In today’s fast-paced digital landscape, data observability plays a critical role in helping organisations unlock the true potential of their data. In this blog post, we’ll explore the key benefits of data observability, how it differs from traditional data monitoring, and why it’s essential for optimising performance, reducing costs, and creating better customer experiences. Join us as we delve into the world of data observability and uncover the secrets to harnessing its power for your organisation’s success.
Data is, quite simply, one of the most valuable assets in modern times, and proactive and strategic organisations and companies have long since realised that results and insights derived from data has to be regular, accurate, reliable, and top-tier when it comes to quality. Only then can critical decisions and plans be formulated. Quality data ensures that outstanding decisions can be made, and one of the key strengths of data observability is that it provides full visibility into an organisation’s data pipelines.
Data observation allows an organisation to identify, troubleshoot, and rectify data issues quickly, and the 2021 Observability Forecast ascertained that 90% of respondents believed that is both important and strategic to their business, but only 26% stated that their observability was mature.
In very simple terms, Data Observability refers to an organisation’s ability to fully understand its data and data systems. Sounds simple, doesn’t it? But as we know, this significantly simplifies and underestimates the relevance, usage, and importance of data in modern times.
Many ask what the main difference is between data observability and data monitoring. To do this, we need to examine what has happened and why it has happened.
Whilst monitoring may inform you that your data pipeline has failed, observability tells you why it has failed and gives you the information to make informed decisions. Two key differences are that data observability is proactive, unlike its reactive data monitoring counterpart. And secondly, data monitoring tells you when something goes wrong, whereas data observability informs why it’s gone wrong. Data (pipeline) observability is not the ability to know simply that your pipeline failed, as monitoring should tell you this. A data observability tool does the detective work to point you to the proximal cause – for example, failure of a Spark job —as well as the root cause, such as the data contained an invalid row. So, do organisations know about the importance of data observability? If not, here’s our quick and easy guide to some of its key benefits…
1. Improves reliability, service, and experience
In a world that has changed hugely because of the Covid-19 pandemic, organisations increasingly rely more and more on digital service, and from that data we can receive greater detail into performance. So, for example, whilst data observability gives a greater purpose beyond just showing how well our app components are performing over time – data can and should be used to show where business results are affected and improve the ability to handle risks.Observability tools empower engineers and developers to create better customer experiences despite the increasing complexity of the digital enterprise. With observability, you can collect, explore, alert, and correlate all telemetry data types.
2. Cost effective
As well as benefiting compliance and security, another of the great benefits of data observability is that it really doesn’t need to break the bank. Data shouldn’t need to be taken from its current location which means that any solutions found should be fast and scalable. Data observability should efficiently connect to your existing stack without requiring any changes to your codebase, pipelines, or programming language, too. And when it is said that it costs ten times as much to complete a unit of work when data is flawed than when data is perfect, then it could also be said that prevention, in other words data observability, is better than cure.
3. Control
Organisations can gain a far firmer grip on active data and resting data when their teams monitor and observe data pipelines. Analytical teams are empowered to develop systems, processes, and tools to quickly identify data problems, bottlenecks, inconsistencies, and that have the potential to prevent any downtimes in data.
4. Simplifies complex systems
Because simple systems have fewer moving parts, they are easier to manage, but distributed systems are constantly updated and have more interconnected parts, which means that the types and numbers of failure that can occur is higher too, creating more unknowns.Data observability is a huge help when data pipelines leak, as it answers many of the key questions as to what has happened and why, which can result in improved efficiency and reduce costs. Observability is better suited for the unpredictability of distributed systems, mainly because it allows organisations to ask serious questions about its systems as issues arise and allows organisations to gain control over data in motion and at rest.
5. Understanding
As organisations and companies’ data usage and systems increases and becomes more complex, these systems and pipelines are more likely to malfunction or break. Data observability gives clearer visibility into data pipelines and infrastructures, detects hard-to-spot problems, and hence gives organisations a greater degree of not just what is happening, but why. It allows you to measure and then improve what you are monitoring.
6. Create better customer experiences and service
It’s an age-old business value – and not simply because of data – if you can measure, you can improve it. Where does data observability come into this? It’s pretty simple: data observability offers better quality data insights to assist your organisation in its planning, decision making, and controlling budgets. The more you know, the better you go.
7. Reduces noise
An observability tool can really help accelerate current processes and really reduce noise. As you know, one of the challenges with data governance is that it just creates a lot of noise. With a proactive mindset, you can actually layer-in an observability tool to reduce the amount of noise and increase the amount of coverage and gradually adopt it as you’re taking advantage of the capabilities of a modern tool.
