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Masooma Memon

Project Data Analytics: Definition, Benefits & Steps to Study Data

Project data analytics enables project managers to make data-driven decisions, enhance collaboration, and predict project outcomes. Here's what it's all about.

Studying your project data, also known as project data analytics, is crucial for:

  • Improving project processes
  • Optimally using resources
  • And, making accurate project timelines and budget forecasts

The catch? Data analytics seems like a technical field to dip your toes into. 

Except, it’s not. In fact, tracking, reviewing, and using data to make decisions is only as hard as you make it.

In this guide on project data analytics, we’ll tell you exactly how simple it can be to gather and use data. We’ll also touch on the many benefits of data analytics and which software to use to automate data mining. 

So without further ado, let’s get on with it. 

What is project data analytics?

Project data analytics refers to using data from past and current projects to improve project management and, with it, business growth. 

Think of it as data analytics (using raw data to draw informed conclusions) in the context of project management.

Since project data analytics aims to improve your project planning and processes, it involves mining data from various aspects of project management including resource data. You can then use this data to:

  • Make informed decisions to improve your processes, achieve business goals, and boost client experience.
  • Convince stakeholders to take change initiatives by using descriptive analytics or presenting data in effective (read: persuasive) ways.
  • Increase pipeline efficiency by leveraging predictive analytics, which involves using past data to forecast capacity and performance. 

The benefits of using project data analytics

Investing in project data analytics benefits everyone — from stakeholders to project managers and HR folks. By reviewing data for better resource utilization, data analytics helps employees too (for example, by saving them from heavy workloads).

Here’s a breakdown of all the advantages of project data analytics:

  • Understand patterns and trends

Studying data helps you identify trends in your team’s performance and project completion. Having this information, in turn, assists in better project and resource planning. 

  • Take corrective measures before it’s too late

Predictive analytics assists in catching early signs of slips in project budgets, costs, and timelines.

By having a strong understanding of your pipeline capacity using past data, for example, you can better plan work deadlines and only take as much work as your staff can deliver. 

  • Achieve business goals

Analytics also gives you data to plan capacity and utilize resources as an effective way to accomplish quarterly and yearly business goals.

In fact, by capturing the rate of work, you can easily predict whether projects will be completed on time. Based on the information, take on more projects to hit revenue goals. 

  • Determine complex projects’ outcomes in real-time

Break down complex project data to better manage projects and improve efficiency in your processes. As a result, better forecast project lifecycle and outcomes. 

  • Make data-backed budgeting decisions

Data analytics assists in driving profit by helping you make better budget- and cost-related decisions. It helps with better forecasting revenue and project costs as well. 

  • Improve resource utilization

Studying resource capacity and performance data is essential for improving resource utilization. Optimal resource allocation and utilization, in turn, contribute to retaining employees.

  • Optimize project performance

Deep insights into what it takes to complete projects in your pipeline are crucial for identifying the right methods to achieve organizational efficiency.

It’s also useful for identifying and monitoring potential issues and developing appropriate project risk response strategies. 

Challenges in project data analytics

Although project data analytics comes with a ton of benefits, it’s not free from its challenges — especially as you gather and review raw data.

The going gets tough if you don’t have clearly defined ways to source and clean raw data.

The first step to better managing these challenges? Understanding what they are and what solutions you have at your disposal. So let’s explore these: 

1. Lack of meaningful data

Often, there is no external data on the problem you’re studying. Internal data, on the other hand, maybe scarce or scattered.

On the flip side, there may be an abundance of data except it may not be meaningful. In cases like these, you’ll need to clean the raw data to filter out irrelevant, unimportant, and duplicate data bites.

Above all, it’s essential to start with a clear focus so you know what data is useful for you. This will largely be based on understanding why you’re collecting data and what it’ll help you improve.

2. Poor data visualization

Data visualization involves presenting data in graphics such as bar charts, line graphs, dashboards, and tables. 

Efficient data visualization is important for understanding data to better identify patterns and trends from it. It’s also what makes data accessible to everyone including executives. 

What’s more, it improves data exploration. That is: you’re better able to spot opportunities to collaborate and use the data.

The problem here? A lot of data visualization is poor. Graphics and tables, for example, may be difficult to read and understand, making data inaccessible and challenging to explore. 

In short, if you create visualizations that are difficult to read and understand, you make it hard for yourself to understand what the data is telling you. In turn, you can’t efficiently use it to make informed project and business decisions and convince stakeholders.

3. Lack of the right tools and skills

Curating, understanding, and presenting data is no easy feat. 

If anything, it takes key skills like critical thinking and data visualization to dabble in data analytics projects. 

However, if you don’t have the budget to hire a full-time or contract data analyst, you can always sharpen your data analysis skills. 

Udemy, Coursera, and LinkedIn offer great courses to build your analysis muscle.

At the same time, find the right tools for mining, cleaning, and visualizing data. 

For project managers and HR folks, we recommend using a project resource management software that offers in-built analytics. This way, you’ll need to focus less on digging out data and more on understanding it for informed decision-making.

Runn, for instance, gives you project and resource data. It also creates easily readable reports that you can share with your team and executives. 

4. Data overwhelm

Yet another challenge that comes with a data analytics project is the confusion that all the complex data causes. 

The problem only amplifies when you deal with raw data in abundance. That kind of information overload hinders, even skews, decision-making by making it hard for you to process available data.

Learning data cleaning techniques is helpful here. More importantly, having a clear direction saves you from overwhelm — making it easy to understand what you’re looking for.

5. Lack of a data culture

Lastly, several companies prefer making assumptions-based decisions rather than relying on data, making this another notable challenge. 

However, the truth is: data-informed decision-making improves business growth. In fact, an old but reliable survey by McKinsey reveals that data-driven organizations are 19 times more profitable than companies that don’t take a data-led approach. 

The solution then is simple: educate and persuade your stakeholders on data analysis and its tangible benefits for achieving business goals.

It’s also helpful to convince them by undertaking some projects on project data analytics to show them how beneficial in-depth analytics can be.

5 simple steps to conduct project data analysis

Even though data analytics is a deep field that requires a specific skill set, you can still get started with it — using the right tools and a clear direction.

And although it can be a tad bit overwhelming in the start, you can get better at it with time, just make sure you’re following this step-by-step blueprint: 

Step 1: Identify your goals

First things first, ask yourself: what do we aim to achieve with the data we gather and analyze? 

For instance, you could gather and analyze data to understand your team’s skill gaps. Or you could invest in it to identify ways to grow your project pipeline.

Whatever your goal may be, answering this question as specifically as possible is key to defining a direction for your data analytics project and deciding which project metrics to track (such as time invested, costs, etc.). 

It also helps you curate data from the best sources for achieving the desired results. And informs your data analysis process too. 

Step 2: Determine your data sources

Once you’ve a grip on the ‘why’ behind your data analytics project, go ahead and list sources for gathering data.

You have two options here:

  • Curate external data on the subject you want to make informed decisions on

This is best suited to new companies with a lack of internal project and resource data.

Some of these sources include the Association for Project Management and Project Management Institute.

The data you gather from these sources, however, isn’t specific enough to your organization so it can only help you make a handful of broad decisions. 

  • Gather your own data from past and current projects

This is a super useful data source to improve your project processes and efficiency.

It’s also specific enough for project timeline forecasting, budget and project cost planning, and efficient resource utilization.

The key to gathering all this internal data though is to choose the right tools for data curation and visualization. Without the right toolkit, the abundance of data can easily baffle you, preventing you from accurately identifying patterns and trends in the available data log.

You can also use a combination of both these data sources — even pair this quantitative approach with gathering qualitative data (more on this in the best practices section).

Step 3: Identify the tools you’ll use

Next, find out which tools you’ll use to gather and visualize data.

For internal data analysis, use your project management tool to extract data from past and ongoing projects.

You can also gather data specific to the resources aspect of your projects using a resource management software like Runn.

Not only does Runn give resources-related data but also visualizes it in easy-to-read charts — presented as shareable reports that you can use to convince stakeholders and make informed decisions.

For instance, you can get the following original data-packed reports in Runn:

Step 4: Clean the data you gather

Once you’ve gathered all the data, you’ll need to clean it — a process that involves removing or fixing incorrect, duplicate, and incomplete data. 

Undertaking a data cleaning project is particularly useful when you combine data from multiple sources (say, different internal project management tools you use). In cases like that, data often ends up mislabeled or duplicated.

Here’s a detailed guide explaining how to clean your data. In short though, start with removing duplicate data, then fix structural errors. Finally, go on to filter data you don’t need before you handle missing data. 

Step 5: Analyze data

This step is where you put your thinking cap on to find out what the data is telling you.

If you’re using Runn to gather project data, gather all the reports relevant to your goal. If you’ve manually gathered and cleaned the data though, start with visualizing the data.

At this point, it’s essential you refrain from jumping to conclusions.

If you’ve a few hunches or think you can already see patterns emerging, write them down as your hypotheses.

After you’ve visualized your data (tools like Tableau and Visme among other free visualization tools can help with this), start tracing patterns.

If you find your findings closely align with the hypothesis you noted down at the beginning of this step, enlist a colleague’s help to double-check the findings. This way, you can prevent bias from clouding your analysis.

Project data analytics best practices

Now that you know how to conduct a project data analysis, let’s walk you through proven tips for improving the process for you: 

1. Limit the project data you gather at a time

Too much data can quickly become overwhelming, therefore, useless — wasting your effort and resources. 

A sustainable approach here? Instead of dabbling with gathering and studying multiple project metrics at a time, start with 2-3 at a time.

Say you want to understand how much time each project of a certain nature takes, begin with tracking the time each task involved takes.

You could also measure how long each team member takes to understand their efficiency. But, for starters, this would dilute your focus and overcomplicate the entire process. 

Once you’ve studied one dataset though, you can study more smaller ones than combine them to get a holistic picture. 

2. Keep your biases at bay

Like it or not, we all make assumptions and are biased toward confirming our suspicions (technically called confirmation bias). However, that prevents us from truly using data to better understand how to improve project management. 

The first step to saving your data analysis from bias though is to be aware of your own biases. Next, write down whatever biases you have as you analyze data.

If your findings are close to what you suspected, ask yourself: am I missing something? Getting a fresh pair of eyes helps too, as we shared above. 

The idea is simple: go in with a curious mind. Most of all, never look for supportive data. Instead, let the data point show you the results.

3. Gather both quantitative and qualitative data

In addition to reviewing your project data, survey team members.

Host polls and one-on-ones to dig deeper into understanding their struggles about completing a project, the typical project timelines, and so on.

This way, you can unearth the context behind what the data is telling you — further improving your work management

4. Automate project data analytics as much as you can

By using Runn, you can automate data gathering and visualization. 

The software gives you reports on your timesheets, team performance, budget breakdowns, and more. In doing so, you get the brain space to review data and make strategic decisions related to optimizing your project workflow and resources. 

5. Support data analytics with governance

Governance refers to giving responsibility to a person related to anything — in this case, gathering, cleaning, analyzing, presenting, and revisiting project data. 

As your data analytics efforts ramp up, this last tip will help you build consistent processes around using data for decision-making. 

The person responsible can gradually establish and refine your analytics process and ensure data is stored and managed carefully.

Start analyzing project data the smart way 💪

Remember: it’s not impossible to study your project data for driving business growth.

In fact, by using a project management software like Runn, you can easily automate data tracking. It also gives you readable reports that you can put together to compare data over a range of time. 

So here’s to using data for strategic decision-making and improving your work efficiency. 

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