Organizations that rely on data to drive their operations and decision-making are increasingly adopting Data Observability as an essential practice. This practice encompasses various aspects such as data validation, anomaly detection, data reliability, and data pipeline monitoring, which enable organizations to ensure the quality, accuracy, and reliability of their data infrastructure and pipelines. Additionally, Data Observability provides insights and tools to manage data costs and optimize data pipelines while minimizing errors and issues.
By proactively observing, analyzing, and troubleshooting data, organizations can make data-driven decisions and maximize its value for their business success. To gain a better understanding of the common use cases of Data Observability, we will explore them in this blog. We partnered with Eckerson Group to summarize these use cases and created an informative whitepaper about Data Observability, which you can find below. Let’s get started!
There are ten common use cases that are grouped into four broad categories. These categories are essential for data teams to understand, because it’s with these approaches that they are enabled to build data products and rely on data to drive their operations and decision-making. Understanding these categories and the use cases with them is crucial in implementing an effective Data Observability practice. They include:
- Data infrastructure design
- Capacity planning
- Pipeline design
- Data supply chain performance tuning
- Data quality
- Data drift
- Resource optimization
- Storage tiering
- Financial operations (FinOps)
- Cost optimization
The key use cases of this category include the design of data infrastructure, capacity planning for infrastructure resources, and the design of data pipelines for delivering data for consumption.
Data Infrastructure Design: In order to meet SLAs, data architects and engineers are required to create data architectures that are both high-performing, flexible, and resilient. To accomplish this, it is essential that they have a comprehensive understanding of their infrastructure’s performance and utilization trends.
For instance, slow network connections may cause delays in fraud prevention ML models, which in turn leads to increased wait times for customers and merchants during large transactions. Additionally, financial analysts may experience lengthy wait times when generating earnings reports due to business analysts overwhelming the CRM database with ad-hoc queries.
To facilitate the design of such data architectures, data observability tools can be employed to analyze performance and utilization trends.
Capacity Planning: After data architects and data engineers have selected the infrastructure components for their pipelines, it is the responsibility of platform engineers to collaborate with CloudOps engineers in determining the necessary capacity requirements. They must ensure that the correct amount of resources is provisioned, maintain optimal utilization levels, and request an appropriate budget.
The use of Data Observability tools can prove beneficial in several ways. For instance, engineers can simulate workloads to anticipate the memory, CPU, or bandwidth required to handle a workload within the designated SLA. This allows them to establish an optimal mix of resources, with ample buffer capacity for future expansion, and avoid unnecessary expenditure on additional resources. Data Observability tools can also assist in monitoring and predicting variances in critical performance indicators (KPIs), enabling engineers to identify points at which workloads may become unstable.
Pipeline Design: To ensure that data is extracted, transformed, and loaded from source to target with the appropriate latency, throughput, and reliability, data architects and data engineers must design pipelines that are flexible. This necessitates a detailed comprehension of how pipeline jobs will interact with various infrastructure elements, including data stores, containers, servers, clusters, and virtual private clouds.
Fortunately, Data Observability can be of assistance in this regard.
With the help of Data Observability, data engineers can gain a detailed understanding of how pipeline jobs will interact with infrastructure elements such as data stores, containers, and clusters. By profiling workloads, they can identify and eliminate unnecessary data, thereby configuring pipeline jobs to filter out such data before it is transferred to the target. Workload profiling also enables them to determine the optimal number of compute nodes required for parallel processing.
In addition to the previously mentioned benefits, Data Observability also aids data teams in managing their progressively more complex environments. Use cases for this category include analyzing and optimizing pipeline performance, detecting and addressing data quality concerns, and detecting data drift that may impact machine learning (ML) models.
Performance Tuning: The untimely failure of a production BI dashboard, data science application, or embedded ML model to receive data can have significant negative consequences for decision-making and operations. To avoid such issues and minimize their impact, DataOps and CloudOps engineers must adjust their pipelines based on indicators of system health, such as memory usage, latency, throughput, traffic patterns, and availability of compute clusters. This process is made easier with the help of Data Observability tools, which enhance the engineers’ ability to meet performance SLAs.
Data Quality: The quality of data is essential to the success of analytics, and Data Observability can aid in identifying and resolving data quality issues before they are discovered by business owners or customers. Without accurate information about the business, a sales dashboard, financial report, or ML model can cause more harm than good.
To mitigate this risk, data teams must locate, evaluate, and address quality issues that may arise with data in motion and data at rest. The ultimate goal is to resolve these issues before they are identified by business owners or customers.
Data Drift: Over time, the accuracy of ML models can diminish, resulting in less precise predictions, classifications, or recommendations. Data drift, which refers to changes in data patterns often caused by evolving business factors, is a primary contributor to this degradation. Such factors might include the state of the economy, customers’ price sensitivity, or actions taken by competitors.
When these factors change, the data feeding ML models changes as well, which lowers the models’ accuracy. Data scientists, ML engineers, and data engineers rely on Data Observability’s data drift policies and associated insights to identify instances of data drift and adjust the ML models accordingly.
Data Observability also assists data teams in adapting their data environment. This encompasses various use cases, such as optimizing resources, tiering storage, and performing migrations.
Resource Optimization: Analytics and data teams often make ad hoc changes that result in inefficient resource utilization. For instance, the data science team might quickly ingest massive external datasets to retrain their ML models for customer recommendations. Similarly, the BI team may start ingesting and transforming multi-structured data from external providers to build 360-degree customer views.
In supporting such new workloads, DataOps and CloudOps engineers tend to consume cloud compute on demand, leading to unexpected costs. Data Observability aids in optimizing resources to keep projects within budget.
Storage Tiering: Analytics projects and applications require large amounts of data, but often only a small fraction is utilized. For instance, an ML model for fraud detection may only require 10 features out of a total of 1,000 columns to evaluate the risk of a transaction. Similarly, a sales performance dashboard may only need a few data points each week to remain current, leaving the remaining data “cold” with limited queries. Data Observability assists data engineers in identifying cold data and migrating it to a lower-cost storage tier.
A Data Observability tool can identify and visualize “skew,” which refers to the distribution of input/output (I/O) across columns, tables, or other objects within a data store. For instance, a data engineer may discover that only 10% of columns or tables in a CRM database support 95% of queries. They may also find that most sales records aged over five quarters are rarely accessed again.
Migrations: Data Observability can provide valuable assistance for cloud migration by helping data teams answer some fundamental questions. These include:
- What is the structure and configuration of the target cloud environment?
- To what extent will the target cloud environment support the analytics tools, applications, and datasets of the data team?
- What will be the performance of their analytics workloads in the target cloud environment?
If data engineers don’t answer these questions before the migration, it can result in considerable problems and undermine the analytics results later on. With the help of Data Observability, these questions can be addressed, and risks can be minimized.
Lastly, Data Observability supports business and IT leaders in funding analytics projects and applications from a business perspective. This set of use cases is specifically centered around the FinOps use case.
Financial Operations (FinOps): Cloud platforms offer companies the flexibility to rent computing resources on demand, which is a cost-effective alternative to purchasing and maintaining servers and storage arrays in their own data centers. However, the use of elastic resources, particularly compute, can result in unexpected and high bills at the end of the month. To address this issue, FinOps has emerged as a discipline that brings together IT and data engineers, finance managers, data consumers, and business owners to collaborate on cost reduction and value optimization of cloud-related projects.
FinOps promotes best practices, automates processes, and holds stakeholders accountable for the costs of their activities. To make cloud-based analytics projects and applications profitable, data teams rely on FinOps and leverage the intelligence of data observability.
Get Data Observability For Your Enterprise
The right Data Observability use cases are essential in requirements engineering and system development. They enable data teams to gain insights into user needs, define requirements, collaborate, and develop solutions that are aligned with business outcomes.
Download the PDF version of the Eckerson Group white paper “Top Ten Use Cases for Data Observability” and get additional information.
No matter where you are on your data observability journey, the Acceldata platform can help you achieve your goals. Reach out to us for a demo of the Acceldata Data Observability Platform and get started.