Andy Chandarana from KnowledgeAdvisors provides insight into the future of dashboards as well as some history on where they have been.
In order to predict where dashboards are heading, it is helpful to think of how they have evolved into their current states. While Drs. Robert Kaplan (Harvard Business School) and David Norton are widely credited for formalizing and popularizing the balanced scorecard in the ’90s, GE began performance measurement reporting in the ’50s and, well before that, a group of French engineers created a “dashboard” of performance measures in the early part of the 20th century. Thus, the foundation of the modern dashboard preceded Bill Gates’ 1999 book “Business @ the Speed of Thought” (widely credited with sparking the broad corporate interest in the use of dashboards) by anywhere from a few years to several decades.
There are plenty of good sources to learn “best practice” tips from the experts on dashboard creation – some that even might help you create Dashboards that People Love to Use! Rightly so, a good majority of them focus on the strategic aspects of dashboard creation such as understanding your audience and purpose first, deciding what types of KPIs and metrics to include, and choosing the best charts to display your data visually for maximum readability and insight. Others are heavier on tactical and aesthetic subjects like color choices, font selection, the use of white space, etc. Several, like the document available in the link above, balance both aspects fairly well. Articles in magazines such as Training Industry Quarterly further narrow the scope for specific industries, audiences, or purposes, providing tips on how to tailor dashboards for your Learning & Development or Talent Management organizations.
If you are not already, it is definitely a good idea to avoid dashboard confusion by getting a good understanding of the fundamental differences between scorecards, dashboards, and business intelligence tools. Knowing how each of these are defined, properly designed, and best used is essential before deciding to build your own. No matter how far down the path you go, as you continue on your journey, it is a good idea to periodically ground yourself in those fundamentals to avoid the déjà vu of dashboard confusion revisited. Like any other lifelong learner, I know it is better to tap the wisdom of others than re-invent the wheel. Therefore, instead of going any further into the foundational subjects mentioned via the links above, I thought it would be helpful to provide a little background for those newer to the subject and then spend the balance of this post exploring the future of dashboard innovation.
Over the course of the last decade or so, traditional scorecards have slowly fallen out of favor in many circles. By adding Learning & Growth and Internal Business Processes metrics to the equation, the balanced scorecard movement was certainly an improvement to widen the typically narrow corporate focus on Financial and Customer metrics. However, many scorecards often consist of primarily lagging measures, require a great deal of manual effort to create (which also increases the risk of human error), and are only produced on a periodic “snapshot” basis. Fortunately, some scorecards and dashboards do a better job of including the KPIs that are truly leading indicators of an organization’s key metrics – a step in right direction.
While the volume of Google searches for balanced scorecards, dashboards, and business intelligence have all declined or remained flat over the last nine years, the interest in big data has increased seven-fold in just the last two years. Future generations of dashboards will tap into multiple, disparate data streams with the computing power to handle the volume of truly big data and the artificial intelligence to make sense of it all. Not only will the dashboard of the future dynamically adapt to the specific role, responsibilities, and challenges of various user profiles, they will someday begin to select and synthesize the right big data streams based on individual usage patterns and a series of auto-generated algorithms.
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