Information Systems and Business Processes

A system that enables business processes by facilitating communication and coordination among different departments; it helps in executing, capturing data of, and monitoring these processes.

Using an IS to carry out business tasks efficiently and effectively; systems can inform users about needed tasks and provide relevant information for completing each step.

IS record and store data from business operations (like dates, times, products, etc).

Using information systems to evaluate how well a process is performing by comparing actual results against standards. Identifying problems with processes.

Data created or captured by business processes.

Using software robots (bots) to automate business tasks.

Two methods for monitoring performance: a specific action or the overall process.

A collection of related records, organized by application.

Storing the same data in multiple locations.

Applications not able to access data used by other applications.

Different versions of the same data are not the same.

A structured collection of related data files.

Protecting database data from unauthorized access or loss.

Ensuring database data meets specified constraints.

Applications don't directly depend on data structure, allowing for changes.

The smallest unit of data in a computer, either 0 or 1.

Big Data comes in diverse formats like images, audio, and social media posts; it's often unstructured or semi-structured, unlike traditional data.

Big Data comes from many sources - internal and external, like emails, social media, and customer databases.

External Big Data sources can be unreliable because the information might be false or misleading.

Inaccurate, incomplete, or duplicate data in Big Data sets. This can include misspellings or repeated information, affecting analysis reliability.

Big Data quality, and the data itself, can change over time due to altered conditions during data capture.

Isolated information systems that need integration.

Databases designed to support decision-making.

Databases organized into tables with rows and columns, each row with a unique record and columns defining attributes.

Processing applications using multiple processors working on different parts.

The process of managing Big Data and gaining value from it.

A smaller, focused data store that draws data from a data warehouse, tailored for specific business needs.

Information about data, describing its structure, format, meaning, source, and usage.

Data organized in more than two dimensions, often visualized as a data cube, unlike relational databases' two-dimensional tables.

Organized repositories of business intelligence data, often by dimensions like product, geography, or time.

Systems providing data to data warehouses or data marts. These could be operational systems, ERP, websites, or external data.

Processes and technologies that prepare data retrieved from multiple sources before loading it into warehouses/marts.

Ensuring the data's accuracy, consistency, and completeness in a data warehouse/mart.

Categories (e.g., product, region, time) that organize data in data warehouses and marts.

Inaccurate or inconsistent data present in source systems; needs fixing or analysis.

Software used to analyze data from source systems at the start of a data warehousing project.

Processes and policies to ensure data quality, integrity, and security in a data warehouse or mart.