"Although it does so much more, the central purpose of a dashboard is to warn the user when any relevant metrics are out of acceptable boundaries. In the dashboard terminology, these alerts consisting of rules and actions add critical value to an enterprise dashboard deployment complemented with strong visual indicators of warnings." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)
"Metrics are measurements of activities to evaluate performance, mostly within a relative framework of time, geography, and aggregation." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)
"The distinguishing feature is that a dashboard is an application with a collection of metrics, benchmarks, goals, results, and alerts presented in a visually effective manner, whereas a portal is a collection of different applications presented together within a personalized framework." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)
"[…] a KPI is a metric, but a metric is not necessarily a KPI. A metric is really a measure of anything. A KPI, however, is meant to be a measure “ that matters ”and that ideally can be acted on. An organization has many metrics, but typically just a few KPIs. This is often the challenge when a management team meets to agree on a short list of key measures for use in its proposed dashboards and scorecards." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)
"A culture of data fluency needs to be built on a shared understanding of the data sources, data analysis, key metrics, and data products. It requires employees to be on the same page about how data is used and why it is important." (Zach Gemignani et al, "Data Fluency", 2014)
"Any presentation of data, whether a simple calculated metric or a complex predictive model, is going to have a set of assumptions and choices that the producer has made to get to the output. The more that these can be made explicit, the more the audience of the data will be open to accepting the message offered by the presenter." (Zach Gemignani et al, "Data Fluency", 2014)
"Goals associated with a few, well-understood key metrics is a powerful combination. For both internal and external stakeholders, there is a strong alignment between organization mission, vision, goals, and tracking of progress. The efforts of everyone can be directed at these measurable goals, and people will focus on the processes that can impact these metrics." (Zach Gemignani et al, "Data Fluency", 2014)
"Metrics can serve two purposes: identifying problems and measuring performance. When the goal is to identify problems and pinpoint areas of operational inefficiency and ineffectiveness, defining the right metric requires a bit of detective work. It requires you to uncover the data residue of a problem and to determine what evidence can be found and how exactly it shows up. When the goal is to measure performance, the right success metrics focus on measures that can be controlled and where improvement in the metric is an unambiguously good thing." (Zach Gemignani et al, "Data Fluency", 2014)
"[…] an overall green status indicator doesn’t mean anything most of the time. All it says is that the things under measurement seem okay. But there always will be many more things not under measurement. To celebrate green indicators is to ignore the unknowns. […] The tendency to roll up metrics into dashboards promotes ignorance of the real situation on the ground. We forget that we only see what is under measurement. We only act when something is not green." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)
"Grouping a bunch of related fine-grained metrics into an aggregate metric works better than handling them individually. This is commonly achieved by defining an aggregate metric as a weighted sum of contributory metrics. It is the sort of technique used in credit scoring, insurance risk scoring, or the points system for immigration. Thresholds are then set on the aggregate score to define different categories of eligibility." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)
"Rolling up fine-grained metrics to create high-level dashboards puts pressure on teams to keep the fine-grained metrics green even when it might not be the best use of their time." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)
"Scaling supervision using metrics is one thing; scaling results is quite another. The former doesn’t automatically ensure the latter." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)
"Simplicity for data visualization often focuses on minimizing the number of elements that do not add value to your display. These include borders, gridlines, axes lines, and boxes, which can easily distract from your core message. This recommendation also relates to the information itself. You should strive to create a visualization that focuses on specific aspects of the data, rather than including all fields and metrics but not saying much about any of them." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)
"The field of [data] visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)
"Data observability platforms often consolidate various data health metrics, such as freshness, accuracy, completeness, and consistency, into a unified dashboard, offering a holistic view of data health. This may take the form of a data scorecard. These tools can also provide metrics such as system uptime/availability, data processing times, error rates in data processing, query performance metrics, resource utilization, and user engagement metrics. They provide customizable key performance indicators (KPIs), real-time alerts, and root cause analysis, and they can help assess the impact of data quality issues on downstream applications, reports, and business processes." (Fern Halper, "Data Makes the World Go 'Round", 2026)
No comments:
Post a Comment