A project management system designed to support a wide variety of infrastructure projects must also support a large variety of data and information types, formats, and methods of data egress/ingress. This includes all types of structured and unstructured data, as well as information created and/or provided by other platforms or systems. We will define “data” as content from a device in its original, unaltered form. Data is most often raw, unorganized facts gathered from an environment with a spatial, temporal and/or elemental component or association. Examples of data include:
- stream of measurements from a tool;
- video, audio, pictures, distances from a camera;
- text gathered from a keyboard,
- sensor readings,
- distances from a laser scanner,
- raw human observations, etc.
Information is data that has been processed, organized and/or presented in a useful and meaningful context. Information will always have some structure or associative properties, while data can be structured or unstructured. For example, the individual sale of items in a store is data, while the identification of the most popular and least popular items in the store is information. A project management system must be able to input and output all forms of data and information in support of the projects targeted by the business. The system must also be able to create and distribute the data and information needed to satisfy the client request, as well as provide the service company incremental insights to yield opportunities beyond the immediate client request.
The key is to create a system that enables the association of structured data with unstructured data, of which both have some temporal, spatial and/or elemental relationship. The challenge is to identify the associative elements that tie the data together, thus yielding insights and information key to making decisions in support of a target objective. The majority of the data collected will be unstructured, making most of a company’s data storage complex and difficult to analyze. But this unstructured data will most likely be the most valuable data to the company. Another challenge is identifying and understanding the characteristics associated with the collection, storage, and analysis of the unstructured data. These characteristics include variety, volume, velocity, veracity, and value.
- Variety refers to unstructured data in different forms such as messages, social media conversations, videos, and photos
- Volume refers to large amounts of data
- Velocity refers to how fast the data is generated and how fast it needs to be analyzed
- Veracity refers to the trustworthiness of data
- Value refers to the worth of the data stored by different organizations