As data becomes more and more abundant and complex, the observability of data also becomes more challenging. Data observability refers to an organization’s ability to ensure that data quality and accessibility are maintained throughout the data lifecycle.
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Data observation tools can be used by an organization to create composite data sets and make it easier and more efficient to extract value from data. An organization using data observation tools can gain actionable business insights and understand system behavior so that they can predict and prevent problems in their systems, such as data outages. Read on to learn more about how data sensing works and some of the best data sensing tools on the market.
Top Observation Tools in 2022
As one of the most popular data observation tools, Dynatrace provides an integrated platform for monitoring data across networks, applications, servers and infrastructure. It supports over 600 third-party technologies and has AI features that can be used for root cause analysis and UX A/B testing.
The platform offered by Dynatrace is delivered via a SaaS or an on-premise implementation. Six plans are available, including application security, full-stack monitoring, open recording, infrastructure monitoring, digital experience monitoring, and cloud automation.
While Dynatrace is easy to integrate, users should be aware that it does not have open source components that allow for faster adoption. Users can try Dynatrace for free for 15 days with the paid version starting at $8 per user. The free version includes several features but has limited integration options and dashboard designs.
new relic is a SaaS-based data observation tool with full-stack monitoring across the network, mobile and browser infrastructure. It integrates with CodeStream, a popular developer collaboration platform, and also provides native support for: OpenTelemetry. Common use cases for the New Relic platform include log management and tracking and integrations with over 470 third-party applications and plugins.
New Relic offers good value for money, excellent application performance monitoring and slack trace features for debugging. However, it lacks its UX. Some pages take a long time to load or are difficult for users to view. Another disadvantage of New Relic is that it is only available on the SaaS platform, with no on-premises option.
A free New Relic plan is available for users to determine if it is the right data observation tool for their requirements. The paid version comes with 100GB of free data per month, with $0.30/GB after that limit is reached.
data dog is a data observation tool that provides excellent versatility for IT operations, developers, business users, security engineering, and other roles and functionalities. It provides a SaaS-based platform with a variety of features, including log management, infrastructure monitoring, and performance monitoring. The dashboard views can be customized through the Playbooks feature.
Datadog is trusted by several reputable organizations around the world, including Samsung and Shell. One of the challenges with Datadog is that it has a steep learning curve for users with some difficult navigational features. A free version is available with two paid models: $15 per host or $1.27 per million log events.
auvik provides a cloud-based data observation platform with real-time mapping, automated configuration backup, and deep insights into network traffic. It supports more than 700 technologies. Auvik’s web interface is easy to navigate and initial setup is simpler than most comparable data observation tools on the market.
One drawback of Auvik is that it focuses on network device monitoring rather than data-level monitoring, so it may not be suitable for every organization. Prices for Auvik start at $150 per month, making it more expensive than the industry standard. Auvik offers tailor-made quotes for large companies.
Grafana is an open-source analytics and visualization tool that provides deep insights into logs, metrics, and traces. One of Grafana’s most notable features is its dashboard, which consists of a group of panels that display telemetry data. Grafana applications can be deployed on-premises or in the cloud. Some of the most famous users of Grafana are eBay, Siemens and Paypal.
The downside to using Grafana is that it can be time consuming to configure, especially with the customizable features it offers. However, this shouldn’t be a problem once it’s set up. Another potential drawback is that it does not provide native data storage, so a third-party storage solution is required.
There are three plans available for Grafana: Free, Pro and Advanced. Pricing starts at $8 per month for one active user. The free version provides access for three team members and stores logs, metrics and traces for 14 days. Grafana also offers custom plans with custom branding, audit tracking, and a customer success manager.
splunk provides full-stack data observation with the ability to ingest telemetry data from across the tech landscape. It supports more than 2,400 apps and add-ons and has built-in AI and automation capabilities. One of Splunk’s best features is its streaming analytics, which provide near real-time analytics for rapid incident response.
One downside to Spunk is that it doesn’t offer a high level of customization for visualization. It is also more expensive than most other data observation tools on the market. Spunk’s advanced features — including security and machine learning settings — can be a challenge to set up.
This Splunk solution is available through the Spunk Cloud Platform and the on-premises Splunk Enterprise platform. A 14-day free trial is available. Splunk’s price starts at $150 per GB ingested per month on annual billing. There are also customized plans based on customer requirements.
An Introduction to Observation Tools
Data observation tools provide a unified and centralized platform to view and analyze data for various applications and infrastructure components. This includes continuous data monitoring and data logging, but unlike individual monitoring tools and applications, data observation tools offer a wider scope and more functionality. These more advanced functionalities include real-time feedback from systems, proactive troubleshooting, incident triaging and machine learning.
Key features in an observation tool
Smooth integration with existing tech stack
A powerful data observation tool should connect and integrate seamlessly with an existing tech stack without the need for new code or modified pipelines. Seamless integration improves return on investment and minimizes business disruption.
Minimal configuration requirements, no prior mapping requirements, and no threshold settings are key features of a great data observation tool. For the enterprise tech stack to run smoothly, data observation tools must form a unified system rather than a collection of poorly integrated subsystems.
Proactive Notifications and Troubleshooting
One of the main purposes of data observation tools is to not only monitor data and identify any anomalies or threats, but also prevent problems from occurring. An observation tool can do this by observing all the data at rest without extracting it.
The tool can also prevent problems by providing valuable and actionable information about data assets so that any changes can be made proactively and not just in response to a problem. This proactive approach is one of the pillars of data observation.
Centralized and customizable dashboards
From a user experience perspective, a data observation tool should have a central dashboard that provides a clear view of the entire system. It should also be customizable so that it meets the needs of different types of users.
There should be alerts, event tracking, event logging, SLA tracking, and automated problem detection features in data observation tools. A quick and easy way to test a data observation application is to manually trigger errors to check how the tool interprets the errors and how it responds.
Benefits of working with observation tools
The basic purpose of data observability is to improve the performance of distributed IT systems. Conventional data monitoring techniques and tools may not be sufficient to handle the increasing complexity of data. That’s why data sensing tools are so often needed for growing businesses. Here are some of the benefits businesses realize quickly after implementing data observation solutions:
An organization that wants to have a holistic view and understanding of data across different IT systems while improving operational efficiency can benefit from using data observation tools. The deep visibility provided by data observation tools allows an organization to perform root cause analysis of most problems.
These tools are also designed to detect issues before they actually surface, helping organizations minimize data downtime or other types of data issues. The solutions provide other components that increase system visibility, including central dashboards, alerts and incident response.
Implementing technologies and processes that improve data team productivity has become a necessity in today’s business landscape. Data observation tools keep data pipelines running smoothly, resulting in more productive teams. Not only is it important to have high quality data, but the data also needs to be up to date and accessible with minimal downtime.
New Collaboration Opportunities
Another key benefit of data observation tools is that they provide enhanced collaboration between users. In fact, this latter benefit is a major reason why so many companies are investing in data observation tools and other solutions that integrate their tech stack and simplify business processes. Recent Research from TechRepublic Premium indicates that many digital transformation initiatives stem from a need to improve collaboration across departments and roles.
With the right data observation tools in your business, data becomes more transparent, secure, and most importantly accessible to the business users who need it to succeed.