Advanced data analytics is a driving power nowadays, covering various human activities and giving businesses worthy insights. Having enough analytical data about your enterprise, employees’ and customers’ satisfaction, finances, and more, project managers can contribute significantly to decision-making, business growth, and overall business prosperity.
The following article will show how this steering lever — data analytics — benefits project managers and can be practically valuable for project management.
Before exploring project management data analytics, let’s decompose the link between data, decision-making, and project management.
Data-driven decision-making in project management
We all know that data in modern business drive many business decisions made. Decision-making relies a lot on data. The more information you can obtain and analyze — the more hypotheses, tactics, ideas, and tests you may generate for your project to succeed.
Primarily, you need reliable approaches and tools to manipulate and evaluate data. Data gathering, synchronization, mining, and various types of analytics are ways to collect essential data and extract crucial insights.
Per Statista, the size of the business intelligence and analytics software application market is likely to reach 12.7 billion U.S. dollars (2022) to above 18 billion in the next three years.
Source: Statista
The business intelligence and analytics software application market is a subsegment of the enterprise application software market. Modern analytical software and business intelligence tools can fulfill the businesses’ goals through better resource management, the boost of the supply chain, and interaction between the parties involved in an enterprise.
Also, you need a scheme for making decisions. A well-thought decision-making framework enhances multiple context analysis and leads to an innovative and efficient solution. Eventually, you can boost your business on different levels using actual records and insights.
By analyzing data, project managers receive opportunities to impact projects and companies. Among others, PMs use data-driven decision-making to:
- define the project’s downsides
- add to project agility
- eliminate bottlenecks
- determine the customer’s needs and pain points
- improve teams’ productivity
- streamline the teams’ collaboration
How data analytics can benefit project managers
So project managers are involved in decision-making constantly, and their decisions are often backed up with data. Where data is, there should be analytics. Further, we’ll explore five practical benefits of data analytics for project managers.
Data analytics for project managers saves costs
Focusing on a company’s income is not everything. You should also know how to cut down project expenditure. With project management tools or simple spreadsheets, a project manager can monitor project financial data and make reports and dashboards to visualize and analyzes ways of saving money.
Poor financial planning may undermine processes and projects that affect the whole company. In this case, data analytics in project management gives you a clear-cut understanding of short and long-term perspectives on expenditures. For instance, you can detect weak operational areas and revise software spending for a team to avoid losses.
Data analytics for project managers improves risk management
Project management intersects with risk assessment often. Handling complex projects may create a risky environment for the entire delivery outcome. Here is where data analytics for project managers comes in handy.
Tracking your project management risks presupposes one’s ability to document all possible risks and carry out accurate troubleshooting.
Compliance and risk are the two major categories that require proper management. Yet, not many organizations already have an active risk management plan. The rest either have no plan or outdated risk management policies.
However, many companies switch to the model where all the business segments collaborate to strengthen and protect the enterprise from the inside.
Source: IT Chronicles
Enterprise risk management (ERM) embraces different levels and departments, and the project managers, who resort to data analytics, should process loads of data. It is very convenient. For instance, if a company has offices (team members) worldwide and operates globally, you must be powered up with data to track risk events and react.
Data analytics for project managers leverages strategic planning
In modern strategic planning, you cannot succeed without data. KPIs, performance metrics, competitor analysis, and risk assessment – it’s all backed up with data that should be evaluated and used for planning.
Such an approach also fits into project management. The bulk of analytical information allows forecasting the work processes effectively and project vision. Moreover, this impressive data library is a real treasure for a person in charge of planning the project budget, estimates, short-term actions, and long-term steps.
Data analytics for project managers adds to the agility
Project management and data analytics create a powerful duo for an agile business. Insights obtained with data analytics help to omit bottlenecks in the work process, improve the speed of delivery and quality, and do a flexible company.
Imagine there is a need to expand the team that works on a short-term project, as the employees are not likely to meet the deadline. In this case, an experienced project manager will see that hiring freelancers would be financially wiser. Otherwise, you will have to spend money on the full-time employees’ onboarding, equipment, etc.
When you analyze and trust your data, you can freely stay flexible, allocate specialists, and experiment with new methodologies and technologies.
Data analytics for project managers enhances business performance
PMs have an impact on business performance. Data analytics in project management also functions as a booster of business performance. It might not be evident at first.
But its vital role will be apparent after project managers make data-driven decisions, use data analytics for planning, etc. It means project managers have enough data to make predictions and create strategic solutions impacting their projects and overall performance. They need to implement data correctly.
As we mentioned, you should use data management and analytics software, collect data from time tracking and project management tools (e.g., Asana to Google Sheets), synchronize databases and spreadsheets, and more – do any core activities for project management data analytics to impact business performance.
The information analysis can save costs, improve risk management, leverage strategic planning, and, most importantly, enhance business performance.
Data analytics in project management practices
Data analytics does offer lots of benefits for project management. Below are some ways to introduce data analytics into your project management practices.
Vision
A well-formulated data analytics vision demonstrates the results or outcomes a project manager targets.
PMs should practice well-honed project scheduling to dig deeper into the departments’ workflows, data, the team processes, strategies, and possible development directions to further align with the company vision.
It is the first thing to do: you must make sure that the data you are going to collect and analyze would be helpful for the goals the company chases.
Data structuring and cleaning
You should understand that data analytics project management is effective only when you know how to transform raw data and apply it to obtain insights. Some information works well for one team. Another bulk of the information is gold for the project management team.
Due to this, data structuring and cleaning must remain the focus of the professional project manager. You can imagine the entire data cleaning process as a circle made of eight stages:
Source: Iterators
When you have a large amount of information, prepare to clean it, as there might be both essential and second-rate data. Analytics’ structuring allows you to find critical data when needed quickly.
Set of tools
A reliable tool for extracting and leveraging data is essential for project management data analytics. Business intelligence (BI) and business analytics (BA) are the primary means for your work with data.
BI presents software specializing in collection storage, management, and analytical data analysis. In contrast, BA focuses on technologies and strategies aimed at predictive practices and descriptive study of the company’s performance to improve further steps.
Project managers who tend to apply BA tools rely on such software as Tableau, Microsoft Excel, Google analytics, and SQL — these solutions help gather and sort information and make graphs.
If talking about BI tools – SAP, Microsoft Power BI, and alternatives – these include KPIs tracking, dashboards creation, predictive modeling, and data mining dashboards.
BI and BA would be excellent additions to your project management stack. Namely, such tools provide you with :
- The opportunity to integrate ‘what if’ analysis for resource planning, and more
- The availability of automatic data synchronization between platforms
- The feature of forecasting
- Real-time customizable and user-friendly dashboard
Monitoring
A project manager deals with data analytics and has to monitor it continuously. In practice, it gives lots of benefits, as you can track the progress and, what is more, compare the actual information with the one that was initially planned by your team and you. As a result, you can apply slight or grand changes to the project if needed after the data monitoring.
Project managers often work with employee feedback. By doing so, they can foresee possible dissatisfaction among employees or think about some new ways to engage them in the process. For example, it is not enough to collect feedback once a year. Project managers should always work with up-to-date information.
It is said that 59% of project managers work on 2-5 projects. After considering this, continuous data monitoring seems to be a wise decision. And if you have enough time, resources, and access to such information, you can also monitor what is happening with the competitors.
When managing a project, you must have updated, clean, and ready to use all project data. Note that you can request it from the team and departments. You can use internal communication platforms to specify details or organize meetings with the parties. Where there is the will, there is a way, so to say.
Easy Adoption
If you introduce new tools to the project management office’s daily routine, you should justify the choice of software or strategy and inform your team.
Besides, the project management team should also know the peculiarity of the chosen BI or BA tool/strategy’s benefits and understand how to use these in practice. Consider some training, presentation, and practicing for this before you start using the set of tools.
Data literacy is essential for the team of specialists who will operate with masses of information and predict the best possible solutions for the company.
Conclusion
Project management data analytics is a powerful tool for modern companies. Project managers able to use data analytics and management practices contribute to the project progress, employee efficiency, and overall company performance.
For those PMs, who want to make data-driven decisions and track performance better, it’s time to make the first steps in data analytics for project management. Remember the five critical practices outlined in this post, and you won’t be disappointed.
