Effective observability of data pipelines ensures the processes providing valuable data are in good health and are running properly, thereby playing a role in business continuity and competitive advantage. For that reason, emphasis must be placed on whether the processes are producing quality data in a timely manner for the consumers relying on it for business decisions.

In today’s cloud-based world, businesses strive to extract maximum value from their data quickly and easily, therefore making observability critical to data-driven decision making.

Cloud changed the need for observability

Because cloud infrastructure scales based on need, robust observability is necessary. Resilient data processes must be built to track the data because this scalability also increases the likelihood that a process might fail along the way. Cloud-based businesses now need to think about building observability for failure.

Cloud computing has also introduced the ability to align data processes more closely with the actual data – a transformation largely due to the increased compute available in the cloud. As data processing within cloud data platforms increases, the need for effective observability solutions to monitor these processes becomes more pressing.

The cloud has also played a role in the evolving nature of data spaces, often leading to new challenges for data analysts and engineers. Sandbox environments, which were typically used for testing, have become commonplace. This has led to an explosion of new data processes that require additional monitoring and observability.

The creation of more production spaces has also heightened the need for strict process and access management protocols. The dynamic nature of the cloud requires robust, automated governance so that only authorized users have access to sensitive data. As data moves from pipeline to production, each step needs to be monitored and controlled to prevent errors and ensure the integrity of the data, emphasizing the need for effective observability solutions.

Observability is critical for organizations

Data observability ensures visibility into the performance and health of data processes. It’s the ability to track, monitor and understand the state of data processing workflows and pipelines in near real-time. Observability ensures that the processes running to serve business needs are operating optimally, generating the insights necessary for informed decisions. This benefits different stakeholders in an organization, from data analysts and engineers to the end consumers of data-driven insights.

• Analysts: Observability empowers data analysts by placing the power of data quality in their hands. It allows them to build their own data pipelines and machine learning models. By monitoring their creations, they can ensure these processes are working correctly and delivering valuable insights. In this environment, they are not merely passive consumers of data, but rather active participants in the data lifecycle, creating an ecosystem where data-driven decision-making thrives.
• Engineers: Data engineers benefit from data observability by monitoring infrastructure robustness and reliability. Observability tools provide them with real-time insights into the system, helping them quickly identify and address issues before they escalate. In the cloud, infrastructure scales based on needs, so observability is critical for engineers to build processes that are resilient.
• Consumers: Proper observability impacts the end consumers of the data—the business decision-makers. Reliable, accurate and timely data is critical for making informed business decisions. Data observability ensures that the insights generated by data processes are trustworthy and available when needed, fostering confidence in the data and the decisions made from it.

Building observability for the cloud

Capital One Software, an enterprise B2B software business of Capital One, designed and built its own monitoring and observability solution for Capital One Slingshot, a SaaS product that helps businesses maximize their Snowflake investment. Preserving the quality and performance of Snowflake data pipelines in Slingshot required a custom-built observability solution.

Using fundamental resources provided by Snowflake, the observability solution was built based on a three-step approach: detect, notify and act. Monitoring activities measure the overall performance of the data processes running in Snowflake and detect abnormalities. Once an error is detected, all impacted stakeholders are informed immediately. The “act” piece varies based on the situation, but timely notifications are critical to rectifying a situation quickly. This anticipatory approach to data monitoring allows Capital One Software to maintain smooth operation of its data processes running in Snowflake, thus minimizing potential interruptions that could hinder downstream processing.

Organizations that want to ensure observability is in place for their own cloud-based data pipelines should focus on:

• Define standards for monitoring and alerting: Develop baseline standards used across all teams and encourage further customization based on specific needs. Also, apply overarching monitors that apply to all teams (i.e., a common process failure or unauthorized access to the system).
• Anticipate and prepare for failure: Given the volatile nature of the cloud, businesses need to design robust and resilient processes. Ensure that efficient alert systems will notify relevant parties across preferred channels in the event of a failure. This needs to be done in a timely manner.
• Automate, but don’t forget the human element: Automate common remedial actions to aid with quick resolution. However, prepare for the potential situations where human intervention is still necessary. For example, depending on the process, it might be an analyst that resolves an issue as it arises or even a product support team if it serves a larger community of analysts.

As the data ecosystem evolves, the need for more robust, customizable monitoring and alerting solutions will only increase. Businesses should invest in solutions that meet their unique needs to ensure data processes are delivering reliable, timely and actionable insights. In the world of data-driven business, organizations cannot afford to ‘fly blind.’ It’s critical to detect, notify and act swiftly to ensure business continuity and decision-making efficacy in the face of ever-increasing data complexity.

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With the help of business intelligence dashboards and easy-to-understand data visualizations, businesses are more efficiently putting data to use. However, one obstacle continues to stand out in the quest to maximize data value: siloed access creating bottlenecks and slowing down the business’ ability to make the most of its data.

So, how can businesses truly maximize the value of their data? By empowering the team.

Putting power in the hands of those closest to the data

Democratizing data ensures that non-technical users outside of a centralized data team can easily find, access, trust, and use data at scale. This approach seeks to eliminate the barriers that restrict data availability to a select group of business analysts and scientists. By putting power in the hands of those closest to the data, data democratization empowers employees, irrespective of their technical capabilities, to get value from data.

To get to this state, organizations could implement a data mesh framework for a decentralized, domain-oriented data architecture. In data mesh, the data is owned by specific teams who are experts in their respective business areas. This is made possible by central governance policies and self-service tooling. Teams are able to trust the data quality while having control over their data and the ability to quickly glean relevant business insights. Domain-oriented ownership also fosters a sense of accountability that leads to better data stewardship across the organization.

To operationalize data mesh, organizations must:

Move to a federated model: Federation is a decentralized approach to data management. The federated model gives power to individual teams within an organization to manage their own data. This model can break down silos that may result in a centralized data architecture. Data is now handled by the domain teams that generate and use the data, empowering teams to take full ownership of their data and feel a sense of accountability.

Implement central policies: Taking a decentralized approach should not compromise on data governance. Organizations should enforce common standards across various teams and data stakeholders. Central policies ensure that while data is more easily accessible, it will remain aligned with organizational standards, internal policies, and ethical considerations. These central policies can be effectively managed when built into tooling, which can help each domain team feel confident that the required governance and controls have been accounted for through the self-service experience.

Use self-service tooling: Self-service tooling ensures that users with varying levels of technical expertise can find, access, trust, and use data. These tools empower users to derive insights from data without needing to rely on data engineers or experts, promoting a more data-driven culture within the organization.

Data mesh represents a paradigm shift in how we manage and value data. A departure from centralized data management, data mesh is designed for scale and brings data closer to the source, treating data as a product with clear ownership and accountability. With each domain handling its own data, there is no longer a single point of failure. Instead, the result is a resilient, distributed system that can scale with the business. This makes the data mesh an ideal framework for large enterprises that manage vast amounts of data in the cloud and want to empower users to get value from data.

Culture plays a pivotal role in increasing data access

Process and technology alone are not enough – organizations must also focus on people and culture in order to democratize data. This culture should emphasize shared power, open information, and an appetite for learning. It will foster collaboration, innovation, and continuous improvement by encouraging all associates to contribute with their own ideas and talents. To democratize data, an organizational culture must:

Encourage information sharing: Information should be seen as a shared resource rather than a closely guarded asset. This perspective aligns perfectly with the goal of data democratization. The idea is to eliminate data silos and create an environment where non-technical users can quickly find, access, and use data.

Promote learning and innovation: Users are encouraged to experiment and learn from failures, which is essential for utilizing and interpreting data effectively. Learning should not be limited to technical skills – it should also include data literacy skills that will empower individuals, like understanding data sources, their uses, and implications.

Empower all users: The culture seeks to break down traditional hierarchies and allow for decentralized decision-making, which can lead to more innovation and an engaged workforce. The idea behind data democratization is to make data accessible so that all users can make informed decisions, and this would not be possible without an organizational culture rooted in this value.

Focus on building trust: Organizations can foster trust amongst users by demonstrating the value of information sharing and respecting user autonomy. A culture built on trust is crucial to data democratization because users will need to trust that the data they use has the appropriate governance applied through self-service tooling, and the organization must trust that its users will be good data stewards.

Putting it into practice

Capital One modernized its data ecosystem on the cloud, adopting Snowflake as its data cloud. But to truly realize the benefits of managing data in the cloud, the company had to reimagine how it could empower both technical and non-technical users within the business to access and use data while remaining well-governed.

Capital One sought to build a scalable architecture that would empower a large number of users to find, access, and use data in the cloud. By operationalizing data mesh, it was able to further federate data management with central policies built into self-service tooling for users.

However, one of the first things Capital One learned was that with that great power comes great responsibility. Unexpected costs may rise when lines of business manage their own compute requests and configuration. Capital One built tools to solve their data management challenges at scale, including Capital One Slingshot, a data management solution that helps Snowflake users improve performance and empower teams with features that provide enhanced visibility and optimization.

Another best practice that is baked into the day-to-day at Capital One is the idea of continuous improvement. Organizations must acknowledge that workloads and user behavior is constantly changing, evaluate these changes, and provide recommendations for remediation. Self-service tooling can automate this, but it is still essential to maintain this focus.

Fundamental steps to maximizing data value

To fully leverage the value of data, organizations must shift how they view, manage, and use data. To recap, organizations should follow these fundamental steps to maximizing data value and empowering teams:

1. Foster an open organizational culture: Culture is the backbone of any transformative initiative, and data transformation is no exception. Encourage openness, collaboration, and experimentation. Empower teams to make data-driven decisions, and ensure they have the tools, skills, and autonomy to do so. This creates an environment where innovation thrives and accelerates the journey towards data democratization.
2. Consider the architectural framework: A centralized data approach may struggle to scale and adapt to evolving business needs in the cloud. Consider operationalizing data mesh, where data is treated as a product and ownership is federated to domain experts. This not only decentralizes data management, promoting agility and scalability, but also enhances data quality and relevance by placing ownership in the hands of those who know the data best.
3. Leverage powerful self-service tooling: Tools like Slingshot can manage and automate critical data management needs, providing a user-friendly experience that users can trust in. Leveraging self-service tooling can greatly enhance the efficiency and effectiveness of data operations, ensuring that data remains well-governed as it moves through the organization.
4. Focus on continuous improvement: Data is constantly changing, and an approach to managing it should be as well. Establishing a rhythm of continuous learning, feedback, and improvement helps organizations stay attuned to the evolving data landscape. This practice instills a culture of growth and adaptability within an organization.

The path to maximizing data value is a continuous journey. By nurturing organizational culture, distributing data ownership, and focusing on improvement, businesses can equip teams to make data-driven business decisions.

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