Knowing who is performing well in your organization — and why — are important factors in knowing what to do to improve. Traditionally, organizations have relied on subjective measures to answer questions of what is working and why it works. While sports and music are examples of the very few areas where it’s possible to isolate the contribution of an individual to the success of the “system”, the link between action and outcome is much murkier in most other areas. As Stefan Henningsson and Christian Øhrgaard point out in their recent article, Follow the Digital Trace: Turning Digital Artifacts into Digital Capital, most people contribute to success through a complex system of influencing conditions that Read more
Advice and opinion about the strategies, technologies and products that allow you to turn your enterprise data and knowledge into a powerful strategic weapon. Topics range from master data management strategies to CRM solutions.
Robotic process automation (RPA) and cognitive automation (CA) tools are getting a lot of attention. But, as reported by Mary Lacity and Leslie Willcocks in their new report, Smart Service Automation: Benefits, Cases, and Lessons, potential adopters of these new types of service automation tools remain skeptical about the claims surrounding their promised business value. These potential adopters are still not sure why an organization would move to service automation, what they’d achieve if they were to do it, and what are the best practices. To answer these questions, Lacity and Willcocks conducted two surveys of 143 outsourcing professionals and interviewed 48 people, including service automation adopters, providers, and advisors. From the interviews, they identified 20 adoption journeys. The 20 Read more
In the 1980s, everyone got excited about the possibility of artificial intelligence. The excitement grew for a few years and then gradually faded as companies found that it was too hard to build and maintain useful expert systems or natural language interfaces. However, there has been a renewed interest in developing software applications that can interact with people in natural languages, perform complex decision-making tasks, or assist human experts in complex analysis efforts. Today these systems are called cognitive computing systems or machine learning. They rely on research from artificial intelligence laboratories and use new techniques like deep learning and reinforcement which seem to overcome some of the problems that were encountered with earlier AI Read more
“How do you architect a lake?” If the question sounds like the opening line of a joke, the answer would clearly come as: “You don’t. You can only discover one.” Whether it is data warehouses or marts, data lakes, or reservoirs, the IT industry has a penchant for metaphor. The subliminal images conjured in the human mind by the above terms are, in my opinion, of critical importance in guiding thinking about the fundamental meanings and architectures of these constructs. Thus, a data warehouse is a large, cavernous, but well-organized location for gathering and storing data prior to its final use and a place where consumers are less than welcome for fear of being knocked Read more
At the recent RSA Security Conference in San Francisco, data-centric security and protection received a lot of attention. Several trends account for this. The main one, of course, is the large number of high-profile data breaches and other cyber attacks continually making the news — a trend that shows no sign of subsiding. In addition to this constantly lurking threat, we can add growing compliance and regulatory requirements as well as the advent of new (difficult to protect) technologies, applications, and architectures. Throw in all the revelations about hacking by various government intelligence services, and it’s easy to see why organizations and security solutions providers have made data-centric security and protection a top priority. The Read more
Analytics — deep, predictive, operational, (insert preferred flavor here) — has climbed to the top of business executives wish lists in the past few years. The explosion of big data from social media sources and the coming supernova from the Internet of Things promises complete understanding of customer needs as well as the prediction/influencing of future behavior. With sufficient data, best of breed algorithms, faster computers, and emerging deep learning approaches — statistical correlation will become a largely exact science. Understanding causation will become an unnecessary luxury. Welcome to the analytics nirvana. Of course, inspiration and implementation often diverge. The day-to-day practicality of big data analytics continues to raise ongoing challenges. The “P-words” — preparation, people, prediction, and production point Read more
The data lake is an attractive use case for enterprises seeking to capitalize on Hadoop’s big data processing capabilities. This is because it offers a platform for solving a major problem affecting most organizations: how to collect, store, and assimilate a range of data that exists in multiple, varying, and often incompatible formats strung out across the organization in different sources and file systems. In the data lake scenario, Hadoop serves as a repository for managing multiple kinds of data: structured, unstructured, and semistructured. But what do you do with all this data once you get it into Hadoop? After all, unless it is used to gain some sort of business value, the data lake Read more