Data analytics: new superpowers for project managers
Forward-thinking project teams recognise the need for decision-making to be supported by accurate data. Here are five superpowers that data analytics can give project managers.
Have you managed a project and wished data from previous initiatives were at your fingertips to help you understand and predict the success of your current work? Maybe to help better understand where to allocate risk budget, build contingency into a schedule, or concentrate resources to avoid bottlenecks?
Sometimes, we end up relying on lessons learned or reviewing similar legacy projects to predict how a current project might pan out. Unfortunately, these hindsight methods fail to capture the complex interrelationships of numerous project factors that combine and influence delivery success or failure.
Fortunately, the growing prominence of data analytics and artificial intelligence (AI) can help find patterns, discern trends, and identify problem areas much earlier on than would have been possible before.
Data analytics, the focus of this article, is the process of analysing raw data to extract meaningful insights and it can arm project managers with new superpowers, helping them to understand more precisely what and when key interventions are needed to halt any divergence of project performance from plan. Automated reporting, intelligent risk monitoring, and financial trend analysis can trigger alerts, allowing project managers to focus more on running teams of people and manage by exception, rather than managing manual and sometimes clunky project management processes.
But where to start with using data analytics?
If you’ve used a macro on a spreadsheet, you’ve had some experience of data analytics already. For example, if your spreadsheet concerns a risk register that is prioritised from highest to lowest impact, you can quickly see what your top risks are. So, by applying basic data analytics, you understand which risks are of most concern and, more importantly, can start to make informed decisions on what actions to implement first to address them.
Let’s explore five superpowers that data analytics give project managers and how integrating it into your project management practices can enhance delivery success.
Risks can come from a range of sources inside and outside of a project and they can appear in many different forms. This makes risk management a fundamental discipline for running a successful project. The threats risks can pose can have a significant impact on a project, but many delivery teams remain reactionary in managing them. Here is where data analytics can transform project managers from reactive to proactive risk management heroes.
Data provides project teams with accurate information allowing managers to better quantify, and model emerging risks. This, in turn, allows smarter mitigation strategies to be implemented and better strategic decisions to be made on where to focus resource, cost, and time to negate threats.
Data drives top-level decisions in competent organisations and decision-making relies a great deal on data. There is a synergy between the two and at a project level, the more information managers can get and analyse, the more ideas and interventions they can implement to assure delivery success.
To move a project towards data-driven decision-making, it’s crucial to have an approach and the necessary tools to manipulate and present data in a digestible form. Data gathering, mining, synchronising, and various types of analytics are ways to collect key data and use them to derive crucial insights.
Analytical software tools can complement project management practices through facilitating resource management, supporting supply chain logistics, and monitoring interactions and communications amongst stakeholders.
A tailored decision-making framework enhances multiple context analysis and leads to ingenious and efficient solutions / strategies. Ultimately, you can improve your project across multiple fronts through using actual records and insights to make better decisions.
Focusing on the profit of a project is not the sole aim of project management, but it is important for project managers to review and challenge their teams to reduce project expenditure. A simple spreadsheet can help project teams implement and monitor costs and budgets and with a few formulae, produce reports and dashboards to visualise net loss and gain – helping to analyse ways to save money.
To enhance this, data analytics can take over and provide a definitive view on understanding the short and long-term trends on project expenditure. Earned value management (an ISO 21508 standard for measuring project performance and progress), can detect profit eroding activities providing a valuable forecast to help project managers review tasks, revise spending, and reverse losses on a project.
Effective planning cannot succeed without data. Metrics, resource profiles, rolling-wave planning, and critical path analysis all require data feeding into them to be used for strategic planning.
And the bulk of analytical information allows forecasting project work far more effectively. Moreover, an impressive data library is a real superpower for a project manager, helping to lead the scheduling of a project’s budget, estimates, short-term actions, and long-term activities in pursuit of goals.
Data analytics supercharges project performance through enabling more successful outcomes. It might not be evident at first, but its role does become apparent after project managers start making data-driven decisions, using data analytics for planning, and deriving key insights from data analysis to inform future work activities.
However, it does require project managers having enough data to be able to predict and develop solutions to positively impact their projects and keep them on track to their cost, time, and quality envelopes.
But having enough data is only one half of a superpower. To make it full, project managers also need to implement data correctly. Accurate information analysis will save costs, improve risk management, leverage strategic planning, and ultimately enhance project performance.
The superpowers of project data analytics
Data analytics gives a fuller picture of a project by providing granular visibility of activities and outputs, generating reports, and visually representing data in easier ways to be interpreted and acted upon by project managers.
Until recently, such analysis has been limited by the high cost and sub-par performance of data collection, scrubbing, and interrogating capabilities. The rapid improvement today in data processing, the growth of data science, and the reducing cost of analytical software means such barriers for project managers are lowered.
With new superpowers emerging courtesy of data analytics, project managers can now make better informed, real-time decisions based on large volumes of data instead of relying on gut instinct or on manually compiled reports days, if not weeks, out of date and limited to a handful of data points.
Project management data analytics is a powerful tool for modern managers delivering major bodies of work. And for those making their first foray into data analytics, remembering the five key superpowers they offer can help overcome any trepidation.
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