Data Analytics for data- driven project management

Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client.

Every company thrives in its business when there is proper project management in place. This, however, is seen seldom, because of the large amount of project failure rates. Project managers have slowly run out of options but to turn to data and data analytics for help.In any organisation, data plays a critical function. Managers and executives can use analytics to spot early symptoms of budget, cost, and timeline slippages and take corrective action.

Use of Data Analytics in improving Project Management controls

Data plays a significant role in any organisation. Using analytics, managers and executives can watch for early signs of slippages in terms of budgets, costs, and timelines and take corrective action. Analytics also helps managers capture the rate of work, so they can easily predict whether the project will be completed on time. Managers can use a burn-down chart, for instance, which is a graphical representation of work left to do over time.

Data Analytics in Project Management

A recent Project Management Institute survey confirms that artificial intelligence (AI) disruption is happening — 81% of the 551 survey respondents report their organisation is being impacted by AI technologies.

Project manager always does a balancing act among the various Constraints set at the start of a project. To be in control of Project he always requires to have check on project performance metrics. Project Performance Analysis is key for project managers to decide the revised rate of efforts to achieve desired goals.

Effective management of projects entails efficient management of the uncertainties and risks on the project. It requires today’s project managers to use analytical techniques to monitor and control the risks as well as to estimate project schedules and costs more accurately with analytics-driven prediction.

Digitization of Project Management process and application of Analytics will provide strategic value creation on the part of organisations.

Project Analytics: Project analytics can help project managers handle complex projects and keep them on-schedule and on-budget.

Quality of Deliverable:Analytics allow managers and executives to deepen their understanding of how ongoing and proposed projects fit into the overall portfolio and organisation vision.

Lowers project costs: This reduces potential project errors and inaccuracies that could cost you a ton of your resources, and, if not corrected, could set back your operations for days, weeks, or months.

Improves resource management:It helps us to predict project outcomes and make better strategic decisions to ensure the most cost-effective resource spending.

Enhances project risk management: Data analytics allow you to analyse your project issues and risks to manage them better and minimise their impact on your processes and results.

Data analytics for project managers enhances business performance: Data analytics in project management also functions as a booster of business performance.

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.

Data structuring and cleaning

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.

4 Types of Advanced Data Analytics

An essential prerequisite for advanced data analytics can be distinguished into four levels that formulate a business intelligence database:

1. Descriptive analytics: analysis of the current operational status without further research.
2. Diagnostic analytics: recognizing data patterns enables conclusions about the causes of previous developments, but no statements about future trends.
3. Predictive analytics: updating the recognized patterns for the future.
4. Prescriptive analytics: concrete recommendations based on the recognized patterns to achieve a predefined goal.

What Project Management Framework might help manage Analytics Projects?

When managing a data analytics project, one key aspect to focus on is ensuring that the team can effectively collaborate and communicate . This can be facilitated by using a framework that supports the key characteristics of an analytical project, such as the need to do exploratory analysis.

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.

In some way, project management data analysis can be seen not as a mere science but as an art process. The key to its success is the consistency of time and approaches. Each project manager may have different preferences in data collection tools or visual presentation tastes.The only thing is that intelligent data analytics allows you to obtain important information, bringing out patterns and trends useful for the business itself.