Author: cprime-admin

The Sprint Backlog – An Actionable Plan to Deliver Value

The sprint is a container for Scrum events. It contains all the work a Scrum team will do to create an increment (which is formed when it meets the quality measures required for the product as defined by the team’s definition of done). The sprint is the heartbeat of Scrum. 

In her blog, 5 Powerful Things About the Sprint, Stephanie Ockerman covers how the sprint provides:

  • Focus (where ideas are turned into value)
  • Predictability (deliver a ‘done’ increment of work)
  • Control (to inspect an increment and adapt)
  • Freedom (for Scrum team to self-manage, collaborate, and experiment)

The first event of a sprint is sprint planning, which lays out the work to be performed for the sprint. The output of sprint planning is the sprint goal—a concise statement of what the team intends to accomplish during the sprint—and the product backlog items selected for the sprint, also known as the sprint backlog.

Why create a sprint backlog?

The Scrum Guide describes the sprint backlog as a plan by and for the developers. It is a highly visible, real-time picture of the work that the developers plan to accomplish during the sprint in order to achieve the sprint goal. 

The sprint backlog is solely owned by the developers, but the Scrum team collaborates over the work on it. For example, if the developers believe the work in the sprint backlog needs to change to better meet the sprint goal, developers would collaborate with the product Owner when making that decision. The sprint backlog is still owned by the developers.

The sprint backlog contains a commitment—the sprint goal—which provides the ‘why’ for the developers. It enhances transparency and focus against which the team can measure progress.  The sprint goal helps to answer questions like:

  • Why is it worthwhile to run this sprint? 
  • What assumptions/hypotheses do we want to test?
  • What is it we are trying to achieve? 
  • How does it get us closer to our product goal? 

How to create a sprint backlog

During sprint planning, developers collaborate with the Product Owner to craft the sprint backlog, which describes how they intend to deliver the increment. The sprint backlog is an actionable plan for delivery.

The developers decompose work (often expressed as user stories) into smaller items (often expressed as tasks). Tasks are small items of work that can be completed in a short timeframe (typically one or two days). 

Tasks are more precise, in detail and in scope, than user stories. When creating tasks, avoid vague statements such as ‘coding’ or ‘implementation’, thinking that you can just refer to the parent user story for the details. Instead, create meaningful descriptions of the tasks to make the scope of work very clear.

A blog by Victor Dantas offers a very good example of how to break a story down to a task level for a requirement for a web app: 

As a registered user, I want to log in with my username and password so that the system can authenticate me and I can trust it.

And with the following acceptance criteria:

Given that I am a registered user and logged out… if I go to the login page and enter my username and password and click on Log in, then the data associated with my user should be accessible.

By getting all the developers together in sprint planning to brainstorm on what is needed, you’re likely to hear things like:

  • “We need a new UI element for Sign-up and Login”
  • “We need to develop encryption functionality for the password”
  • “We need to create a table in the database for user information”

Now, to do things in a more structured way, let’s ask the developers:

How can we break this down into executable, scope-bound tasks? Here, the team may agree on the following tasks for the user story:

computer with code lines

  • Define Sign-up/Login form style and develop new CSS class
  • Develop HTML and Javascript Sign-Up/Login presentation layer code
  • Develop Javascript sign up form validation code 

Now you can see how the sprint backlog gets formed and grows during sprint planning.  However, during sprint planning, you will not create the perfect plan. The sprint backlog is an adaptive plan by and for the developers. It is a highly visible, real-time picture of the work that the developers plan to accomplish during the sprint in order to achieve the sprint goal. 

Consequently, they will update the sprint backlog throughout the sprint as they learn more and, as such, they create, re-order, add more detail, and delete as needed. 

Sprint backlog misconceptions and anti-patterns

Developers cannot change the sprint backlog during the sprint as it is a commitment

The myth is that the sprint backlog is fixed during the sprint and that developers must implement all the work items in the sprint backlog because they have committed to deliver them. If not, the sprint is a failure. Changes to the sprint backlog are not allowed and no work can be added or removed from it, as this creates a lack of focus and there is a risk of ‘goal’ creep. 

That’s not the case. 

The sprint backlog should not be static

The sprint goal is an objective set by the Scrum team during sprint planning. The sprint goal describes what the Scrum team wants to achieve during the sprint (to test an idea, hypothesis or run a test) and how it intends to be closer to the product goal.

Remember, Scrum was created to ‘help people, teams and organizations generate value through adaptive solutions for complex problems. The Scrum team—more particularly the developers who craft the sprint backlog—cannot predict the future and create the perfect plan. Complex work is highly unpredictable, so they cannot set a detailed plan in stone during sprint planning. The developers should refine the work that needs to be done based on what they learn once work begins. 

For example, let’s assume the developers create a new feature and change several existing features during the sprint. All this needs testing, regression testing, and code refactoring, which they expected; they could not anticipate the effort and amount of work required. So, the developers will need to add work items to the sprint backlog. Nevertheless, they remain committed to the sprint goal.

The sprint backlog changes based on ‘inspect and adapt’

The sprint backlog supports empiricism—the idea that knowledge comes from experience and deciding based on what the team observes (The Scrum Guide 2020). The Daily Scrum gives developers an opportunity to inspect and adapt their progress to the sprint goal and make any adjustments to the sprint backlog. 

A metric developers can use in a Daily Scrum to help support empiricism and manage the sprint backlog is Work Item Age. Work Item Age looks at current active work (the amount of elapsed time between when a work item started and the current time). Work Item Age is a leading indicator related to unfinished work items. It is a great metric; it enables transparency to which work items are flowing well and which are stuck in the mud and not progressing as expected. Using Work Item Age in combination with cycle time can help developers to focus on those items of work which are at most risk of missing the teams’ service level expectation, and make the necessary adjustments.

The Product Owner controls the sprint backlog and therefore can pull work items in and out whenever they feel like?

If a Product Owner pulls and adds work items into the sprint backlog at will, and developers just shrug and continue working, the developers are no longer committed to the sprint goal and lack ownership of the sprint backlog that is rightfully theirs. They have just become a feature factory and no longer align with a sprint or product goal, or value creation.

Developers own the sprint backlog and must maintain accountability

The Scrum Guide states that developers are always accountable for:

  • Creating a plan for the sprint—the sprint backlog
  • Instilling quality by adhering to a definition of done
  • Adapting their plan each day toward the sprint goal
  • Holding each other accountable as professionals

The Product Owner owns the product backlog

The Product Owner is accountable for effective product backlog management, which includes:

  • Developing and explicitly communicating the product goal
  • Creating and clearly communicating product backlog items
  • Ordering product backlog items
  • Ensuring that the product backlog is transparent, visible, and understood

To learn more about the sprint backlog and other important aspects of Scrum, check out our popular FAQ, What is Agile and What is Scrum?

Unlock Productivity and Innovation With Our ChatGPT Primer

In today’s fast-paced digital landscape, efficiency and innovation are more than goals; they’re necessities. Generative AI, particularly ChatGPT, can empower you in this quest. But it’s not quick and intuitive—you need actionable strategies and best practices to get the most out of this transformative technology. 

As a first step down the road of leveraging generative AI for your business, let’s cover some basics. 

What is generative AI?

Generative AI is a broad category of tools and applications designed to automate and innovate various aspects of business and personal tasks. It has a wide range of applications, from content creation to data analysis. 

Knowing where to apply generative AI, whether in automating customer service or enhancing creative processes, is essential. Interestingly, the rise of generative AI can be likened to the “big data” buzz of 2011, indicating its transformative potential.

A brief ChatGPT primer

ChatGPT has emerged as a particularly accessible and popular form of generative AI. Its ease of use and real-world applicability make it a compelling choice for those looking to explore the world of AI. 

OpenAI’s juggernaut has gained considerable attention for its ability to perform tasks ranging from drafting emails to generating code. Enterprises in every industry are scrambling to figure out how to put this powerful application—and ones like it—to use solving real world business problems.

Leveraging ChatGPT in the enterprise: not just a tool, an assistant

In an enterprise setting, ChatGPT can serve as a valuable assistant, aiding in tasks like content generation and data analysis. Its capabilities extend far beyond simple text generation; it can help kickstart projects, providing a foundation upon which to build.

For instance, if your marketing team is working on a new campaign, ChatGPT can generate initial drafts for email copy, social media posts, or even whitepapers. This not only speeds up the creative process but also allows your team to focus on fine-tuning the content. 

Similarly, in the realm of data analysis, ChatGPT can sift through large datasets to identify key trends or anomalies, serving as a first pass before human analysts dive deeper into the data.

The “second-year intern” analogy

The model’s capabilities can be likened to that of a “second-year intern”—someone who has enough experience to handle a variety of tasks but still requires supervision. This has implications for job roles in the future. 

As ChatGPT takes on more routine tasks, professionals can focus on strategic, creative, and more complex aspects of their work. For example, a data scientist could use ChatGPT to handle initial data cleaning and basic analysis, freeing them to focus on more complex modeling and interpretation.

Technical expertise required

To maximize the utility of ChatGPT, a team with some technical expertise may be required, especially for tasks like scripting or using APIs. 

For example, integrating ChatGPT into your customer relationship management (CRM) system to automate certain customer interactions would likely require knowledge of APIs. Similarly, if you’re looking to use ChatGPT for more advanced data analysis tasks, some familiarity with scripting could be beneficial to customize the model’s queries and interpret its outputs effectively.

Caveats and limitations: know before you go

While generative AI and ChatGPT offer numerous advantages, it’s essential to be aware of their limitations. These limitations can impact everything from the quality of the output to data security, and being aware of them is crucial for responsible and effective use.

Error replication

One of the first things to note is that the model can replicate errors. For example, if you’re using ChatGPT to generate code snippets or automate parts of your software development process, it’s essential to double-check the output. An error in the code could lead to bugs that might be costly to fix later. Therefore, while ChatGPT can accelerate the development process, human oversight is still necessary to ensure accuracy.

The model is also notorious for replicating user errors. Users have reported being able to “trick” the AI with all manner of false information, with sometimes hilarious and sometimes nefarious results. In an effort to learn, ChatGPT has been known to absorb some very ugly ideas.

Outdated training data

Another limitation is the model’s training data, which cuts off in 2021. This makes it less reliable for tasks requiring real-time updates or current information. 

For instance, if you’re in finance and looking to get the latest insights on emerging markets or investment trends, ChatGPT out-of-the-box might not be the best tool for the job. Its data is not up-to-date, and therefore, it can’t provide real-time market insights.

Some other generative AI applications offer limited access to current online content, but this can be problematic in its own way. ChatGPT experimented briefly with a real-time browser plugin in beta, but shut it down fairly quickly when it found that the AI was bypassing security protocols and absorbing tremendous amounts of false or inappropriate data from the internet. Eventually, those problems will be solved. But until then, ChatGPT’s knowledge of the world ends in 2021.

Data security concerns

Data security is a significant concern, especially for enterprises dealing with sensitive or confidential information. Some companies are cautious about using models like ChatGPT due to potential data security risks. For example, if you’re in healthcare and considering using ChatGPT for automating patient interactions, you’ll need to be extremely cautious due to the sensitive nature of medical data—using the public ChatGPT application means accepting that every bit of data passing through it can be stored and reviewed to train the model going forward.

To address data security concerns, solutions like private instances of these models are being developed. These private instances would reside within a company’s own infrastructure, providing an additional layer of security. 

This is particularly useful for companies that need to adhere to strict compliance regulations, such as those in the financial or healthcare sectors. But really, every organization that wants to fully leverage generative AI would be well served to consider establishing a private instance to ensure proprietary and protected data remains safe.

Effective communication with ChatGPT: more than just commands

Interacting with ChatGPT or any other Language Learning Model (LLM) is not a dialogue to be taken lightly, especially in a corporate environment. The importance of iterative conversations and feedback loops is paramount for achieving precise and useful outcomes.

Clear and specific prompts

You might be looking to generate marketing copy for a new product launch. Instead of asking the model to “write some marketing content,” you could specify, “Please draft a compelling product description for our new line of ergonomic office chairs.” 

The more detailed your prompt, the more aligned the output will be with your marketing objectives. You can mold the AI’s responses by requesting specific tone, telling it who your target audience is, and describing the way the finished content will be used.

Being specific is crucial when you’re dealing with business data analysis. For instance, if you’re looking to understand quarterly sales data, asking “Provide insights into Q2 2023 sales data for our software products” will yield a more focused and actionable analysis than a vague query like “Tell me about our sales.”

The deeper you drill down into details, the more insights ChatGPT can provide, as long as the broader context is available to work from.

Iterative process and feedback

ChatGPT learns from the feedback you provide, which is invaluable when you’re iterating on complex projects like a business proposal. If the initial draft isn’t aligned with the client’s needs, you can refine your prompt or provide additional context. 

For example, if the first draft is too technical, you could say, “Revise the proposal to focus more on business outcomes and ROI.” Or, you could reference a particular sentence, paragraph, or section and say, “Expand on this statement by providing two examples of how it can be applied by HR professionals.”

Chain prompts for contextual outputs

Chain prompts allow you to build upon previous queries for more nuanced and contextual outputs. 

For instance, after generating a list of potential leads, you could ask, “What would be an effective email subject line to engage these leads?” The model, remembering your previous query, can suggest a subject line that aligns with the type of leads you’re targeting. 

Used in conjunction with iterative feedback, chain prompts can produce exceptional results with a little time and effort.

Identifying opportunities for generative AI: a framework for success

Understanding what machines excel at versus human capabilities is crucial when considering the implementation of generative AI. When evaluating tasks for automation, three key factors come into play: repeatability, scalability, and data orientation.

Repeatability

Tasks that are repetitive and follow a set pattern are prime candidates for automation. Generative AI excels in these areas because it can execute the same task consistently without fatigue or error, provided the task is well-defined. 

For example, if you’re looking to automate the generation of monthly reports, generative AI can be programmed to pull the same types of data and format them in a consistent manner, saving valuable human hours.

Scalability

Another factor to consider is scalability. If a task needs to be performed on a larger scale, generative AI can easily handle the increased workload without requiring a proportional increase in resources. 

For instance, customer service chatbots powered by generative AI can handle hundreds or even thousands of queries simultaneously, providing quick and consistent responses. This is something that would be incredibly resource-intensive if done by human agents.

Data Orientation

Generative AI shines in tasks that are data-oriented. These are tasks that require the analysis or interpretation of large sets of data. 

For example, generative AI can sift through vast amounts of market research data to identify trends or patterns, tasks that would take a human analyst a significant amount of time. The AI can then generate summaries or even predictive models based on this data, aiding in decision-making processes.

The transformative potential  

Generative AI and ChatGPT are not just technological novelties; they are tools that are already significantly impacting how we work and innovate. To truly grasp the transformative power of these technologies, we invite you to dig deeper by watching a comprehensive webinar that covers these topics and includes live demonstrations: How to Unlock Productivity and Innovation With Generative AI and ChatGPT.

By embracing these advancements, you’re not just staying ahead of the curve; you’re shaping it. Welcome to the future.

Former Senior Cognizant Executive to join Cprime as President and Member of the Board of Directors

Srinivasan Veeraraghavachary named Cprime’s President, bringing tech leadership experience to accelerate growth

CARY, NC, September 13, 2023 — Cprime, a leading provider of agile ways of working and technology consulting services, today announced the appointment of Srinivasan Veeraraghavachary as the company’s President and member of its Board of Directors.

Srinivasan Veeraraghavachary, a seasoned leader in the technology consulting sector, has joined Cprime as our new President and a member of our Board of Directors. This appointment comes at a time when we are actively expanding our services and entering new markets.

Mr. Veeraraghavachary previously spent more than two decades with Cognizant (Nasdaq: CTSH), holding several leadership positions, including most recently as Chief Operating Officer and Executive Vice President. During his tenure at Cognizant, he played a key role in driving strong and sustainable growth by defining go-to-market strategies, initiating and deepening relationships with customers, and driving best-in-class execution. Prior to assuming his role as Chief Operating Officer, Mr. Veeraraghavachary was responsible for several scaled business units within the organization and managed several strategic client relationships.

He joins Cprime on a full-time basis during a new chapter of growth for the company. In January, Cprime announced a majority investment by Goldman Sachs Asset Management and Everstone Capital, and the company is currently expanding its footprint into new technology services and global markets.

“We are thrilled about Mr. Veeraraghavachary joining Cprime’s Board,” commented Harsh Nanda, Partner and Head of Technology for Private Equity within Goldman Sachs Asset Management. “As a highly respected technology executive with decades of experience, he brings a blend of strategic vision, significant experience in scaling businesses, and focus on operational excellence to Cprime. With his successful track record of growing technology consulting businesses and driving organizational and operational execution he will be an important addition to Cprime’s leadership team.”

“We are excited to welcome Mr. Veeraraghavachary to Cprime,” said Zubin Irani, CEO of Cprime, and Gerald Attia, Chairman of the Board of Directors. “His extensive background, strong leadership skills, and client-centric focus align perfectly to Cprime’s mission to provide clients a more productive future where process and technology converge for better results and increased speed to market. We are confident that he will play a key role in driving our company’s growth and delivering exceptional value to our clients.”

“I am honored to join Cprime,” said Mr. Veeraraghavachary. “Cprime is a highly respected company and a leader in driving digital transformation via the implementation of Agile and DevOps frameworks for its clients. I am excited about the opportunity to join Cprime’s talented management team and use my experience to contribute to the next chapter of Cprime’s expansion.”

About Cprime

Cprime is a full-service global consulting leader helping clients modernize ways of working and gain the best out of their processes, people, and technology to innovate and thrive. Cprime’s team of experienced practitioners help businesses achieve agility, improve visibility and alignment, speed time to market, and realize significant operational and cost saving improvements. With over 20 years’ experience, Cprime is trusted around the globe to provide strategic, agile, and technical consulting, coaching, and training to businesses leading their industry in digital transformation.

To learn more visit http://www.cprime.com and LinkedIn.

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Media Contact
Lisa Flattery, Cprime, lisa.flattery@cprime.com

Validating Product Ideas Through a Startup Proof of Concept

In a new business where a lot of things are unpredictable, a startup proof of concept can be an essential tool for validating product ideas. 

In order to make your concept thrive, you will need to calculate and define its viability and feasibility. You need insights on what you need to implement in terms of technology, finance, infrastructure, etc. to bring the idea to life. You also need to prove your concept is technically feasible and desirable to investors and other stakeholders.

That’s where you will need to develop a proof of concept (POC). Join us as we explore some of the ways to write a proof of concept and how creating it can help a startup validate its product and improve the overall chances for future investment and sustainability over time. 

What is a Proof of Concept

Before implementing any startup, there is a need for solid and undeniable proof of whether the idea will work or not. A POC or proof of principle fits in here. It helps you decide whether you can proceed with the hypothesis or not. 

Basically, a POC for a startup is a presentation of the proposed product and its potential viability. POCs describe the functionality of the product, including its general design or specific features, and how feasible they are.

A POC can prove that building the proposed solution, program, product, feature, or method is achievable. POCs further allow decision-makers, investors, and even users to explore the potential of the idea, giving them a glimpse of the bigger picture or the situation once the company launches the product.

Types of Startup Proofs of Concept

POC for a startup is a type of demonstration or prototype that shows how a new technology or business model can be implemented and tested. A POC can be either technical or business-based.

Programmers cooperating brainstorming at information technology company

Technical POCs

Technical POCs are useful for testing new technologies, evaluating feasibility, and gauging the interest of potential customers. POC in technology can also help developers learn how to build something from scratch and identify any potential problems with their designs.

There are three main types of technical POCs:

  • User Experience (UX) Proofs: UX proof is a simple demonstration that a product or service can be used by real people. This might involve testing out the design on dummy users or simulating user behavior in an analytical way.
  • Product Proofs: A product proof is a more complex demonstration that the product or service can be developed and launched successfully. This might involve building a functional prototype, completing customer interviews, or conducting market research.
  • Prototype Proofs: A prototype proof is the most complex type of proof of concept for a startup and requires the most time and investment to produce. It is typically an early version of the final product that has been simplified for testing purposes.

The developers at Cprime can help entrepreneurs who are looking to build a tech product to validate their idea and assess the technical requirements needed and define the tools and resources necessary to build the product.

Business POCs

Business POCs are especially important for testing new business models in the real world. They can help entrepreneurs determine whether their idea is viable, identify potential market niches and gain feedback from actual consumers. Furthermore, they can help entrepreneurs validate their assumptions about customer behavior and marketplace trends.

In many cases, both a Business and Technical POC may be needed. For example, to effectively test out a business model that revolves exclusively around a tech product. Imagine you want to launch the next Uber. You would want to put together a Business POC to establish that there’s room in the ride share market for a new contender, and that your concept is different enough to compete. But you’d also want to develop a Technical POC to ensure the app you’re envisioning is feasible.

Benefits of Validating Product Ideas With a Proof of Concept

While startups are all very different, 90% of them have one thing in common—they fail. One of the things startups can do to improve their chances of being in the 10% is to develop a POC. The benefits of a successful POC are long-lasting and can touch on every department within the startup, from product to sales.

To the untrained eye, a POC may feel like an extra step in the process, but developing one can help increase your odds of success. It can:

  • Help you assess risks: Creating a startup proof of concept can help you and your team identify potential risks and issues before a product goes live. You can then decide whether to make changes to the product or go back to the drawing board.
  • Get your team on the same page: Proofs of concept can help align your team and introduce them to your prospective product. For example, a POC can show your sales and marketing teams the unique selling points of your product and who your competition is. You can also use a POC to get feedback from employees who may not have been involved in developing your product.
  • See if your idea can adapt and grow: A proof of concept can help you see how scalable your idea is. As an example, would it be easy to mass produce in the future? What would you do if you needed to add additional features for new markets?
  • Get investors: Finally, a POC can be great to showcase to potential investors. Investors will want to see that you’ve done your research before they provide you with funding, and a POC is a great way to do this. 

How to Write a Proof of Concept

The goal of a startup proof of concept is to test the viability and feasibility of an idea. Once the startup POC has been completed, it can be used as part of the business plan in order to assess whether there’s potential for this idea to become a reality. If all goes well, then the next stage, which involves further development, can begin.

Basically, there are five essential stages of a startup proof of concept that teams can follow, from developing the idea to firming it up and presenting it to investors.

Stage 1: Conduct research and development

When you write a proof of concept, the first thing that comes into mind is Research and Development (R&D). The team needs to conduct extensive research around the history of similar work across the globe.

If there is none, the next step should be to analyze existing guides, PDFs, scholarly articles, or tutorials that would act as a key point of reference for the team.

If it is not available in the market, consider your idea to be a novel one that can mark you as a leading pioneer.

It may take time to build a POC. It may sometimes seem infeasible. But challenging the team’s technical abilities is the key here. Setting your own standards can lead to success when a business is still in the development phase.

Stage 2:  Specify the need for your idea

Once you are done with research around your idea, it is time to specify who needs it and why. Consider listing user personas your product will target and their pain points to understand their needs.

However, don’t just assume things. As you collect evidence at this stage, interview potential users and ask them questions regarding what they desire and need to solve the problem being addressed with your project. To make your proof of concept document look authentic, consider conducting in-depth interviews or online surveys.

Stage 3: Ideate the right solution

From the sample group’s answers, you can now start brainstorming with your team for the right solutions to customer pain points, keeping in mind that they should also be feasible and within the company’s capacity.

The team should then assess each brainstormed solution according to the likely costs, timeline, technologies needed, required operational capacities, competition, resources, and other factors.

Additionally, to firm up the proposal, you should discuss how your solution can support the fulfillment of the organization’s or stakeholders’ goals.

Stage 4: Create a prototype and test it

Once you have come up with a feasible idea, you should create a prototype based on the decided requirements, features, and solutions.

Your team must let the individuals in their sample group test the completed prototype so they can quickly determine whether the product truly addressed the pain points shared by the group.

Testing it with the same group enables you to document their feedback more easily, which is essential to the next step.

Stage 5: Gather and document feedback

During the prototype testing, you must gather and document the sample group’s feedback about their experience, their reactions, and any other valuable details, including what they think of the user interface.

The gathered feedback lets you initially verify the usability and feasibility of the solution. It also informs the team of any needed improvements to the proposed product and gives significant insight for other relevant actions moving forward.

Stage 6: Present POC for approval

With the concept tested and improved based on the feedback, you can now start the last proof of concept stage and prepare your presentation for the stakeholders.

You must present, among other things, the pain points that the product solves, features that address those problems, and technologies integrated to demonstrate the value of the idea.

You should elaborate on the product development and project management components, which you should also note in your project tracker.

These include clearly defined success criteria or project management metrics, evaluation measures, timelines, next project management plans, resources needed, and other aspects discussed earlier.

Once you’ve successfully presented the idea and persuaded the stakeholders to approve and invest, you can begin to implement it.

Wrap-up

Ultimately, startups looking to be a part of the successful minority need to do all they can to stand out and differentiate themselves from competitors—and that means proving their product has a market need and works on a large scale.

A POC helps startups see if a proposed idea is practical and attractive for the target market and achievable for the company. Through the business POC, you can explore the planned components and functionalities of the ideated product, along with the costs, resources, and capacities required to make it work.

In order to further increase the chance of success, startups should not question whether or not they should run a POC, but rather see how they can increase their capacity and create more POCs with more companies in more verticals in hopes of defying the odds.

The custom development experts at Cprime are well aware of the need for proof of concept. We also know how to create attractive prototypes to use for further user testing or attracting investors. Cprime developers can validate the product at the idea stage to see if it’s worth further developing and we can continue to collaborate to develop your product and launch it for success.

Ready to get started? Contact our experts to schedule a consultation.

How to Streamline Strategic Planning in Jira Align – Part 1

Strategic planning is essential for companies to align on priorities, establish goals, and measure performance. However, the process can often become complex and disjointed when managed through static documents and spreadsheets. Jira Align provides a powerful way to streamline strategic planning and enable real-time tracking of key initiatives.

In this post, we’ll walk through how to optimize strategic planning in Jira Align. We’ll cover:

  • Getting started with structuring objectives and initiatives
  • Developing dynamic roadmaps tied to your strategy
  • Fostering collaboration across teams
  • Tracking progress and measuring strategic impact
  • Ongoing best practices for maintaining alignment

Let’s dive in!

Getting started with strategic planning in Jira Align

The first step is structuring your strategy in Jira Align. 

Using OKRs

This involves setting up company objectives and key results (OKRs) to define measurable goals. You can then map initiatives and projects to each objective to operationalize your strategic plan.

When creating objectives, clearly define the desired outcome and how it will be measured. For example, an objective could be “Increase customer retention by 5% by Q4”.

With the objective set up, you can then establish key results—the quantifiable metrics that measure achievement of the goal. For our example, key results could be improving net promoter score to over 8.0, and decreasing churn rate below 10%.

Using Initiatives

Once your OKRs are established, you can start adding initiatives into Jira Align. Initiatives are the high-level programs and projects required to hit your objectives. 

Adding owners, estimates, and dependencies helps manage stakeholder involvement and execution.

Using permissions

You can also manage permissions in Jira Align to control who can view and edit various plans based on their role. This helps keep strategic plans visible to executives while allowing project teams to collaborate.

Developing strategic plans and roadmaps

One of the most powerful features in Jira Align is the ability to visualize strategic plans in roadmap form. This provides a high-level timeline of all initiatives mapped to objectives across planning horizons.

  • Establish milestones – Roadmaps can be structured across custom tiers to represent key milestones like quarterly goals, product releases, or project stages. 
  • Organize initiatives – Initiatives are then placed on their respective tiers based on estimated delivery dates.
  • Define dependencies – Project managers can define dependencies between initiatives to automatically sequence them. For example, an initiative to “Build CRM Integration” could be set to depend on “Complete Customer Database Migration” finishing first.
  • Experiment with scenarios – Jira Align enables scenario roadmapping with base case, aggressive, and conservative plans. This allows modeling tradeoffs to optimize investment mix and capacity planning.
  • Keep it up to date – As initiatives get underway, owners can update completion percentage to denote progress. Roadmaps auto-recalculate timelines when changes occur, keeping stakeholders aware of shifts.
  • Monitor and adjust – Different views like Gantt charts allow drilling into initiative details like assigned resources, budgets, and risks. Calendars make it easy to check for conflicts across resource utilization.
  • Share and report – Roadmaps can be shared via interactive dashboards or exported as presentation-ready slides. The visuals provide executives an end-to-end view of strategic plans, facilitating better decision making.

With Jira Align roadmapping, organizations gain a living, breathing plan that updates in real-time versus static documents. This enhances coordination across business units and teams to drive strategy execution.

Collaborating across teams in Jira Align

Strategic planning requires alignment between executives, managers, and frontline teams to be successful. Jira Align provides several ways to break down silos and improve cross-functional collaboration.

  • Interactive roadmap sharingInitiative owners can share live roadmap views with other teams or groups. This fosters transparency into how their work ties into larger company goals and interdependent initiatives. Commenting allows discussions right on roadmaps.
  • Integrations with communication toolsJira Align integrates with Confluence and Slack for real-time collaboration. Status updates made in Jira Align can automatically flow into Confluence docs or Slack channels. This keeps everyone looped in.
  • Managing stakeholders – Initiative owners can tag stakeholders from other groups. These stakeholders then receive alerts on progress updates related to their work, facilitating coordination.
  • Team progress reporting – Managers can pull progress reports filtered by team or department. This enables checking alignment across the organization and having data-driven conversations to resolve execution gaps.
  • Organization-wide access – With flexible permissions, Jira Align can provide company-wide transparency while limiting editing access as needed. This enables top-down and bottom-up visibility.

In summary, Jira Align breaks down team and departmental silos through seamless information sharing, notifications, integrations, and access controls. This leads to improved coordination and higher likelihood of successfully executing strategic plans enterprise-wide.

Tracking and measuring strategic plans

Jira Align provides robust capabilities for tracking objective progress and strategic plan KPIs in real-time.

Check out our four-part series on Jira Align reporting, starting with The Power of Team Level Reporting in Jira Align (Part 1 of 4).

Customizable dashboards offer at-a-glance views of portfolio health, budget-versus-actual costs, and completion percentage for company goals. Drilling into objectives shows up-to-date progress towards key results as well.

For a more detailed analysis, Jira Align’s reporting allows you to:

  • Monitor initiative delivery and identify late projects
  • Spot resource bottlenecks across plans
  • Analyze burn rates and forecast future progress
  • Track progress by department, product line or other dimensions
  • Export presentation-ready reports to update executives

As teams execute on initiatives, they can update progress directly on Jira Align roadmaps. This allows timelines to dynamically adjust based on real-world changes, keeping strategic plans reality-grounded.

For example, if a product launch gets delayed, initiative owners can easily drag-and-drop milestones on their roadmaps. Dependent initiatives then automatically shift based on the new timelines.

This lets organizations pivot gracefully versus rigidly sticking to outdated plans when business or market conditions change.

To maintain alignment, initiative owners can continually update key result metrics as outcomes are measured. Adding comments also enables teams to collaborate and provide context on progress changes.

With Jira Align’s robust tracking and measurement capabilities, organizations can closely monitor strategic plan effectiveness and rapidly adapt execution to drive better results. The key is maintaining up-to-date plans and making data-driven decisions based on real-time insights.

Tips for ongoing success

Here are some best practices to get continued value from Jira Align for strategic planning:

  • Review and update roadmaps quarterly – Set time on the calendar to evaluate progress and realign as needed.
  • Create templates for consistent plans – Build on templates each planning cycle rather than starting from scratch.
  • Automate data integrationsLink Jira Align to other systems to maintain up-to-date plans.
  • Assign initiative owners – Ensure every initiative has an owner responsible for execution.
  • Train all stakeholders – Provide training on Jira Align to foster adoption across the organization.

Following these tips will help ingrain Jira Align into your recurring strategic planning process.

In conclusion

Jira Align provides an optimized way to streamline strategic planning and gain organization-wide transparency. 

  • By structuring goals in Jira Align, teams gain clear line of sight into how their work ties back to company objectives.
  • With real-time roadmaps and progress tracking, organizations can dynamically adapt as business conditions change; cross-team alignment also improves through integrated planning.
  • Getting started with Jira Align involves laying out objectives, initiatives, and key results. From there, teams can develop roadmaps, collaborate across tools, and track progress towards strategic goals.
  • Following best practices around consistent reviews, updates, automation, and training will ensure continued success. 

With Jira Align, companies can connect high-level planning to execution and make strategic planning a living, breathing process. Stay tuned for Part 2, where we will consider How AI is Transforming Strategic Planning in Jira Align.

What to Consider When Moving Your CMDB Into Jira Service Management Cloud

Migration of a Configuration Management Database (CMDB) from on-prem architecture to Jira Service Management (JSM) Cloud is a topic receiving increasing attention, particularly since Atlassian Server applications will no longer be supported as of February 15, 2024. 

That aside, there’s still been a significant push towards cloud-based solutions. Whether you are considering or planning a migration, understanding the intricacies of the process is crucial. This post outlines the vital aspects of CMDB and its migration to the cloud.

(This content is based on a webinar entitled, A Pre-flight Checklist for Moving Your CMDB Onto Jira Service Management Cloud. Click to watch the full webinar with a deeper dive into the material and software demos of both Jira Service Management and Device42.)

The business value of a CMDB

As companies are trying to demonstrate mindfulness and effectiveness in the way they steward their resources and IT budget, the need for a CMDB has become increasingly apparent. A CMDB is a tool used to capture all the different components of the IT ecosystem of a company. It is a clear path to demonstrate the business value of IT investments. The rising demand for CMDB and CMDB best practices indicates its growing importance in ensuring efficiency and effectiveness in business operations.

Many companies are pushing towards the cloud to reap the benefits of scalability, accessibility, cost-effectiveness, automation, and innovation. However, migrating to a cloud-based CMDB can present challenges and risks.

Challenges of a CMDB migration

Migration of CMDB metadata is a complex and intricate task, requiring careful planning and meticulous execution. The challenges faced can be multifaceted and present serious risks if not handled with the utmost caution.

  • Data Loss: The transfer of information between different systems or versions might lead to inconsistencies or outright loss of vital data. Implementing a robust backup system and using specialized tools for migration can mitigate this risk.
  • Downtime: The migration process can lead to downtime, affecting the day-to-day operations of the business. Coordinating with all stakeholders and planning migration during non-peak hours can reduce the impact on business functionality.
  • Missing Integrations: A migration may result in missing connections or integrations with other systems. Ensuring that all necessary integrations are identified and re-established post-migration is crucial.
  • User Acceptance and Enablement: People are at the core of any system, and changes can lead to resistance or difficulties in adaptation. A clear communication strategy pre- and post-migration can help to educate users about the changes and benefits. Tailored training programs can further ensure user enablement.
  • Compliance and Security Concerns: Ensuring that all regulatory requirements are met during migration is vital. Conducting regular audits and consulting with legal teams can ensure that all compliance aspects are addressed.

The migration of CMDB metadata is a challenging task, but with careful planning, stakeholder collaboration, and adherence to industry best practices, these challenges can be transformed into opportunities for growth and innovation.

Mitigating the risks: The Discovery and Design phases in migration

To best mitigate the risks and challenges associated with migrating your CMDB, we recommend carrying out both a Discovery phase and a Design phase in preparation for the migration.

Discovery phase

A successful migration of the CMDB requires a comprehensive understanding of its current state. This stage is instrumental in recognizing what needs to be transferred and how to do it efficiently. Below is a more detailed breakdown of the key elements:

  1. Use Cases Examination: Identifying the specific use cases of the CMDB within the organization will help define what functionalities must be retained or enhanced during the migration. This ensures that the system continues to meet the unique needs of the business.
  2. Stakeholders Identification: Recognizing who is affected by the CMDB, from IT professionals to business leaders, ensures that their needs and concerns are addressed. Engaging with stakeholders during the discovery phase can foster collaboration and reduce resistance.
  3. Investigating Existing Data Models: Understanding the current data structure, including how information is stored, categorized, and accessed, is paramount. Analyzing this structure helps identify potential challenges and opportunities for improvement.
  4. Analysis of CMDB Objects: Investigating the specific objects within the CMDB (e.g., hardware, software, services) provides insights into what assets are managed and how they are interrelated.
  5. Catalogs Exploration: This involves reviewing the existing catalogs and classifications within the CMDB. Knowing what assets are categorized and how they are organized can guide a more effective migration process.
  6. Integrations Analysis: Mapping out the current integrations with other systems is critical. A clear understanding of how the CMDB interacts with other platforms ensures that these connections can be re-established post-migration without disruptions.
  7. Dependencies Mapping: Identifying the dependencies between different objects within the CMDB is crucial for maintaining the integrity of relationships post-migration. It’s important to map these connections to ensure a seamless transition.
  8. Risk Assessment: Conducting a comprehensive risk assessment at this stage helps in foreseeing potential issues and planning mitigations. By anticipating challenges, the organization can be better prepared.
  9. Regulatory Compliance Check: Ensuring that all existing compliance measures are understood will make it easier to maintain adherence to regulations during and after migration.
  10. Alignment with Business Goals: Ensuring that the migration plan aligns with overall business objectives, including Agile transformations and digital strategies, is vital for creating synergy with broader organizational initiatives.

The Discovery Phase sets the stage for a successful CMDB migration by offering a clear, well-defined understanding of the existing system. It lays the foundation for informed decisions and strategic planning, turning the complexity of migration into a manageable, step-by-step process that aligns with the needs and goals of the organization.

Design Phase

The Design Phase plays a crucial role in shaping the migration process, transforming insights gathered during the Discovery Phase into a structured plan. It ensures that the migration aligns with organizational goals and meets user expectations. Here’s an in-depth look at the key components:

  • Defining the Desired Data Model: 
      1. New Structure: Understanding the needs of the organization allows for the creation of an optimal data model that fits current and future requirements.
      2. Mapping Old to New: A crucial part of this stage involves mapping the existing data structure to the new one, ensuring no loss of essential information.
  • Assessing Target Platform Limitations:
      1. Technology Constraints: Evaluating the capabilities and constraints of the target platform ensures that it can support the newly designed data structure.
      2. Compatibility Analysis: Checking the compatibility with existing systems and integrations is vital to avoid potential conflicts or performance issues.
  • Identifying New Capabilities:
      1. Functional Enhancements: This includes outlining features that may enhance the user experience, such as improved search capabilities, analytics, or custom reporting.
      2. Alignment with Agile and Digital Strategies: Ensuring that the new design supports Agile transformations and digital strategies within the organization.
  • Establishing Acceptance Criteria:
      1. Quality Standards: Setting clear quality standards for data integrity, performance, and usability ensures that the migration meets organizational expectations.
      2. User Acceptance: Determining what will be considered a successful migration from the users’ perspective, including functional requirements and user-friendliness.
  • Creating a Detailed Migration Roadmap:
      1. Phases and Timelines: Defining a clear schedule, including milestones, dependencies, and deadlines, keeps the project on track.
      2. Resource Allocation: Planning who will be responsible for each part of the migration, from technical teams to stakeholders, ensures efficient execution.
      3. Risk Mitigation Strategies: Identifying potential risks and planning how to address them helps in avoiding unexpected challenges.
  • Enhancing User Experience and Data Organization:
      1. User Interface Design: Creating an intuitive interface that aligns with the users’ needs enhances their interaction with the system.
      2. Data Access and Permissions: Planning how data will be organized and who will have access to what ensures that the right information is available to the right people.
  • Compliance and Security Measures:
      1. Regulatory Alignment: Ensuring that the design adheres to all relevant regulations protects the organization from legal issues.
      2. Security Protocols: Implementing robust security measures protects the data during and after migration.
  • Monitoring and Feedback Mechanisms:
      1. Performance Metrics: Setting up ways to monitor the migration’s success against the defined criteria ensures continuous alignment with the goals.
      2. Feedback Loops: Incorporating mechanisms for continuous feedback from users and stakeholders fosters a more responsive and successful migration.

The Design Phase is where vision turns into strategy, crafting the blueprint for a successful migration. A thoughtful and well-structured design ensures that the migration will be carried out efficiently, meeting the goals, enhancing user experience, and aligning with the broader strategies such as Agile transformations and workforce management. It provides the road ahead, filled with clear directions and well-defined success criteria.

Migrating the CMDB to Jira Service Management (JSM) in Atlassian Cloud

Cprime’s runbooks detail the steps involved in migration, including:

  1. Deploying the Target Environment: Procuring licenses, configuring settings, and instituting a change freeze.
  2. Migrating in a Test Environment: Doing a full dry run with zero impact or disruption, just to make sure everything is functioning as expected.
  3. Migration Execution: Handling Jira issue data and CMDB metadata separately using tools like Jira Service Management Cloud migration assistant, site import, CSV, or API.
  4. Going Live: Continuous monitoring, maintaining the old environment in read-only state, and customized enablement support.

Migrating your CMDB onto Jira Service Management Cloud is a complex yet rewarding process. Understanding the business value, recognizing and overcoming challenges, reaping the benefits of the cloud, and carefully planning the discovery, design, and migration phases can set the stage for a successful transformation. 

To gain a deeper understanding and see thorough software demos of both Jira Service Management and Device42 applications, we invite you to watch the full webinar, “A Pre-flight Checklist for Moving Your CMDB Onto Jira Service Management Cloud“. Don’t miss this opportunity to explore real-life examples and insights from industry experts.

SAFe 6.0 Deep Dive – Flow Metrics (Part 2 of 2)

To quickly summarize Part One of this two-part series, we are looking into the recently released Flow Metrics within the updated Scaled Agile Framework® (V6.0) which offers interesting insights into tracking and monitoring flow of work through your Agile Release Train. 

In Part One, we covered the first three of the six metrics:

  • Flow Distribution
  • Flow Velocity
  • Flow Time 

Now, we will wrap up this series with the latter half of this collection of metrics, which will include 

  • Flow Load
  • Flow Efficiency
  • Flow Predictability

As a refresher, many Agile concepts originated from the Toyota Manufacturing / Toyota Production System (TPS), considered to be the foundation for today’s Lean manufacturing processes. Hence, we will use this comparison to help illustrate how these metrics may map to building hardware and software solutions within an Agile Release Train.

The 6 Flow Metrics

Here’s another review of the six flow metrics that SAFe® recommends.

Metric Definition
Flow Distribution Proportion of work items by type
Flow Velocity Number of completed work items over a fixed period
Flow Time Time elapsed from start to finish for a work item
Flow Load Number of work items currently in progress
Flow Efficiency Ratio of the time spent in value-added work divided by total time
Flow Predictability Level of consistency with which teams/trains/portfolios meet their objectives

 

Continuing where we left off in Part One, we will now focus on Flow Load. 

Flow Load

If we look at the definition of this term, we realize that this seems to resemble another popular Lean-Agile concept: Work In Progress (or WIP). In a flow-based system, often referred to as a Kanban system, the WIP limit provides a throttling mechanism to minimize over-burdening the system (or the staff) by controlling the total amount of work that an individual (or a system) can perform‌ at once. 

Flow Load is essentially the same idea; by tracking the total number of work items in play, we can glean interesting insights into the health of the system. 

Using the automobile manufacturing example, the load can represent the total number of cars currently moving through the assembly line at a time, which may vary depending on the seasonality, time of the day, etc. More than likely, we are going to need to look at multiple metrics in order to make sense of the data. We will touch on that a bit later.

Flow Efficiency

The fifth metric is Flow Efficiency, which measures the amount of value-added work (or productive work) as a function of total time spent. 

Many trains struggle to track this metric because it is often difficult to distinguish good use of time versus poor use of time. For example, is “big room planning” or any other meetings that may be perceived as administrative work considered value-added work? That may be debatable. In order for this metric to have meaning in your organization, your team may need to clarify what is actually considered “value-added work”. 

Within the car manufacturing world, any idle time is non-value added work; for example, the time it requires for a technician to walk from the car to the tool bench to retrieve a tool is usually considered “travel time”, and is not value-added. Even if each trip only requires a few seconds, over ‌millions of cars, those seconds add up and will lead to lost overall productivity that can equate to significant lost revenue.

Flow Predictability

Lastly, we have Flow Predictability, which may seem familiar. The Predictability metric has been part of SAFe for several years, even if it was not specifically referred to as a “Flow” metric. 

The concept of predictability is often difficult to grasp because very few organizations are effective at tracking this metric. The ability to produce results consistently should be the goal of any Agile Release Train, and this metric will enable trains to monitor how they are doing over time.

Putting it all together

Now that we have a better understanding of each of the six flow metrics, what are we supposed to do with them? How do we know if the train is running optimally or is in serious trouble? 

In isolation, it is difficult to make a judgment on the state of the system by looking at any single metric at a point in time. We must have a reference point against which to compare in order to determine whether your train is taking on too many work items simultaneously, or not enough. This is where we need to pair the flow metrics to draw a useful and intelligent conclusion about how your train is operating.

For example, by looking at the relationship between velocity, load, and efficiency, we can put together a picture of what is going on with your train. Is the train running effectively, or is it heading for disaster? Even if you are tracking all six metrics rigorously, you will probably need to think about what “good” looks like for your specific context. To achieve this, consider the following:

  1.     What does the customer truly care about?
  2.     What can you do differently to move the needle on those things that are important to the customer?
  3.     How can your teams use metrics to improve transparency and create a sense of purpose?

There are no right answers to these questions. You will need to think about these within your own context to decide which of the metrics make sense for you. It is possible to apply only a subset of the six metrics and still get value out of them. 

If you aren’t sure where to start, engage your train and encourage your teams to come up with their recommendations; if they are given the opportunity to define meaningful metrics, they are much more likely to provide quality data and apply them effectively.

And of course, if you need any help with this or other SAFe concepts, consider our catalog of SAFe-related learning courses and certification programs.

How to Establish Lean Budgets for Agile Success

Implementing Lean budgets is a crucial step for organizations adopting Agile practices in the context of the Scaled Agile Framework® (SAFe®). Traditional project portfolio management and budgeting approaches often inhibit delivering value.

Lean budgeting takes a different approach focused on empowering teams, decentralizing decisions, and delivering value fast. Read on to learn how to move beyond traditional budgeting to establish Lean budgets that fuel Agile success.

SAFe® and Scaled Agile Framework® are registered trademarks of Scaled Agile Inc.

Problems with traditional budgeting approaches

Many organizations rely on traditional project portfolio management approaches that create bottlenecks. Here are some of the most common challenges:

  • Project-based cost accounting – Traditional project cost accounting requires constantly moving people between projects and renegotiating budgets. It limits flexibility and causes waste.
  • Functional silos – People organized in functional silos struggle to deliver end-to-end value. Handoffs and misaligned priorities across departments inhibit flow.
  • Overly detailed business cases – Requiring big, detailed upfront business cases delays funding and forces big batch work efforts. This inhibits delivering value iteratively.
  • Waterfall phase gates – Gating funding based on completing waterfall-style phases optimizes for utilization, not flow. Teams cannot pivot quickly in response to feedback.

Embracing Lean budgeting

Transitioning to Lean budgeting is a mindset shift focused on empowering teams to deliver maximum value. Adopting Lean budgeting requires rethinking some core practices:

Fund value streams, not projects

  • Organize long-lived Agile teams around delivering value for a value stream rather than temporary project teams.
  • Provide teams a budget to cover capacity over time rather than funding project-by-project.
  • Allow teams to flexibly reprioritize work within their budget as they learn and conditions change.
  • Decentralize funding decisions and trust teams to spend funds responsibly to meet objectives.

Right-size upfront planning

  • Avoid large batches of upfront planning and estimation.
  • Plan just enough to gain commitment for the next stage of learning and feedback.
  • Use rolling wave planning to provide visibility 2-3 months ahead.
  • Re-plan frequently in smaller batches based on feedback.

Economic prioritization

  • Train teams on economic thinking and prioritization techniques like Cost of Delay.
  • Empower teams to make data-driven tradeoff decisions on what will provide the most value.
  • Let go of sunk cost bias. Pivot when the economics suggest it makes sense.

Inclusive portfolio budgeting

  • Use Participatory Budgeting sessions to transparently allocate portfolio budgets.
  • Include diverse stakeholders and facilitate inclusive decision-making.
  • Make funding criteria and guardrails clear.
  • Provide transparency into funding rationales and how budgets are spent.

Visualize flow

  • Use Kanban, Cumulative Flow Diagrams and other visuals to reveal dependencies and bottlenecks.
  • Identify wait states, handoffs, and other areas of waste in the end-to-end value flow.
  • Improve flow by addressing root causes, not just expediting.

Implementing these Lean budgeting practices reduces friction and puts funding decisions closer to teams delivering the value. This fuels faster feedback and flexibility.

Seeing the Investment Horizons

The SAFe Investment Horizon model provides a portfolio perspective on allocating funding to Solutions across multiple timeframes. This helps balance short-term and long-term investments.

Horizon 3: Evaluating (3-5 years)

This stage funds experiments and prototypes to validate new ideas that may provide future growth:

  • Conduct market research to identify potential new product or geographic opportunities.
  • Develop minimum viable products (MVPs) to test demand and usability with a small set of early evangelists.
  • Run crowdsourcing campaigns or design sprints to gather customer feedback on new technologies or features.
  • Implement small pilot projects to evaluate new partnerships, business models, or marketing approaches

Horizon 2: Emerging (1-2 years)

This stage transitions the most promising solutions from Horizon 3 closer to readiness:

  • Scale successful MVPs into wider beta releases to assess product-market fit.
  • Build out integrations and infrastructure to support scaling market-validated solutions.
  • Grow partner and early adopter communities to co-create solutions.
  • Refine business models and operational processes for solutions demonstrating value.

Horizon 1: Investing & Extracting

This stage focuses on improving existing systems while maximizing profit:

  • Fund initiatives to enhance capabilities and the competitiveness of current products.
  • Maintain solutions with stable revenue streams (cash cows) that require minimal investment.
  • Expand customer segments and acquisition channels for established offerings.
  • Improve delivery pipelines, DevOps, and automation for faster time-to-market.

Horizon 0: Retiring

This stage winds down solutions that no longer provide strategic value:

  • Develop sunset plans to transition customers off obsolete or unprofitable solutions.
  • Reassign teams from retired solutions to new initiatives.
  • Extract lessons learned from solutions being retired to apply to future efforts.
  • Prioritize divesting solutions that are cash and resource traps.


Aligning budgets and roadmaps to these horizons balances short-term returns with long-term bets.

Participatory Budgeting in action

Participatory Budgeting (PB) brings diverse stakeholders together to transparently decide how to allocate portfolio investments.

Done well, PB builds engagement, accountability, and trust. Follow these tips to maximize its impact:

Involve diverse stakeholders

  • Get better ideas by broadening your perspectives
  • Increase adoption across organization for a smoother implementation
  • Achieve higher quality decisions by tapping a wider base of knowledge and experience
  • Boost morale and inclusion by encouraging open participation

Communicate clearly

  • Improve understanding across all groups to support more effective processes
  • Increase participation by making the process accessible and engaging
  • Find better ideas and promote transparency by enabling dialogue
  • Reduce confusion and questions to create efficiencies

Foster collaboration

  • Break down silos to improve alignment
  • Leverage collective intelligence to fuel innovation
  • Encourage greater commitment by building shared ownership
  • Develop relationships and empathy for a sense of community and connection

Establish clear guidelines

  • Promote fairness by setting consistent expectations
  • Enable effective preparation and yield higher-quality proposals
  • Accelerate reviews and decisions for faster results
  • Reduce risk through improved governance and compliance

Provide support

  • Boost collaboration skills to create synergistic proposals
  • Overcome barriers to participation and promote diversity
  • Resolve issues quickly with smoother processes
  • Upskill on budgeting best practices to achieve more impactful spending

Ensure accountability

  • Improve trust and engagement by enhancing transparency
  • Facilitate lessons learned to support continuous improvement
  • Demonstrate the ROI of investments to inform better future decisions
  • Establish a positive culture and high morale by celebrating successes

Reaping the benefits

Transitioning to Lean budgeting unlocks tangible benefits:

  • Greater agility – Teams can respond quickly to learnings and new opportunities
  • Improved transparency – Stakeholders have visibility into how funds are used
  • Increased engagement – Cross-functional partners feel ownership in decisions
  • Faster value – Removing bottlenecks accelerates delivering customer value
  • Higher satisfaction – Adapting to evolving needs increases customer delight

Is your budgeting process getting in the way of Lean-Agile results? Follow these guidelines to establish Lean budgets that fuel faster flow. Empowered teams supported by participatory processes can achieve amazing things. Get out of their way and let them delight customers!

Dive deeper by reading our white paper, “Using Lean-Agile Principles to Execute Organizational Transformations”.

SAFe 6.0 Deep Dive – Flow Metrics (Part 1 of 2)

If you are applying SAFe® (Scaled Agile Framework®) or are considering doing so, you are most likely aware of at least some of the key updates to the V6.0 release. One of the most critical additions to this update is the concept of “flow”. 

What is Flow in SAFe 6.0?

Scaled Agile’s official definition of Flow is as follows:

“Flow is characterized by a smooth transition of work through the entire value stream with a minimum of handoffs, delays, and rework. In SAFe, we consider flow to be present when teams, trains, and the portfolio can quickly, continuously, and efficiently deliver quality products and services from trigger to value.” (© Scaled Agile, Inc.)

SAFe 6.0 provides a detailed account of the various flow concepts and metrics that you should consider when implementing SAFe. While this information is very thorough, many practitioners I work with expressed an interest in more specific examples related to how to deploy these metrics. Hence, I thought it might be worthwhile to illustrate the use of these metrics through a simple, everyday example that most of us should be able to relate to so we can gain a better understanding overall.

An example from manufacturing

Because of the level of detail that will be covered, it made sense to split this into a two-part series to ensure we provide adequate coverage of all six of the flow metrics that are recommended by SAFe 6.0. Let’s first go over the details of a fictitious project which we will use to clarify the flow concepts.

When I facilitate training sessions for my clients, I often think about a common example that is not specific to any industry or technology so that I can try to reach as many of my students as possible and help them connect to their own contexts. Although there may not seem to be a clear alignment to Agile principles and practices, I often use manufacturing concepts as a backdrop to explain Lean-Agile concepts. For this exploration of flow metrics, we will use the concepts of automobile manufacturing to clarify the ideas.

The Toyota Manufacturing / Toyota Production System (TPS) is considered the foundation for today’s Lean manufacturing processes. Hence, we will connect to SAFe’s flow metrics by inspecting how these may map to building hardware and software solutions within an Agile Release Train.

The 6 Flow Metrics

Let’s first look at the 6 Flow Metrics that SAFe recommends.

Metric Definition
Flow Distribution Proportion of work items by type
Flow Velocity Number of completed work items over a fixed period
Flow Time Time elapsed from start to finish for a work item
Flow Load Number of work items currently in progress
Flow Efficiency Ratio of the time spent in value-added work divided by total time
Flow Predictability Level of consistency with which teams/trains/portfolios meet their objectives

 

Flow Distribution

The key concept behind monitoring the distribution of different types of work is to understand how long-term and short-term objectives are being supported. For example, work may include infrastructure/enablers, sustainment/maintenance, and new capabilities/features. If one type of work is dominating the overall distribution, the risk to the overall health of the product life cycle may be elevated. 

Using an automobile manufacturing assembly as an example, the assembly line will typically contain a variety of car models that are in distinct stages of the overall assembly process. 

Within this context, we may want to see how much of the resources (human capital and technological assets) are deployed for the development of the chassis, painting, electronics, etc. 

From another perspective, at the portfolio level, it may be worthwhile to examine how funding is allocated to research and development for future car models or manufacturing of current-year models. 

By looking at the flow distribution of how work is executed for various types of work, we can assess trends (i.e. peaks, valleys, outliers, etc.) which will enable the team to make informed decisions on potential shifts in the allocation.

Flow Velocity

Using the automobile manufacturing example, flow velocity is easy to explain—it is simply the number of vehicles produced within a time horizon. For example, 1,000 cars per day, or something to that effect. In your world, depending on what type of product or service you are building, this metric may not be as simple to measure, especially if you are not shipping a physical product. However, if you are producing a service, you will probably depend on some type of technology to support that service, which I would suspect to be a digitally enabled system, in which case you can measure the number of features and/or functions that your team is deploying within a specified timeframe. That can also qualify as flow velocity.

One key thing to keep in mind is that the purpose of collecting this data is to evaluate the performance of the team in terms of productivity and efficiency. Trends will be valuable to determine whether the overall performance is improving or degrading over time.

Flow Time

In delivering products or services to the customer, nothing is more noticeable than speed; customers can often compromise on the complexity and sophistication of a solution, but they are almost always eager to receive something as quickly as possible, no matter how incomplete it might be. 

We need to keep in mind that this does not mean we can put low-quality goods into the hands of the customers and expect them to be happy. Faster delivery means we will provide a high-quality product, but possibly without all the special features that may be perceived as “nice-to-have”.

Flow time is one method of measuring how much time your team needs to put that capability into your customers’ hands. It is a trending metric that allows you to determine if your team is improving its efficiency (reduction in time), stabilizing (by a plateauing effect) or even degrading (taking more time to deliver value). 

Within the context of automobiles, flow time can be measured as the number of hours/minutes required to build a complete, functioning car that is ready to be driven.

In Part 2 of this Deep Dive, we will cover the remaining three Flow Metrics:

  • Flow Load
  • Flow Efficiency
  • Flow Predictability

In the meantime, if you need any help with this or other SAFe concepts, consider our catalog of SAFe-related learning courses and certification programs.

Perfecting Your ITSM Customer Management Using JSM

Customer Management is a vital component of a thriving ITSM practice. But what is it? Why is it vital? And how can you go about perfecting it so you see all the impressive benefits?

This is the third in a three-part series covering ITSM principles and applying them using JSM:

(This content is based in part on the webinar, “Perfecting Customer Management Using Jira Service Management”. To learn more and see an in-depth software demo showing how to practically apply this information, watch the video!)

What is customer management?

In this context, customer management refers to the process of managing and optimizing interactions with internal and/or external customers over the life cycle of the relationship.

It’s about putting the customer first

This is vital because it supports one of the key values of the popular ITIL framework for ITSM: customer-centricity. In contrast to “the technology orientation” to which many organizations default—the IT team is solely focused on handling their own tasks, and the customer’s requests are viewed as an interruption or even a burden—a customer-centric view puts the customer’s satisfaction first and foremost, prioritizing other IT tasks and updating processes accordingly.

An example of a change that reveals the adoption of a customer-centric approach could be the wording used in the form fields on a customer service portal:

 

Technology-oriented Customer-centric
“Hardware and peripherals” Laptop, printer, phone
“SAML/SSO validation error” Trouble logging in?
“IP address?” <blank field> “IP address?” <tool tip that links to an article on the knowledge base: “How to locate your IP address in three easy steps”>

 

Since many customers are not, themselves, IT professionals, adjusting the terminology to be simpler and clearer exemplifies customer-centricity.

Defining the IT value stream

This aligns well with the Agile concept of value streams. The value the IT department provides is not measured in items checked off a list, it’s measured in satisfied customers. So, an ITSM “value stream” begins with the end in mind—a satisfied customer—and identifies every point along the path from problem to solution, with the customer at the forefront.

Once value streams are identified and established for every customer request type, customer-centric processes can be standardized and (to the extent possible) automated. This allows for quick and efficient decision making and solutioning without sacrificing the customer’s satisfaction in the pursuit of speed.

Key components of Customer Management

Effective Customer Management requires three vital elements:

Understanding the customer’s needs and expectations

Logically, you can’t put the customer first in your service delivery or effectively establish value streams if you don’t fully understand what the customer wants in the first place. And, you need to understand the customer’s expectations—whether they’re realistic or not. (Sometimes, effective Customer Management will involve managing those expectations kindly but firmly.)

Capturing customer feedback

The way you come to understand the customer is by constantly soliciting feedback from them. Keep the lines of communication open before, during, and after the ticket resolution process. 

Realistically, customer needs and expectations change over time. So, feedback should be an ongoing loop. Effective Customer Management—and all other aspects of high-quality ITSM—is not just a set-it-and-forget-it proposition. It should constantly evolve with your customer.

Continually improving service delivery

If you’re focused on the customer, and you keep that communication flowing, then you will routinely uncover opportunities to improve and streamline your service delivery. Don’t put off making those changes. Continuous improvement is the key to maintaining your competitive edge.

The power of “shifting left”

In the context of ITSM, “shifting left” refers to arranging tools and processes in such a way that problem resolution occurs as close to the customer and their initial request as possible.

The ITIL framework defines five support tiers that your team can utilize to solve a customer problem:

 

Tier 0 Tier 1 Tier 2 Tier 3 Tier 4
What is it? Self-service Initial human contact (via phone, email, chat, or in-person) Routine technical support Expert technical support Third-party technical support
Who is involved? Self-service portal, knowledge base, Service Catalog, automated ticketing solutions, and increasingly, AI chatbots Customer service representatives with limited technical expertise IT specialists and analysts with general knowledge and proficiency  Subject-matter experts with deep experience Outsourced help desk resources, often from the manufacturer, developer, or a niche consultancy
What do they do? Answer simple, general questions and perform routine or automated tasks (password resets, how-to instruction) Triage the situation, offer a single point of contact, resolve the situation (if it falls within their scope of knowledge and experience) or escalate to Tier 2 Analyze and resolve the situation, or escalate to Tier 3 Analyze and resolve the situation, or escalate to Tier 4 Analyze and resolve the situation, or propose an alternate resolution (i.e. replacement, refund, etc.)

 

The IT team that focuses on building their Tier 0 capabilities will experience significant benefits, including:

  • Reducing resolution time
  • Optimizing the use of IT resources, reducing cost
  • Enhancing the customer experience

Building up the knowledge and experience of your Tier 1 support team offers all the same benefits in those more complex situations that may have previously required the help of busy (and expensive) Tier 2 staff. And the pattern goes on.

Putting Customer Management into action

Putting the theory into practice will require a tool that supports effective Customer Management. Jira Service Management (JSM) offers many features and capabilities that align with the concepts described above. 

For example:

  • Defining the IT value stream – JSM offers tremendous customization so you can build portals, workflows, a knowledge base, and integrations, all based around the value you need to deliver to your customers.
  • Capturing customer feedback – A robust Customer Satisfaction (CSAT) feedback and scoring module and other communication and collaboration tools within JSM keep the lines of communication open, supporting continuous improvement.
  • Shifting left – JSM supports self-service through customizable portals, automated ticketing, and streamlined integrations with knowledge base materials, chatbots, and AI.

For an in-depth demonstration of how to put all these concepts into practice using JSM, watch the webinar, “Perfecting Customer Management Using Jira Service Management”. And, if you’re ready to move forward with perfecting your own Customer Management practice, speak to our ITSM experts today.