Discovering the Perfect Data Observability Tool for Your Ecosystem!
We live in a thrilling time where the incredible potential of Gen AI is being explored in diverse industries. The question on everyone’s mind is: what is the pivotal factor that will steer companies toward AI success?
Join us as we unravel this exciting mystery!
Below is a snippet from one of the letters from one of Open AI employees, which states that:
“It’s not the model weights; the quality datasets that will drive your organization’s success with decision-making (AI/BI/Analytics).”
Quality datasets are essential because they directly impact the accuracy and reliability of information used for decision-making. Quality data is key to making accurate, informed decisions. While all data has some level of “quality,” various characteristics and factors determine the degree of data quality (high-quality versus low-quality).
Have you ever wondered how to guarantee that the datasets you use for your organization’s decision-making processes are high-quality?
“Delivering quality datasets is a journey, not a destination.”
We need to keep marching towards that goal perpetually. The term for this journey is coined as Data Observability which is the ability to understand, diagnose, and manage data health across your ecosystem throughout its lifecycle.
We will not delve into the details of “What is Data Observability” as numerous articles are available online (References to some of these articles can be found at the bottom of the page). This article aims to aid you in choosing a suitable data observability tool amidst the many choices available in the market.
In large enterprises where we deal with thousands of datasets, the scale from the data reliability aspect becomes extremely difficult to handle, and that's where Data Observability comes to the rescue. By implementing a Data Observability tool in your ecosystem, you can ensure that your data is reliable and accurate, no matter the scale. But how do you choose the right tool for your needs? It all comes down to the essential elements you must consider during the evaluation process.
Deployment, Performance & Security
Each enterprise has a specific infrastructure and security standards/guidelines they strictly follow when onboarding new software/solutions in their ecosystem. When selecting a data observability tool, evaluating it based on these standards and procedures is crucial.
What deployment options are available per your organization’s infra requirements, like SaaS (VPC or VPC Peering), or can you deploy it in your cloud tenant or as containerized workloads? Can it scale seamlessly as the organizational data ecosystem grows? It is also recommended that an infrastructure-as-code approach be used to ensure consistent deployment.
What are your enterprise security standards, and how does the solution fit within them? What are the various security compliance certifications for the tool? How is the data security handled in the tool?
Integration with Data Systems
It is imperative to identify your ecosystem's different data tech stacks that need to be monitored with the data observability tool, such as operational(OLTP, MDM, ERP, HRMS, etc.) and analytical systems (OLAP, BI Tools, Feature Stores, Vector Databases, etc.). Ensuring that the data observability tool supports those tech stacks is crucial. Integration with enterprise scheduling tools is mandatory for a seamless experience.
“Data Quality is everyone's responsibility.”
Therefore, ensuring data quality checks are closer to that of the data producers is always advantageous. Consequently, it is necessary to consider the critical data producers' (systems of record) connectivity options in the data observability tool candidates.
Monitoring Quality Rules & Anomaly Detection
Monitoring and anomaly detection are critical aspects of data observability. These practices allow organizations to identify when data pillars such as volume, freshness, schema, quality, and distribution do not meet data product expectations. What we should be looking for in the tool is its efficiency in monitoring aspects like dataset/table level monitoring, field level monitoring, custom monitoring, ready-to-monitor metrics, monitor frequency, and control for validation coverage.
Anomaly detection is worth expecting from the tool in terms of proactive detection for schema changes, historical load patterns, missing rows, distribution changes, volume changes, etc.
Custom Quality Checks and Configuration Options
Every organization is unique regarding its needs for quality definitions and expectations, so the questions are as follows. Does the tool offer enough flexibility to tailor it to the needs of those custom quality rules and checks? Is there a programmatic way to define and execute rules/expectations? A plus would be that the rules can be managed with source code management tools. Do configuration-driven options exist to dynamically modify quality checks and validation rules that can quickly be updated with changing business processes?
Alerting & Ticketing
Data Quality incidents/issues are unavoidable. The faster you detect problems, the sooner you can bounce back and exceed expectations. Imagine the thrill of delivering a fantastic experience to your stakeholders with 99.999% data uptime!
“Resilience is the key in Data Space, as incidents/issues are inevitable.”
What integration options for alerting are available in the data observability tool compared to your organization’s alerting tech stack (Teams, Slack, Pagerduty, Webhooks, etc.)?Does it have integration options with Ticketing(Jira) and Incident Management Tools(ServiceNow)? Can a priority be assigned to the alerts based on the type of table/issue in question?
Resolution
It would be an excellent feature for all users in your data ecosystem if we have a representation that covers all the impact radius of a data issue regarding lineage, root cause, impact analysis and issue management. To achieve this, we need to ask some questions.
Does the product document detect issues, provide feedback alerts, list users involved, and include resolution notes for future reference? Does the product use lineage to help users navigate to the root cause of issues in tables or data pipelines that have a cascading effect? Does the lineage extend beyond the data lake/warehouse to analytics tools such as Tableau?
Reporting the Value Delivered
The most crucial challenge for any data team is demonstrating the value delivered by their platform/application. Therefore, the data observability tool should be capable of maintaining history and bucketing all data quality issues without fail. Does it possess the ability to generate comprehensive reports that accurately reflect the total number of problems detected, acknowledged, and resolved while ensuring that the complete lifecycle of each issue is preserved?
Additionally, they should translate the specific details of each issue into a meaningful metric that can represent the overall data availability for the leadership of an organization. Reporting and the ability to build dashboards that provide a bird's eye view of the quality aspects of the organization are non-negotiable requirements.
Governance
The data observability tool can be leveraged for monitoring by different teams in your ecosystem, and it becomes vital that different teams work within their respective boundaries and do not overstep into each other’s domains. It is imperative to be able to define and group assets by domains and implement role-based access control to restrict visibility and permissions. What level of governance flexibility does the tool offer for day-to-day operations?
User Experience
Who are the various personas that may leverage the data observability tool in your ecosystem? These personas may include Data Engineers, Data Scientists, Operations teams, Data Producers, Product Owners, Data Analysts, Business etc. Identifying your organization’s user base and POCing the tools with them will drive adoption.
“Ease of use is the most underrated feature in data space. Rarely are products marketed and demoed with this feature as a selling point.”
How user-friendly is the tool in terms of leveraging and navigating across its various features, and how quick and easy is it to set up a quality check for a new source?
Pricing Model
As organizations collect more and more data, selecting a suitable pricing model can be a daunting task. Take a closer look at the various pricing models for the tools, and which one suits your organization's needs the best? Some vendors have a pricing model by the number of schema objects (Tables/datasets, etc.) being monitored, some go by the amount of data processed, and some have flat-tier pricing.
Support Model
When you onboard any tool in your ecosystem, understanding the support model provided by the data observability partner is crucial. What are the different support models for the tool? How quickly can we expect support tickets to be resolved? Can we expect 24/7 coverage, or is support only available on certain days/hours? How are SLA misses handled with the availability of the tool?
Maturity & Adoption of the tool in various industries
It's crucial to determine the maturity level of the tool and its adoption rate in the industry before making a decision. It's also essential to assess the tool's product roadmap and vision by consulting with leaders of Data Observability companies(candidates). With new players emerging daily, selecting the right strategic partner is paramount.
Before embarking on any POC evaluation, conducting a market analysis and evaluating the available tools in the data observability space is crucial. You can carefully select the most appropriate candidates by creating a matrix of integration and other capabilities required for your specific needs. This approach saves time and resources and increases the chances of success in your data observability initiatives. E.g.
Act quickly and efficiently with data observability architecture in the fast-moving data space. Focus on the most valuable data product and build operational muscle around it. Set metrics for success, ensure data is on time, complete, and correct, and proactively communicate the steps to achieve these goals.
References for “ What is Data Observability” :
https://www.montecarlodata.com/blog-what-is-data-observability/ (Credit: Monte Carlo)
https://www.telm.ai/blog/data-observability-the-reality/ (Credit: Telmai)
https://www.dqlabs.ai/blog/what-is-data-observability/ (Credit: DQ Labs)
https://www.bigeye.com/blog/data-observability (Credit: Bigeye)
I hope you found it helpful! Thanks for reading!
For other Data Space topics, please click on the below link.
https://medium.com/@DataEnthusiast
Let’s connect on Linkedin!
Authors
Himanshu Gaurav — www.linkedin.com/in/himanshugaurav21
Bala Vignesh S — www.linkedin.com/in/bala-vignesh-s-31101b29
