A Detailed Guide To Data Analytics for Project Management

The project life cycle includes evaluating risks, budgeting, project scheduling, and sticking to deadlines from the idea stage through the implementation stage. Using project management systems like Wrike, Jira, and others to fulfill the demands of more complicated projects by gathering intricate data at various stages is a terrific concept. But how can we glean intelligence from project data? The quick answer is through Project Analytics. Consider it a plug-in for your project management tool, allowing data from the tool to be processed, projected, and visualized for better decision-making. Analytics has evolved dramatically in recent years, and its power may now be applied to project management. This article will provide you with details about data analytics for project management.

Who is a Project Manager?

Project managers oversee the planning, execution, monitoring, control, and conclusion of projects. They oversee the overall project scope, the project team and resources, the project budget, and the success or failure of the project. They are involved with all aspects of project management. 

What is the Importance of Data Analytics for Project Management?

When good project management is in place, every organization thrives in its business. This, however, is uncommon due to the high number of project failure rates. Project managers have gradually run out of options. So, project managers have turned to data and data analytics for assistance.

Data is extremely important in any firm. Project Managers and executives can use data analytics to detect early warning signs of budget, expense, and timeframe overruns and take corrective action. Managers can also utilize analytics to track work velocity, making it easier to predict if a project will be completed on time.

If approached traditionally, project management can be time-consuming. However, with adequate research and data analysis, the difficulty can be eliminated. Project managers, who play a critical role throughout the project’s lifetime till completion, can avoid tiresome work and manage projects in an orderly manner with the use of data, resulting in increased efficiency.

The Influence of Data Analytics on Project Managers

In terms of project management, the project manager has the most reliance. This includes everything from project planning to effective project management throughout all phases of the project till the project is completed. He or she has a set of tasks and responsibilities that define a project’s pass/fail status. When it comes to project management in general, some of the functions and responsibilities of a project manager are as follows:

• Resources that are compatible
• Providing the finest resources to stakeholders
• Organizing available resources.
• Real-time data management
• Managing risks, issues, and test cases, among other things

All these duties performed by a project manager become repetitive and time-consuming when they are carried out using traditional methods and approaches, such as the usage of excel sheets to access data. Projects of any scale, however, can be efficiently led with the support of data and analytics produced from the existing system.

How Data Analytics is helpful for project managers?

Visualization:

Wouldn’t it be fantastic if project managers could reproduce the project in charts and dashboards with the most important data points? That is the power of analytics, like entering a data warehouse and self-serving the output from the data sets.

Visualization is a crucial component of data analytics since it simplifies the data so that you can quickly understand the broader picture. Bar charts, pie charts, and 3-D charts such as segmentation charts, treemaps, and so on simplify project decision-making and make predicting project results easier.

Forecasting:

Handling large or essential tasks necessitates some extrapolation. Project forecasting frequently entails analyzing the project’s performance history to determine whether the project is likely to be profitable in the future. If forecasts are negative, managers can decide to abandon new projects or halt current ones.

Forecasting Allows You to Simply Accomplish the Following:

• Trend analysis – It is the process of identifying trends in past data and applying those patterns to accurately estimate the risk, cost, and variance of a project.
• Break-even analysis – As the name implies, break-even analysis determines whether the project’s profits can cover its costs.
• Predictive analytics entails using predictive models and AIML algorithms to forecast the project’s future Key Performance Indicators (KPIs).
• Prescriptive analytics explains how we can fix something in the project before it happens.

Integration of Processes Across Disciplines:

Assume you are managing many projects with stakeholders from various departments. Your reports will not be complete unless you include data and processes from other departments. To acquire a holistic picture of the project KPIs, the project scope might be expanded across multiple disciplines such as marketing, sales, and customer support. We require analytics software that can assist us to gather insights from different sources to cover project dependencies. 

Project Portfolio Management:

Project Portfolio Management is one of the useful applications of analytics (PPM). Strategizing is more important than execution, and no one knows this better than Project Managers. When there are several initiatives and limited resources, it is vital to select and prioritize the most valuable ones. Project Portfolio Management refers to the process of picking the correct project while also managing projects for lucrative outcomes.

Good analytics software may assist identify initiatives that should be prioritized for the greatest results and forecast outcomes, allowing managers to focus on the correct projects at the right time.

Analytics Ranging from Descriptive to Prescriptive

Previously, project management was limited to descriptive analytics – analyzing what went well and not so well in the project. While descriptive and diagnostic analytics investigate what happened and why it happened, predictive and prescriptive analytics forecast and correct potential risks.

Companies can use Project Analytics to move project management into the predictive and prescriptive analytics stages. Project managers can then maintain complete control of the project rather than succumbing to less-considered project execution tactics.

Responsibilities of a Project Manager 

A project manager is responsible for several responsibilities that cover the five project phases of a project life cycle (initiating, planning, executing, monitoring, and closing) outlined below –

Integration, scope, time, money, quality, human resources, communication, risk procurement, and stakeholder management are all knowledge areas that intersect with the project management phases.

The Beginning Phases:

• Creating a project charter for integration management
• Stakeholder identification and management

The phase of Preparation:

• Creating a project management plan for integration management
• Scope management entails defining and controlling scope, developing a work breakdown structure (WBS), and obtaining requirements.
• Time management is the process of planning, defining, and designing schedules, and activities, estimating resources and determining activity durations.
• Cost management entails budgeting, planning, and estimating expenditures.
• Quality management is the process of planning and determining quality requirements.
• Human resource management entails planning and identifying human resource requirements.
• Management of communications: Communication planning
• Risk management entails anticipating and recognizing potential risks, conducting qualitative and quantitative risk assessments, and devising risk-mitigation measures.
• Procurement management entails planning for and identifying necessary purchases.
• Stakeholder management entails anticipating stakeholder expectations.

Executing:

• Integration management entails directing and supervising all project work.
• Quality management entails performing all areas of quality management.
• Human resource management includes the selection, development, and management of the project team.
• Communication management entails overseeing all areas of communication.
• Management of procurement: Take action to get necessary procurements.
• Stakeholder management is the process of managing all stakeholder expectations.

Controlling and Monitoring:

• Monitoring and supervising project activity, as well as managing any necessary modifications, are all part of integration management.
• Scope management entails validating and controlling the project’s scope.
• Time management entails controlling the project’s scope.
• Cost management entails keeping project expenses under control.
• Controlling the quality of deliverables is referred to as quality management.
• Controlling all team and stakeholder communications is part of communications management.
• Controlling procurements is what procurement management is all about.
• Controlling stakeholder engagements are referred to as stakeholder management.

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Closing: 

• Integration management entails the completion of all project phases.
• Procurement management entails the completion of all project procurements.

Stages of Project Analytics:

Project analytics are available to assist you in answering inquiries. More difficult issues can be answered as project analytics processes mature. Let us now examine the stages of project analytics:

Descriptive Analytics:

Descriptive analytics is the first stage of maturity. They can provide backward-looking reporting, addressing simple inquiries such as “What happened?” Or when did it occur? Most company reports come into this group. It is the previously discussed status quo.

Diagnostic Analytics:

The ability to examine prior performances and explain why they occurred is the next stage. Analytics can help you analyze, spot anomalies, detect patterns, and determine links between things like cost and performance if you can efficiently acquire project data.

Predictive Analytics:

At this level, you start to look ahead, answering questions such as, “What is likely to happen?” 

Prescriptive Analytics:

The following level employs the information offered by predictive analytics to answer the question, “What should we do?” 

Cognitive Analytics:

As you use the previous tiers of analytics, you can get very complex. Using machine learning and artificial intelligence (AI), your statistical models will define new models.

Challenges Faced in Performing Data Analytics by Project Managers

Inaccurate Data:

One problem is that project data sources are isolated. Project teams are becoming inefficient by using so many different point solutions, spreadsheets, and in-house tools to record project data. As a result, they frequently spend most of their time looking for data, compiling it, and fixing errors. Without a centralized source of correct project data, companies will struggle to progress beyond the Descriptive level of maturity.

Unstructured Information:

Unstructured information means the information exists in various forms and is not well defined. This is a significant issue for businesses because it makes data difficult and expensive to maintain, organize, and use for decision-making. In fact, unstructured data is cited as a key concern by 95% of firms. This can push firms to pursue AI and machine learning solutions to make sense of their data, attempting to skip forward to the most mature degrees of maturity.

Inadequate Project Controls and Management Foundation:

However, this raises another potential issue. If firms focus primarily on data analysis technology while ignoring best practice methods for project management and project controls, AI and machine learning efforts will fall short of expectations.

It would be like to constructing a high-speed train but failing to construct the track.

The junction of people, processes, and technology is where success is found. The capabilities of the correct technological platform as the source of project data should be considered while designing project analytics. A platform based on industry best practices will guarantee that project teams capture and leverage data in a consistent manner.

Steps to Improve Performance with Data Analytics for Project Management

Establish Your Vision:

The first stage, like any transition, is to define a vision for where you want to go. The vision should be comprehensive, and it should reinvent how your data may be used to unlock more value.

Establish a Foundational Platform:

Adopting a centralized project performance management solution is another critical step. This will assist you in consolidating your project data, breaking down information silos, and developing the processes required to exploit your data. Connecting project data and achieving a single source of truth can help you swiftly and accurately answer descriptive and diagnostic maturity-level inquiries. It will also serve as the foundation for more complex project analytics.

Begin Small:

Any alteration that is too powerful is doomed to fail, so don’t go too big right away. Iterations should be used to implement your idea. Begin by answering one question that you couldn’t or couldn’t answer as readily as before. You may begin to comprehend your business after you have a deeper understanding of your data. It is critical to view project analytics as a journey that will offer incremental value each time a new model is deployed.

Get a Quick Time to Value:

People want immediate results. As you progress through your journey, it is critical that you answer questions that will have an immediate impact on the organization. With these new insights, you may be able to identify other areas of effect or data that needs to be gathered.

Facilitate User Adoption:

It makes no difference if you have the greatest powerful tool in the world if no one uses it. You require willing adoption. Companies that make digital technologies available, self-service, and part of routine operating practices have a significantly better chance of success. You are restricting the possibilities of your project analytics if you rely on experts to identify insights. A solid, user-friendly data visualization platform will empower decision-makers to access what they need faster.

Capitalize on Your Successes:

As you demonstrate progress, you will gain more buy-in and support. Then you’ll have a reason to invest additional money. As you gain momentum and confidence in your project analytics, you will be able to answer more questions. Then, on top of your project data source, you may overlay an integrated analytics platform to create quick, scalable data models.

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How to Introduce Data Analytics in Project Management

Data analytics has numerous advantages for project management. Here are some ideas for incorporating data analytics into your project management methods – 

Vision

A well-defined data analytics vision indicates the goals or results that a project manager seeks.

PMs should use well-honed project scheduling skills to delve deeper into departments’ workflows, data, team processes, strategies, and potential development orientations to better match with the company vision.

The first step is to ensure that the data you are going to collect and analyze will be beneficial to the company’s aims. 

Data Organization and Cleansing:

You should recognize that data analytics project management is only effective if you understand how to transform raw data and apply it to gain insights. Some information is useful to only one team. Most of the information is also valuable to the project management team.

As a result, data organization and cleansing must remain the professional project manager’s primary priority. 

When you have a huge amount of data, be prepared to clean it because there may be both vital and secondary data. The structure of analytics helps you to easily identify vital data when needed.

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Data Analytics For  Project Management – Tools

For project management data analytics, a dependable solution for extracting and exploiting data is required. The key tools for working with data are business intelligence (BI) and business analytics (BA).

BI software focuses on collecting storage, management, and analytical data analysis. BA, on the other hand, concentrates on technologies and tactics geared at predictive practices and descriptive analysis of the company’s performance to better future actions.

Project managers who use BA tools rely on software such as Tableau, Microsoft Excel, Google Analytics, and SQL to gather and sort data and create visualizations.

When it comes to BI tools, these include KPI tracking, dashboard generation, predictive modeling, and data mining dashboards (SAP, Microsoft Power BI, and others).

BI and BA Would Be Fantastic Additions to Your Project Management Toolkit. Specifically, Such Tools Give You With:

• The ability to use ‘what if’ analysis for resource planning, among other things
• Automatic data synchronization between platforms is available.
• Forecasting’s feature
• A dashboard that can be customized in real-time and is easy to use

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Let us go through some of the common questions for our topic – data analytics for project management

Frequently Asked Questions

Q1. How is data analytics useful for project managers?

Making decisions is part of project management. Data mining and machine learning approaches must be used to support the decision-making process.

Project managers can use data in two ways: business intelligence and business analytics. Using data analytics, project managers, and executives may keep an eye out for early warning signs of budget, expense, and timeline issues and take corrective action. Data Analytics is also helpful for project managers in capturing the rate of work, allowing them to accurately anticipate the schedule for their projects.

The ability to objectively examine project performance and make rational and informed decisions is critical to project success.

The appropriate Project management requires proper planning, tracking, and analysis of project progress. Project managers now have a plethora of project metrics at their disposal to manage, measure, watch, and analyze the performance of their projects.

Q2. How is data analytics used for managing risks by project managers?

Every project contains inherent risks, but by introducing data analytics into the project management process, you can identify, rank, and prioritize those risks. As a bare minimum, any review of an organization’s risks should contain the following factors:

• The project’s size and complexity
• The risk tolerance of the organization
• The project or risk manager’s expertise

This degree of risk identification and analysis will enable the organization to reduce the likelihood of a certain risk occurring and predict the potential consequences if the risk occurs.

It is critical to have the correct knowledge set to comprehend Data Analytics in Operations and Project Management by project managers and assist the firm in growth. Executive education is critical for gaining a better understanding of the process. 

Q3. What is project analytics?

Project analytics is the systematic analysis of data to gather knowledge that will assist you to make better decisions. You can acquire critical insights into your data by applying statistical models to it, something you would not be able to do otherwise.

Project analytics are a lifeline for project managers to stay on schedule and on a budget in an environment where projects are growing increasingly complicated. Project managers can use data analytics to go beyond merely collecting data. They can see how projects are performing and whether they are generating the desired business benefits. Project analytics can also be predictive, providing insight into what is likely to occur on a project and informing the best course of action.

Conclusion on data analytics for project management

When managers had to just keep the project going a decade ago, the project management approach was different. It is now more about strategizing, team collaboration, agile project management, and project portfolio management. Data Analytics for project management can help modern project managers ease these duties.

In this article, we have discussed how useful is Data Analytics for project management and project managers and helps in pursuing an analytics-driven project management strategy. In conclusion, project analytics can help project managers make strategic decisions and increase project success rates.