Jul 142015

The New Needs of Digital Business

Digital business requires change across a very wide range of areas. There is an increasing use of storage, vastly expanded networking requirements, and a rise in the virtualization of all equipment. Digital systems deployed on the network can be replicated, modeled, and situated anywhere, so we have seen virtual networks, virtual servers, virtual mobile solutions, and virtual workstations of all types. Virtualization creates a need for new management techniques that control, replicate, and abandon virtual components on an automatic basis and manage their various interactions. Information technology is moving outside the firm to the public cloud, either directly or connected through a hybrid cloud mechanism. All aspects of IT are becoming increasingly connected to all the artifacts and processes of the firm.

The frameworks used in EA are also continuing to evolve and include elements such as big data, the cloud, mobile, and the other familiar elements of the changing environment. But what has not evolved so swiftly is the ability to rapidly change the models themselves and what they include as the cycles of technology change continue to accelerate. Continued development of digital business creates a space of massively interconnected data and processing, which must evolve into a more effectively governed system.

While EA was initially viewed as a solution to a problem of managing and optimizing a relatively static processing environment and its associated connections, the issues it was designed to solve remain, in many cases, critical. It is more necessary than ever for equipment to process optimally, for data to be accounted for, for network connections to be optimized, and for a company’s investment in technology to fully reach its maximum ROI. To achieve this, it is still necessary to chart, document, and plan the components of the IT infrastructure. But, in digital business, that infrastructure is much larger and more ambiguous. This means that new tools need to be deployed to provide the services that we have become accustomed to from EA.

What Does It All Mean?

For the data center manager, all of this means that architecture is becoming more visible. At the same time, it is necessary to create a solution that is capable of high-speed change and linked to analytics and the increasing stream of infrastructure data that has become available from the Internet of Things. The possibilities with this new architecture are immense since it will be possible to create automatic optimization based upon real-time data from logs and other related information provided by the equipment itself in its operation. This will mean greater efficiency and more opportunities to develop a powerful business-focused environment of processing for the benefit of the firm.

Few attempts have yet been made to apply a big data approach to EA. One reason is that architecture is too central to the IT mission and there is fear of change and of losing control. Yet real control was already lost several decades ago, and IT management needs to move to an advisory, consultative, and analytic role more than retaining the mantel of master of internal services.

As we move into this new era, it is clear that greater automation of the EA processes will be required. In a move somewhat like “physician, heal thyself,” we can begin to apply the kind of technology to this task that has been advised for other processes. This is, of course, the operating environment of the big data revolution.

Photo: Bruce Nauman via CC Share-Alike license.


Brian Dooley

Brian J. Dooley is a Senior Consultant with Cutter Consortium's Data Analytics & Digital Technologies practice. He is an author, analyst, and journalist with more than 30 years' experience in analyzing and writing about IT trends.


  3 Responses to “The New Needs of Digital Business”

  1. The possibility of integrating analytics and automatic optimization from real time data through architecture operation is ideal, not just for smooth functioning of IT but also for maximization of business goals. However, it is indicative of a need for restructuring of the organization, so that, as in the case of infrastructure, optimization of skill can also be achieved in the field of human resources, particularly the management.

  2. avatar

    While i see certainly a potential to leverage data to automatic optimisation in some areas, i would not expect too much bottom line benefit from that,
    Standards for “Self-Organizing Networks” have been around in Telco for quite some time. Troubleshooting/Optimising decentralised systems of semi-autonomous agents requires rare(=expensive) skills and is often more effort than expected. Additionally there is the drawback, that Complex Systems require emergent practices rather than comfortably relying on existing “Best Practice” available for simple systems or “Good Practices” for complicated systems.
    Looking ahead:
    Ubiquous Machine Learning will make large system behaviour much more unpredictable, than what we are used to today. Do we have appropriate coping strategies for the higher probability of turbulent behaviour?

    Example: Complex(and fairly unpredictable) swarm behaviour is created by a few simple rules.

    • Yes, indeed! Changes are coming, and benefits will be irregularly distributed. Some areas will develop more swiftly than others. But this will also lead to new complexities, and decreased transparency in tactical decision-making–not to mention a need for new skills and changes in oversight.

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