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A guide to data analytics for project management
Data Analytics

A Complete Guide To Data Analytics For Project Management

Reshmi Muralidharan Nair

Data analytics as the name suggests involves analyzing the data for the growth of the business. A data analyst collects, organizes, and analyses the data to guide businesses for betterment. Businesses can choose their area to prosper and develop efficiencies, like where to allocate the resources and the best strategies to be followed. From the viewpoint of business, data analytics plays a vital role in analyzing the popularity of the products, cost reduction, deciding the marketing strategies to be followed, selecting the target audience, and so on. Hence, there is a wide scope of data analytics for project management.

Data Analytics For Project Management

Data Analytics helps in improved project management by providing insights for decision-making, optimizing resources, and risk management and also by monitoring the resources accurately and promptly.

It does the prediction of the past project data thereby facilitating improvement in the outcomes of the company, helping in the enhancement of future projects of the company.  It is a broad concept and a booming field to keep a keen eye on.

Improved interest of the stakeholders in the company is driven through data mining and predictive analytics, as in data mining or data analytics discovering historical data, patterns, or insights is done to support decision-making and finding hidden relationships.

Predictive Analytics guides project managers to foresee the impact on future projects and make an informed decision for the company by analysing historical reports using statistical algorithms and machine learning techniques which facilitate making predictions of future outcomes.

Thus, Data Analytics for project management is vital as it leads to more successful project outcomes.

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Decoding Project Management

For successful management of a project, there are certain guidelines to be followed and that is when project management comes into the picture. Apart from data analytics for project management, proper utilization and organization of resources are required for the successful completion of a project.

In simple words, Project Management aims to lead the work of a team in a direction to accomplish the required task in given constraints. Projects are undertakings that require proper allocation and execution by a capable leader for its success and that is why project managers’ role is crucial in any business.

The five stages of successful project management include initiation, planning, organizing, executing, monitoring, and closure.

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Role of a Project Manager

In short words a Project Manager is a person who is responsible for planning, organising, timely delivery, and execution of a project. They also define goals, make the maximum use of resources in a desired budget, and reduce potential risks by tracking the key performance indicators and insights-driven through data mining and predictive analysis.

They are responsible for overseeing the trends, insights, collection, and execution of data to make a well-reasoned decision for the business.

They use data analytics for project management and many tools to identify what’s going well, what could go wrong, and what could be done better for project management and the future growth of the business.

Data collection, Data mining, and predictive analysis help the project manager to improve the overall growth of the business by getting an idea about the whereabouts.

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Exploring the Duties of a Project Manager

Project Manager Responsibilities in Detail:

  • Project Initiation – Outlining the requirements and deciding on the start of a project by obtaining the initial approvals and setting the foundation of a project lifecycle.
  • Project Planning – Planning the details of a project requirement by deciding on the resources, dependencies, timings, and deadlines.
  • Resource Allocation – Deciding on the proper allocation of resources for the completion of the project efficiently by managing the available resources and dividing the workload according to competency.
  • Risk Management – Using Data Analytics for Project Management to analyse the risks and take precautions to manage risk for improved performance of projects.
  • Stakeholder Interaction – Communication and timely information in regard to the project is to be provided to the stakeholders. Stakeholder satisfaction is important for the success and to ensure the prolonged interest of stakeholders in the company which can contribute to trust, positive feedback, and Goodwill of key stakeholders.
  • Quality Control – Maintaining the preset quality standards.
  • Task Scheduling – Pre-Planning and scheduling the task, updating the task priorities regularly, and identifying the challenges involved in the tasks.
  • Tracking & Reporting – Regular tracking and managing a sheet to identify the progress of the project. Key Performance Indicator Identification and generating and reporting of the project progress to the stakeholders.
  • Issue Solving – Identifying and making a resolution for solving any criticality in the project. Open communication is to be encouraged for better problem-solving.
  • Change Management – Managing the changes in areas relating to budget, resources, or any other matters in a project. Taking approvals for modifications.
  • Budget Control – Deciding the project budget, expense management, cost saving, and allocating resources efficiently.
  • Team Guidance – Guiding the team on improvements and challenges about the project.

Demand for Project Manager

Project Management is a booming field that has a great demand in today’s market. The demand for the area is increasing rapidly as they have a great role in the success of a project.

Project managers are important in maintaining the timeline and specified parameters of the project. In the growing competition project manager’s role is critical in leading the team through challenges, maximum utilization of resources, timely completion of tasks, and following a budget.

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Project Management Challenges

Project Managers face many challenges in handling the project. There are many tools used by them to overcome the challenges in planning, execution, and supervising the project development and come up with a solution to the issues day to day.

Data Analytics for Project Management is a key tool used by Project managers to overcome the challenges.

The common challenges include creep requirements, lack of proper communication, unclear goals, lack of accountability, poor risk management, adapting to changes, and resource management.

To get ahead of these challenges many measures could be inculcated e.g. to overcome creeping requirements project managers can break the project into small tasks and make use of the best resources. Poor communication can be overruled by using the correct channels of communication including the team and stakeholders.

Resource management is a key task for any Project Manager. Analysing the skills and assigning any task accordingly can do wonders for the strategic completion of any project. Risk management and sticking to a budget a crucial steps to be followed as well.

Thus, by following and sticking to a preplanned strategy Project Manager’s role is critical in a project’s success they take various measures and tools to achieve their target with greater ease.

Importance of Data-Driven Project Manager Strategies

Data is the prime source of advancement in companies across various industries and therefore, it becomes crucial for Project Managers also to inculcate a Data-driven approach in Project Management strategies.

  • Risk Management
  • Better Process Management
  • Cost Reduction
  • Improved Stakeholder Communication
  • Adaptability to change

Risk Management –

Including data in Project Management can benefit Project Managers in many ways it assists project managers in understanding project requirements and analyzing the potential risks involved.

Data-driven project management is the best way to ensure better risk management and eradicate possible human errors.

It helps in tracking the project status, predictive analytics for future improvement, stakeholder communication, evidence-based strategy valuation, and proactive issue resolution.

Better Process Management –

Project Management involves managing different aspects so it’s important to have a systematic process to be followed to ensure timely and efficient completion of the project.

A data-driven approach can help improve efficiency in project management by facilitating better resource allocation, performance measurement & continuous improvement.

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Cost Reduction –

A data-driven approach can help management utilize the best resources, inventory management, analyze supplier performance, identify inefficiencies, and predictive maintenance which can facilitate cost reduction.

Improved Stakeholder Communication-

Following a data-driven approach can foster project-based insights to stakeholders. A data-driven approach includes forecasting trends, identifying risks, and stakeholders to be provided with timely updates, reports, and relevant and accurate information.

Adaptability to Change

Data-driven approach enables project Managers to change and adapt to the current trends in the market. It ensures the constant monitoring of key factors and performance indicators.

Project Managers can connect with the recent trends and are ready for upcoming challenges in regards to the project. This will motivate the project team to adapt to any upcoming changes without compromising on the project timelines, budget, and overall success.

How is Data Analytics for Project Management Beneficial?

Data Analytics as explained above plays a vital role in analysing and studying the history of data, thereby facilitating the growth of the business. It can involve data mining and predictive analysis which helps in the reduction of risks, proper resource allocation, and enhancement of project outcomes.

What are the Types of Data Analytics?

There are 5 types of Data Analytics. Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics and Cognitive Analytics.

  • Descriptive Analytics: Collection of historical data for insights, trends etc.
  • Diagnostic Analytics: Investigation of insights and patterns to understand the root cause for the challenges.
  • Predictive Analytics: Involves the study of algorithms, and machine learning for deeper understanding of the past data to achieve future outcomes.
  • Prescriptive Analytics: Providing recommendations for future outcomes by strategic planning and implementation.
  • Cognitive Analytics: Including AI and Machine learning for addressing advanced queries and finding solutions to avoid any human errors.

Are both Data Science and Data Analytics for Project Management a Requirement?

Data Science along with Data Analytics can give a great idea about the business for project management, the core difference between the two is Data Science includes machine learning and is a broad concept to analyse the futuristic performances relating to a business, however, data analytics is a study of past data and analysing the high-performing projects in a business. You may decide based on the relevance of your project.

Is It Necessary to Include Data Analytics for Project Management?

No, it entirely depends on the project’s requirements. However, nowadays as business is competitive, including data analytics for project management can bring favourable outcomes.

Which is Better: Data Analytics or Project Management?

There is no comparison between the two as they serve different purposes. Data Analytics for project management is crucial using insights and historical Data, on the other hand, Project Management is used by managers for the successful completion of tasks in a project, deals with managing a team, making timelines, and following projects aligned with the guidelines to be followed. Both are important for a project and play a critical role.

Areas or Fields in which Data Analytics is Beneficial

Data Analytics plays a pivotal role in today’s market and competitive businesses utilize data analytics for better prediction of data, utilization of resources, and following trends. Data Mining and predictive analysis are included in Data Analytics which facilitates any business growth by providing insights about the history of Data.

Here Are Few Businesses Where Data Science is Used:

  • Banking Sector: Data Analytics can be beneficial in risk management, anti-money laundering, and fraud minimisation in banking and financial industries.
  • Media – In the Media and the entertainment industry data analytics can help provide customer insights, recognize the content, and analyze content performance.
  • Healthcare – Predicting patient behavior, discovering drugs, and improving medication or diagnostic techniques by study is possible through data analytics are the benefits of data analytics in healthcare.
  • Education – Data Analytics is implemented in the Education Sector, to implement new strategies, analyze student performances, and predict the success ratio of the institution.
  • Insurance – In the Insurance sector, data analytics can assist with risk assessment, customer segmentation, and detecting fraud.
  • Retail – To improve customer experience by promoting supply chain management, cost savings to improve sales are the advantages of Data Analytics in the retail and logistics industry.
  • Government – In the Government Sector as well Data Analytics can play an irreplaceable role in the implementation of new policies, the rise of the public sector, and the smart allocation of resources.

Benefits Of Data Analytics For Project Management.

Project Managers use Data Analytics for project management and the success of a project. Here are a few points on how Data Analytics facilitates Project Managers:

  • Prediction of the challenges
  • Helps in following a timeline by identifying the challenges relating to the project.
  • Helps in maintaining communication with stakeholders by timely project reporting.
  • Adapting the changes in the project.
  • Studying the historical data for continuous improvement.
  • Facilitate Cost Saving, Resource Allocation, and Budget Control.
  • Quick decision-making

Tools Used in Data Analytics

Data Analytics involves the collection of historical data for insights and analysing future trends. Data Analytics is a field in demand and is a booming market. It involves data mining and predictive analysis for identifying the challenges occurring in any project thereby assisting faster project completion with accuracy. Moreover, it also fosters a customer-friendly approach and employee satisfaction.

Here is a List of a Few Tools That Benefit a Data Analyst:

  • Python
  • R
  • SQL
  • Tableau
  • Power BI
    Excel
  • SAS
  • Google Analytics

Project Portfolio Management

Project Portfolio Management(PPM) is software used by project managers to optimize resources, prioritize projects, and achieve project goals. Project Portfolio Management includes Data Mining and Predictive Analysis which helps in product portfolios and aligning the project with the strategy of the business.

Few Tools Used In Project Portfolio Management.

  • Planview
  • Oracle Primavera
  • Adobe Workfront
  • Sciforma
  • LiquidPlanner
  • Changepoint
  • KeyedIn
  • Celoxis
  • Clarity PPM
  • Jira Portfolio

Besides, Project Portfolio Management, Project Managers follow other methodologies and techniques for the execution of a project. Some methods include:

  • Water Methodology
  • Agile Methodology
  • Scrum
  • Kanban
  • Critical Path Method
  • Lean Project Management
  • Extreme Programming

Role of AI in Data Analytics for Project Management

AI role is pivotal in any area of business currently. In data mining and predictive analysis, AI’s role is irreplaceable. By 2030, 80 percent of the work of today’s project management (PM) discipline will be eliminated as artificial intelligence (AI) takes on traditional PM functions such as data collection, tracking, and reporting, according to Gartner, Inc.

AI algorithms use trends, insights, machine learning, and historical data to study what is in demand and what could lead to significant growth in a business, this allows the project managers to overcome the challenges.

Project Managers can take proactive measures to identify potential risks and overcome the challenges in the project leading to higher chances of success of a project. AI can help in resource allocation and analysing big amounts of data which can help time-saving.

This can help in the reduction of risk involved and assist with any human error as well. Overall, AI can be useful in managing big data with time effectiveness and by providing real-time insights.

Although AI’s role is critical in Project Management it is unlikely to replace the Project Manager, as the role of a Project Manager in managing a team, budget Control, and planning and execution is inevitable also the inclusion of data analytics in project management is broad and irreplaceable.

Project Management Reporting and Metrics

Proper measures need to be taken for impactful project management. To keep the project in line proper management is required and leadership quality is essential for project management success.

Project Managers generate timely reports on the progress of the project. The project report provides the status of the project at a particular time. Project reports may be required weekly or monthly according to the guidelines of the project.

Project reports are provided to stakeholders to capture their interest and ensure the required time, budget, and scope of the project. To name a few Project Reports include Status Reports, Time Tracking Reports, Resource Management Reports, Risk Assessment Reports etc.

Every Project requires certain metrics to be followed for its progress. To track the progress of a project, the project metrics role is paramount. Productivity, Gross Profit Margin, Return on Investment, Customer Satisfaction, and Actual Cost are a few Project Management Metrics.

There are certain guidelines and metrics for a Project Manager as well. These guidelines are for the evaluation of project progress. Adhering to Project Scope, Risk Management, Budget Control, and Stakeholder Satisfaction are a few metrics set for a Project Manager.

FAQs

i) What is Data Analysis in Project Management?

In Project Management, Data Analytics is a medium assisting Project Managers with insights, trends, patterns, and historical data that supports and provides valuable feedback for data-driven decisions which assist in better outcomes and the strategic completion of a project.

ii) What is the difference between GANTT Chart & RACI Chart?

A GANTT Chart and a RACI chart both are used in Project Management. Though not compulsorily they are highly valuable tools for Project Management. GANTT Chart promotes visualising the project timelines and RACI chart guides with roles and responsibilities for every task in a project.

iii) Can a Data Analyst become a Project Manager?

Certainly, a Data Analyst can become a Project manager with expertise and experience by gaining knowledge about project management and handling various projects. Data Analytical skills can be crucial to becoming a successful Project Manager.

iii) Why is Data Analyst important?

Data Analysis is widely popular in today’s world. By analyzing data, businesses can optimize resources, gain a competitive edge, eliminate human error, and save time. Data Analytics can aid in finding loopholes in a project and rectifying them well ahead of time which facilitates customer satisfaction and trust.

Conclusion:

In conclusion, integrating data analytics for project management includes several benefits regarding cost-effectiveness, and enhanced risk management, and contributes to better project management. Project Managers are prepared well in advance for the challenges in the project with the study of data, helping them achieve the targets in budget constraints and with allocated resources. Project Managers decide the scope and aim of a project through Project Management this ensures what is to be achieved in a project and all the team members are in line with their respective roles.

Data Analysis helps researchers find accurate and evidence-based findings from research. By incorporating data analytics project managers can make wise decisions, enhance resource management, monitor the progress of the report, and contribute to stakeholder satisfaction. Integration of data analytics also facilitates team empowerment, leading to better adaptation to changes, time-saving, and improvement in individual and organizational goals.

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