A data scientist is somebody who is inquisitive, who can play with data and spot trends. Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.
Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.
Data Scientists perform data science. They use technology and skills to increase awareness, clarity and direction for those working with data. The data scientist role is here to accommodate the rapid changes that occur in our modern day environment and are bestowed the task of minimising the disruption that technology and data is having on the way we work, play and learn. Data Scientists don’t just present data, data scientists present data with an intelligence awareness of the consequences of presenting that data.
The three components involved in data science are organising, packaging and delivering data (the OPD of data). Organising is where the physical location and structure of the data is planned and executed. Packaging is where the prototypes are build, the statistics is performed and the visualisation is created. Delivering is where the story gets told and the value is obtained.
However what separates data science from all other existing roles is that they also need to have a continual awareness of What, How, Who and Why. A data scientist needs to know what will be the output of the data science process and have a clear vision of this output. A data scientist needs to have a clearly defined plan on how will this output be achieved within the restraints of available resources and time.
A data scientist needs to deeply understand who the people are that will be involved in creating the output. And most of all the data scientist must know why there is a motivation behind attempting to manifest the creative visualisation.