Database Reference
In-Depth Information
This person would have deep knowledge of a field of study, such as oceanography,
biology, or genetics, with some depth of quantitative knowledge.
At this early stage in the process, the team needs to determine how much business
or domain knowledge the data scientist needs to develop models in Phases 3 and
4. The earlier the team can make this assessment the better, because the decision
helps dictate the resources needed for the project team and ensures the team has
the right balance of domain knowledge and technical expertise.
2.2.2 Resources
As part of the discovery phase, the team needs to assess the resources available to
support the project. In this context, resources include technology, tools, systems,
data, and people.
During this scoping, consider the available tools and technology the team will
be using and the types of systems needed for later phases to operationalize the
models. In addition, try to evaluate the level of analytical sophistication within
the organization and gaps that may exist related to tools, technology, and skills.
For instance, for the model being developed to have longevity in an organization,
consider what types of skills and roles will be required that may not exist today.
For the project to have long-term success, what types of skills and roles will be
needed for the recipients of the model being developed? Does the requisite level
of expertise exist within the organization today, or will it need to be cultivated?
Answering these questions will influence the techniques the team selects and the
kind of implementation the team chooses to pursue in subsequent phases of the
Data Analytics Lifecycle.
In addition to the skills and computing resources, it is advisable to take inventory
of the types of data available to the team for the project. Consider if the data
available is sufficient to support the project's goals. The team will need to
determine whether it must collect additional data, purchase it from outside
sources, or transform existing data. Often, projects are started looking only at the
data available. When the data is less than hoped for, the size and scope of the
project is reduced to work within the constraints of the existing data.
An alternative approach is to consider the long-term goals of this kind of project,
without being constrained by the current data. The team can then consider what
data is needed to reach the long-term goals and which pieces of this multistep
journey can be achieved today with the existing data. Considering longer-term
goals along with short-term goals enables teams to pursue more ambitious projects
