The purpose of this post is about creating dashboards. This is not financial advice. All views expressed on this post are my own and do not represent the opinions of any entity whatsoever with which I have been, am now, or will be affiliated. I used TIBCO Spotfire 7.14 to create this process, and a free trial version is available here
Leveraging data in your daily life may sound unrealistic, and the practicality of apply a dashboard to help decision-making can sound harrowing. However, to better understand how the same logic can translate from life to business, and vice versa, knowing and using data will no longer be a daunting hurdle to cross.
Here’s how you can do the same.
Step 1: Start with a great question
More than ten years ago, end of April of 2008, I started an amazing adventure. I moved from Bogotá, Colombia to Singapore. One of the first interesting cultural shocks was the extremely low-interestrates for a Savings Account. If I recall, it was about 0.05% per annum. The rates I was used to getting ranged between 5.00% to 8.00%, so 0.05% was not only shocking but completely unacceptable in my opinion.
So the first question was: How can I obtain a better return for my savings?
Step 2: Get the best data available
After checking in with some financial advisors and local friends, I got introduced both to Unit Trusts and REITs investment options. I explored further and found some interesting resources online like http://www.sreitinvestmentblog.sg/ and http://reitdata.com/ (Figure 1). REITs made sense at the moment, and I loved the advice from some of the investment blogs so, I decided to create my own rules against the benchmarks:
- Rule #1: Buy when the price is lower than its NAV.
- Rule #2: Sell when the price is higher than its NAV
- Rule #3: Try to avoid high gearing and get more than a 7.5% yield.
Figure 1: Screenshot of REIT data table from August 2008. Retrieved from https://reitdata.com/2008/08/
Figure 2: Screenshot of REIT data table from August 2018. Retrieved from https://reitdata.com/
Step 3: Create easy to read charts
The tables in Figure 1 and 2 are useful, but not easy to read. We want to know what is good for buying, what is good for selling (if we need the cash) and if our portfolio is a well-balanced mix.
Cleaning the dataset a bit, and adding one column called Value (basically, the difference between the market value and the NAV (Net asset value) we can sense if the market has overvalued something or undervalued it. To help with the visualization, I created a gradient color scheme for this new value column to tell us easily whether something is cheap [Green], or if it is expensive [Red].
Finally, I used a Scatter Plot visualization, with the Yield on Axis-X and Value on Axis-Y to get the same idea of the Gartner Magic Quadrant and thus, we can conclude that we’d want to buy something with yield better than 7.5% and that its value is below the NAV.
The result visualization will be something like this:
Step 4: Creating interactions for the dashboard
Finally, for the interaction part, we want the dashboard user to see things like what are this month’s good deals (things to consider to buy), using the marking and label features:
Or, if we have already bought, what things are the market overvaluing:
And this is it, a simple 4-step process to bring a logical and practical way of using data and dashboarding into your daily life!
- Ask a question
- Get your data sources tidy (Clean it!)
- Create your charts to answer the question
- Add interactions to your dashboard for easy decision-making
Note: I used TIBCO Spotfire 7.14 to create this process, anda free trial version is available here.
- December 2018(20)
- November 2018(48)
- October 2018(76)
- September 2018(55)
- August 2018(63)
- July 2018(74)
- June 2018(64)
- May 2018(65)
- April 2018(76)
- March 2018(82)
- February 2018(65)
- January 2018(80)
- December 2017(71)
- November 2017(72)
- October 2017(75)
- September 2017(65)
- August 2017(97)
- July 2017(111)
- June 2017(87)
- May 2017(105)
- April 2017(113)
- March 2017(108)
- February 2017(112)
- January 2017(109)
- December 2016(110)
- November 2016(121)
- October 2016(111)
- September 2016(123)
- August 2016(169)
- July 2016(142)
- June 2016(152)
- May 2016(118)
- April 2016(60)
- March 2016(86)
- February 2016(154)
- January 2016(3)
- December 2015(150)