"Alerts and KPI thresholds are two sides of the same coin. Alerts are actions taken once a KPI threshold is reached. However, alerts are not defined for every threshold boundary. For the most part, they serve as a warning system when a KPI shows poor performance or an undesired trend. Alerts must always be accompanied by attention-capturing actions such as automatic e-mails and/or visual indication such as blinking or animation on the dashboard. The other variable for alerts is the recipient. There may be one or more appropriate recipients for each alert." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)
"Alerts are integral to the dashboard concept in that they transform the dashboard from a graphical information presentation into a live console for managing organizational processes and performance. Effective dashboard deployment must facilitate easy management of alerts. This management process involves three components: (1) rules, (2) actions, and (3) recipients." (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 chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)
"To establish a uniform performance benchmark across the organization, it is important that variance of a specific KPI be consistent across all of its possible grains." (Shadan Malik, "Enterprise Dashboards: Design and best practices for IT", 2005)
"A key sign of successful business intelligence is the degree to which it impacts business performance." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"When the purpose of forecasting is to generate accurate forecasts, it is useful to define performance metrics that measure predictive accuracy. Such metrics can tell us how well a particular method performs in general, as well as compared to benchmarks or forecasts from other methods.
"Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model rarely starts with the data; instead it originates with identifying the business opportunity and determining how the model can improve performance." (Dominic Barton & David Court, "Making Advanced Analytics Work for You", 2012)
"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)
"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)
"In order to get measures to drive performance, a reporting framework needs to be developed at all levels within the organization." (David Parmenter, "Key Performance Indicators: Developing, implementing, and using winning KPIs" 3rd Ed., 2015)
"[...] the real power of the data lake is to enable advanced analytics or data science on the detailed and complete history of data in an attempt to uncover new variables and metrics that are better predictors of business performance." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)
"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)
"When it comes to data lakes, some things usually stay constant: the storage and processing patterns. Change could come in any of the following ways: Adding new components and processing or consumption patterns to respond to new requirements. […] Optimizing existing architecture for better cost or performance" (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)
"Observability extends the concept of reliability. It’s about having the ability to monitor the health and performance of the data product. By using tools to track various metrics like response times and error rates, organizations can proactively manage the data product’s health. This proactive management plays a crucial role in maintaining the product’s reliability, as it allows for the early identification and resolution of potential issues before they escalate." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)
"The lakehouse combines the best elements of data lakes and data warehouses for OLAP workloads. It merges the scalability and flexibility of data lakes with the management features and performance optimization of data warehouses. [...] A lakehouse eliminates the need for disjointed systems and provides a single, coherent platform for all forms of data analysis. Lakehouses enhance the performance of data queries and simplify data management, making it easier for organizations to derive insights from their data." (Denny Lee et al, "Delta Lake: The Definitive Guide", 2025)
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