Analytics Quality
Data Stewardship & Quality
Overview
Improving the quality and contextualization of data across the organization increases the velocity and impact of your analytics solutions. This course aligns the business and technical teams on how to improve context and quality from data capture through curation.
Learning Outcomes
After this course, students will be able to:
Improving the quality and contextualization of data across the organization increases the velocity and impact of your analytics solutions. This course aligns the business and technical teams on how to improve context and quality from data capture through curation.
Learning Outcomes
After this course, students will be able to:
- Understand the responsibilities and expectations of a data steward.
- Enable self-service datasets in the organization.
- Define, measure and improve data quality for analytics solutions.
- Understand data lineage within data pipelines.
- Data quality definition, calculation and improvements.
- Understand use-case centric vs data centric improvements.
- Monitor and troubleshoot data quality concerns with the organization.
Length
2 Days (8 hours/day)
Pre-Requisites
This course requires a basic experience and understanding of architecture and data pipelining.
Course Content
This course contains the following modules:
This course contains the following modules:
Module 1: Data stewardship
- Students will learn the role, importance, and key responsibilities of a data steward. This includes how to provide context, structure, and quality for the data the organization relies upon.
- They will also learn the components of an operational analytic solution as a translation from traditional spreadsheet reports.
Module 2: Data storytelling
- Data stewards will learn how to provide the crucial context for the organization.
- Students will learn best practices for data schemas, business process analysis, and documentation of data sets.
Module 3: Data quality
- This module covers how to define, measure, and improve the quality of a data set. This includes the key aspects of quality including availability, timeliness and accuracy.
Exercises
- Translate the components of a spreadsheet report into the components of an analytic solution.
- Create a data walk and schema to document the context of a data set including the collection and business process.
- Define data quality for a data set including reporting and improvement opportunities.
Overview
Defining quality and testing in analytics requires understanding the areas to test including the data pipeline, analytics, and integration. This course provides an overview of the importance and keys to success quality in the analytics process.
Learning Outcomes
After this course, students will be able to:
Defining quality and testing in analytics requires understanding the areas to test including the data pipeline, analytics, and integration. This course provides an overview of the importance and keys to success quality in the analytics process.
Learning Outcomes
After this course, students will be able to:
- Explain the importance and drivers of quality in analytics.
- Understand the cost of re-work and poor quality.
- Root cause analysis on quality issues.
- Identify the key points and activities to increase analytics solutions’ quality (Data, Analysis, U.I. etc.).
Length
1 Day (8 hours)
Pre-Requisites
This course requires basic experience building analytics solutions.
Course Content
This course contains the following modules:
This course contains the following modules:
Module 1: How are analytics solutions tested for quality?
- What makes an analytics solution high quality?
- Learn how to identify the business impact and critical features for adoption.
Module 2: Anatomy of an analytics solution
- This module will familiarize you with the core components of an analytics solution.
Module 3: Find the root cause of quality issues
- Techniques to identify and isolate quality issues within analytics solutions
Module 4: Stage gates to adhere to quality standards
- Learn the best practices to set up in a testing framework to ensure all solutions are adhering to established quality standards.
Module 5: The impact of poor quality
- Learn how poor quality can significant impact an organizations trust to use analytics for critical decision making.
- Understand how to establish a quality standards and ensure solutions are consistently being produced against that standard.
Exercises
- Identify the points in the analytic process which determine the quality of the solution.
- Identify the key areas and components to test for an upcoming analytics solution.
- Example root cause analysis on analytics solutions.
Analytics Testing & QA
Overview
Analytic solutions require a unique approach to testing to ensure reliable predictions. This course covers how to define and execute test cases for analytic solutions.
Learning Outcomes
After this course, students will be able to:
Analytic solutions require a unique approach to testing to ensure reliable predictions. This course covers how to define and execute test cases for analytic solutions.
Learning Outcomes
After this course, students will be able to:
- Leverage quality & testing concepts and methods to increase the value and impact of analytics solutions.
- Write and execute test cases that define and confirm the quality of the functionality and data of analytic solutions.
- Expand business requirements for edge-cases and data patterns to test.
- Document test cases to educate and communicate to the broader business.
Length
2 Days (8 hours/day)
Pre-Requisites
Students are expected to have some experience with the analytics development cycle and basic understanding of statistics.
Course Content
This course contains the following modules:
This course contains the following modules:
Module 1: Test case development
- Leveraging user requirements to develop test cases that deliver value to stakeholder.
Module 2: Edge case creation
- Set strategy around the execution of the models test strategy to deliver a quick and more quality assurance.
Module 3: Identifying critical risk factors
- Dive into risk classifications and development.
Module 4: Documentation best practices
- Understand best practices to capture and communicate data risks to the key stakeholders/users of model.
Exercises
- Understand the foundations of automated and manual analytics testing.
- Distinguish the strengths and weaknesses of random testing, symbolic analysis, static analysis, and model checking.
- Create executable requirements for test cases and understanding key criteria for risk memos to communicate with the broader organization.
- Create an analytics test plan that utilizes both manually-written tests and automated tests towards maximizing rigor, minimizing effort and time, and minimizing test costs for analytics solutions.
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