What is the Data Analytics Hierarchy Process of Needs?
Data analytic hierarchy process (DAHP) is a variation of the Analytic Hierarchy Process (AHP) that specifically involves the use of data analytics in the decision-making process. DAHP involves applying data analytics techniques and tools to the data related to the decision problem in order to extract insights and inform the decision-making process.
In DAHP, the data related to the decision problem is first collected and analyzed in order to identify relevant trends, patterns, and relationships. This data is then used to inform the development of the hierarchy of subproblems and the evaluation of the options within each subproblem.
DAHP can be useful in situations where there is a large amount of data available that needs to be analyzed and synthesized in order to inform the decision-making process. It can also be useful for making data-driven decisions when the data itself is a key factor in the decision. DAHP can be ...
10 ways BI tools to improve Analytics Hierarchy Process
How to create a data story?
Telling a compelling data story is a valuable skill that can assist you in communicating and sharing insights and knowledge gained from your data. A dataset containing interesting and relevant information is required to tell a compelling data story. You can start crafting your data story once you have your data and knowledge. This may entail organizing the data, performing analysis and visualization, and selecting the key points and insights to highlight.
Overall, telling a compelling data story is a valuable skill that can assist you in communicating and sharing the insights and knowledge gained from your data. You can help your audience appreciate and understand the key points and insights you present by carefully crafting your data story and making it engaging and easy to understand.
Setting the stage in a data story is a first step in telling a compelling and engaging story with your data. Setting the ...
Have you already tried GPT-3 chat demo?
ChatGPT is a prototype artificial intelligence chatbot developed by OpenAI in 2022 that specialises in dialogue. The chatbot is a large language model that has been fine-tuned using both supervised and reinforcement learning techniques. It is based on OpenAI's GPT-3.5 model, which is an improved version of GPT-3.
This time it was trained on publicly available data to create general intelligence. In fact they did a great job as it understands the questions and can generate unique responses with really relevant feedback.
You can check it out by yourself with asking anything, which is related to NLSQL. We tried and were impressed about details, which comes up from the dialogue.
Nevertheless, ChatGPT's factual accuracy has been questioned, among other concerns. Mike Pearl of Mashable tested ChatGPT with multiple questions. In one example, he asked the model for "the largest country in Central America that isn't Mexico". ChatGPT responded with ...
Unlocking the Power of Big Data with NLSQL
Big data is becoming increasingly important for organizations of all sizes and in all industries. With the ability to process and analyze large amounts of data, businesses can gain valuable insights into their customers, operations, and markets. The six Vs of big data include Volume, Velocity, Variety, Veracity, Value, and Verification. These 'Vs' represents the main characteristics of big data and are essential when designing a big data solution.
Volume refers to the large amount of data that is generated and collected, often measured in terabytes or petabytes. Velocity refers to the speed at which data is generated and needs to be processed, often in real time. Variety refers to the wide range of data types and formats that need to be managed, such as structured, unstructured, and semi-structured data. Veracity refers to the accuracy and trustworthiness of data, and Value refers to the potential business value that can be ...
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What are decision support systems?
An interactive information system called a Decision Support System (DSS) analyses enormous amounts of data to help guide business decisions.
A DSS supports the management, operations, and planning levels of an organisation in making better decisions by evaluating the impact of uncertainties and the considerations involved to make one choice over another.
To assist users in making decisions, a DSS uses a variety of raw data, papers, personal knowledge, and/or business models. Relational data sources, cubes, data warehouses, electronic health records (EHRs), income estimates, sales projections, and other sources may all be utilised by a DSS.
Decision Support Systems consist of three key components: the database, software system, and user interface. The size of the DSS database will vary based on need, from a small, standalone system to a large data warehouse. Microsoft Teams or other user interfaces allow users to interact and view analytics results.
Decision support systems ...