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Process Tempo Insights

  • Daria Chadwick

Why Cloud Migration Projects Fail

There is a difference between what contributes to the failure of cloud migration projects and what the root cause is. So what should CIOs be looking for?

Migrating to the cloud is a daunting task for even the most experienced IT executive. What makes the task all the more daunting is knowing that 90% of CIOs have experienced failed or disrupted data migration projects, and only 25% of those surveyed in the same study met their deadlines for migrations (CSA). Fortunately, CIOs that are soon to begin a cloud migration project - or CIOs that are actively planning a cloud migration project today - can take comfort in knowing there are two key ways they can increase the likelihood of success. But before we take a look at those two key ways, it's critical to understand something about the state of cloud migration today: There is a difference between what contributes to the failure of cloud migration projects and what the root cause is. If CIOs can begin their cloud migration process by tackling the root cause first, they will avoid contributing issues that would otherwise lie ahead of them. Essentially, they can clear the path of obstacles before they even set foot on it. Let first examine what contributes to cloud migration failure:


What Contributes to Cloud Migration Failure?


  • Planning limitations. Project leaders understand that trying to execute a migration without a thorough plan of action creates a cloud migration risk in itself. But despite substantial planning efforts, migrations still fail. It's not that project leaders are failing to plan outright or that they don't plan enough; they face limitations around the extent to which they can prepare.

  • Collaboration limitations: A great deal of movement needs to occur regarding cloud migration. This movement occurs not just around data but also people and processes. Each step of a cloud migration project - planning, executing, monitoring, or optimizing - requires a massive amount of human feedback, input, and collaboration. Project leaders know this and understand that practical cooperation and coordination between these moving parts are critical. It's why these projects kick off a massive influx in meetings, emails, and zoom calls, and it's why spreadsheet usage goes through the roof. But, as with planning, project leaders face limitations. Only this time, it's around the degree of collaboration/coordination they can and should be affording to all these moving parts.

  • Scope limitations The combination of the two elements above means that project leaders face severe restrictions in understanding the true scope of their migration project. Cloud migration efforts are often rife with blindspots that can be massively disruptive to critical migration processes like wave planning. Before starting to plan a cloud migration, project leaders must have a clear and accurate understanding of the scope of the project at hand.


The Root Cause of Cloud Migration Failure


CIOs can navigate around these limitations with the help of data. Data helps remove many limitations around digital transformation projects when applied intelligently. This is because data maturity is strongly correlated to digital maturity, and attempts to digitally transform without transforming data in tandem often lead to problems.


When data is not fit for purpose, it creates a slew of compounding problems around planning, collaboration, and decision-making that can cause migration projects to fail. But having mature data and mature approaches to that data can help CIOs expand the extent to which they can plan their migration, expand the degree of collaboration they can and should afford to moving parts, and understand the true scope of their migrations to make better, more informed choices.


So what should CIOs do to get their data fit for cloud migration purpose, fast?


Step 1: Establish Transparency with Digital Twins

A digital twin is a virtual representation of a real-world physical system or product that serves as an indistinguishable digital counterpart, providing extensive, contextual data and information about the real-world system. Creating a digital twin of your infrastructure can provide numerous benefits that can help Chief Information Officers (CIOs) deliver successful cloud migration projects, such as:

  • Reduced Risk: A digital twin allows CIOs to test out different scenarios before they are implemented in real life, reducing the risk of system incompatibility issues or other problems that could arise during the cloud migration project.

  • Improved Performance: By looking at detailed data about every infrastructure element, CIOs can identify areas for improvement and make changes to increase efficiency. This can help improve the performance of existing services and ensure that any new cloud migration project runs smoothly.

  • Increased Insight: The ability to simulate potential changes provides insight into how various infrastructure elements interact, allowing CIOs to understand potential issues better and develop strategies for mitigating them before they become major problems.

By harnessing the power of a digital twin, CIOs can create an excellent starting foundation of information for their cloud migration projects by providing enhanced observability that can provide maximum benefits to their organization.

Step 2: Apply Observability with Graph Applications

Applied observability is the applied use of observable data in a highly orchestrated and integrated approach across business functions, applications, infrastructure, and operations to enable the shortest latency from action to reaction and proactive planning of business decisions. (Gartner)


Digital twins provide an unparalleled level of observability into an organization's infrastructure. But the ability to apply that observability is where the value of a digital twin is truly realized. This is where graph applications shine.


Graph applications are graph-native data applications that can be overlaid on top of complex datasets, like digital twins. When overlaid, they become powerful, purpose-built solutions for cloud migration stakeholders. Graph applications simplify and centralize complex data projects, like cloud migration efforts, ensuring that cross-departmental or cross-organizational data aren't carried out in data isolation. They provide a secure, foundational, centralized data environment that revolves around stakeholders' needs in sharing and collaborating around cloud migration data. And, they have embedded data, analytics, and workflow capabilities within the application to accelerate the critical data efforts that need to take place.


Ensuring Success


Armed with a digital twin + graph application, project leaders and stakeholders can now build their cloud migration projects on a robust data foundation. This approach delivers some key benefits.


The Ability to Optimize Before Migrating. Because of cloud migration efforts' perceived complexity, many organizations lift and shift directly to the cloud. While this approach may save time and effort in the short term, it can lead to problems down the line around planning or application functionality and can lead to increased cloud costs. The digital twin + graph application foundation allows for the lift-and-shift approach and its pitfalls to be avoided entirely. Next-gen observability from the digital twin makes blind spots and inefficiencies apparent, and the capabilities of the graph application allow stakeholders to take action on plugging and addressing these blind spots with ease. Stakeholders can address these blind spots by tapping into existing datasets, tools, or other systems to fill information gaps. They can also leverage another bonus of graph applications: human knowledge capture. Human knowledge capture is a feature unique to Process Tempo that allows stakeholders to blend human knowledge with collected system data to provide more intelligent and contextual information. Stakeholders can also use this same feature to correct inconsistencies in data, helping to improve data quality over time. Optimizing before migrating allows each step of the migration process to run smoothly, results in more cost-effective cloud spending, and helps stakeholders leverage high-quality data to plan, execute, monitor, and adapt their cloud migration efforts.

The Ability to Intelligently Plan, Execute, Monitor, and Adapt Your Cloud Migration Armed with the cleanest system of record around their cloud migration data, stakeholders can begin planning, executing, monitoring, and adapting the migration effort within the graph application itself. Taking these steps within the digital twin-backed, graph application itself vs. using external project planning tools or spreadsheets allows stakeholders to:

  • Apply ML + AI techniques to help build smarter wave plans

  • Create accurate, data-driven, realistic deadlines

  • Orchestrate the input of multiple stakeholders

  • Leverage executive-level summaries and insights into the project

  • Monitor progress through critical KPIs

  • Adapt quickly to new priorities or unseen changes

  • Proactively respond to anticipated challenges

  • Accelerate migration efforts as needed

 

CASE STUDY: How A Top Fortune Automotive Manufacturer Migrated Thousands of APIs to Google Apigee with Neo4j's Digital Twins + Process Tempo's Graph Applications

  • 4,000+ APIs Requiring Migration

  • 30 Days From Idea to Production

  • 11 Teams Involved in Planning

  • 300+ Stakeholders Involved in the Effort


Consolidating capabilities to the cloud continues to be a top priority for top US auto manufacturers. For one manufacturer, reducing API traffic via a single cloud gateway was critical and required careful planning. The company selected Process Tempo + Neo4j to help make this effort - and future efforts - a success because of the unique combination of offerings.


Before implementing Process Tempo + Neo4j, stakeholders attempted to collect and analyze data in shared spreadsheets. These spreadsheets quickly broke due to the constantly changing environment, could not cover the scope of the project, and were not able to support the collaborative needs of the planning team.


Process Tempo + Neo4j's first step was to establish an accurate representation of the API landscape for the manufacturer by creating a digital twin. The manufacturer knew their landscape had numerous applications, teams, and consumers that would be potentially impacted by the shift to the cloud, but had very little visibility or single source of truth into such a large spread of moving parts.


Neo4j's ability to store API landscape data as a graph made it easy for stakeholders to consolidate, integrate, blend, and analyze this data into a central location, allowing them to conduct critical impact and dependency analyses. Process Tempo's graph-native applications, overlaid on top of the digital twin infrastructure, then made it easy for stakeholders to access this information, collaborate around information, understand the potential downstream impact, and make decisions with real-time data.


The next step was to develop and execute the migration plan, a coordinated and collaborative effort across several teams. When these teams attempted to plan using the spreadsheet approach, they found key details were overlooked. Lack of visibility into the infrastructure often AssumptionsFor example, because no two services were alike, they could not be treated equally during planning, as each needed to be weighted based on the complexity involved. Before having Process Tempo + Neo4j, assumptions were made about services and applications that caused problems down the line.


The graph algorithms native to the Neo4j infrastructure made it easier for stakeholders to develop a more intelligent plan that accounted for these differences. This planning, knowledge sharing, and collaboration was made possible by the native workflow features found in Process Tempo, and was enhanced with AI & ML techniques. Stakeholders also used the combination of dashboarding, workflow, and advanced analytics to help them adapt their schedules to changing conditions as they arose.


For the first time, the manufacturer had an established proven framework in place that not only delivered a successful migration project, but was very easy to replicate. Additionally, implementing the solution opened up a number of other use cases outside of cloud migration that leveraged the power of digital twins + graph applications, including cybersecurity, decisin support, reduced cloud spend, and more.


 

Getting Started with Process Tempo + Neo4j


Only Process Tempo + Neo4j can help you avoid the root cause of cloud migration failure. By elevating and accelerating your data maturity levels around your specific cloud migration project, you can immediately understand and effectively manage the true scope of your migration project from the get-go and maintain that clear scope even if plans change. From the moment your migration planning kicks off to the final stages of post-migration optimization, you'll be able to create and meet your realistic deadlines, avoid costly mistakes, and migrate successfully.


We start by tapping into your critical information and processes to create a digital twin of your infrastructure. We then overlay purpose-built, graph data applications that deliver essential data, analytics, workflow, and collaboration capabilities to migration stakeholders in a no-code, intuitive environment. Armed with this capability, we work to help you close any data management and digital transformation gaps that may exist before you migrate. With everything in place that you need, you can now quickly and easily provide accurate planning and collaboration capabilities that keep your entire team organized and focused every step of the way.


Let Process Tempo + Neo4j intelligently put your data to work to help save you time, money, and effort on your cloud migration project. Know that your migration will run smoothly and intelligently with our industry-leading solutions.


Process Tempo + Neo4j Cloud Migration Data Sheet 2022-2023
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