Author Archives: Gadi Yedwab

About Gadi Yedwab

Gadi Yedwab is the founder and CEO of Explore Analytics. Prior to founding Explore Analytics, Gadi served as VP of Product Development at ServiceNow, a leading provider of cloud-based services that automate enterprise IT operations. Prior to ServiceNow, Gadi Yedwab held executive positions at Quest Software and Brio Technology (which was acquired by Hyperion and then by Oracle). You can reach Gadi on twitter at @GYedwab or using the Feedback Form.

US Treasury Historical Yield Curve 1990 – 2013

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The yield curve is a useful tool in forecasting near-term economic conditions. For example, the US Treasury yield curve is a chart with the yield (interest rate) on the Y axis and the term of the Treasury bond, from 30 days up to 30 years, on the X axis.

The yield curve on November 15, 2013, looked like this:

This curve increases from a very low rate for short-term bonds to significantly higher rates for longer-term maturities. This would be considered a normal or even steep curve that may signal quick economic improvement in the future.

The yield curve is not always shaped as it is today. It could become flat with short-term rates near long-term rates and even inverted, when some short term rates are higher than the longer-term rates. Such curves may signal uncertainty in the economy.

To see how the yield curve changed historically, consider the following chart. The chart is actually not showing the yield curve, but instead is showing a line for every bond duration, from 3-months to 30 years. It shows data for the last 23 years.

Here’s how to look at this chart:

  • The red line represents the yield on the 30-year bond. In a normal yield curve, this duration should have the highest rate and indeed you can see that most (but not all) of the time, it’s on top.
  • The unusual straight line in the 30-year bond from February 2002 to February 2006 is due to the fact that the US discontinued this bond and then reintroduced it.
  • It’s easy to see periods in which short-term and long-term rates bunch together and even invert, with lines representing short-term rates crossing over the lines representing long-term rates. It’s no surprise that they correspond, for example, with the stock market downturn in 2002 and the stock market crash in 2008-2009.

I invite you to explore this interactive chart by zooming in and scrolling through the timeline.

Data is from the U.S. Department of the Treasury.

About Explore Analytics

The charts in this article were created using Explore Analytics, a cloud-based self-service data analysis and visualization tool for individuals and teams.

Tracking Trend for Self Service Trend Analysis

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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