Private Equity

Transform Portfolio Data into Actionable Insights in 3 Steps


August 9, 2017

While most organizations understand the value of the data they collect, the majority still struggle with turning that information into actionable insights.

That is the conclusion of a new study from Interana, which looked at the experiences organizations are having with data and analytics in 2017. The study revealed that 70% feel they do not get critical insights from their data.

EY reported a similar pain-point in their 2017 Global Private Equity Survey. With 40% of portfolio managers reporting that they weren’t confident in their current data analysis process. 

That's why we've  outlined 3  steps  to help you  mould portfolio data into actionable insights. 

1. Define the Data

Gaining critical portfolio insights means figuring out what you want from the data. In his book Thinking with Data, Max Shron offers a helpful framework for narrowing the scope of a project like data analysis. Similar to a story, a project will always include exposition (the context), some conflict (the need), a resolution (the vision) and, hopefully, a happily-ever-after ending (the outcome).

Answering the following questions will help you best plan for how to use your portfolio data. 

  • Context: What are you trying to achieve? Are there any larger fund goals that can help prioritize the project? An exit event perhaps?

  • Need: What specific needs could be addressed by intelligently using data? What will this project accomplish that was impossible before?

  • Vision: What will meeting the need with data look like? Is it possible to mock up the final result?

  • Outcome: How and by whom will the result be used and integrated into the fund? How will the success of the project be measured?

2.  Filter the Data

 Once you’ve identified how you’re going to use the data, it’s important to brainstorm how you’re going to approach it. Sometimes it's too imprecise, formatted incorrectly or incomplete. This is where the importance of validating the data comes into play.

  • Filtering: Cut the noise and focus on the data that matters most.

  • Sorting: Rank data by importance.

  • Grouping: Summarize data and segment different groups.

3. Visualize the Data

Although fund managers are constantly investing in new technology solutions, many CFOs still rely on old-fashioned spreadsheets to interpret data.

“Numerical quantities focus on expected values, graphical summaries focus on unexpected values,” said mathematician John Tukey.

Tukey's quote alludes to the fact that the real power of visualizations lies in their ability to display unexpected findings in a clear, easy-to-grasp manner. Which is why we recommend fund managers move away from spreadsheets and towards visualizations. 

To help bring your portfolio data to life, there are several tools available, for example, Hockeystick has a customizable dashboard that allows users to illustrate their data.

The following principles will help guide the kind of visualization that evokes compelling portfolio insights from your validated data.

  • Be aesthetically mindful: People are naturally attracted to beauty, so why not make a chart as pretty as possible? Aesthetics can also help make the charts easier to comprehend — pinpointing the resulting action.

  • Focus on trends: The best insights often come from looking not at singular data points but at trends, especially when they change direction.

  • Try different perspectives: Because one individual can't see everything, invite others to delve into the data. It's vital to have several pairs of eyes looking for actionable insights.

  • Don’t trust the data: Always analyze data from at least two angles. For example, plot the same data multiple times using different chart types. Data has the power to mislead, so make sure it's telling the story accurately.

Turning data into actionable insights can be an unstructured process. Fortunately, there are several techniques (like the ones listed above) to help guide you. We encourage fund managers to establish such guidelines to work with data more effectively.

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