Automated and Deep IT Business Impact Insight
Digitization of value chains turns ITOA into a strategic asset for management
Digitization explodes through organizations’ business processes and throughout complete B2B value chains spanning external customer, suppliers and partners. Digitization is increasingly real-time with low tolerance for latency and performance problems. Digitization is directly tied to business processes that can be directly translated to revenues or costs. The increasing reliance and vulnerability to performance issues in the IT stack becomes increasingly critical with P&L impact transparency.
2017 will see automated and deep IT business impact insight become increasingly deployed, understood and leveraged by management as a key competitive advantage to deliver shareholder value.
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Ivar Sagemo CEO, AIMS Innovation |
In 2017 more purpose-built analytics-driven apps will emerge. They will focus on a specific domain, aiming to solve a real business problem. It may leverage open source tools and platforms, but will bring in significant enhancement in the area of domain expertise and ease of deployment / maintenance. Instead of offline analysis mode, these apps will interact with core business functions in a real-time fashion therefore become more front and center.
IT Transformation is all about optimizing and automating IT alignment with business outcomes. ITOA has changed the game from Tools driven IT to Insights driven IT. ITOA solutions will start addressing multiple stakeholders beyond IT Operations. Next generation advanced ITOA solutions further change the game from Insights driven IT to Outcomes driven IT. Advanced ITOA solutions will take the holistic platform approach and have multiple touch points across operational domains and application layers for a given business outcome.
Digital Transformation is the biggest trend in enterprise technology and the Inability to provide business level reporting is one of the key pain points for IT performance of organizations.
The way datacenters are designed today doesn’t allow datacenter to wisely “right manage” and “right size” their Business IT, Lack of visibility into key business indicators resource utilization and too much time spent on troubleshooting and preventing performance issues.
In 2016 we saw container technologies such as Docker become production ready. As applications are further split into microservices and containerized, the number of metrics to monitor becomes unmanageable. Containers are constantly added and removed, and this at a much faster pace and larger scale than servers or VMs. In order to monitor such ephemeral infrastructures that generate an overload of constantly changing metrics, adapted ITOA tools will be required. Such tools will need to embed more knowledge about the infrastructure orchestration and service dependencies, and include techniques such as anomaly detection to rapidly identify abnormal behaviour without having to constantly reconfigure alerting rules.
IT Operations Analytics has been growing in the past years and offers organisations a lot of benefits in keeping their IT operational. However, with the increased data security threats for 2017, IT Operations Analytics to discover these threats is increasingly becoming important for organisations. Especially those organisations that offer Internet of Things products, or any connected device, need to be aware that those products are part of the IT network as well. Each connected device is a potential entry point for hackers and knowing how these devices interact with your IT network is vital for organisations to keep the gates closed. 2017 will therefore see an increased need for ITOA, not only to optimise the IT network, but more importantly to protect is from potential hackers.
General operational analysis of machine data, without any understanding of the context and domain knowledge, has its limits.
Last year we saw significant progress in the ITOA space blending and correlating multiple data sources. However, most of the ITOA solutions still require customers to slice and dice outcomes of blended analysis to interpret them or present these outcomes in a complex, specialized manner. I expect that this year ITOA technologies will expand use of recent advances in machine learning to automate data interpretation. The result will be a generation of specific, easy to understand insights that can be utilized by Operations teams without significant training and investigation overhead. The machinery of the analytics will be hidden from the users that will be just consuming and acting on automatically generated findings, guidelines and instructions, which will be presented in a native language.
IT operations will converge with DevOps practices, enabling the delivery of faster, better code, central to the notion that the application (user experience) is king. The adoption of DevOps as a cultural practice will accelerate and become broadly adopted as the best way to deliver software in the enterprise. Because of this, IT teams will need more flexible networking architectures and will require a new generation of ITOA monitoring capabilities. As insights are increasingly gleaned from ITOA, intelligence will move up the stack into software, and the commoditization of underlying network hardware will only increase.
As enterprises become increasingly digital, ITOA will drive business growth plans as an integral part of any digital transformation strategy. ITOA tools need to interoperate with emerging technologies, such as cloud, virtualization, mobility, big data, containers, micro-services and IoT – the foundation for successful delivery of digital services. An end-to-end correlated view between the end-user performance of digital services, the supporting applications and infrastructure, across complex environments, will become a critical requirement. Predictive and prescriptive analytics, automation, close-loop functions and artificial intelligence will continue to grow as top ITOA trends in 2017.
2017 will be defined by a significant shift towards the adoption of cognitive capabilities. One of the ways in which this will manifest itself is through cognitive chatbots that apply machine learning capabilities to demonstrate the ability to learn on the job. Chatbots will become the new SME that assists IT first responders to resolve incidents with greater speed and efficiency.
The data is out there ...
Enterprises, already slightly disillusioned by the failure of mainstream vendors to deliver machine-learning enabled “predicative analytics”, will take a more pragmatic approach to automating their management of IT Operations data.
Deep machine learning is expanding the automation and analytics capabilities of IT operations
With deep learning, machine learning and many other sub-domains moving into the mainstream in the past 12 months we will see these approaches applied to IT Operations data in 2017. Pattern recognition in unstructured no-SQL data will really start to blossom, providing predictive outcomes before they happen. For the first time in many years overall critical business down-time will decrease as the machines come to the aid of the human tech-worker.
There’s no doubt that hybrid IT is the reality for the majority of organizations today and in the foreseeable future. And not only that, the center of technology itself is becoming increasingly hybrid. IT professionals must start thinking about management in a hybrid context. But what does it actually look like in practice?
The recent election is going to heighten security in general because of the amount of backlash with foreign affairs that it has caused.
