Data analytics turns raw information into insights that can be used to optimize performance and improve the output of a process, system, or business. As powerful analytical tools become more readily available, big data and business analytics is growing into a multi-billion-dollar industry. Companies are leveraging data analytics in support of acquiring opportunity, making quicker and better decisions, creating efficiencies, and reducing costs.
Similarly, data-driven project management is an approach that improves project work processes. Data analytics support more efficient monitoring and control of project uncertainties and bring greater objectivity to decision-making activities.
From Data-Informed to Data-Driven
The definition of a project as a coordinated, one-off effort to produce a unique product, service, or result implies working to create something that did not exist previously. With each project essentially being something “new”, it’s natural for a project manager to be influenced by past experience and subjective perceptions, with decisions made based on what has worked before along with healthy doses of gut instinct and intuition.
However, it is obvious that using measurable, objective data to guide decision making is a sound strategy. In fact, project management has always involved the collection and processing of information for the purpose of gauging work progress, monitoring expenses, assessing the percentage of deliverables completed, measuring output quality, and determining how to proceed. Using data in this manner comprises a data-informed approach to management.
Moving to a data-driven mode entails establishing and emplacing pre-defined, metrics-based rules that trigger specific management decisions based on incoming information. This has the advantage of supporting quicker response to change, since the decision-making process has already been completed. Another efficiency gain is realized by freeing the project manager from entanglement in operational details and allowing focus to remain at the project overview level.
Setting Up a Data-Driven Management Strategy
A simplified general framework for data-driven decision making can include four primary elements. The first is choosing project measurement metrics. Select metrics that are closely tied to project objectives. Once metrics are identified, data must be collected. Identify detection and storage tools appropriate to each type of data to be gathered and decide how frequently measurements will be taken.
Processing is the stage that transforms raw, detailed data into actionable information. Ideally, this will go beyond simply understanding aspects of current project status to using statistical techniques in the identification of trends and patterns that can be used in the development of predictive models of events and outcomes.
The most complex part of this process is establishing decisions and actions to be taken based on information received and the trigger levels or elements that will set those actions in motion. A very useful project management tool at this point is a dashboard-type arrangement (click here for more information), summarizing the output of key project metrics.
Making Better Decisions
Project managers can engage in higher-quality decision making with support from data analytics. When carried out correctly, objective criteria replaces reliance on experience and intuition, and pressure associated with decision making in emergent situations is reduced. Ideally, your organization’s project management culture can shift from a reactive to proactive orientation.