Roambi Trends Best Practices

Roambi Trends provides the ultimate interface to monitor, zoom, and drill into measures (sales, profit) that change over time in consecutive intervals (days, months, quarters). Organize and trend information in real-time while maintaining historical context using a thoughtfully designed line and area chart user interface. Rotate your iPhone to uncover Trends’ powerful zoom capabilities that provide detailed focus required to quickly assimilate information. Slice by multiple time dimensions (day, week, month, quarter, year) and visualize any measure from sales ($) volume to energy consumption (kWh).

Example Use Cases
Trends by default will display the latest information available, with an option to toggle inline alerts that measure change from the previous interval. Any information that changes frequently where historical trending is required is a perfect use case for Trends. Whether you monitor market data to assess risk, or require historical sales trends at your fingertips, Trends’ powerful interface will accommodate any data or time series. For example, a retail energy sales executive can utilize Trends to reference their customer’s historical energy usage and demand data. While dynamically comparing two data points, the retail energy sales executive can address on-peak and off-peak demand inquiries from their customer while un-tethered from the computer.
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Roambi Trends Features
Aside from the sleek aesthetics and thoughtful design execution Roambi Trends features:

  • Multi-level categorization of key dimension to simplify navigation
  • Inline alerting that indicates change from the previous interval by units and %.
  • Slicing feature to display multiple grains of data by time dimension
  • Touch focus to display any value within the chart.
  • Dynamic trending with automatic best fit calculations that use the data shape to choose a correct trend (linear, logarithmic, polynomial, power, exponential, polynomial, moving average)


  • Moving average trend will evaluate every “x” data points plot an average line
  • Multi-touch comparison of two values within Trends’ chart interface
  • Landscape rotation expansion of chart including sliding micro-chart for pan and zoom functionality
  • Bookmarking option saves your place so you can re-call it at anytime
  • Locate specific trends with built in Go-to feature and trend selection
  • Instant email and screenshot capabilities to share content with non Roambi users or  with Roambi users via Roambicast
  • Email Trends views while keeping  with bookmarks intact

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Data Visualization Best Practices
Trends employs the use of a line and area chart visualization control, which is best applied to consecutive interval metrics which are always time-based (month, day, etc). To ensure that Trends will properly display your business data, the dimension that will ultimately display as the X-axis will contain consecutive values (days, months, hours) to communicate performance over time. Trends’ best-fit line will evaluate the data shape and then generate an appropriate trend using one of 6 statistical calculations. Included as a trend option is a moving average that will evaluate every “n” values to re-calculate the average.
Appropriate uses for Trends where the listed dimension (quarter, week, etc) is intended as the chart X-axis.

  • Sales by quarter
  • Expenses by week
  • kWh by day
  • Safety incidents by month
  • Market Price by hour

Inappropriate uses for Trends where the listed dimension (region, category, etc) is intended as the chart X-axis.

  • Sales by region
  • Expenses by category
  • kWh by customer
  • Safety incidents by department
  • Market price by commodity

If your data does not contain appropriate data for line chart visualization through Trends, other Roambi views like CataList, and Cardex can fulfill other requires for navigating and visualizing your business data.
Implementation Best Practices
6While Roambi provides tremendous flexibility for implementing intuitive views, formatting reports and Excel worksheets that provide data require best practices that can save you valuable time, while facilitating a usable data structure for your data visualization needs. When working with Excel, all Roambi Views require a flattened vertical table where any combination of columns can be grouped and then assigned to charts and tables within the view.
By default the last non-data row is utilized as the KPI name for each column of data. All other rows above are grouped into category names. While data sources like, Crystal Reports, etc automatically identify categories, Excel will require you to use the cell grouping feature, to automatically create Roambi categories. Trends will support one level of grouping, which is visually represented in the Trends interface.

7Roambi Trends will use up to the first 5 columns to identify consecutive time measures for which the graphs will be displayed. It is a best practice to insert the largest time dimension grains from left to right, to ensure the user can intuitively drill into the data. Roambi Designer provides flexibility to re-label the measure label.
Example: year=Y, quarter=Q, month=M, day=D, hour=H, minute=M

8Roambi Trends will display the last value within the trend view, and calculate the change from previous value. To visually indicate change, Roambi Trends leverages color coded indicators to display a “good” or “bad” change as the values change. Every measure (column) can differ in how they are measured for performance. For example, as revenue increases the Roambi view would display a green alert value. However, if expenses increase the Roambi view should display the alert indicator as red. Roambi Designer can accommodate unique alert definitions for every column of data.

Data Formatting
Applying number formatting to data: Roambi is intelligent enough to interpret multiple data formats from reports and Excel files. When applying number formatting, you want to ensure that each column only contains a single numeric format ($, %, “,”, etc).
The Trends component can utilize MUTIPLE ranges in the workbook or one big dataset (containing different numerical formats).

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