Before leveraging data and turning it into value, are you concerned with accessing the right data, transforming it, and setting up (ideally reliable, replicable, and secure) pipelines? At the same time, are you aiming to ensure data quality and smooth collaboration with other teams and stakeholders?

Some stakeholders want to leverage data for business or operational insights and dashboards. Others need it for their machine learning and AI initiatives. And then we hear about DataOps coming to the rescue for critical data operations. However, what does that mean? And how can you leverage best practices, such as Reliability Engineering and Observability, to advance your DataOps efforts and set up unified analytics platforms?

In this blog post, we will discuss both Data Reliability Engineering and Unified Analytics Platforms. In the Data Reliability Engineering part, we will examine in-depth how to collect, ingest, interpret, and transform your data by preserving high standards of data quality, flexibility, and agility. In the subsequent part, we will explain the key aspects of Unified Analytics, where users can perform various data processing tasks, from data cleaning and transformation to advanced analytics and ML/AI model training and deployment, all within a single architecture.

We will also provide insights into the Effective Management of the Data Lifecycle, which is essential to ensure that data remains accurate, secure, and accessible throughout its full lifespan while minimizing associated risks and costs.

Data Mishaps: Why Organizations Still Struggle with Critical Data Operations?

Many organizations struggle with critical data operations. Numerous primary challenges in the data lifecycle can be considered an inhibitor for organizations to bring business value to the market and to stay competitive.

In 2010, JPMorgan Chase & Co. was fined $153.6 million by the U.S. Securities and Exchange Commission (SEC) for improper handling of its data architecture. The bank had failed to maintain accurate and complete data about its mortgage-backed securities, which made it difficult for regulators to understand the true value and risk of these assets. Therefore, SEC accused the bank of misleading the investors in a complex mortgage securities transaction. The bank's data architecture was fragmented, with different systems and processes used to manage data across different departments and business units. This resulted in inconsistencies and errors in the data, which were not effectively identified or corrected. As a result, regulators could not effectively monitor and regulate the bank's activities, contributing to the subprime mortgage crisis and the 2008 financial crisis. The incident highlights the risks associated with incomplete data architecture and fragmented data systems, which can lead to errors, inconsistencies, and compliance failures.

We see many organizations, big and small ones, facing critical challenges that impede their ability to guarantee high data quality standards, security, scalability, and agility. Some of the primary challenges are listed below:

  • Lack of technical expertise in big data:
    One of the primary challenges is the sheer volume of data that organizations are now generating and processing. As not only data sources and systems become more complex but also the stakeholders and their needs to leverage data for different business objectives, it becomes increasingly difficult to manage and maintain them in an end-to-end manner. Organizations often lack the necessary expertise to manage their data operations effectively. It takes different skill sets to fulfill all expectations.
  • Fragmented data pipelines:
    Although various data tools and platforms are available in the market, they either specialize in specific capabilities or offer only some of the fundamental features, while other essential critical components of a holistic architecture view are absent. For example, Apache Hadoop is an open-source software framework that is widely used for distributed storage and processing of large datasets. It includes Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. However, Hadoop lacks some critical components that are essential for a complete data architecture, such as real-time stream processing, data ingestion, and data visualization. To address these limitations, other tools and technologies need to be integrated into the Hadoop ecosystem.
  • Deficient collaboration and communication:
    Another common challenge is the siloed nature of many organizations, where different teams and departments may use their own systems and processes for managing data. This can lead to inconsistencies in data quality and difficulties in establishing a comprehensive view of the organization's data. Moreover, in the process of developing projects, different teams within an organization often end up creating similar components or processes independently without realizing that others may have already created similar solutions. This leads to duplicated effort and wasted resources, creating multiple standards or frameworks that serve the same purpose.

By addressing and handling these challenges, organizations can lay the foundation for a reliable data engineering practice to support data-driven decision-making and drive business success. It is also important in any development project to have a clear understanding of what the desired outcome is and what the successful completion looks like. Having this understanding helps to guide the development process and ensure that the end result meets the intended goals and expectations. Without a clear idea of what success looks like, it can be difficult to measure progress and determine when the project is complete.

Streamlining Success with DataOps: The Power of Agile Operations

As defined by Gartner, “DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver (business) value faster by creating predictable delivery and change management of data, data models and related artifacts.” Like and inspired by DevOps, it aims to speed up the delivery of data applications while emphasizing data workflows.

DataOps involves using automation to manage infrastructure resources to accelerate the development and deployment process. By the utilization of monitoring and logging tools, we track the performance of the system, as well as we detect critical issues, and develop recovery plans to ensure operational continuity and continuous data quality assurance. That makes DataOps also a collaborative engineering approach, including various practices and tools to deal with the end-to-end approach to data management, emphasizing the automation of data ingestion, transformation, storage, and analysis.

Hence, the definition of DataOps comprises three key aspects:

  • Continuous Integration
    is a software development practice where code changes are frequently integrated into a shared repository and then automatically tested and verified. In DataOps, Continuous Data Integration is the process of continuously integrating and consolidating data from multiple sources into a single, unified view of the data. This concept is an important part of modern data architecture because it enables organizations to gain a complete and accurate view of their data, which can be used to drive business insights and improve operational efficiency.
  • Continuous Monitoring
    is a software process of observing and assessing an application in real-time to ensure that is performing as expected. Continuous Data Monitoring, in DataOps, is the process of continuously monitoring data in a modern data architecture to ensure that it is accurate, consistent, and meets the organization's data quality standards. This concept involves monitoring data as it is collected, stored, processed, and analyzed to ensure that it is accurate, up-to-date, and relevant. Therefore, continuous data monitoring is a critical component of modern data architectures, ensuring that organizations can trust their data and make informed decisions based on accurate, high-quality data.
  • Continuous Verification
    is another software development practice that ensures the data integration’s accuracy and high quality by detecting and eliminating errors. Various types of automated tests are performed at every stage of the development process. By implementing a continuous data verification strategy, organizations can ensure that their data is verified in real-time, reducing the risk of incorrect decisions and outcomes.
Figure 1: DataOps is a set of practices and principles that help organizations improve the speed, quality, and reliability of their data analytics initiatives. The DataOps main components involves the aforementioned elements of Continuous Integration, Continuous Monitoring, and Continuous Verification. [Image inspired by: DataTalks.Club]

Considering the three aspects of DataOps, we can ensure scalability, security, reliability, and agility of data architectures by leveraging the concepts of Site Reliability Engineering (SRE) and Observability.

Site Reliability Engineering (SRE) is a software engineering approach for managing and maintaining large-scale software systems. SRE teams focus on the reliability, scalability, and efficiency of systems and the speed of deployment and change management. For a more detailed description on this concept, you can check this blog post from our network partner team, the Digital Architects Zurich.

Observability is a related concept that refers to the ability to proactively monitor, measure, and analyze the performance and behavior of software systems in real time. Observability helps SRE teams identify and diagnose problems rapidly, which enables them to take the right actions before critical issues affect end-users. We wrote a more detailed blog post about this concept here.

To leverage SRE and Observability for DataOps, organizations can:

  • Apply Observability: Implement tools and processes for monitoring and analyzing the performance of data workflows, including data quality, latency, and processing time.
  • Use Automation: Streamline data workflows and reduce the possibilities of severe errors caused by manual processes.
  • Prioritize Reliability: Prioritize reliability in data workflows to ensure that data is available, accurate, and secure.

Following the best DataOps practices we can improve simple data engineering since data engineering is primarily concerned with the technical aspects of building and maintaining data infrastructure, while DataOps is focused on improving the efficiency and agility of the entire data pipeline, including processes, tools, and team collaboration.

A robust data management strategy is crucial to achieving operational efficiency and gaining actionable insights from data. An effective data management approach should consider the following key pillars as explained in Figure 2. These pillars provide a framework for managing data throughout its lifecycle and ensuring that it is reliable, replicable, and secure.

Figure 2: The key pillars of DataOps. These pillars establish a structure for managing data from beginning to end while also guaranteeing its dependability, reproducibility, and safety.

To effectively implement DataOps, a unified data architecture approach is essential. This involves integrating and standardizing data from various sources, while also ensuring that data is consistent, accessible, and secure across the organization. By adopting a unified data architecture approach, organizations can improve data quality, reduce data integration complexity, and enhance security. These benefits are critical to achieving a successful DataOps implementation and gaining valuable insights from data.

The Unified Data Architecture

A Unified Analytics Architecture involves several steps and layers following the principles of continuous data integration, monitoring, and verification, as described previously in this blog post. As illustrated below in Figure 3, the first step is to collect data from various sources and formats, which could include structured, unstructured, or semi-structured data. Once the data is collected, it needs to be ingested into a data lake or data warehouse. This involves validating the data, performing data profiling, and ensuring that the data conforms to specific standards. The data is then stored in a data lake or data warehouse, which involves selecting an appropriate data storage technology, configuring storage settings, and defining access controls.

The data is then transformed into a suitable format for analysis. This includes cleaning the data, normalizing it, and performing any necessary transformations to make it ready for analysis. Depending on various possible objectives, the transformed data can then be for different kinds of business, operational or scientific insights, or intelligence purposes. This could involve building dashboards, creating reports, or using advanced analytics techniques such as machine learning to generate insights. The data integration process is continuous, meaning that new data is continuously collected, ingested, stored, and transformed. This ensures that the data is always up-to-date and available for analysis. DataOps also involves monitoring and verifying the quality of data continuously. This includes checking for data quality issues, data breaches, and data anomalies.

Figure 3: The Unified Analytics Architecture is a comprehensive reference model that enables organizations to streamline and simplify their data analytics workflows by consolidating multiple technical capabilities, layers, and operational best practices into a single architecture view.

By following these steps and principles, teams and organizations can build a robust end-to-end data architecture that provides a solid foundation for project teams to build reliable data pipelines and thus enable them to continuously extract valuable insights from their data.

Machine Learning Architects Basel

Are you struggling to become data- and ML-driven with different data and software architectures, tools, and best practices that might be new to you? Managing data and machine learning end-to-end initiatives and operations can be challenging and time-consuming, including assessing and implementing required technologies and effective DataOps and MLOps workflows and practices. It might be worthwhile to consider collaborating with an experienced and independent partner.

Machine Learning Architects Basel (MLAB) is a member of the Swiss Digital Network (SDN). We have created our effective MLOps framework that combines our expertise in DataOps, Machine Learning, MLOps, and our extensive knowledge and experience in DevOps, SRE, and agile transformations.

If you want to learn more about how MLAB can aid your organization in creating long-lasting benefits by developing and maintaining reliable data and machine learning solutions, don't hesitate to contact us.

We hope you find this blog post informative and engaging. It is part of our Next Generation Data & AI Journey powered by MLOps.

References and Acknowledgements

  1. The Digital Highway for End-to-End Machine Learning & Effective MLOps
  2. Introduction to Reliability & Collaboration for Data & ML Lifecycles
  3. Observability for MLOps
  4. Effective MLOps: Maturity Model
  5. Designing Machine Learning Systems, by C. Huyen, O'Reilly Media, Inc.
  6. Reliable Machine Learning: Applying SRE Principles to ML in Production, by Cathy Chen,, O'Reilly Media, Inc.
  7. Practical MLOps, by Gift, Noah and Deza, Alfredo, O'Reilly Media, Inc.
  8. Folder icons created by lakonicon - Flaticon