Let’s break down the terms data, data science, business intelligence, and analytics to understand their meanings and relationships:<\/p>\n\n\n\n
Data refers to raw facts, figures, and statistics collected for analysis.<\/p>\n\n\n\n
Structured Data: <\/mark>Organized and easily queryable data, often found in databases. In a business context about data & data science, the word „data“ is also related to the practical implementation of handling data. Therefore using the term „data engineering<\/mark>“ would be probably better, less generic and specific. <\/p>\n\n\n\n Data Engineering focuses on the infrastructure and processes needed to make data accessible, reliable, and usable. It is responsible for creating the pipelines and systems that allow data to be collected, stored, and processed efficiently. Further it plays a crucial role in ensuring that data is of high quality, consistent, and available for analysis.<\/p>\n\n\n\n At least to me, it makes a lot of sence relate „Data“ to „Data Engineering in this context. Providing a solid ecosystem for data frist and then make a hand over to data science and it’s related fields.<\/p>\n\n\n\n Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. Apply, use or provide:<\/p>\n\n\n\n Statistics and Mathematics: <\/mark>Fundamental for data analysis and modeling. Business Intelligence involves the use of technology, processes, and tools to analyze business data and present actionable information to help executives, managers, and other corporate end-users make informed business decisions. Apply, use or provide:<\/p>\n\n\n\n Data Warehousing: <\/mark>Centralized storage of data from various sources. Analytics involves the systematic computational analysis of data or statistics. It is the discovery, interpretation, and communication of meaningful patterns in data. For some reason people relate „descriptive analytics“ to Business Intelligence and „predictive analytics“ to Data Science and that makes absolutely no sence to me.<\/p>\n\n\n\n Data and Data Science: <\/mark>Data (Engineering<\/em>) is the foundation of data science. Data scientists use various techniques, including statistical analysis and machine learning, to extract meaningful insights and patterns from data. In summary<\/mark>, data is the raw material, data science is the process of extracting knowledge from that data, business intelligence is the use of technology and tools to analyze business data, and analytics is the overall process of examining data to draw conclusions. They are interconnected and often used together to support informed decision-making in various domains.<\/mark><\/p>\n","protected":false},"excerpt":{"rendered":" Let’s break down the terms data, data science, business intelligence, and analytics to understand their meanings and relationships: Data Data refers to raw facts, figures, and statistics collected for analysis. Types of Data Structured Data: Organized and easily queryable data, often found in databases. Unstructured Data: Information that doesn’t have a predefined data model and […]<\/p>\n","protected":false},"author":1,"featured_media":156,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-155","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigpicture"],"_links":{"self":[{"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/posts\/155","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/comments?post=155"}],"version-history":[{"count":10,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/posts\/155\/revisions"}],"predecessor-version":[{"id":237,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/posts\/155\/revisions\/237"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/media\/156"}],"wp:attachment":[{"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/media?parent=155"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/categories?post=155"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bitwise.exposed\/index.php\/wp-json\/wp\/v2\/tags?post=155"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Unstructured Data: <\/mark>Information that doesn’t have a predefined data model and is not organized, such as text documents, images, videos, etc.
Semi-structured Data: <\/mark>Falls somewhere between structured and unstructured data, having some organizational properties but not to the extent of structured data.<\/p>\n\n\n\nIn a business contex<\/h6>\n\n\n\n
Data Engineering<\/h6>\n\n\n\n
Data Science<\/h4>\n\n\n\n
Programming Skills: Often involving languages like Python or R.
Domain Knowledge: <\/mark>Understanding the industry or field to interpret results effectively.
Machine Learning: <\/mark>Algorithms that allow systems to learn patterns and make predictions.<\/p>\n\n\n\nBusiness Intellignece (BI)<\/h4>\n\n\n\n
Reporting and Querying: <\/mark>Generating reports and querying data for insights.
Data Visualization: <\/mark>Presenting data in a visual format (charts, graphs, dashboards) for better understanding.
Performance Metrics: <\/mark>Monitoring and measuring key performance indicators (KPIs).<\/p>\n\n\n\nAnalytics<\/h4>\n\n\n\n
I want to bring the term „Analytics“ into play, cause at least at the moment I read a lot of articles about Data Science and Business Intelligence, where I’m couldn’t agree with the interpretation and definition of these terms.
Short, Business Intelligence is focused to the past (descriptive analytics) and Data Science to the future (predictive analytics). To be clear, that’s not my opinion and here is why:<\/p>\n\n\n\n
There are different types of Analytics:
Descriptive Analytics: <\/mark>Examining historical data to understand what has happened.
Predictive Analytics:<\/mark> Using data and statistical algorithms to identify the likelihood of future outcomes.
Prescriptive Analytics: <\/mark>Recommending actions based on predictions to optimize outcomes.<\/p>\n\n\n\nRelations<\/h3>\n\n\n\n
Data Science and Business Intelligence: <\/mark>Business Intelligence uses data science methods to provide stakeholders in a company with data and insights they need. Litteraly „business“ in business intelligence means a business context, while data science could be everything, from chemistry, physics, engineering, etc …. and of course economy.
Analytics, data science and business intelligence: <\/mark>Analytics, for me that word is far to generic, to give it a specific meaning in a business or any other context. There are different types, like descriptive, predictive, prescriptive, to generate insights out of data.<\/p>\n\n\n\n