6 minutes

Today, data is touching practically every area of business. Project management is no exception. A project manager’s goal is always to get the most out of their team and plan the best strategy going forward – that’s where project management analytics comes in. 

The nature of project management, however, is changing. New challenges constantly present themselves, such as managing remote teams, and keeping up with new technology. The old ways are no longer enough and thorough planning can only get you so far. 

But what’s the best way to embrace data-driven project management? This article will explore that, and more. 

Why is data-driven project management important? 

Data-driven companies collect a great deal of data, both internally and externally. With the right analytics software, this can provide all sorts of actionable insights to improve project management. Below we’ve looked at some of the different benefits of a data-driven approach. 

1. Better risk management 

Project Management Analytics

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Every project manager makes a mistake eventually. It’s far better to understand the risks early and minimize the effects. However, there is a growing acknowledgment that the old methods of risk management are no longer enough. 

Relying on a project manager alone leaves the door open to error. They overlook key risks and find your teams unprepared when something goes wrong. A study shows that 79% of organizations describe the rate of technological change to be a challenge to risk management. 

Data-driven project management is the only way to ensure comprehensive risk management. This uses historical data and machine learning to identify risks. Some organizations are switching to an approach called enterprise risk management. This method gathers data from all departments. The data is then analyzed and fed back to teams to warn of any potential risks.     

2. Reduced costs 

Too many project managers fail to properly account for costs during the project planning phase. This can have a significant effect on the overall revenue of an organization. This can be avoided by having better financial planning. By keeping an eye on financial data, project managers can better reduce costs. This could mean: 

  • Creating spreadsheets documenting expenditures. 
  • Building visualizations so that teams can better understand how money is being spent. 
  • Monitoring team operations and cutting back on wasteful processes.  

3. Sped up processes 

Project managers oversee many different operations from their teams. This means it’s easy to miss processes, such as inefficient workflows,  that are slowing overall performance. The longer this issue goes unnoticed, the more damage is done to your overall business progress. 

Just like GTM tools can help speed up processes on your website, data-driven project management software can help remove bottlenecks and find alternatives going forward.

How you can embrace data-driven project management 

You should now have a better understanding of how data-driven project management can help you. But what are the best ways to embrace this approach?

A) Use the right BI tools 

Project Management Analytics

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Business intelligence for project management is key to a data-driven approach. Your BI tools use software to power data-driven decision-making. BI is a combination of many different forms of software. This includes analytics, data visualizations, data mining, and other data-related tools.  

Popular examples of BI software include Microsoft Power BI, Domo, and Tableau. No matter which software you choose, ensure it can accomplish the following functions: 

  • Connectivity – BI software should be able to integrate with many different systems. This includes databases, spreadsheets, and other data sources.
  • Accessibility – Data insights should be presented in simple and informative visualizations. This might include charts, graphs, and tables. 
  • Comparisons – Users should be easily able to compare datasets side by side. 
  • Segmentation – Your tool of choice should make it possible to dig deep into data. BI should allow users to segment data into smaller subsets. 
  • Forecasting – The ability to predict future trends using historical data and AI-powered machine learning.

B) Don’t overlook BA 

Business analytics (BA) is essential for a data-driven approach. BI uses historical data, helping past experiences to inform future decisions. BA looks at new historic and new data to help with decision-making.

There are many tools that provide project management analytics. For example, if a project is website based, it’s important to learn Google Analytics. The tool provides access to a wealth of insights into website data. Information is presented in different reports and visualizations to aid with decision-making. 

Other examples of BA tools include Microsoft Visio, Oracle Net Studio, and Pencil. 

C) Use project management metrics  

For the reasons mentioned above, the insights you’ll gain from data are invaluable. But as you make improvements to your projects, how do you know you’re getting the results you need? Project performance tracking is a key part of a data-driven approach. 

Below are some key project management metrics to use. 

  • Cycle time – This is the amount of time to complete a certain process or task within your project. As you make changes, this should reduce. 
  • Line items – By keeping an eye on line items in your budget, you can know expenditures for different areas of your project. Are your attempts to reduce costs proving to be successful?  
  • Budget variance – Does the budget at the end of a project align with your projections? 
  • Resource profitability – Are team members making productive use of their time? Could resources be better used elsewhere? 
  • Average cost per hour – This combines all the different factors involved in completing a project. This includes the salary of employees, the cost of equipment and software, office space, etc. 
  • Customer feedback What are customers saying about your projects? Are they happy with your progress? A customer doesn’t always mean an external party. It could include a member of your organization. 
  • Return on Investment (ROI) – Did your project result in a positive payback for your client? In many ways, RoI is the most crucial metric, especially for digital marketing project management. It should be monitored closely. 

D) Always present valuable data 

For data to be useful, it always needs to provide the best possible insights. To show how you can achieve this, we’ve broken this section into two parts: data cleaning and data structuring. 

Data Structuring 

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Data-driven project management involves working with many different data streams. It’s the job of the project manager to ensure that data reaches the right places. After all, not every piece of information will be useful to all teams. Where one team might find a piece of data to be invaluable, another may view it as useless. 

To make sure that data always reaches the right teams, data structuring is necessary. This is the process of organizing your data so that it can easily be identified. Listed below, are some examples of data structuring.  

  • Graphs and charts – A visual representation of data, making it easier to understand. For example, a pie chart, where the largest slice represents the highest value. 
  • Progression or regression – Data is grouped sequentially to facilitate a certain action.  
  • A ranked list – A linear data structure. This might be a table of customers in order of the average amount each customer spent.  

Data cleaning 

Data structuring is important but alone isn’t enough to ensure that teams get the information they need. Whilst a team might receive the correct data set, it may be filled with corrupted or incorrect information. For effective data-driven insights, a process called data cleaning needs to occur. 

Below is a simple description of the different steps of data cleaning.

Step 1 – Removing duplications and irrelevancies 

Instances of duplicate data can be extremely frustrating. They can lead to costly mistakes and make it longer for teams to find the information they need. Similarly, irrelevant data provides no value and simply distracts from useful data. Take time to remove instances of these issues from your data.  

Step 2 – Address issues with the data structure 

When looking at your data, you may notice some structural issues. For instance, headings might not match naming conventions or contain typos. Inconsistencies can cause lots of problems, making it more likely teams will overlook important data. 

Step 3 – Remove unneeded outliers 

Outliers are instances of data that differ significantly from general observations. Not all outliers are wrong, some can be valuable. It’s important to look through your data and find and remove irrelevant outliers.

Step 4 – Deal with lost data 

Lost data is always unfortunate. When this occurs, you need to decide what to do next. Options include: 

  • Replacing missing values based on other observations 
  • Remove any instances of data that contain missing values
Step 5 – Validate your data 

Now that the cleaning process is complete, how effective has it been? Can you understand your data? Does it contain valuable insights? Are there no instances of duplicates or structuring issues? It’s useful at this stage to undertake QA testing to remove any lingering doubts. 

The future is data-driven  

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There’s no stopping it, project management is changing. If project managers want to keep up, they must embrace data-driven decision-making. As explored in this article, there are many benefits to data-driven project management, from reducing costs to speeding project deliveries. 

When adopting a data-driven approach, try to stick to the tips outlined above. Let’s recap: 

  • Invest in the right BI and BA tools 
  • Choose the best metrics to monitor progress 
  • Always clean and structure your data 

Adopting data-driven project management is the first step to a more efficient team. So, why delay? The future is here.