Data Analytics¶
Todo
add some text about this constituency
Data Engineer¶
- About
- A Data Engineer is responsible for expanding and optimizing our data and data pipeline architecture, data flow and data collection. They have a clear understanding of data pipeline and data wrangling, optimizing data systems and building them from the ground up. The Data Engineer supports our software developers, database developers, and data scientists on data initiatives, and ensures optimal data delivery architecture is consistent with the established or objective project cloud architecture.
- Key Responsibility Areas
- Create and maintain optimal data pipeline architecture on On-Premises and/or on any cloud platform.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using data solution tools.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Develop, construct, test and maintain data architectures.
- Align architecture with business requirements.
- Data acquisition from multiple sources.
- Use data to discover tasks that can be automated.
- Deliver updates to stakeholders based on analytics.
- Links
Data Scientist¶
About
A Data Scientist supports leadership & other project teams with insights gained from analyzing data. A Data Scientist should be adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining & data analysis methods, using a variety of data tools on premise and clouds, building and implementing models, creating algorithms. They must have an ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. They must have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
- Key Responsibility Areas
- Drive projects driven by questions and insights rather than goals and objectives.
- Implement & execute DSLC (Data Science Life Cycle) process for data science projects.
- Gain insights from data using statistical methods.
- Use Visualization and storytelling to communicate insights.
- Prepare data science processes for operationalizing analytics.
- Work with stakeholders throughout the organization to identify opportunities for leveraging Innova’s data to drive business solutions.
- Mine and analyze data from Innova’s resources to drive optimization and improvement of product development, marketing techniques and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop both out of box and custom data models and algorithms to apply to data sets.
- Work closely with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
Links
Data Steward¶
About
While data governance focuses on high-level policies and procedures, data stewardship focuses on tactical coordination and implementation of these policies.
A functional role in data management and governance, with responsibility for ensuring that data policies and standards turn into practice within the domain. Data stewards assist the enterprise in leveraging data assets to full capacity. It is crucial to the success of an enterprise data governance strategy. It is always evolving role as expectations of enterprise data governance gets more complex. They are responsible for assuring quality and trust in the data and maintaining a consistent use of data resources across the organization.
- Key Responsibility Areas
- Create processes and procedures usability, integrity, security, and availability of data used in an enterprise along with access controls to monitor adherence. This includes establishing internal policies and standards and enforcing those policies. Monitors and ensures compliance with formal data handling agreements, such as a Memorandum of Agreement.
- Maintain quality of the data using customer feedback, concerns, questions evaluating and identifying issues and coordinating and implementing corrections regularly.
- Ensure compliance and security of the data. Data stewards are responsible for protecting the data while providing information on potential risks and offering regulatory guidance. Provide checks and balances of how data is accessed and data breaches compromising data related activities.
- Monitor data usage to assist teams, share best practice trends in data use, and provide insight into how and where teams can use data to help in day-to-day decision-making.
- Closely coordinates with corresponding customer data steward to facilitate appropriate access to required data.
Links
Data Science Project Manager¶
About
The Data Science Project Manager is responsible for the flow of knowledge creation and ultimately the realization of value which comes from delivery of the product outlined in the vision statement. Data Science Project Manager owns the life cycle of the project from end-to-end. The main objective is to deliver business value within the agreed upon schedule and budget. He or she works closely with the Research Lead/SME, the Data Scientist and the larger group to refine team capacity, efficiency and velocity. The Data Science Project Manager is charged with planning and scheduling duties including developing project plans, monitoring and reporting status, identifying and managing issues to closure, and identifying and mitigating risk.
- Key Responsibility Areas
- Ensure the data science team focuses on discovering actionable results aligned with current customer interests and larger strategic objectives. Actionable results are defined as insights the customer can use to inform decision-making (resource allocation, risk mitigation, etc.).
- Ensure team insights are distributed to the enterprise with full transparency and appropriate governance.
- Ensure full enterprise transparency on any gaps in required data and ensure data acquisition plans are developed as necessary.
- Liaison between data and analytics team, product or data ownership, development/ implementation teams, and client stakeholders.
- Regularly assess execution, review remaining work against forecast, analyze and submit changes to the plan to rapidly respond to business needs, risks and variances.
- Plan for the delivery of business value by ensuring that all work performed is within project scope, forecasting Release schedule, orchestrating non-software deliverables, and communicating milestones and commitments to the plan to ensure customer needs are met.
- Organize and coordinate all activities necessary to deliver contractual obligations by documenting, scheduling, communicating, and budgeting for all activities required to deliver the required project scope to the customer.
- Build and manage the product backlog, including documenting and organizing planned work artifacts and acceptance criteria (e.g., utilizing AzDO).
- Ensure the team is prepared for meetings and an accurate record of meeting outcomes is maintained.
Links
Software Engineer¶
About
The Software Engineer is focused on implementation of the Data Science product in the production environment. Specializes in Software Engineering in the Data Science discipline versus Software Engineering for Web Applications.
The Software Engineer will be responsible for packaging the Data Science solution and integrating the solution with other tools and products. The Software Engineer determines how to export a solution from traditional Data Science languages such as R, Python, Matlab, etc. into production software environments such as Java, C#, etc. In this role, the Software Engineer manages configuration control of the Data Science solution; builds applications to prove integration concepts; develops Machine Learning libraries for deploying algorithms into production; and optimizes the production solution for performance and scalability.
- Key Responsibility Areas
- Acts as the technical caretaker for Data Science solution integration into other tools and products.
- Ensures the Data Science solution is stable and scalable when implemented in production.
Links
Data Visualization Engineer¶
About
The Data Visualization Engineer focuses on representing data in a manner that connects technical findings to a non-technical audience. The Data Visualization Engineer leads the effort to craft a story from the data science discovery and/or designs the experience that users of the Data Science product will have. In this role, the Data Visualization Engineer may use a presentation to tell a story, a collection of graphics, a user interface, or some other medium that communicates the insight(s) of the Data Science product to a general audience.
The choice of communication medium and the design of the communication is the responsibility of the Data Visualization Engineer. The Data Visualization Engineer may choose to utilize business intelligence tools such as PowerBI, concept design tools like Adobe, statistical tools such as Python, dynamic visualization tools such as d3, etc. Depending on the customer demand and the story behind the data this role will utilize any number of tools to communicate the findings of the data science team. Appropriate skills for the role may be technical writing, programming, or UI design depending on the customer requirement. Therefore, the individual that fills this role will vary.
- Key Responsibility Areas
- Integrates together the insights of the data science sprint into a coherent story.
- Leads the Visualization Design activity but does not necessarily create all the graphics for the team. Acts as the technical lead responsible for ensuring that all relevant graphics are produced within the sprint.
- Creates documentation of customer reporting requirements and finalized reports.
Links
Process Guidance Version: 10.4