Good, reliable data is often the key to making an agile project successful. But project managers often struggle to get the data they need — or to find it in a sea of data they don’t. A project to address this problem at Honeywell Inc. can be a model for companies struggling to build useful dashboards that can help project managers make decisions. Bringing in perspectives from stakeholders across the organization they successfully met their project managers’ needs by following a few design principles: 1) start with the users’ perspectives, 2) simplify visuals with intentional data engineering, 3) focus on making the end interface user-friendly, and 4) create user-friendly documentation and training.
Today’s organizations are under pressure to adapt their infrastructures and processes to a more digitally based, dynamic marketplace. To stay competitive, companies have increasingly adopted an agile project management framework — and access to timely and relevant information is more valuable than ever. As such, project managers increasingly rely on activity-based dashboards for reporting timely and user-friendly information to project stakeholders who want to understand how project activities affect interim performance objectives.
Dashboards have an obvious appeal. They provide timely and easy-to-understand numeric and visual reports of KPIs corresponding to project activities, offering stakeholders a high-level view of essential metrics that show them the state of project performance. Project managers can more easily see variances between actual and expected metrics that signal process breakdowns, allowing them to more quickly take necessary actions to maintain project progress. Dashboards’ underlying components (e.g., data sources, analytics, and functionality) have rapidly improved, too, as the digital era has produced additional descriptive data sources, enhanced real-time data feeds, and robust visualization, all of which make them useful to a wider and wider audience.
However, despite their advantages, dashboards also often have limitations. They can include too much data, producing information overload and making clear decision support difficult. On top of that, dashboard reports provide retrospective information. Simply put, the available information provides an update on what is, or what has happened with a key performance indicator. Users may ask, “Why are my KPIs in the red zone?” or “What is causing our performance to be so far from our estimates?” but access to the underlying data is typically not part of the dashboard functionality. Recent research has shown that managers and executives have trouble extracting fuller potential from the information provided in dashboards. Business leaders struggle to find a balance between dashboards that take sweeping views of operational or financial indicators and those filled with detailed KPIs or a tight focus on particular indicators.
Despite the growing reliance on dashboards for managing projects, we found that a majority of project managers feel they don’t have sufficient decision support from analytic platforms to effectively manage projects. In a survey conducted to better understand the importance of analytics in project management, we acquired feedback from 25 practicing project managers across industries including pharma, manufacturing, technology, and health tech. Their responses indicated that while they leaned heavily on these reporting platforms — a majority used them almost daily — nearly three quarters felt that their dashboards were missing the tools they needed to succeed.
This was a familiar problem: One of us (D’Cruz) had recently navigated it in role as senior manager of analytics at Honeywell Inc. There, he led an initiative to redesign the company’s dashboards to give project managers access not just to KPIs but also the more detailed descriptive data behind them. By providing this timely, accessible, and in-depth information, he found that project managers were better able to understand what was driving their KPIs, establish more accurate budget forecasts. and manage the ongoing performance of their projects. This seemed to be the kind of tool our respondents felt they were missing. Here’s how the company did it.
Building a Better Dashboard at Honeywell
The process Honeywell developed was in response to one team’s problems. A project initiative was experiencing a high variance in its costs relative to what was projected. This quickly brought in the finance division because of the need to readjust capital requirements, which in turn created delays.
The effort to fix this situation started with the finance team, who noticed high variances in proposed project budget estimations. Project managers were either understating or overstating their forecasts which gave rise to two problems: 1) Overstated forecasts resulted in capital being misallocated, preventing spending on other investments, and 2) Understated forecasts caused change requests for budgets leading to slower time-to-market.
Honeywell’s data and analytics staff were able to adapt the functionality of a dashboard to provide effective decision support by involving stakeholders in the design and roll-out processes. By providing an intentionally tailored interactive dashboard — that is, a dashboard that enabled users to access pertinent, near real-time data — stakeholders could acquire the detailed and timely information they needed to understand what was driving KPI variances, enabling them to better manage resources throughout the project.
The Design Stage
To fix the cost-forecasting problem, the team decided create a front-end dashboard that would allow project managers to have access to more detailed financial actuals and estimates in a more real-time interface than they had before. The thinking here was that in an agile framework with evolving priorities and requirements, project managers needed the ability and flexibility to understand how their projects and expenses were evolving. As such, it was necessary to transform the way project managers viewed and planned finances for a project.
Start with the user’s perspective: The first step in designing the analytic framework was to clearly understand the data involved and how project managers relied on and accessed this data. The primary sources of data were from internal spend on resources and external spend (partner/vendor expenses – labor, technology, etc.). These were pulled into a centralized database. The next step was to decide the list of KPIs/metrics that would be useful to stakeholders. This involved interaction with the project management team to identify how these would be reported.
Data engineering can help: Data that is input into source systems is then loaded into a data warehouse, which requires an extract, transform and load (ETL) process. In order to feed the visual capabilities of the dashboard, the engineering team used reporting requirements described by the project managers to build different tables, joins, and filters in the database. This allowed the visualization layer to connect to source data, facilitating a more seamless ability to interact and display desired information and create visuals of required metrics.
Make your interface user friendly: Through the incorporation of tabs, which provided drill-down direction for user navigation, PMs were able to access detailed attributes of what was driving cost or labor hours and also displayed cost drivers over time. These project activity metrics were updated as they occurred, giving users near-real-time decision support. The interactive dashboard connects PMs to access a variety of detailed data such as:
- Internal labor hours applied to activities according to functional area (down to an individual employee level)
- External vendor/partner costs as they occur by attribute (e.g. billed software, labor hours etc.)
- Cost drivers of project resources and variances verses projections on a daily basis and over time
Create user-friendly documentation and training: In order to drive use of the platform the analytic team provided ongoing support to project stakeholders on how to access data via the dashboard and included self-service instructional documentation and video content that users could access to quickly get up to speed on how to identify the data they needed to gain an understanding of resource consumption.
Outcomes
The value of analytics in a dashboard roll-out is to provide decision support, or more simply put, to provide relevant information on a timely basis in order for users to better understand the tasks they are managing. The scenario depicted in this project achieved its objective on a few fronts. The analytics team monitored usage of the dashboard after the roll out and determined that a majority of PMs logged into the system on a regular basis, which addresses a major obstacle of systems roll-outs (e.g. user adoption).
As time elapsed, the team also identified greater forecast accuracy of projects which reduced project “pings,” or disruptive pauses in projects and ripple effects of corresponding activities. A major result was a more optimal allocation of capital resources at the organizational level as PMs were better equipped to request additional funding depending on additional scope, which helped avoid over-budgeting to cover potential changes of scope in an agile framework. It was accomplished through access to more detailed cost driver information as it occurred, which empowered PMs to maintain a flexible project structure. In some cases, PMs were able to actually downsize budgets from excess, unused capital.
As companies rely more and more on agile approaches, having the right data on hand to make important decisions only become more essential. Dashboards can be great tools to guide project managers along the way, but it’s clear that right now many of the managers feel they don’t have all the tools they need. But these employees shouldn’t be expected to make due with incomplete information in such a vital task. Giving them the tools they need to succeed will benefit everyone.