"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)
"The storytelling mind is allergic to uncertainty, randomness, and coincidence. It is addicted to meaning. If the storytelling mind cannot find meaningful patterns in the world, it will try to impose them. In short, the storytelling mind is a factory that churns out true stories when it can, but will manufacture lies when it can't." (Jonathan Gottschall, "The Storytelling Animal: How Stories Make Us Human", 2012)
"There is another important distinction pertaining to mining data: the difference between (1) mining the data to find patterns and build models, and (2) using the results of data mining. Students often confuse these two processes when studying data science, and managers sometimes confuse them when discussing business analytics. The use of data mining results should influence and inform the data mining process itself, but the two should be kept distinct." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)
"Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more. Each of these is used by different communities and has different associations. Some have a long half-life, some less so." (Pedro Domingos, "The Master Algorithm", 2015)
"The human side of analytics is the biggest challenge to implementing big data." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)
"Exploratory data analysis is the search for patterns and trends in a given data set. Visualization techniques play an important part in this quest. Looking carefully at your data is important for several reasons, including identifying mistakes in collection/processing, finding violations of statistical assumptions, and suggesting interesting hypotheses." (Steven S Skiena, "The Data Science Design Manual", 2017)
"One important thing to bear in mind about the outputs of data science and analytics is that in the vast majority of cases they do not uncover hidden patterns or relationships as if by magic, and in the case of predictive analytics they do not tell us exactly what will happen in the future. Instead, they enable us to forecast what may come. In other words, once we have carried out some modelling there is still a lot of work to do to make sense out of the results obtained, taking into account the constraints and assumptions in the model, as well as considering what an acceptable level of reliability is in each scenario." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017)
"The goal of data science is to improve decision making by basing decisions on insights extracted from large data sets. As a field of activity, data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets. It is closely related to the fields of data mining and machine learning, but it is broader in scope." (John D Kelleher & Brendan Tierney, "Data Science", 2018)
"Analytics provides a way to demonstrate the linkage between people and business outcomes. HR analytics (also called people analytics or talent analytics) use measurement and analysis techniques to understand, improve, and optimize the people side of business. Data are the raw numbers you track. [...] Metrics focus on counting, tracking, and presenting past data. Analytics uses statistics to help you see patterns in the data." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)
"Data analytics is a powerful tool to increase the likelihood that you have the right problem. Both quantitative and qualitative data serve a purpose in supporting a hypothesis. They allow you to objectively measure and identify patterns and relationships." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)
"Data mining is a common way of handling big data. It’s used to analyze big data and overcome some of the limitations of human information processing and traditional analytic techniques. This approach applies machine learning algorithms to find patterns of relationships between elements in large, messy data sets. The objective of data mining is to use the detected patterns to predict future outcomes and make better decisions." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)
"Missing data can pose more or less of a problem depending on how random they are. Missing values scattered haphazardly throughout your data set may be inconvenient, but they don’t necessarily pose a threat to your analyses. When missing data follow a clearer pattern, there may be a problem. In those cases, you might need to revisit how you collect your data." (Shonna D Watters et al, "The Practical Guide for HR Analytics: Using data to inform, transform, and empower HR decisions", 2019)
"Big data is revolutionizing the world around us, and it is easy to feel alienated by tales of computers handing down decisions made in ways we don’t understand. I think we’re right to be concerned. Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily be mysterious and commercially sensitive black boxes." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)
"The data mesh pattern doesn't feature a central node and is loosely coupled compared to a hub-spoke architecture. It has different data lakehouse nodes that are independent of each other. The node data lakehouses are domain-driven. A domain can be oriented in multiple ways. The original idea of data mesh alludes to a source-oriented domain aligning to business processes. However, a more practical approach would be to define a domain based on the organizational setup and practicality; for example, a domain can be a product group, it can be separate organizational entities, and it can also be a specific business process, such as marketing. Each domain has its own data lakehouse that is managed and maintained by that domain." (Pradeep Menon, "Data Lakehouse in Action", 2022)
"[...] organizations are working to unify the data silos that often exist across their data ecosystem. Some are doing this through a centralized physical approach, such as implementing a data lakehouse pattern, which allows them to store and manage all types of data in one place. Many lakehouse implementations such as those from vendors such as Snowflake and Databricks have evolved into what is often referred to as the modern data platform. This is an architectural pattern that combines the lakehouse with tightly integrated tools for data ingestion, transformation, analytics, observability, and governance. This pattern reflects a shift toward cloud-native architectures that are designed to support end-to-end data workflows with scalability and flexibility." (Fern Halper, "Data Makes the World Go 'Round", 2026)
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