one per row. Manufacturing Batch SKU Category Stock Keeping Unit of various Manufacturing Batches. Order note Category/ Document Collection Free form text – notes taken at the moment the vendor ordered items. This will be used in Text Analytics and is in a separate data set. Product Brand Category 2 brands of products: Novelty and Toys. Product Line Category 8 lines of products, falling in the two product brands. Product Make Category 77 product make, falling into the 8 product lines. Product SKU Category 779 product SKUs produced, falling into the various product styles. Product Style Category 355 product styles, falling into the various product makes. Sales Rep Category Identification of the sales representative who made the sale. Transaction date Category/Date Date of the sale. Transaction Day Of Week Category/Date Day of the week of the sale. Transaction Month and Year Category/Date Day and Month of the sale. Transaction Month of year Category/Date Month of the sale Transaction year Category/Date Year of the sale
  102. 102. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. INSIGHT TOY COMPANY – DATA DICTIONARY PAGE 2 OF 3 Field Type Description Vendor Date Ended Category/Date When the vendor stopped doing business with us Vendor Date Started Category/Date When the vendor started doing business with us Vendor Loyalty Program Category Binary field (Y/N) representing whether or not this vendor is in our loyalty program. Vendor Type Measure (Category) Numerical category. 5 types of vendors: (1) Convenience store, (2) Discount store, (3) Department store, (4) Kiosk or (5) Other. Facility Geography Unique identifier of the selling facility Facility City Geography City where the selling facility is located Facility Continents Geography Continent where the selling facility is located Facility Country/Region Geography Country where the selling facility is located Facility State/Province Geography State or Province where the selling facility is located Manufacturing Facility Geography Identifier and location of the manufacturing facility Vendor Geography Identifier and location of the vendor (customer) Country Hierarchy Hierarchy A custom hierarchy made up of Facility City, State, Country Geo Hierarchy Hierarchy A custom hierarchy made up of Facility City, State, Country, Continent Product Hierarchy Hierarchy A custom hierarchy made up of Product SKU, Style, Make, Line and Brand Gross Margin Ratio Aggregated Measure Sum of Gross Margins divided by Sum of Sales (‘Order Total’) Gross Margin Measure (calculated) Gross Margin for each sale = ‘Order Total’ – ‘Order product Cost’ Gross Margin link color Measure (calculated) A duplicate of Gross margin to be used to drive the color of certain visualizations.
  103. 103. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. INSIGHT TOY COMPANY – DATA DICTIONARY PAGE 3 OF 3 Field Type Description Market Penetration Measure For each transaction, the corresponding % of market share in that particular region at that time. Order Count Measure (calculated) Count of the number of distinct Orders – based on field Order. Order Distribution Cost Measure Distribution cost associated with that transaction Order Marketing Cost Measure Marketing cost assigned to that transaction (through an activity-based costing exercise) Order Product Cost Measure Direct manufacturing costs associated with that transaction. Included I the calculation of gross Margin. Order Sales Cost Measure Sales-related costs assigned to that transaction (through an activity-based costing exercise) Order Total Measure Revenue from that sale. Sales Rep % of Target Measure A ratio of Sales Rep Actual sales divided by Sales Rep target. Calculated DAILY Sales Rep Actual Measure Cumulative DAILY sales for each sales representative. This value should not be summed across the transactions (since it has already been aggregated). Sales Rep Target Measure Daily sales Target (goal) for each sales representative. This value should not be summed across the transactions (since it has already been aggregated). Sales Rep Vendor Base Measure Potential revenue (funnel) from all the vendors (customers) assigned to a sales representative. This value should not be summed across the transactions (since it has already been aggregated). Sales Rep Vendors Measure Number of customers (vendors) assigned to a sales representative. This value should not be summed across the transactions (since it has already been aggregated). Vendor Distance Measure Distance from the vendor location to our selling facility. Vendor Rating Measure Subjective evaluation, from 0% to 100%, representing the potential value of a customer (vendor) for insight Toy. Vendor Satisfaction Measure Satisfaction of the customer (vendor) based on a marketing survey. From 0% to 100%. Return to Table of Content

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