Project Process

Analytics Project Process

How the data analytics team intakes requests and creates projects.

Depending on the project, the process may vary slightly, but the overall steps will remain the same.

General Analytics Project Request Process

Definitions of the Steps in the Process

1. Project Request: The project request is the first step in the process. This is where the business team or individual requests a project from the data analytics team. The request can be made through a variety of channels, including email, phone, or in person. The request should include a description of the project, the goals of the project, and any other relevant information. It can also be as simple as a statement of ‘I need help with this data’ or ‘I need a report on this data’.

2. Request Evaluation: Requests are evaluated to determine if they are a legitimate project,a support request or something we shouldn’t be working on.

   • Legitimate Project: This is a project that meets the criteria for a project (TBD) and is not a support request. This includes projects that are new, complex, or require significant resources.
   • Support Request: This is a request for support from the data analytics team. This includes requests for help with data, reports, or other analytics-related tasks. These requests are generally handled through a support ticket system and are not considered projects.
   • Not Analytics Project: This includes requests that are outside the scope of the data analytics team or do not require the data analytics resources to solve.

3. Project Type: The project is evaluated to determine if it is a novel project or a routine project.

   • Novel Project: This is a project that is new, complex, or requires significant resources. These projects are generally more time-consuming and require more resources than routine projects. These projects are generally gated and require approval from the stakeholders before they can proceed.
   • Routine Project: This is a project that is either simple or has been done previously and thus it is well understood. These projects are generally less time-consuming and require fewer resources than novel projects.

4. Project Definition: This includes defining the scope of the project, identifying the stakeholders, and determining the resources needed for the project. This can include the following:

   • doing exploratory data analysis (EDA) to understand the data and the problem that needs to be solved
   • defining the data sources that will be used in the project
   • testing a modeling approach to see if it is feasible
   • etc.

5. Quick Project: This is a project that is simple and can be completed quickly. These projects do not require approval from the stakeholders before they can proceed. The project is initiated by the data analytics team and is executed by the data analytics team with assistance from subject matter experts (SMEs) and other stakeholders. This process includes:

   • creation of the data pipeline to ingest and transform the data
   • creation of the data model to analyze the data
   • creation of the reports and dashboards to visualize the data
   • creation of the machine learning models to predict the data
   • creation of the documentation to support the project
   • etc.

6. Project Initiation: The project is initiated by the data analytics team generally at the request of a business individual or team. This process includes:

   • creating a project plan that outlines the steps that will be taken to complete the project
   • identifying the resources needed for the project
   • determining the timeline for the project
   • identifying the stakeholders and their roles in the project
   • etc.

7. Project Execution: The project is executed by the data analytics team with assistance from subject matter experts (SMEs) and other stakeholders. This process includes:

   • creation of the data pipeline to ingest and transform the data
   • creation of the data model to analyze the data
   • creation of the reports and dashboards to visualize the data
   • creation of the machine learning models to predict the data
   • creation of the documentation to support the project
   • etc.
     The project is monitored and controlled by the data analytics team to ensure that it is completed on time and within budget.

8. Project Closure: The project is closed out by the data analytics team and the stakeholders are notified of the completion of the project. This usually includes a testing phase where the project is tested to ensure that it meets the requirements of the stakeholders. In some cases the project is handed over to the business team for them to take over the project. In other cases the project is handed over to the data analytics team for them to maintain the pipelines, models, dashboards or other artifacts.

9. Project Review: The project is reviewed and the stakeholders to ensure that it meets the requirements of the stakeholders and that the project was completed on time and within budget. Not all projects are reviewed, but it is a good practice to review projects that are large or complex.

How a Project is Resourced

The Data Analytics team is made up of 4 distinct teams that support the project process. Each team has its own set of responsibilities and processes that they follow to support the project process. The teams are:

• Data Services: This team is responsible for the data pipeline and the data model. They are responsible for ingesting and transforming the data and creating the data models. They are also responsible for maintaining the data pipeline and the data model after projects are completed.
• Advanced Analytics: This team is responsible for the machine learning models and data analysis to suport projects. They are also responsible for maintaining the machine learning models and the data analysis after the project is completed.
• Enablement and Reporting: This team is responsible for the reports and dashboards. They are also responsible for maintaining the reports and dashboards after the project is completed.
• Information Management: This team is responsible for the data governance, ensuring that the data is secure and that the data is used in accordance with the data governance policies and procedures.

Depending on the project, the teams may work together or independently to support the project process. Generally one team will take the lead on the project and the other teams will support the project as needed.

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