Operationalizing Big Data

What is the primary difference between processes and workflows in data-intensive environments?

• Processes are task-oriented, while workflows are designed for decision-making.

• Processes are end-to-end structures, while workflows are high-level structures.

• Processes are designed for decision-making, while workflows are task-oriented.

• Processes and workflows are synonymous in data-intensive environments.

Why might substituting a big data source for a standard source in a workflow not work seamlessly?

• Big data sources lack processing approaches and performance.

• Standard workflows are inherently incompatible with big data.

• Big data workflows are less efficient than standard workflows.

• Standard data-processing methods are superior to big data approaches.

What is the recommended best practice for integrating big data into workflows?

• Assume existing workflows will handle big data seamlessly.

• Ignore data store selection and processing speed considerations.

• Modify existing workflows without considering big data types.

• Identify big data sources, map types to workflows, and ensure proper processing support.

Why is data validation particularly crucial when big data moves from exploratory to actionable stages?

• Exploratory data is more accurate than actionable data.

• Big data is always accurate in exploratory stages.

• Actionable data impacts operational conditions and decision-making.

• Validity becomes less relevant as big data progresses.

What is a key consideration regarding data volatility in the context of big data?

• Big data sources are always stable and long-lasting.

• Data volatility is irrelevant in big data analytics.

• The length of time data needs to "live" depends on various factors.

• Volatility is only a concern for standard data settings.