DataOps Methodology

IBM via Coursera

Go to Course: https://www.coursera.org/learn/ibm-data-ops-methodology

Introduction

### Review and Recommendation of the Coursera Course: DataOps Methodology In an era where data is deemed the new oil, the ability to manage and derive insights from this valuable resource has become paramount. The **DataOps Methodology** course on Coursera emerges as a fundamental learning experience designed to empower organizations to harness data management effectively. With a curriculum rooted in collaboration, integration, and automation, this course caters to data managers, engineers, analysts, and anyone involved in data-driven decision-making processes. Here's an in-depth look at what this course offers, along with my recommendation. #### Course Overview DataOps, as defined by Gartner, is akin to the software engineering principles found in DevOps. It is a collaborative practice that aims to facilitate better workflows among data managers and consumers, ensuring that organizations can flexibly respond to their evolving data needs. The **DataOps Methodology** course is structured to provide learners with a solid foundation in implementing these principles effectively within their organizations. #### Syllabus Breakdown The course divides its content into six comprehensive modules, each designed to cover critical aspects of the DataOps framework: 1. **Establish DataOps - Prepare for Operation** - This module introduces the foundational principles of DataOps. You’ll gain insight into the roles of various stakeholders in defining and curating data, setting the stage for effective data delivery that meets organizational purposes. 2. **Establish DataOps – Optimize for Operation** - Here, participants will lean into understanding the business value of their data operations. This module emphasizes the importance of articulating value to the broader organization, which is crucial for aligning data management objectives with business strategies. 3. **Iterate DataOps - Know Your Data** - A deep dive into data discovery techniques, this segment emphasizes understanding data repositories and highlights the automation of recognizing data semantics and patterns. Key focuses include identifying regulated data, fostering organizational insight, and enhancing data classification practices. 4. **Iterate DataOps – Trust Your Data** - An essential aspect of data management is establishing trust in data quality. This module addresses how to evaluate and improve data quality, ensuring that data-driven decisions are based on reliable information. Particularly, it touches on compliance and ethical considerations regarding data use. 5. **Iterate DataOps – Use Your Data** - This lesson covers the transformation process necessary for making data consumable and useful. You'll explore various data preparation methods, optimization for use cases, and learn from a real-world project involving AI-based analytics in Google Cloud. 6. **Improve DataOps** - Continuous improvement is vital for any data operation. In this module, participants learn to evaluate previous data sprints, analyze outcomes, and make iterative improvements for future cycles. 7. **Summary & Final Exam** - The final module synthesizes knowledge gained throughout the course, reinforced by a practical exam that assesses your understanding of DataOps principles and practices. #### Recommendation The **DataOps Methodology** course is highly recommended for professionals looking to enhance their data management skills and for organizations aiming to improve their data operations. Here are a few reasons why you should consider enrolling: - **Comprehensive Learning**: The course covers essential DataOps principles and practices across multiple facets, from team preparation and data optimization to trust and usability of data. - **Practical Application**: Accounting for real-world scenarios like implementing AI in supply chain management ensures the course is not only academic but also practical and applicable. - **Skill Development**: It provides you with a robust skill set to advocate for and drive data quality initiatives, making you a valuable asset within your organization. In conclusion, the **DataOps Methodology** course on Coursera not only fosters an in-depth understanding of data operations but also equips you with the tools necessary for successful implementation within your organization. In a data-centric world, mastering these principles is crucial for enabling effective decision-making and achieving strategic goals. Whether you're a seasoned professional or a newcomer to the field, this course is a worthwhile investment in your career.

Syllabus

Establish DataOps - Prepare for operation

In this module you will learn the fundamentals of a DataOps approach. You will learn about the people who are involved in defining data, curating it for use by a wide variety of data consumers, and how they can work together to deliver data for a specific purpose:

Establish DataOps – Optimize for operation

In this lesson you will learn the fundamentals of a DataOps approach. You will learn about how the DataOps team works together in defining the business value of the work they undertake to be able to clearly articulate the value they bring to the wider organization:

Iterate DataOps - Know your data

In this lesson you will learn about the capabilities that you will need to use to understand the data in repositories across an organization. Data discovery is most appropriately employed when the scale of available data is too vast to devise a manual approach or where there has been institutional loss of data cataloging. It utilizes various techniques to programmatically recognize semantics and patterns in data. It is a key aspect of identifying and locating sensitive or regulated data to adequately protect it, although in general, knowing what stored data means unlocks its potential for use in analytics. Data Classification provides a higher level of semantic enrichment, enabling the organization to raise data understanding from technical metadata to a business understanding, further helping to discover the overlap between multiple sources of data according to the information that they contain:

Iterate DataOps – Trust your data

In this lesson you will learn that understanding data semantics helps data consumers to know what is available for consumption, but it does not provide any guidance on how good that data is. This module is all about trust, how reliable a data source can be in providing high fidelity data that can be used to drive key strategic decisions, and whether that data should be accessible to those who want to use it; whether the data consumer is permitted to see and use it. This module will address the common dimensions of data quality, how to both detect and remediate poor data quality. And it will look at enforcing the many policies that are needed around data quality, not least the need to respect an individual’s wishes and rights around how their data is used:

Iterate DataOps – Use your data

In this lesson you will learn that providing useful data in a catalog can often necessitate some transformation of that data. Modifying original data can optimize data ingestion in various use-cases, such as combining multiple data sets, consolidating multiple transaction summaries, or manipulating non-standard data to conform to international standards. This module will examine the choices for data preparation, how visualization can be used to facilitate the human understanding of the data and what needs to be changed, and the various options for single use, optimization of data workflows and ensuring the regular production of transformations for operational use. Furthermore, this module will show you how to plan and implement the data movement and integration tasks that are required to support a business use case. The module is based on a real-world data movement and integration project required to support implementation of an AI-based SaaS analytical system for supply chain management running in the Google cloud. The module will cover the major topics that need to be addressed to complete a data movement and integration project successfully:

Improve DataOps

In this lesson you will learn about evaluating the last data sprint, observe what worked and what did not, and make recommendations on how the next iteration could be improved.

Summary & Final Exam