 Название: From Data to Insights: The Strategy of a Data Analytics Team
Автор: John Mackay
Издательство: Springer
Год: 2025
Страниц: 125
Язык: английский
Формат: pdf (true), epub
Размер: 17.6 MB
From Data to Insights is a practical guide to building a strategy that addresses the common issues faced by Data Analytics teams. Not enough time, too many requests, unhappy stakeholders, and colleagues with low team morale are just some of the problems caused by poor strategy and design.
This guide will help managers avoid these pitfalls and improve team performance by focusing on long-term goals and strategic planning, ensuring your Data Analytics team can effectively support their business and its stakeholders.
Practical examples and explanations are provided to help understand how a Data Analytics team’s strategy can be implemented and why certain approaches will or won’t work.
Big Data refers to very large collections of data. These data sets are typically too large to be processed traditionally and are usually held in non-structured or NoSQL data stores. Non-structured Data includes files such as photos, emails, audio, or sensor data. We can still use integration tools to extract the data from these files, though we will need to convert this data to a format we can report with.
SQL and NoSQL: If SQL is “Structured Query Language”, then NoSQL just refers to “Not Only Structured Query Language”. What we really mean is that SQL data is tabular, whilst noSQL uses a different structure that is not so easy to process for reporting. There are a few types of NoSQL datastores, the most common for business being JSON file storage. A JSON file provides a key and value to represent elements of the data. Multiple values can easily be added to each key. We can report directly from a noSQL data store using some visualisation tools, or we can transform this data into a tabular structure.
To understand Artificial Intelligence and Machine Learning, we need to start at the base of computer programming, algorithms. An algorithm is an automated instruction. It is typically one of the first things you will learn when using formulas in excel, or a new coding language. Machine Learning can be incredibly complex, and it can relatively simple. This depends on whether we are using existing libraries and models, or developing our own. For most business uses of Machine Learning, we utilise libraries that have been created for us. This is similar to whether we use off-the-shelf software, enterprise software, or in-house built software. Python is a Data Science language commonly used in Machine Learning, with a large range of libraries we can utilise to create models, which can analyse and predict data for us.
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From Data to Insights: The Strategy of a Data Analytics Team 
