Data Analytics for Project Managers: Benefits and Practices

Having a prosperous business largely depends on resources. However, with the rise of the information age, a decisive element of business performance constitutes the data.

Under the new conditions, project managers became capable of generating data strategies that favor business growth and project progress. This article will answer why and how it is best to apply data analytics to your project.

Project managers must be fond of numbers. Most of their goals are related to crunching numbers and analyzing the results. A perfect and accurate performance analysis or project dashboard is a suitable solution to help.
It is where project maintenance and analytics trends come together in agile project management: conditions and tasks dictate the form of execution. But it's not all as easy as it might seem.

In fact, a recent report shows that 97% of executives said their organizations aspire to a data-driven culture, while only 26.5% said they were successful. Organizations have invested trillions of dollars in modernizing their businesses, but 70% of these initiatives failed. It happens because companies prioritize technology investments without creating a data culture to support them.

A toolset capable of collecting all the company's information, both internally and externally, helps to avoid falling into bad practices that promote exploitation and mismanagement of data. In this way, we can state: anything that is not measured cannot be improved. Project management is no different. Just as you can track and analyze any other business field, you should apply the same analytics approaches to project management in real time.

Advanced analysis entails effective management of project uncertainties and risks. It requires modern project managers to use analytical techniques to monitor and control risks and estimate project schedules and costs more accurately with analytics-based forecasting.

Using a BI connector or data integration solutions enabling streamlined data management to decipher specific patterns and trends could come in handy. Project managers can use such tools to determine how projects are performing, establish a need for resources, and make strategic project decisions based on the insights.

An essential prerequisite for advanced data analytics can be distinguished into four levels that formulate a business intelligence database:

1. Descriptive analytics: analysis of the current operational status without further research.
2. Diagnostic analytics: recognizing data patterns enables conclusions about the causes of previous developments, but no statements about future trends.
3. Predictive analytics: updating the recognized patterns for the future.
4. Prescriptive analytics: concrete recommendations based on the recognized patterns to achieve a predefined goal.

Using these four pillars of modern data analytics, you can overview, track, and improve your projects.

From our point of view, this binomial 'Project Management' and 'Analytics' symbiosis will have the most significant benefits in the following 4 axes:

Using analytics, project managers can watch for early signs of slippage in terms of costs and timelines and take proactive action.

Additionally, insightful analytics can help improve resource utilization and forecast revenue and costs. With analytics, organizations can take a broader view and combine unrelated data streams to deliver deep insights into projections and early warning signs on complex projects.

As a project manager, you need to understand how analytics can reduce your workload, improve processes, and improve your project outcomes.

Quality is a definitive measure of the success of your project at the time of delivery. Analytics help you plan, monitor, and review quality across your project.

Advanced analytics helps organizations make decisions based on facts rather than instincts. Real-time project evaluation reveals a wealth of information to help organizations align with their strategic goals.

Using tools for project management like Asana, Jira or Trello, you can evaluate performance. However, if a team gets bigger or the project scope increases, you might need more advanced analysis to make rational and data-driven decisions. For this purpose, you can use data analytics tools, spreadsheets, or databases.

For instance, by exporting records from Asana to BigQuery, you'll have a detailed overview of projects, costs, progress, team productivity, and more. With careful analysis, a project manager can make strategic decisions easier.

By analyzing the Jira data in BI systems like Microsoft Power BI, you’d get comprehensive reporting across all business functions, combining multiple data sources in single analytics, and automated reporting processes. Thus gaining greater control and making your business decisions based on actual insights.

Correctly managing and having up-to-date reports and assessments allows you to organize your company's financial resources more accurately.

The financial information of your business is much more than numbers. Its analysis is essential to obtain relevant findings and make key decisions in this area, keeping your company's information secure, reducing errors and unforeseen events, or detecting leaks that could harm the organization's longevity and success.

While shifting to a data-driven approach and using available tools makes a lot of sense, most project managers might either be unaware of the analytics approach or comfortably move towards their largely subjective approach to decision-making.
The high availability of data analytics technologies can enable project managers to use various detailed reports and graphs to break down complex project data and predict their behavior and results.

Nevertheless, their utilizers should know the possible practices of working with them to maximize the help from existing features, depending on the final expectations.

A good method to produce estimates (time, effort, and resources) is the three-point estimation, which evaluates three possible scenarios:

• the best one (optimistic)
• the most likely one (realistic)
• the worst one (pessimistic)

Rather than simply relying on the project manager's experience, use data to estimate the duration of the various project planning levels using real values ​​from past projects, current trends, and forecasts.
An algorithm predicts the length of the project phases or individual tasks based on the metadata, the planned project tasks, and previous results. Even during a project, you can continue with these analyses using the current project information. You can display their results in dashboards and reporting tools such as Power BI or Tableau.
A project manager receives suggestions for measures to eliminate the risk, for example, alternative resources, including updated task durations directly in his planning tool – Jira, Asana, or others.

Another analytical model directly addresses data-driven decision-making, guided by multiple evaluation criteria, is AHP (Analytical Hierarchy Process).

You can use it in any scenario that includes some factors in the decision-making process, such as which project to have in a portfolio or whether to create a deliverable or part of it internally or acquire it from the outside (make or buy).

The method involves modeling the scenario by identifying the criteria for making the decision and the possible alternatives. For example, in a project management scenario, the criteria could include flexibility in the scope, time, and costs. To make a decision, you should consider the relative importance of these three factors in your project context.

Regardless of the chosen practice of data analytics, the fundamental steps of any project management analytics process are the following:

Identify data patterns: extracting data from different sources, such as project management software, ERP, CRM, etc., and identifying repeatable actions, metrics, and results.

Derive inferences from data patterns: once obtained, the information is processed automatically to be homogenized, ordered, and categorized.

Display them in meaningful visuals: when the data is already in order, it is crucial to execute advanced data visualization techniques.

Use inferences to develop predictive models: the company's executives and strategists move on to the information analysis phase, which depends on the type of problem to be solved.

Use all the above to support decision making: finally, according to the analysis made, the most appropriate decisions come to solve the problem as quickly as possible.

In some way, project management data analysis can be seen not as a mere science but as an art process. The key to its success is the consistency of time and approaches. Each project manager may have different preferences in data collection tools or visual presentation tastes.

The only thing is that intelligent data analytics allows you to obtain important information, bringing out patterns and trends useful for the business itself.