Data and analytics is a growth industry. It seems that most people are interested in what they can get out of their data, in terms of actionable information, so that they can make better informed decisions. Data alone is not a panacea that will replace experiential decision making. These two, when used together and balanced correctly, comprise what I would suggest is the optimal decision making process. In my experience, certain industry sectors are far ahead in terms of data and analytics (such as banking) whilst others (such as construction) behind.
Similarly, some job roles are more mature than others when it comes to having an effective data and analytics function. Unfortunately, project, programme and portfolio management tend to lag behind most other professions, yet they have some of the highest demands for reporting.
What could be
It doesn’t need to be that way. There are some simple techniques and modern tools that can be learned and implemented at relatively low cost that will help us reap major benefits. Not least of which is the reduction, or elimination, of the time taken to wrangle data in spreadsheets or project management tools into a shape that is useful.
It is very likely that there are benefits and insights hidden within the data that you are yet to discover. We have certainly found this to be the case with several clients; showing data in ways they had not seen presented previously radically changed the way they operated and, often, materially altered profitability. Often data is inaccessible, due to the lack of skills and experience within the team, for extracting and structuring data from the source systems.
Data visualisation principles
What data we show is very important but how we show the data is also of equal importance; something that most of us (including data professionals!) have never been taught. Data visualisation is a separate skill to graphic design. The emphasis on data visualisation is to tell the story with the data whilst aesthetics (although still important) play a lesser role. Which of these graphs, for example, conveys the message from the data more efficiently?
The first, using a lot of colour a background image and coloured and angled text, obscures the data. It makes the readers’ job more difficult than it should be. The second is a good example of where “less is more” which, combined with ordering by “Actual”, helps the message from the data to jump out. The customer’s life is made easier.
The important take-away is that we, as conveyors of information, will greatly improve the experience and ability of our customers to make better informed decisions if we understand some basic principles of how the brain functions and what types of visualisation to use for what purpose. A rare skill which is easily learned.
Project management challenges and data solutions
Project, programme and portfolio managers face some difficult challenges and, I would suggest, that they need all the help that they can get. If we can get access to the data they need, present it well, guide them to exceptions and outliers and allow them to explore key issues these professionals can use their expertise to make better decisions faster.
What if, by filling out a few fields each week, the project manager’s weekly returns were largely done for him or her, whilst the programme and portfolio managers were simultaneously presented with a “forest view” (with outliers and areas of concern highlighted) on a single screen? This is possible!
For examples of application of data analytics as applied to portfolio management, good data presentation practice and some live demonstrations, please join our webinar on this topic on 13 June 2018.