A guide to data analytics in project management

You’ve got a project at work. It could be anything — launching a new website, setting up a company event, or rolling out a new software. You start by collecting data. This could be the hours your team logs in, cash flow, progress reports, you name it. 

Now, data alone can be overwhelming; it’s just a bunch of numbers. However, analytics turns those numbers into insights. For example, analytics could flag that one phase of the project is sucking up too much time and money. With that insight, you can figure out what’s up and adjust your plan to get things back on track. Data analytics predicts how your project will unfold before it happens. This kind of foresight lets you prepare and adapt.

In this article, you will learn how to use data analytics to forecast your project’s progress. We will also introduce you to the most powerful project management tool, Wrike, which Gartner recognized as a leader in collaborative work management. We’ll show you how big names like Walmart Canada and Sony Picture Television use Wrike’s powerful analytics tools to visualize and manage their projects.

Analyze your project in Wrike — start your free trial now.

What is data analytics in project management?

Data analytics refers to the use of statistical and quantitative methods to analyze datasets and extract insights that can be applied to project management processes. It involves collecting, organizing, and analyzing these datasets to uncover patterns and trends that can inform business decisions. This may include data mining and machine learning, among others.

For an IT team, this might involve tracking the time it takes to resolve support tickets or figuring out which system updates lead to fewer customer complaints. You’re always collecting this data but with data analytics, you can actually use it to improve your work. You can also look forward and predict what could happen. For example, say your analytics show you that server outages spike every time you get a surge in web traffic. Now, you can plan for that and get ahead of the game. And the best part is that you don’t need to be a data scientist to get it. 

There are tools out there that can help any project manager become a data analytics pro. These tools turn that sea of numbers into easy-to-read charts and reports, meaning you can spend less time digging through data and more time managing your project.

Benefits of implementing data analytics in project management

Are you leading a small team or managing large-scale projects? Let’s see how data analytics can significantly enhance your operations. 

Data analytics can help you:

Improve project efficiency and resource allocation

Companies often identify areas where they can optimize their project processes to reduce costs and improve efficiency. They may flag certain tasks that can be automated or outsourced to reduce labor costs, or they may use predictive analytics to forecast resource needs and allocate resources more effectively.

Enhance risk management and mitigation

Businesses can address potential risks and mitigate them before they become major problems. Predictive analytics algorithms can forecast potential risks and enable project managers to take preventive action before they occur.

Optimize project scheduling and time management

Organizations can optimize project scheduling and time management by identifying bottlenecks or areas for more time or resources. Project managers can adjust their schedules or reallocate resources to ensure projects are completed on time and within budget.

Boost stakeholder engagement and communication

Companies can improve stakeholder engagement and communication by providing real-time data on project performance and other key metrics. This can increase transparency and accountability and help to build trust between project managers and stakeholders.

Essential data analytics tools for project managers 

How do you choose from the mountain of data analytics tools at your fingertips? We’ve broken it down into four categories to help you out.

Project management software with analytics features

One essential tool that every project manager should consider is project management software with robust analytics features. But what does that mean? Put simply, it’s a platform that keeps your tasks in order and provides deep insights into every aspect of your project. That’s where Wrike can be a game changer in how you manage your project. 

We have a range of features designed to enhance project tracking and data analysis, making it an ideal choice for project managers. Wrike’s advanced analytics can show you at a glance how resources are being allocated, where bottlenecks might be forming, and how closely project timelines are being adhered to. 

Let’s say you’re rolling out a new software update. You can track each stage with an analytics board, monitor team performance, and predict potential delays based on historical data. You can also generate custom reports to gain insights into various aspects, such as task completion rates, team performance, and resource allocation.  

How to implement data analytics in your project management process

Project management is a complex process that involves many moving parts. From managing resources and timelines to ensuring customer satisfaction, project managers have a lot on their plate. 

Now, how do you factor data analytics into all of that? We’ve got you covered.

1. Identify key performance indicators (KPIs)

Imagine you’re managing a software development project. One of your main goals is to release the product on time. So, what’s your KPI there? Deadlines met, right? You want to track how often your team hits or misses these dates. Miss too many, and you know there’s something wonky going on that needs your attention.

But it goes beyond hitting milestones alone. Say you want to ensure the code is quality — no one likes buggy software. You might look at the number of defects reported after a release. If that number starts climbing, it’s a red flag that something’s not quite right in the development process.

Tracking project completion rates can help you identify areas where your team may need to catch up on or improve deadlines. On the other hand, tracking resource usage can highlight areas where you may be overusing (or indeed, underusing) resources.

2. Collect and organize data

Next, collect and organize the data you need to analyze. This may involve pulling data from multiple sources, such as project management software, CRM systems, or other data sources.

For instance, if you manage a chain of cafés, the data you get from peak sales hours, popular menu items, and customer feedback is gold. You gather this data through your POS system, customer surveys, and social media chatter. Once you’ve got this information, you organize it in a way that’s easy to understand.

Collecting and organizing data can be a time-consuming process, but it’s critical to ensure your analysis is accurate and meaningful. This will give you a complete picture of your project’s performance and allow you to make informed decisions based on that data.

3. Analyze data for insights

Now it’s time to analyze your data for insights through predictive analytics algorithms, data visualization tools, or other analytical methods to uncover patterns and trends that inform your decision-making process.

Using predictive analytics algorithms helps you forecast project outcomes based on historical data, allowing you to adjust your project plan before issues arise. Meanwhile, data visualization tools let you identify trends and patterns in your data that may be difficult to spot otherwise, such as spikes in resource usage or dips in customer satisfaction.

4. Make data-driven decisions

Finally, the insights generated through data analytics should be used to inform your decision-making process. This may call for making changes to your project processes, reallocating resources, or taking other steps to optimize project performance based on the insights gleaned from your analysis.

Let’s say you run an online boutique store. You’ve got sales data, customer feedback, ad click-through rates, and even the time spent on different webpages ready to go. By analyzing this data, you could determine which products are flying off the shelves and which ads are bringing in the most eyeballs. This helps you decide where to invest your marketing dollars and which products to feature more prominently.

Speaking of fashion, clothing rental service Gwynnie Bee overcame significant challenges, such as scaling up operations while maintaining visibility over project timelines, using Wrike. Previously, this was not easily tracked, and risks were difficult to predict. As a direct result of implementing Wrike, Gwynnie Bee achieved a remarkable 60% reduction in order processing time, and shipping quality and speed also saw dramatic improvements.

Phillip Hoffman, Senior Program Manager, says:

“Wrike allows us to plan a project with enough detail that we really reduce the risk, or likelihood of the delay, of the project not going correctly.”

Boost your data analytics skills with Wrike

Wrike provides a sharp lens through which project managers can view their projects’ present state and predict the future. With Wrike, you have a dashboard that displays what each team member is working on and predicts when they might hit a roadblock. And that’s not all. 

If you’re in the middle of a phase in your project, unforeseen delays or oversights won’t catch you off-guard because our AI project risk prediction helps you stay on track. You’ll be notified of any potential overruns so you can adjust schedules or resources accordingly to hit deadlines.

You can also build a repository of data over time, which is like gold for any project manager. This historical data can help you see which processes or project types have been most successful, guiding you to replicate those strategies in the future.  

Want to increase the use of data analytics in your project management processes? Start your two-week trial of Wrike today.