DATA and IoT (Internet of Things) / IoE / IoX
Data is used to give support to guide your business decisions.
IoT is data acquired from real-time computing or computing devices such as sensors, GPS, cell phones, robots, drones, and so forth.
DATA ranges from very small to very large.
BIG DATA is very large data, too large for humans to mentally process in their own brains. Computing power is recommended when working with BIG DATA.
DATA IN THE CLOUD is data that is acquired, stored, or computed from accessible storage that is physically located outside the connected devices that use the data
TRAINING DATA is carefully selected to find important analytical patterns that can support better informed decisions
PREDICTIVE ANALYTICS uses your data to make sense of it in ways that help answer your business questions
IoT is data acquired from real-time computing or computing devices such as sensors, GPS, cell phones, robots, drones, and so forth.
DATA ranges from very small to very large.
BIG DATA is very large data, too large for humans to mentally process in their own brains. Computing power is recommended when working with BIG DATA.
DATA IN THE CLOUD is data that is acquired, stored, or computed from accessible storage that is physically located outside the connected devices that use the data
TRAINING DATA is carefully selected to find important analytical patterns that can support better informed decisions
PREDICTIVE ANALYTICS uses your data to make sense of it in ways that help answer your business questions
DATA SERVICES
Data Understanding• Work with your team to determine what data is needed to support the answer to your business question
• Discover if the data exists and is available • Access and review a sample data set • Clearly state if the data may answer none, part, or all of your business question |
Data Acquisition• Identify the locations, formats, and accessibility of the data
• Recommend tools to use for acquiring the data • Work with your team to acquire and test access to the data • Perform a proof of concept to acquire training data and validation data • Quality-check the data |
Data Preparation
• Transform the acquired data to ready it for analysis
• Communicate any issues uncovered in the data during transformation • Report the quality and usability of the transformed data • Work with your team to store the transformed training data and validation data |
Data Reports, Dashboards, Displays, Transfers, and Storage
• Work with your team to determine the recommended output tools, layouts, and content
• Review options and possibilities for optimal output results •Perform a proof of concept to write the output to the output locations |