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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 Guatemala, …
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 (DSS) …
How to Enable BI Self-Service Analytics for your Company?
We assume managers at all organisation levels are key business decision-makers. But when managers hear about big data and analytics, what do they think? Do they comprehend how these technologies can be applied to decision-making? To take advantage of the capabilities brought by these varied mindsets, it is crucial to comprehend the distinctions between technical and non-technical employees. The gap caused by disparities between technologists and managers mindsets and education.
Due to their varied backgrounds, experiences, and training, managers and data professionals have different mindsets—different approaches to comprehending and solving problems. To take advantage of the capabilities brought by these varied mindsets, it is crucial to comprehend the distinctions between them. Thinking at these distinctions in terms of computational knowledge and contextual knowledge is one way to approach the topic. High degrees of computational knowledge are typically possessed by data professionals who specialise in the collection, analysis, and interpretation of …
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