Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 32

As a freshman at Indiana University in the mid-1990s, one of your authors took an Introduction to Business class taught by Professor Tom Heslin. He didn't have to tell you he was from Brooklyn, as his accent stood quite apart in southern Indiana, but he loved to work his Brooklyn origins into his lectures two or three times each Thursday evening anyway, and his students loved him for it. Oddly, by contrast, he never mentioned that he was a Navy veteran and a World War II war hero. We did know that he had come to the Indiana University Kelley School of Business to teach and to give back, after a lengthy career at Bell Labs. He was no-nonsense and full of energy, and had a way of giving students punchy one-liners that would stick. Several come to mind, but one was “You gotta have a plan!” which he employed liberally to drill into his students that they must exercise forethought, be purposeful in their actions, and leave little to chance. But importantly for this section, when introducing the concept of business controls to bright-eyed freshman, he said “You can't manage it if you can't measure it.”

It is this last aphorism that has really stuck. This is mentioned because in all of our businesses, there are key metrics that are actively managed (and frequently reported) to allow individuals, managers, or executives to closely monitor process performance. These measures are referred to as key performance indicators (KPIs) and are used to measure and report on the health and performance of an organization, a division, a function, or even a process within them. They tend to be some of the most widely reported numbers for internal audiences, and a portion of them may find their way to external stakeholders and regulators. A whole book could be written on how to make a thoughtful selection of KPIs for a given process, in order to convey health across a number of dimensions. However, for purposes of this book, we will assume that these have been arrived at separately and are effectively conveying business performance to allow for active and rigorous management. What we do want to cover in this section is the ways that KPIs can be compiled and displayed efficiently through the use of dashboards and visualization tools.

Most, if not all of our readers will be familiar with common temporal data visualization formats – bar charts (to show value comparisons), line charts (to show time-series movements), scatterplots (to show large numbers of observations), and sparklines (for trending). Some may be familiar with hierarchical visualizations like tree diagrams, sunbursts, and ring charts. A more select few will be familiar with multidimensional data visualizations that can communicate more than one variable for each observation. Examples of multidimensional data visualizations include pie charts and stacked bar charts that show observation values relative to the whole, and Venn diagrams that can show observations that meet either one or both of two population definitions or constraints. Visualizations are widely in use to make both interpretation and comparison of KPI observations as easy as possible to understand at a glance, or at least in a handshake, rather than after a prolonged study.

Dashboards build on data visualization by pulling together all critical KPIs that are necessary to manage an enterprise, a business, a department, or even a process, in a single view. Measuring and displaying multiple business indicators together can offer context that communicating a single fact or value alone can fail to do. They are built to deliver the key and necessary performance metrics in a visually rich frame that can provide stakeholders with an instant comprehension and more complete understanding of the relative health of business processes.

Anyone who has ever owned the production and distribution of a metrics dashboard or scorecard would likely tell you that they are surprisingly thought- and labor-intensive to produce and maintain. From gathering and agreeing the KPIs that best convey the full picture, to the design and layout of each individual component metric in a visualization, to structuring the page layout to feature the most key of the key metrics prominently – all of these design steps represent a lot of work. When there are multiple recipients, it is always a challenge to navigate the conflicting preferences of each, and we all know that recipients are never bashful about suggesting additions or format changes. We have all been to meetings that have been sidetracked completely by the one audience member who spends the bulk of the session asking questions surrounding the format of the visualizations or the array of components and their order on the page, rather than engaging in a productive discussion on how to improve any of the key metrics. Beyond the design, perhaps even more time-consuming are the maintenance steps that are required each time the metrics dashboard is to be communicated. The slides must be dusted off and refreshed with the updates that have occurred across any of the dimensions from any of the various data sources. From there, date headings must be refreshed, any changes that have been requested must be made, and of course commentary must be updated, before it is sent off.

Over the last decade, a number of dashboarding and visualization vendor tools have emerged to simplify dashboard design, to enable the efficient capture and assembly of KPIs, and importantly to allow for low-latency refresh of visualizations on demand. Key among them are Tableau, QlikView and Qlik Sense, SAP Business Objects, IBM Cognos, Microsoft Power BI, and Oracle BI. This is an ever-moving list, but these are names readers should recognize, as they represent prevalent and widely subscribed visualization platforms – and they are increasingly tied to business intelligence and data analytics.

The evolution from flat reports on continuous form paper that had the perforated strips with holes on each side, if anyone remembers folding them over, licking the edges, and tearing them off (sorry team, yes one of your authors licked the edges to get a cleaner tear, while your second author claims not to have and cringes in disgust— you'll never know which was whom), to brighter reports featuring better fonts and some color graphs and visuals that we got accustomed to in the 1990s and 2000s, to the highly flexible, visual, dynamic, and interactive digital dashboards we have today that convey business intelligence insights is startling – and game-changing. We cannot introduce significant emerging and enabling data analytics technology without making mention of the advancements in data visualization and dashboarding that puts key information in the hands of end-users and decision-makers in an intelligent, versatile, and insightful way.