Tracking Trend for Self Service Trend Analysis

Trend analysis is one of the most useful tools for understanding the current state of things and their outlook. Is the business improving or deteriorating? Once visualized, trends are easy to spot. A single glance can tell us whether things are converging, diverging, or moving in a certain direction.

The Problem: Ready Historical Data is Out of Reach

For users, creating a trend chart is easy provided that historical data is readily available. Alas, this is often not the case. Let’s consider an example. An application keeps track of projects and all their tasks. Progress is recorded at the task level and information is rolled up to the project level. So far, this is all very typical. We can calculate the “% complete” of each project simply by adding up the hours completed and dividing by the total hours planned. Yet that only gives us the current status.

For trend analysis, we need to know the “% complete” for yesterday, and the day before, and last week going back in time. We may want to see this information broken down by task and grouped by team or subproject. To do so, the system must keep a history of the updates to each task and be able to tell us the number of hours completed on the task at any point in time.

Operational systems, as opposed to specially-designed data warehouses, typically fall short of this requirement in two ways:

  1. The system might simply update the hours on the task without keeping a history. In that case, our user is out of luck.
  2. The system may separately keep timecards. It may be possible to reconstruct the history from the timecards, but that would be outside the reach of our self-service user, if we assume they don’t have SQL and programming skills.

The Solution: Tracking Trend

“Tracking Trend” is a name for a simple feature that makes life easy for self-service users and allows them to trend data. It breaks the problem into three easy steps.

Step One

The first step is for the user to create a report showing current information. The report would show the “% complete” by project, subproject and team as of the time of running the report.

Step Two

The next step is to “Track Trend” on the report that was created in step one. To “Track Trend” means to create a job that runs the report on a schedule specified by the user, for example every day at 10pm, and capture the output into a table.

Step Three

The table created in step two is perfect for creating trend reports. The table has a date/time field indicating each time the report ran. A trend report can show trends at the project level and allow drill-down to the subproject and team level. Creating such a report is well within reach of a self-service user.

Other Example Applications for Tracking Trends

Tracking trend is useful for any calculated metric, such as inventory on hand, net worth, membership renewal rate, support backlog, top-10 list, and much more. Think for example how easy creating a burn-down chart becomes.

Track Trend in Explore Analytics

Explore Analytics makes tracking trend as easy as scheduling a recurring meeting in outlook. You specify the report (created in step one), the schedule, and a name for the table to hold the output. It’s that simple.

Burn-Down Chart

When to Animate Data Visualization

Animation for the sake of catching the viewer eye without adding information can quickly become an annoyance. However, when used correctly, animation can greatly improve the visualization by increasing the information density.

A great use of animation is adding the time dimension to an already busy chart. Before animation became available for data visualization, a typical method was to produce a series of charts, each showing data for a single time period.

In this article, we’ll see examples of animated XY, Bubble, and Map charts. Using Explore Analytics we can create animated charts that are interactive. They allow you to play the animation forward or back, step through, search through, and pick any page of the animation. You can also set the frame rate to control the speed of the animation.

In the following chart, we show crime statistics for the 50 US States. The chart has 50 bubbles, each representing a state, with its property crime rate on the X axis, its homicide rate on the Y axis, and population represented by the size of the bubble. We pack data for 51 years by using animation.

This chart is powered by Explore Analytics.

Two additional examples:

Historical US Unemployment: See how US unemployment by county changed over 22 years from 1990 to 2011. This chart is a little slow to load because there’s a lot of data packed into it. Green represents less than 6% unemployment and red represents higher than 6%. See the dramatic change from 2007 to 2009. Also, try zooming in on your state and run the animation.

Historical Yield Curve: see how the US treasury yield curve (a graph of bond rates for different maturity periods) changed over time from January 1990 until February 2013. Watch for times when the curve flattens or even reverses, signaling the onset of a recession.

In conclusion, animation can greatly increase information density and effectively illustrate trends in the data.