Today, companies are beginning to understand why they should use Big Data in project management. The opportunity that comes with the possibility of analyzing large volumes of data, from the many sources available. Thanks to the use of the internet and social networks and use this data to make much faster and more accurate predictions.
Although we have been hearing the term Big data for decades, it is now when access is easiest and storage costs are most economical. Therefore, it is time to consider the benefit we can get from optimizing processes in project management.
But to understand the impact that Big Data can have on project management we must first understand its main characteristics.
What is Big Data?
A large volume of digital data coming from different sources.
Big data in project management describes the large volume of structured, semi-structured and unstructured data analyzed to understand trends.
- Structured and semi-structured data: they are part of a predefined structure and are easily catalogued. Example: Excel sheet or SQL database.
- Unstructured data: not part of a defined structure It is complicated to create reports and make analyses because the information is not well structured and catalogued. Example: an email, Word file, Skype conversation.
The 5 Vs of the Big Data:
New data constantly generated from various sources.
Data is generated at high speed so efficient data analysis technologies are needed.
The management of the information received is key to classify all data received from each of the sources.
It is essential to select the data that is really important and generates value for the company. A good definition of objectives and strategy prior to data storage will save a lot of computing time and facilitate long-term management.
Data must be relevant and also true. False data is one of the biggest challenges we will face when analyzing information.
Challenges of Big Data:
- Only 20% of the information is structured. The large amount of data, coming from different sources, generates problems for companies when extracting real and quality data.
- It takes a long time to transform unstructured data types into structured ones and to process these data.
- The data changes quickly and therefore has a very short validity. It requires a very high processing power, if we do not do it well the analysis can produce wrong conclusions that will lead us to make mistakes in decision making.
- There are no unified data quality standards. The standards related to data quality still need to be improved and there are still hardly any results from research on Big Data data quality.
Big Data in project management:
Numerous conclusions drawn through Big Data have allowed companies to find patterns of behavior and predict future results with great accuracy. So the success of any project depends on making decisions through the information available and Big Data is a great way to get information easy to process.
Some processes of project management in which Big Data can achieve a great impact and in what way:
Project planning and execution creates a large amount of data, resulting in an opportunity to develop relevant predictions through Big Data. The volume and variety of data can help project managers reorganize planning processes and develop creative and innovative solutions.
Therefore, Big Data’s “Elastic Search” tool allows us to make queries within a large volume of complex data. Its main feature is the ability to index and analyze all this data in real time, resulting in process optimization and more efficient management.
Every day, innumerable information is collected about different professionals working in a multitude of sectors. The data extracted refers to experiences in past projects, team members’ skills, academic training, complementary training, personal and team performance evaluations, leadership, etc.
Big Data can provide conclusions on how to organize teams more efficiently. Such as optimizing team size and structure, the skills needed to build a successful team or how to choose the most efficient leaders for each type of project.
A significant amount of information is generated as a result of knowledge management in both projects and organizations. In addition, this information includes good practices, records, lessons learned, among others. Normally, this information is stored in huge files where it is almost impossible to find the necessary information, losing its value.
Through Big Data we also intend to extract the value of all this information and transform it into a shared knowledge platform. In this way we can easily find ways to manage problems and develop new practices to work more efficiently.
Project management is usually affected by external and internal agents that can jeopardize the success of the project. Risks must be managed to minimize the negative impact on project performance, so risks should always be documented. When risks occur and become problems, the resolution of these problems must be documented. The creation of all this information will allow us to analyze it to improve the management of risks.
Big Data can be a fundamental tool for this analysis, developing techniques and processes to identify, analyze, prioritize, control and create strategies to react to risks.
Quality management involves a considerable amount of work during the planning, design, construction and testing phases. During the development of these phases a lot of information is prepared, processed and analyzed. This information includes development policies, decisions when choosing quality criteria and thresholds, or the use of quality standards such as ISO standards.
Big Data can analyze this information to develop new quality controls, techniques in controls and processes, control panels to monitor quality during project execution and new thresholds, criteria and parameters to measure quality according to initial requirements.
The resources of a project include human resources, infrastructure, technologies, financial resources, knowledge, processes and procedures.
As in the previous processes, a large amount of information related to the use of resources is collected. Such as types of resources, units of measure, quantity required, quantity used, quantity of resources wasted and control mechanisms for resource use.
Resources are usually converted into money and therefore the analysis of resource management can generate conclusions to improve resource management and consequently achieve cost savings. Big Data can play an important role in the development of new procedures for the acquisition, allocation and management of project resources.
In my opinion, the adoption of Big Data in project management and in the processes of organizations in general has a very positive impact. Especially in terms of the opportunity to take advantage of the amount of data that is currently generated daily.
In addition, with a correct management it could solve the problems related to the excess of information, helping us to interpret this great volume of data. And thus give meaning to the time to make predictions, reduce time in research, search and presentation of results.
In summary, the adoption of Big Data in project management in companies should not be taken lightly, it should be a great project in itself. The advantages that it can bring are many, but if all this data is not treated in the right way we will be making decisions based on wrong information and we will suffer the failure of the decisions.