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Big Data and Machine Data for CEOs

Moving big data using machine image

The idea that the amount of data is rapidly growing has come to be called “Big Data” in journalistic and analyst circles. “Big Data” refers to the fact that machines are producing data in volumes never before seen in recorded history. There are significant opportunities to analyze and exploit that data to find business insights.

The nature of Big Data is that it is complicated to manage and complicated to understand, and therefore the CITO must play a crucial role in helping transform a company from one that ignores the opportunities of Big Data—also sometimes called machine data—to a company that actually takes advantage of it.

This problem statement focuses on how CITOs can lead the transformation to allow a company to take advantage of Big Data.

Context and Background

If you look around the average data center, you see huge amounts of data being produced by all the systems involved. There are call detail records from telecommunications systems.

There are network activity logs that show a variety of network activity. There are applications that produce records of application events, such as noting when people sign on to an application. There are web logs that produce a URL-by-URL picture of how people are downloading pages.

There are RFID and other “Internet of things”-oriented systems that are producing a variety of data collections. All of the above can be called either “machine data” or Big Data.

The question is, how on earth can this data be of use to the business?

As we wrote in the blog on business intelligence and the data center, there is significant opportunity for these datasets to be mined and for business insights to be gathered out of them.

How Can this Happen?

The research in this problem statement would explain a playbook for understanding how a CITO can find out what the business needs to know, and then figure out if the machine data can handle it.

For example, a simple playbook might consist of walking the floors of a company and querying the business units about what questions that they cannot answer now that would be of high value to them.

Then the CITO could analyze the machine data available, and determine if any of those questions could be answered. If those questions could be answered, then the value of answering those questions would have to be determined.

Then, the investment in technology to answer them would have to be determined. Then, capital could be allocated if it turned out that the answering these questions proved to be a valuable use of resources.

In addition, once this type of data is providing regular business insights, it’s likely that the business side would like to use it on its own. Then it becomes possible to create environments that present this machine data to the business, just the same way that business intelligence systems present transactional data through business intelligence.