When considering Big Data projects and architecture, list and describe five challenges designers should be mindful of in order to make the journey to analytics competency less stressful

What will be an ideal response?

• Data volume: The ability to capture, store, and process the huge volume of data at an acceptable speed so that the latest information is available to decision makers when they need it.
• Data integration: The ability to combine data that is not similar in structure or source and to do so quickly and at reasonable cost.
• Processing capabilities: The ability to process the data quickly, as it is captured. The traditional way of collecting and then processing the data may not work. In many situations data needs to be analyzed as soon as it is captured to leverage the most value.
• Data governance: The ability to keep up with the security, privacy, ownership, and quality issues of Big Data. As the volume, variety (format and source), and velocity of data change, so should the capabilities of governance practices.
• Skills availability: Big Data is being harnessed with new tools and is being looked at in different ways. There is a shortage of data scientists with the skills to do the job.
• Solution cost: Since Big Data has opened up a world of possible business improvements, there is a great deal of experimentation and discovery taking place to determine the patterns that matter and the insights that turn to value. To ensure a positive ROI on a Big Data project, therefore, it is crucial to reduce the cost of the solutions used to find that value.