Archive for Enterprise IT

From Services to Cogs and Journey to Cognitive BPM

Earlier this year, I gave a keynote at 2017 IEEE Joint Conferences on BigData Service, and 11th IEEE International Symposium on Service-Oriented System Engineering (SOSE 2017) with a focus on Cognitive Assistant in the Enterprise, and Cognitive Services. There was requests to share the slides, which is released below with a bit of delay, hopefully it’s still relevant.

The talk went into two key areas:

  • Services, and cognitive: how the software architecture for services is impacted by cognitive technology, and the software architecture and methods for cognitive services, and in particular enterprise cognitive assistants, usually with a chatbot interface, as well
  • Cognitive business process management: how cognitive technologies is fundamentally impacting and enabling the automation of a large school of manual processes in the enterprise, and in personal space, for that matter, through cognitive understanding of processes that are described and interacted among people, as opposed to prescribed in models.

In each of technologies, I also provided concrete examples and, some from my own work, with related references.

The big issue in enterprise big data: linking massive number of data islands

 

Reports are coming on studies that suggest organizations started to see the opportunity and benefits of big data technology adoption for driving business decisions (Forbes on IDG survey). While IDG survey suggests that the investment related to big data analytics in the enterprise will increase steadily in 2014, other surveys still do not show signs of rapid growth in investments by organizations (CNN iReport on Bain & Company survey).

The real issue that may be underlying this observation is the nature of big data problem in the enterprise that have to be understood and addressed to support greater adoption. The enterprise big data is characterized as enabling analytical tools and technique to process large volume of data efficiently (mainly on top of the stack of HDFS, Hadoop, noSQL, etc.), however, arguments emerging that the enterprise big data problems is not about size, or even small data analysis is the next big thing, and the fact the loosely coupled small data could be more interesting aspect of big data in the enterprise.

Philippines-Hundred-IslandsWhile I strongly assert the importance of small data in the enterprise, I would go a step beyond by saying the big data problem in the enterprise today is how to make sense of massive number of data islands, a lot of small and some large, some centered around employees and generated by them, some shared in group settings using sharing and social media inside the enterprise, some stored in large enterprise application databases and document repositories and other information outside of the enterprise wall that the enterprise may care about to serve their customer better. The overarching problem in this context is how to link this data, interpret and understand it and make it available for data and business analytics purposes.

One trend to watch for in this space is development in the graph databases and graph knowledge representation, and how they are evolved to intelligently discover entities, and their relationships and make the graph available for analysis. The graph database providers are focused and advanced a great deal in improving the performance of data analysis on top of knowledge graphs, but more innovation needed on forming knowledge graphs over data islands.

Adaptive Case Management: the road ahead?

This past July (2013), Keith Swenson and I were invited by Jorge Sanz (IBM Research) to write a position paper on the current state of adaptive case management, and challenges and the next steps ahead for the industry and academia, for IEEE Conference on Business Informatics. Here is an excerpt, you may read the complete paper, here.

The landscape of work in the organizations has changed significantly. Over the last decade automation has been a major focus of organizations in IT and in other work segments. As the result, a lot of less skilled workers have given their place to machines and software [25]. Workers today spend less of their time on routine tasks, most of which are often automated, and more of their time on things that really require thinking, than was possible just ten years ago. The challenge today is how to support higher skilled modes of work: knowledge work. We can also call this kind of work “unpredictable work” because one cannot predict in advance the exact course of what will be done. It requires thinking in order to figure out what to do. The exact course of what needs to be done cannot be known in advance, and this is the central challenge to the traditional way of designing IT systems. The name “case management” is used to talk about an approach that supports the knowledge worker, without requiring that the work be constrained to a set of pre-defined actions.
Indeed, between 25% and 40% of the workforce can be classified as knowledge workers today [1]. Knowledge workers include managers, decision makers, executives, doctors, lawyers, campaign managers, emergency responders, strategist, and many others who think for a living. While extensive software and tooling support are provided for routine tasks, this has been less the case for knowledge workers and case management. The state of the art in technology support for case management can be described as systems of record, today. These approaches rely on people maintaining consistent information records, using disparate applications and manually tracking pieces of information related to a case across different systems. Substantial information related to cases lives outside the applications, often in the personal inboxes of knowledge workers without being linked to and shared with other relevant applications. This fragmentation makes it hard to reconcile case information.

As technologist, we are biased to see this change in the work landscape as a technology trend. However, what the current practice in case management needs to realize is that we are seeing a fundamental shift in our workforce, and in the ways they are managed. Not only are companies engaging their customers in new ways — using social media, mobile computing devices, and social networks — but managers are engaging workers in similarly transformed ways. The office is being transformed from an assembly line for the processing of forms, to far more agile and effective patterns for accomplishing organizational goals. While knowledge workers try to leverage recent technology developments in managing case work, there is a need for new approaches to support knowledge work in an integrated, flexible, worker-driven and holistic manner.
The term adaptive case management refers to managing the work needed to handle a case in a flexible manner by adhering to the principle of planning-by-doing, considering the work context, and the ability to accommodate changes in the environment and the work context [3]. Today, knowledge workers use a mix of applications (emails, communication, document and where applicable workflow management applications) and human work. Indeed, the majority of cases (74%) in Fortune 1000 companies are managed using multiple applications or are mostly done manually [3]. Some of the issues in this context include the fact that critical information to the handling of cases live in disparate systems, information loss on workers’ hand offs, workers who are not on sync, and the fact that communication and information exchange tools (such as email, chat and other tools used for sharing case information) are un-aware of the work context.
In this writeupe, we provide a brief overview of case management historically and offer a framework for understanding the work spectrum in the enterprise (doing a comprehensive survey is beyond the goals of this paper). We highlight research challenges in supporting knowledge workers, and review few recent work and products that take initial steps in this supporting knowledge workers. We describe a grand vision for an architecture of software systems for supporting knowledge work.  Continue reading here.

The Future of Enterprise IT and CIOs in the Cloud World

Enterprise IT has witnessed a series of changes over the past decades. With the rapid move towards adoption of cloud services, the impact of changes on enterprise IT has never been more profound. This is changing the traditional role of enterprise IT as the main entity for providing and supporting IT systems and services to becoming a broker and manager of IT services for a hybrid portfolio of IT services acquired externally or provisioned internally. Currently, much of IT service innovation in cloud services domain is focused on the service provider side. Not much attention has been given to the challenges at the service consumer side for designing, acquiring, operating and managing a portfolio of services.
In the following article, we discuss the trends and the changes that cloud services bring to enterprise IT and present a framework for a novel technology solution that supports the service consumer role of IT (published in IEEE Computer, and slightly modified version available as an HP Labs Technical Report)