Unlocking the Value of Real-Time Analytics

Chapter 1. The Four Requirements of Real-Time Analytics

Data alone is not valuable. It needs analysis to turn rows of attributes and numbers into actionable insights, and these insights usually have more value when you generate them sooner. Real-time analytics is the ideal situation when data is available for reporting as soon as it is collected. Any delay in the process of moving data from data storage to transactional systems to analysis can leave business value on the table.

Consider this customer service example:

1. A company collects data on a customer unable to find a particular product during a web interaction.

2. The data sits in the transactional system until it is loaded into an analytic database hours later that night.

3. An analyst writes a query to pull information about what customers are unable to find. This takes several hours to write and run.

4. The report arrives in the salesperson’s inbox 24 hours after the transaction occurred.

5. The product the user was looking for was available at another nearby store, but because it took so long to collect, store, and analyze the data, the customer moved on to another vendor.

So how do companies reduce or eliminate the delay from transaction to reporting? More importantly, how can that company generate and use that insight at the point of sale at the very moment that customer is looking for that product? There are four key requirements for supporting real-time analytics. They are latency, freshness, throughput, and concurrency, ...