Big Data: Data analysis is an integrated part of the project management routine, as this information allows you better to understand the market, the purpose of the proposal, how it is carried out, the best practices for its development, and much more.
Determining a positive result at the end of the project, this data cannot, in any way, be dismissed. Still, it can be better worked on to maximize efforts and make project management more accessible, agile, and effective.
Although this optimization is not yet a reality for most companies, this transformation has been taking shape little by little and bringing with it unique opportunities for improvement that could not be achieved in any other way — after all, processing and analyzing in-depth terabytes and petabytes of information is, unfortunately, humanly impossible. And that’s where Big Data comes in!
What Is Big Data?
Big Data is a data analysis technology. It uses large amounts of structured and unstructured information to guide teams to gain insights into their workflow. In this way, the company’s planning becomes more robust and prepared to face the day-to-day challenges of the corporate world.
Extensive Data-driven routines, in other words, our data analysis-driven routines. This strategy ensures more predictability for decisions, reduces risks, and maximizes companies’ innovation levels. After all, they will be able to find market trends more efficiently and thus achieve more profitability.
How Can Using Big Data In Projects Improve Results?
Adopting Big Data in corporate projects can bring great results to companies. Teams can deliver better results and present higher quality work as it is disseminated in stages such as planning and risk management. Check out some benefits of data analysis in project planning and management routines below!
Optimization Of The Critical Path Of Activities
One of the most significant difficulties in project management is determining the critical path, that is, the sequence of activities to be developed so that they are linked in the best possible way, aiming to optimize the team’s time and resources.
Since time is one of the three pillars of project management, with the help of Big Data, it is possible to determine the most flexible points where inevitable delays can be allowed and the points where it is simply not possible to make mistakes.
Efficient Vulnerability Analysis
Analyzing project vulnerabilities also requires a good base of experience, which cannot always — or should — be solely based on the memory of the project manager or a team member.
In this scenario, Big Data can provide relevant information about points of vulnerability in the project just by confronting structured and unstructured data from different sources, thus creating patterns and detecting trends, connections that the human mind is unlikely to be able to make with the same precision or due agility.
Overall Improvement In Project Quality
Knowing strictly at what point a problem occurred, when it was detected, when it was solved, and how much of the project budget the solution consumed is an essential set of information to maintain the quality of the project.
Big Data can be used for more intelligent data analysis from past projects and stages. From this crossing of information, the manager will be able to know how the teams reacted to different scenarios and, thus, build more robust routines for the future. As a consequence, the chances of errors will be much less.
Waste is a big problem for corporate projects. When they occur, teams spend more than they should and delay stages. In addition, the company has less chance of success throughout the project.
Investing in Big Data allows teams to work with more appropriate resource usage forecasts. The management team will use data analysis to identify patterns from previous projects and build a more robust plan. In this way, the project will function more adequately and with more ability to deliver everything that is expected with an adequate final expenditure.
One of the benefits of reducing waste is maximizing the use of available resources. A company that manages to do this has the ability, for example, to make better use of equipment. This contributes to extending its useful life and the investment obtained with each tool.
In the case of the solutions used during the project, having a complete view of how they will be used facilitates the application of measures such as preventive maintenance. They extend the life of each material and reduce unscheduled repairs. This way, the business spends less and has tools for longer.
Standardization Based On Parameters And Specifications
A functional project is often a project with well-structured quality and resource standards. A company that manages to keep teams running based on a unified routine and a well-standardized workflow has fewer errors and more agility. This allows teams to deliver a more robust result with a greater chance of success.
In other words, using Big Data to learn about common mistakes and chronic problems of the team helps to optimize training and facilitates the definition of activities in the medium and long term.
High productivity is a crucial factor for any project to succeed. When teams are agile, they have more time to deal with unexpected changes and correct errors more safely. All this without compromising the deadline that was defined in the planning stages.
From Big Data, the company finds it easier to assemble a project knowing all its strengths and weaknesses. The manager will have a complete view of everything relevant even before a step is started. In this way, he can act actively to mitigate possible problems and, thus, maximize the productivity of all professionals.
More Risk Predictability
Every project has risks that can affect its success. Many managers employ methodologies and strategies that reduce their possible impact. They are aimed at eliminating, facilitating the identification, and speeding up the correction of any risk.
Big Data facilitates identifying and preparing to deal with risks involving projects. The business can use previous experiences to validate possible problems and create robust mitigation techniques. Thus, if any risk becomes a reality, the team will spend less time dealing with them.