This three-part series introduces you to the art of managing the many necessary decisions for managing your multicloud environment.
If you missed it, check out Part One.
Cloud Decision Management Process (CDMP) is an application-centric process that ultimately recommends which target cloud platforms that individual applications should be landing. You can execute this in two stages: Workload Filtering and Application Grouping, followed by Cloud Platform Evaluation and Recommendation. Each of these stages has its unique objectives and target outcomes.
To enable speedy decision making with concerted efforts, CDMP acts as a funnel to filter those applications not suitable for cloud computing. For those deemed suitable, they are associated with viable cloud platform options. Essentially this step limits each application to a small number of cloud platforms for further consideration.
Figure 1: High-level planning for multicloud strategies
Using the example in Figure 1, the CDMP establishes a sufficient number of filtering questions (mostly closed questions asking for binary ‘Yes’ or ‘No’ response). CDMP then uses these questions to direct individual applications to their potential destinations, including public clouds, private clouds, SaaS, OpenShift, or simply retained in the current data center. The key mechanism is a decision tree that fits each application to a single destination platform, if possible, or to associate them with a few platform options. The outcome of the latter can then inform the evaluation process taking place in Stage 2.
Figure 2: CDMP Stage 1 Decision Tree
Figure 2 shows a possible decision tree framework the company can build for the scenario illustrated in Figure 1. Each circular shape is an abstract of one or more filtering questions that help determine each application’s fit-for-purpose cloud platform. In this illustration, the filtering process takes two levels. The first level aims to immediately disqualify those applications that are not suitable for cloud computing. Perhaps the applications are at their end of lifecycle or their usability and compatibility with other systems after migration to the cloud. The second level aims to determine the appropriate cloud platforms for individual applications. The sequence in which the filters are processed should reflect the company’s cloud adoption strategy. In this illustration, the company prefers SaaS over public clouds and private clouds, assuming everything else is equal. You can expect that if the company has a different strategy or policies related to cloud adoption, then the illustrated decision routing in Figure 2 would be quite different.
Within each circular filtering function, the company needs to develop one or more questions to effectively associate each application to a single platform or several options. The appropriate and relevant questions differ from company to company when we put their cloud adoption strategies in context. However, in general, these filtering questions would most likely be centered amongst four different domain areas of consideration, namely: Business, Platforms, Technology, and Operations.
Figure 3: Domain Areas for CDMP Filtering Questions
Figure 3 illustrates examples of the filtering questions that you can set for the four domain areas. You should focus on those questions that will help accelerate the identification of appropriate platforms for applications to keep the overall CDMP stage-1 decision tree relatively simple. For example, determining proprietary technologies and frontloading that determination in the decision tree would help you quickly identify a handful of applications not suitable for cloud computing. For systems with stringent data privacy and security requirements, the public cloud might be the unlikely option. Under such scenarios, the decision tree might branch out to the current data center, or the incumbent private cloud operator might be the only viable option.
The company should also limit the number of questions sitting on the decision tree. Each application should traverse no more than a dozen of filtering questions in total before reaching a recommendation. Eventually, you should identify a small subset of available options out of public cloud, private cloud, SaaS, or some other variations before entering CDMP Stage-2. Suppose you can determine a single cloud platform as the most appropriate destination for any particular application. In that case, there is no need to go further within the CDMP for this application.
By the time you reach Stage 2 of the CDMP, you have associated each application with a subset of platform options identified in Stage 1. The purpose of Stage 2 is to evaluate these platform options to select the most suitable destination. Because you are comparing different platform options, you should establish a finite number of evaluation criteria to enable an objective and consistent assessment.
You can perform platform evaluation quite intuitively with common guardrails or more methodically with a due-diligence process. If the applications and their associated infrastructure are not complicated, many companies might just handpick one of the platform options recommended from Stage 1. Perform this based on simple criteria such as cost of ownership, technology affiliation, risks of migration or transformation, or systems interdependency, whichever makes the most sense for the key stakeholders. However, when the company manages a massive installed base, and several applications carry a substantial inventory of compute, storage, and interacting systems, the evaluation should be undertaken with a more structured approach to generate consistent and objective outcomes. These systems could be quite mission-critical, have several key users and stakeholders, and be architecturally complex. Under such circumstances, the evaluation should be systematic and constitute the following major components:
This component focuses on what the company wants to measure when assessing potential cloud platforms to host a particular application. The evaluation criteria mainly encompass four different perspectives: technical feasibility, operational efficiency, service resilience, and business agility.
Technical feasibility is concerned with the technological challenges the cloud platform might present. These include risks and challenges attributed to migration, systems integration, use of opensource or other cloud-native services, modernization of pre-existing systems, and so on.
Operational efficiency focuses on time, manpower, and cost-savings that come from the future environment. These might include considerations of the future operating model, governance and control, support structure, people transformation, and skill requirements. The more cost-efficiently the company can transition into its steady-state, the better the platform option is.
Service Resilience is the key security, compliance, and availability baselines the company would like to pursue from the future cloud computing environment. As part of their cloud transformation objectives, most companies would like to improve these baselines so that they are getting more protection against unpredictable outages, satisfying industry regulations, and meeting corporate audit requirements.
Business agility, which could be the most neglected perspective if the transformation is led by IT, would grant credits for a target platform that accommodates the best route to business innovation, business change, and ad-hoc experimentation. These are typical customer-facing business unit priorities that need to be articulated into respective infrastructure capabilities, both functional and non-functional.
Figure 4 outlines some examples of evaluation criteria across the four perspectives, but this is not an exhaustive list.
Figure 4: Cloud Platforms Evaluation Criteria
These perspectives and criteria are relevant consideration regardless of how the company wants to approach assessing the target cloud platforms, be it with intuitive judgment or through a formal due diligence process. To keep the overall process streamlined, the company should also limit the number of criteria to those most impactful to business and IT performance.
After the company has criteria selected for evaluation from one of the four perspectives above, we need an epic to put the evaluation criteria into context to drive the evaluation process. The epic should state the scenario to which the criteria applies so that the platform options can be assessed for their suitability of hosting the particular application.
By reaching stage 2 of the CDMP process, you have determined all individual platform options to be suitable for hosting the subject application. The challenge is to assess the extent of suitability among different choices and surface the best one. Because we are comparing different platform options, one approach is to define a list of conditions for reflecting the extent of suitability, or credit tiering, and assign a score to each tier. The company can then score each platform option by applying the evaluation epic against the credit tiers. After passing through all evaluation criteria, you could then recommend the platform option that comes up with the highest total score at the end.
You should not treat the multiple criteria used for platform evaluation equally since they have different business impact levels. For instance, criteria related to systems availability to the business are likely to be more important than criteria purely on how the subject application’s maintenance can be automated. Because of this, you can introduce a business multiplier to amplify the score, backing up the most suitable platform option to gain weight towards becoming the final recommendation.
Figure 5 illustrates an example of how all these components work together to provide a platform recommendation. The example assumes three target platforms are deemed suitable for an application that requires heavy integration with another system. Depending on where the other system resides, the evaluation schema incorporates an evaluation epic (question for qualifying the target platform) and an exhaustive credit tiering to assign a score to each of the target platforms based on a list of scenario options. The business multiplier, which models the business impact, doubles the initial scores to capture more weight for this evaluation epic in the final recommendation.
Figure 5: CDMP Stage 2 Cloud Platform Evaluation Schema
This example also shows why CDMP is essentially a consensus-building process. The evaluation epic has to be a common concern from the majority of stakeholders. The credit tiering has to be exhaustive to leave no gaps for credit assignment. The scenario options need to be relevant to what the epic is trying to assess, and the business multiplier must be appropriate to reflect the relative importance of these criteria, among others. All these are defined with inputs from different stakeholder groups with checks and balances to ensure CDMP’s recommendations are consistent, objective, and representative for the company’s overall interest.
To learn more, read Part Three of this series.
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