It seems that the bloom has left the rose for the concept of bimodal IT among some organizations. Not only can the transition from one mode of operating to another be challenging, but it’s made more so when unclear definitions lead to unrealistic expectations and assumptions. Unfortunately, this has all occurred in the two short years since Gartner Inc. coined the phrase “bimodal IT.”
What’s important to remember is that any kind of transition is often tricky. There’s no guarantee of success — merely a set of recommendations for the journey. Nobody said transitioning to a more dynamic and continuous process would be easy. However, failure, fear and skepticism should not give people license to remain stuck in their legacy systems or rush headlong into change.
Let’s examine what this term “bimodal IT” actually means, why it makes sense in some cases and how to ease the pain of transition.
Fundamental transformation ahead
Let’s start with a clear understanding of just what Gartner meant by bimodal IT. The analyst firm defined this process within IT infrastructure and operations as “the practice of managing two separate but coherent styles of work: one focused on predictability, the other on exploration.”
- Mode 1 focuses on predictability and has a goal of stability. It is best used where requirements are well understood in advance and can be identified by a process of analysis. It includes the necessary investment in renovating and opening up the legacy environment.
- Mode 2 is exploratory. In this case, the requirements are not well understood in advance. Mode 2 is best suited for areas where an organization cannot make an accurate, detailed, predefined plan because not enough is known about the area. Mode 2 efforts don’t presume to predict the future but allow the future to reveal itself in small pieces. Gartner ended its original, short definition with “Both play an essential role in the digital transformation.”
Transformation is a process, not a destination. Bimodal IT creates two separate groups that work at different speeds on segregated systems. It is typically characterized by a waterfall vs. Agile scenario. Waterfall methodology follows linear, sequential development with distinct goals for each phase. By contrast, Agile processes seek to help teams respond to unpredictability through incremental, iterative work cadences and ongoing feedback.
This two-speed method may be the current de facto way of doing things and may remain this way for a while; but slowly and steadily, IT is undergoing massive and fundamental transformation to address customer and enterprise needs for agility.
Radical change, serious strain
Once upon a very recent time, it took six months to a year to develop and release a new version or update. Today, it’s not uncommon to see weekly or biweekly releases. What accounts for such a radical change in delivery speed? Five overarching trends contribute to IT transformation:
- Cloud services, including public cloud, have become increasingly popular with Amazon, Azure and Google, providing end-to-end services for application development and deployment. Private cloud environments have become commonplace as well. This leads to dynamic and agile application life cycle management.
- Continuous delivery and integration (CD/CI) that guarantees code is maintained in a deployable state at all times. While thousands of developers make changes, detailed hardening and testing phases are eliminated, leading to faster delivery.
- The DevOps movement is a close cousin of CD/CI, in which developers and operations team become more collaborative and work together. Hybrid DevOps models are coming into play in which operational staff is embedded into development teams in the interim.
- Dynamic architectures and technologies that can be scaled, like micro-services architectures that help products and services be developed incrementally, scale better and lend these products well to CD/CI. Investment in containers and virtualization has also helped this trend. These architectures are also dynamic and ever changing with application components that come and go. Containers are quite short-lived, with — on average — one-sixth the lifetime of virtual machines.
- Agile methodology helps enable more customer-driven and faster development of software and may enable CD/CI. However, an organization doesn’t have IT transformation solely by adopting Agile methodology.
All these trends lead to rapid-fire development, which burdens an organization’s underlying infrastructure. It poses new challenges to IT operations teams. It requires teams to manage unparalleled amounts of data while predicting and preventing outages, in real time, and maintaining and delivering agile, reliable applications. This increased complexity makes some organizations fearful about transitioning from Mode 1 to Mode 2 completely, as concerns loom over new processes and operational complexity.
In order to ensure availability, reliability, performance and security of applications in today’s digital, virtualized and hybrid-cloud environments, new approaches must be employed to provide operational intelligence to ease the transition from Mode 1 to Mode 2.
Toward a peaceful transition
Here’s a quick example of why the concept of bimodal IT came to be: the reason that Agile was created was to enable a faster, more responsive process than waterfall practices can offer. However, switching to continuous delivery and integration mode too quickly could prove disastrous for certain systems, as some change carries more inherent risk than other changes.
Underlying Gartner’s discussion of bimodal IT is the idea of providing breathing space so that organizations can transform and innovate without crashing and burning. With that in mind, following are three best practices to help ease the transition and ensure that applications continue to run at optimal levels.
- Both Mode 1 and Mode 2 applications are running on increasingly complex infrastructures, with underlying resources constantly changing to meet these applications’ performance requirements. You need visibility into all your data — including performance data, logs and topology — and the ability to visualize all layers of your application infrastructure stack in one place at any point of time. This allows you to identify the root cause of an outage or performance degradation in the past or the present.
- Tools that offer this visibility can also provide the capability to understand the impact of a software release on the operations in the Continuous Delivery and Integration mode (Mode 2). In the absence of such tools, conducting definitive post-mortem analysis is a costly, manual and confusing process — if it can be done at all.
- There is a big data problem brewing in IT. As IT goes through this transformation, it is becoming increasing complex and dynamic. Relying solely on traditional IT monitoring tools that trigger numerous alarms makes the job of IT operations teams even more difficult. Understanding all the raw data to make intelligent decisions in real time and sifting through the sea of alarms and telemetry data at the same time poses major challenge to IT operations teams. Artificial intelligence (AI) — especially machine learning — is well suited to take all the data and generate the necessary operational intelligence to distinguish critical, service-impacting events from false positives that do not require the immediate attention of an operator. As IT transitions, you need IT operations intelligence that can handle both modes of operations.
Traditional monitoring tools trigger alerts only after a problem has already occurred. This doesn’t prevent outages; it just tells you there’s already a problem. Look for solutions that incorporate predictive analytics to alert you to anomalous trends or potentially dangerous issues before they impact your application.
In summary, to ease and manage the transition from Mode 1 to Mode 2, automated solutions that analyze and provide insight into ever-changing applications and infrastructure topologies are essential. Equipping users with the ability to replay and analyze past incidents and to pinpoint performance degradation and root cause, while cutting out the noise and preventing future costly outages and downtime, is important to facilitate the transition. This operational intelligence connects enterprise DevOps and TechOps teams, giving them what they need to quickly address issues as they arise.
Individualized change
No two organizations are alike. There is no cookie-cutter solution that will work for every organization. Instead, each organization must take careful stock of its operations and determine which parts would do better to remain in Mode 1 and which can move to Mode 2 right away. Bimodal IT is a useful construct for IT teams along the continuum of digital transformation— but it is not a parking lot. Instead, it is a trajectory toward uni-modal IT that is suitable for the digital world: continuous, dynamic and agile. IT operations analytics can help lend the visibility and insight needed to create a successful transition.
Dr. Akhil Sahai is VP product management at Perspica. He is an accomplished management and technology leader with 25+ years’ experience. At HP Enterprise as senior director of product management, he envisaged and managed the solutions program. At Dell, he led product strategy and management of Dell’s Converged Infrastructure product line. He led Gale Technologies to its successful acquisition by Dell. At Cisco, he undertook business development for VCE Coalition. At VMware, he managed global product strategy and management for vCloud Software.