As data analytics have become increasingly critical to an organization’s operations, more data than ever is being captured and fed into analytics data stores, with the hope that it will help enterprises make decisions with greater accuracy. Data reliability is therefore essential to enable enterprises to make the right decisions based on the right information.
This data comes from a variety of sources. Internally, they come from applications and repositories, while external sources include service providers and independent data producers. For companies that produce data products, it’s typical that they get a significant percentage of their data from external sources. And since the end product is the data itself, reliably bringing together the data with high degrees of quality is critical.
The starting point for doing that is to shift left the entire approach to data reliability to ensure that data entering your environment is of the highest quality and can be trusted. Shifting left is essential, but it’s not something that can simply be turned on. Data Observability plays a key role in shaping data reliability, and only with the right platform can you ensure you’re getting only good, healthy data into your system.
High-quality data can help an organization achieve competitive advantages and continuously deliver innovative, market-leading products. Poor quality data will deliver bad outcomes and create bad products, and that can break the business.
The data pipelines that feed and transform data for consumption are increasingly complex. The pipelines can break at any point due to data errors, poor logic, or the necessary resources not being available to process the data. The challenge for every data team is to get their data reliability established as early in the data journey as possible and thus, create data pipelines that are optimized to perform and scale to meet an enterprise's business and technical needs.
In the context of data observability, "shift left" refers to a proactive approach of incorporating observability practices early in the data lifecycle. It is a concept borrowed from software development methodologies, where it emphasizes addressing potential issues and ensuring quality at the earliest stages of development.
When applied to data observability, shifting left means integrating observability practices and tools into the data pipeline and data infrastructure from the beginning, rather than treating it as an afterthought or applying it only in the later stages. The goal is to catch and address data quality, integrity, and performance issues as early as possible, reducing the chances of problems propagating downstream.
The data within the data pipelines that manage data supply chains typically operate in one of three sections:
In the past, most organizations would only apply data quality tests in the final consumption zone due to resource and testing limitations. The role of modern data reliability is to check data in any of these three zones as well as to monitor the data pipelines that are moving and transforming the data.
In software development, as well as other processes, there is the “1 x 10 x 100 Rule” which applies to the cost of fixing problems at different stages of the process. It says that for every $1 it costs to detect and fix a problem in development, it costs $10 to fix the problem when that problem is detected in the QA/staging phase, and $100 to detect and fix it once the software is in production. In essence, it’s far more cost-effective to fix it as early as possible.
The same rule can be applied to data pipelines and supply chains. For every $1 it costs to detect and fix a problem in the landing zone, it costs $10 to detect and fix a problem in the transformation zone, and $100 to detect and fix it in the consumption zone.
To effectively manage data and data pipelines, data incidents need to be detected as early as possible in the supply chain. This helps data teams optimize resources, control costs, and produce the best possible data product.
We mentioned earlier how data supply chains have gotten increasingly complex. This complexity is manifested through things like:
Consider the diagram below where data pipelines flow data from left to right from sources into the data landing zone, transformation zone, and consumption zone. Where data was once only checked in the consumption zone, today’s best practices call for data teams to shift left their data reliability checks into the data landing zone.
The result of shift-left data reliability is earlier detection and fast correction of data incidents. It also keeps bad data from spreading further downstream where it might be consumed by users and could result in poor and misinformed decision-making.
The 1 x 10 x 100 rule applies here. Earlier detection means data incidents are corrected quickly and efficiently at the lowest possible cost (the $1). If data issues were to spread downstream they would impact more data assets and become far more costly to correct (the $10 or $100).
The ability for your data reliability solution to shift left requires a unique set of capabilities to be effective. This includes the ability to:
There needs to be continuous monitoring of data pipelines to detect issues early and keep the data healthy and flowing properly. A consolidated incident management and troubleshooting operation control center allows data teams to get continuous visibility into data health and enables them to respond rapidly to incidents.
To support continuous monitoring, data reliability dashboards and control centers should be able to:
To quickly identify the root cause of data incidents and remedy them, data teams need as much information as possible about the incident and what was happening at the time it occurred. Acceldata provides correlated, multi-layer data on data assets, data pipelines, data infrastructure, and the incidents at the time they happened. Armed with this information, data teams can:
Shifting left your data reliability allows your data teams to detect and resolve issues earlier in a data pipeline and prevents poor-quality data from flowing further downstream. Shifting-left helps:
Get a demo of the Acceldata Data Observability platform and learn how you can shift left data reliability for smooth and effective data reliability processes.