The Premise of Analytics

By Tim Ackroyd on

The premise of analytics is to leverage data and statistical methods to gain insights and make informed decisions. Analytics involves the systematic analysis of data to uncover patterns, trends, relationships, and other valuable information that can guide decision-making, problem-solving, and optimization.

The Premise

The premise of analytics can be summarized in the following key points:

  1. Data-driven approach: Analytics relies on data as its foundation. It recognizes that organizations generate and collect vast amounts of data from various sources, such as customer transactions, operations, social media, sensors, and more. By analyzing this data, businesses can gain valuable insights to drive strategic decisions and improve performance.
  2. Extracting insights: Analytics aims to extract meaningful insights from data. It involves applying statistical techniques, data mining, machine learning, and other analytical methods to identify patterns, trends, correlations, and anomalies within the data. These insights provide valuable information for understanding past and present scenarios, predicting future outcomes, and identifying opportunities or areas for improvement.
  3. Evidence-based decision-making: Analytics facilitates evidence-based decision-making by providing objective and data-supported insights. Instead of relying solely on intuition or gut feelings, analytics empowers decision-makers to make informed choices backed by quantitative evidence. It helps reduce biases, increase accuracy, and optimize decision outcomes.
  4. Continuous improvement: Analytics is not a one-time process but a continuous and iterative one. It involves analyzing data, evaluating outcomes, and refining models or strategies based on new insights. By continuously monitoring and analyzing data, organizations can adapt to changing conditions, identify emerging trends, and make adjustments to stay competitive and achieve their goals.
  5. Actionable outcomes: The ultimate goal of analytics is to drive action and create value. It aims to provide actionable recommendations or insights that can be translated into tangible outcomes. Whether it's optimizing operations, improving customer experience, enhancing marketing strategies, or making strategic investments, analytics enables organizations to make data-backed decisions that lead to positive outcomes.

Overall, the premise of analytics revolves around leveraging data, statistical methods, and analytical techniques to extract insights, support decision-making, and drive continuous improvement and value creation in organizations.

Causal Analysis

Causal analysis, also known as causal inference or causal reasoning, is a process of determining cause-and-effect relationships between variables or events. It involves identifying and understanding the causal mechanisms that link one variable or event (the cause) to another variable or event (the effect).

Causal analysis aims to go beyond mere correlation or association between variables and seeks to establish a cause-and-effect relationship. It helps answer questions such as "What causes a particular outcome?" or "What are the effects of a specific intervention?"

I primarily use Causal Analysis in Software and System Engineering and Delivery. First we plan for failures and how they may impact human resource utilization and delivery objectives. The  old saying, "hope for success plan for failure" applies here.

The Progression of Analytics


This model can be applied to Personalization where the vision is to tailor an effortless customer experience to accommodate specific needs related to content, products, and services.

The model is made up of vertical and horizontal partitions (or pools). The vertical partitions consist of Class elements to act as templates for building analytic flows in progression to achieve effective Machine Learning.



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