Strategic advice to leverage new technologies

A paradox increasingly confronts a number of researchers and analysts: to derive value from data, you need a lot of it. We now have the tools to analyze these large amounts, but few institutions, at least in the private sector, are willing to make it available to others.

For those not familiar with the structure of subways, the third rail is the one that carries the power for the trains. Under no circumstances do you ever want to touch it.

Big Data describes the evolutionary results of digitization, storage growth, and processing capability. It encompasses the growth of data in volume, variety, and velocity resulting from the increasing amounts of digitized material and data generated on the Internet.

As more traditional enterprises start to move their Hadoop projects into production, they are confronting the big question: How do we ensure data security and compliance in Big Data environments like Hadoop?

Big Data is inevitable and low latency is the need of the hour. Effectiveness in processing data has never been so relevant. If not engineered well, data processing systems that operate on Big Data are sure to suffer from performance problems. In this Executive Update, we explore an intuitive heuristic that will enable the user to understand the technical tradeoffs and learn how to performance engineer as well as tune a data processing system to effectiveness.

One of the most popular articles ever written for the Harvard Business Review was authored by Frederick Herzberg in 1968. 1 In it, Herzberg proposed a re

Manipulating "data in flight" versus "data at rest" is the next frontier in the analytics discipline. The most recent exuberance from the IT industry centers on Big Data parallel processing, NoSQL technologies, and the in-memory capability for business intelligence queries. These trends enable more agility for the practitioner and provide enterprises a greater ability to react to information with lower latency than ever before. These factors contribute to enhancing the value that companies can extract from data assets. Much of the current focus lies with data at rest, in existing relational and NoSQL databases, but these systems don't have the correct physics to react to data in flight. Data in flight refers to real-time data streams and requires a unique toolset, skill set, and mindset to implement successful systems. In this article, we'll examine real-time data streams in practice.

As I wrote in the last installment of CBR, I'd like to close each issue by asking (and answering) the question, "Now what?"; pulling together some of the key action points provided by our contributing authors and then throwing in my own two cents. So now that we have had a clear look at the state of play of real-time analytics, what should your organization be doing next?