Monday morning brings about a fresh start. It’s a time for realigning and starting the week on the right foot. Nothing can kill that productivity mindset faster than an app outage, unexpected website maintenance, or another tech snafu that leaves you without the tools you need to succeed. It’s also what data downtime can feel like to enterprises.
Data downtime refers to a period when your data quality is lagging, incorrect, or missing completely — and it can be a costly occurrence that requires intervention from engineering or development teams. According to research firm Gartner, organizations believe poor data quality is responsible for an average of $15 million per year in losses as well as significant time lost on productivity as data engineering and DevOps teams scramble to investigate the root cause of the issue.
Luckily, data downtime is something companies can plan for and, ultimately, avoid with the right strategy. Pulling information from our most recent ebook detailing the ins and outs of data observability, we’ll hone in on three actionable steps that can help your organization avoid data downtime and keep your teams moving forward.
Step 1: Opt for Data Observability Tools Over Monitoring Tools
Put simply, monitoring tools find problems, while observability tools not only identify errors but also provide context and illuminate the root cause — meaning you can quickly resolve issues.
An effective data observability solution provides a higher level of data monitoring sophistication, giving data teams additional tools to mitigate data downtime. Data observability tools, technologies, expertise, and processes are integrated with existing systems and act as an automated defense system that enables users to identify, troubleshoot, and resolve data errors more quickly than before. The result? Data downtime is minimized or prevented, and the integrity of the data is preserved. It’s a proactive solution that can minimize the number of resources allocated to correcting an issue and, instead, prevent it altogether.
Step 2: Protect Your Schema
As one of the five pillars of data observability, the schema is a key part of the data gathering process that defines how data is structured and organized among tables, columns, and views. Along with recency, distribution, volume, and lineage, the schema is a vital tenet of the data observability process that maintains the health and reliability of an organization’s data. When changes occur to the source data structure, the result is data downtime. As an example, a newly added field upstream can cause a pipeline to fail if not accounted for in the pipeline’s logic, which means maintaining and keeping your schema error-free is one of the major ways to dodge data downtime.
Due to the prevalent threat of system downtime, you should take extra care when updating your schema. To avoid data downtime, follow best practices, including:
- Locking data tables to take them offline while making updates to the schema.
- Performing updates using a replica model of your data so that data gathering can continue as you update the schema. Then you can migrate your application to the new schema when complete.
- Considering dependencies before touching a database. Most data doesn’t operate in a vacuum, so consider any applications or servers that may be affected and act accordingly.
- Restricting access to the data as the schema is updated to avoid “too many cooks in the kitchen.”
Updating the schema is a necessary part of data upkeep that ensures businesses can evolve their data collection over time. For those hoping to ease their schema woes altogether, there are “schema-less” solutions IT leaders can explore to see if they might be a fit. However, decision-makers should be aware that such solutions may result in decreased consistency and performance, and it might not be realistic to move away from structured data altogether depending on your organization’s needs.
Step 3: Keep Your Systems Optimized
You’ve likely heard that the best defense is a good offense, which is an apt way to think about avoiding data downtime. It’s a no-brainer that well-maintained systems — especially systems with built-in redundancy and resiliency — are the first place to begin optimizing your data observability process.
Furthermore, instead of thinking about if a downtime-causing event occurs, you should think about when. With the rise of cybersecurity threats, natural disasters, and unexpected outages, having a good data backup and recovery strategy is just as important as having a solid system in place from the start.
Think back to that Monday morning we talked about at the beginning of this blog, and imagine, on the following Friday, you learn a project you poured your heart and soul into is suddenly missing from your computer. Going through that experience is often all it takes to never forget to back up your information again.
With regular data backup or a well-orchestrated backup-as-a-service solution attached to the cloud, organizations can ensure business continuity and, ultimately, avoid significant downtime.
Not sure where to start with putting these steps into action? That’s where Maven Wave comes in. With a team of experienced data experts who can troubleshoot your data observability strategy, we can help you put a solution in place that affords you minimal downtime (as we did for this client).
To learn more about data observability — and see it in action via a recent enterprise POC (Proof of Concept) — download Maven Wave’s ebook here.
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