Large-scale IT infrastructure projects require an enormous amount of planning, design, configuration and testing to ensure that everything is working correctly and properly transitioned once the work is done. This takes time and resources. Like their software counterparts, IT infrastructure projects are more likely to be successful — more efficient, secure, and reliable — when accompanied by robust estimation and planning processes. In this article for ProjectManagement.com, Larry Putnam, Jr. and Joe Madden identify best practices for applying parametric estimation to IT infrastructure projects.

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It’s that time of year again for many C-level executives: time to figure out the IT budget for next year. This is to bring the business side of the organization to the table with the technical side to forecast how much IT is going to spend. It can be a complicated process, but there are ways to make it easier and more accurate; and there are ways to save a lot of time and money. The challenges often relate to short planning time frames, minimal information available to generate accurate forecasts, political agendas within the organization, and, unfortunately, only a small number of estimation methods in place. But there are tools and processes available to help face these challenges. Here are the basic steps that we recommend for cost optimization in the budgeting process.

Start by analyzing the historical data that is available. The process can be streamlined by focusing on the core metrics within the organization. This data can include release level size, effort, staff, and duration information. Historical data showing typical effort by role by month spending is also valuable to leverage. Ideally, this type of data should be captured on 8-15 projects.

The next step is to pull together scope level sizing data on projects that are being considered for the new year. This information can include epics, themes, user stories, business requirements, or use cases, to name a few. The goal here is to get as close as possible to determining how much work needs to be done on each release in the pipeline. Once there is a large enough sample of data, then release level estimates can be created for the coming year. There are tools available to help streamline this process and the best ones allow for risk mitigation and sanity checking with historical data.  

The Scaled Agile Framework (SAFe) has realized widespread adoption by organizations desiring to accelerate product delivery without sacrificing quality. The alignment of product vision, business value, and development velocity is an key contributor to successful large-scale agile development.  Presented by QSM's Laura Zuber on June 20 at 11:00 AM EST, this upcoming ITMPI webinar demonstrates how scope-based estimation techniques can be used to model the relationship between vision, value, and velocity at different levels of the framework and stages of implementation to guide release planning and management decisions.

Laura Zuber has 25 years of experience in software development consulting, training, and support. She has conducted training and coaching sessions for all QSM SLIM-Suite tools and helped customers implement SLIM across a wide variety of processes and platforms. Laura has managed software development projects, served as a senior software process improvement specialist, performed process assessments, designed and implemented best practices, and authored numerous training programs. She is a Certified Scrum Master and lead consultant for using SLIM with agile development.

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For a number of years, the federal government has been on a mission to reduce waste and enhance efficiencies across departments, including IT. But according to the CIO Council’s 2017 State of Federal Information Technology report, 43% of the federal government’s $80 billion in IT projects cataloged in September 2016 were listed as over budget or behind schedule. In this article for GCN, Doug Putnam takes a look at some of the common pitfalls that lead to project cost and schedule overruns and how parametric estimation can help government CIOs and their teams avoid these traps.

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With agile projects, we hear a lot about the planning benefits of having a fixed number of people with a fixed number of sprints.  All great stuff when it comes to finishing on time and within budget. But one of the things we also need to focus on is the quality of the software.  We often hear stories about functionality getting put on hold because of reliability goals not being met.

There are some agile estimation models available to help with this, and they can provide this information at the release level, before the project starts or during those early sprints. They provide this information by leveraging historical data along with time-tested forecasting models that are built to support agile projects. 

In the first view, you can see the estimate for the number of defects remaining. This is a big picture view of the overall release. Product managers and anyone concerned with client satisfaction can use these models to predict when the software will be reliable enough for delivery to the customer.

In the second view, you can see the total MTTD (Mean Time to Defect) and the MTTD by severity level. The MTTD is the amount of time that elapses between discovered defects. Each chart shows the months progressing on the horizontal axis and the MTTD (in days) improve over time on the vertical axis. 

Developing software within the DoD presents a unique set of challenges, including but not limited to budget cuts, Congressionally mandated changes, changing software requirements, and so on. It should come as no surprise, therefore, that cost estimators have faced significant challenges when estimating systems in the Defense arena. A recent initiative put forth by the DoD was to improve its estimation process by leveraging historical data collected from forensic analyses of recently completed software development efforts. This article by Taylor Putnam-Majarian and John Staiger, discusses (1) some of the challenges faced throughout this initiative, (2) the data collection process, and (3) how one can leverage data to improve cost estimates. This article was originally published in Crosstalk Magazine.

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Organizations often come to us in the early stages of shopping for a software estimation tool and, oftentimes we find that they could be asking some additional questions. They often focus on the tool’s operating system, database structure, and architecture, when they could also be focusing on the quality of the data behind the tool. They also ask a lot of questions about inputting detailed information when really it would be in their best interest to focus on solid project-level information since detail-level inputs are often not available early in the planning lifecycle. Instead of focusing on the number of hours allotted to each individual person, it would be more beneficial to focus on how much work the overall team needs to finish.

In our 30+ years of experience in this industry, we've found that, no matter what tool an organization ultimately chooses, they need to be asking the right questions. Here is the criteria they should consider.

Tool Capability

As with any tool, it is important to match the tool with the job at hand. Using a screw driver to perform the task of a chisel will yield poor results. The same is true with trying to use a detailed planning tool in place of a software estimation tool. Make sure that you consider at what point in time formal estimates are required and how the resulting information is used to support negotiation and business decision making. Here are the main issues that should be taken into consideration when assessing an estimation tool.

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While creating technology is about solving everyday problems, the act of creating technology is about solving design problems. This applies to any type of technology project, regardless of its purpose or size. Organizations must put as much thought and consideration into the design of their underlying IT infrastructures as they do in the design of their software projects. Both require careful sizing, estimation and planning.

Of course, installing, configuring and testing IT infrastructure is different than developing a piece of software. A typical IT infrastructure project could include:

• Server room buildout (clean power, fire prevention, disaster recovery, cabling, etc.)
• Network connectivity (local and wide area network)
• Installation of computer server hardware (can be physical or virtual)
• Configuration of system software on the servers (operating system, database, email server, web server, security template, etc.)
• Configuration of computer desktops, laptops, smart phones. peripherals and other Internet of Things (IoT) devices 

Together, the hardware and software formulate a truly complex system where all of the parts are interconnected.

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If you were thinking about purchasing a driverless car, and the salesperson told you that there’s a “slight” chance that the car will fail during transit, would you still feel comfortable laying down your money? Or, if you faced an emergency, would you trust an automated robot to perform open-heart surgery, rather than the hands of a skilled physician?

While these questions might seem like the stuff of a science fiction novel, they’re quickly becoming a part of our normal, everyday world. We’re hearing a great deal about artificial intelligence and how it is replacing tasks that were once done by humans. AI is powered by software, and that software is becoming increasingly vital to our lives. This makes ensuring its reliability more important than ever.

But here’s a sobering thought: right now, IT operations teams are building software that is, on average, 95% reliable out the door. That’s right; today, a 5% unreliability gap is considered “good enough.”

What if you could leverage summary level cost, duration, and productivity data to support estimates for future projects, at the release and enterprise level? C-level executives, development managers, and project stakeholders are all involved at some level in project planning. They want quick access to information on a regular basis and they want web-based solutions to make it happen. So how does it all work? There are web-based analytics tools that allow you to create a centralized database for all of your projects. These tools store the data, leverage it to generate project and portfolio estimates, and then provide a communication vehicle throughout the organization to ensure that everyone involved is on the same page. It all starts with having the data in one place. 

Once you have all of your project data in one place, then you can focus on analyzing the completed projects. You can compare them against industry trends and leverage a 5-star report to show how they rate on performance in the industry. The initial measures to focus on would be size, duration, effort, reliability, and productivity. A project's productivity will be calculated automatically once you have entered the size, duration and effort. We call this measure a Productivity Index. This measure can be compared to industry and used as a benchmark to measure process improvements over time.  These numbers give you a quantitative picture of your current project environment.