Today’s businesses collect massive amounts of data to make informed decisions that improve outcomes. Popular types of data analytics include:
While many businesses currently know that they need to collect and analyze as much data as possible, plenty of organizations struggle to use information effectively. Applied observability helps organizations do more with data and avoid common barriers that prevent success.
When describing applied observability, Gartner emphasizes the importance of making observable data readily available across departments and making it an intrinsic part of the business applications, functions, and infrastructure.
By taking this approach, data becomes more valuable because more people can access, analyze, and draw conclusions from it. That doesn’t mean we recommend giving everyone access to all data. That could lead to enormous regulatory and security issues. Instead, we encourage businesses to take a strategic approach that emphasizes the value of applied observability while maintaining essential security measures.
Applied observability is much more than a useful strategy that can help your organisation meet goals. It’s quickly becoming an essential aspect of remaining competitive.
Gartner estimates that “70% of organizations that successfully applied observability will achieve shorter latency for decision making…” by 2026. Groups that don’t embrace the technology now risk getting left behind by competitors that understand the benefits of using available data to prepare for the future and pivot quickly to emerging trends.
We’ve already seen how other types of observability improve business operations. We already use observability tools to monitor IT performance. The ability to see, for example, how software performs makes it much easier to avoid problems and optimize processes.
Applied observability is a more recent extension of this strategy. Now, we more fully understand the advantages of making data easier to find, share, and analyze. It matters just as much — if not more — than monitoring your IT infrastructure's performance.
A lot of advice you can find about adding applied observability to your technology strategy focuses on specific environments. For example, Google has a comprehensive guide to building observability into applications and infrastructure. Unfortunately, it mostly speaks to those using the Google Cloud Architecture Framework.
What if you don’t rely on Google exclusively? We want to offer more generalised advice so everyone can benefit, regardless of the IT assets they already use.
Do you know all the types of data your business collects and generates? Probably not. How could you when you have so many teams working with discrete datasets instead of collaborating?
Mapping your data layers helps identify sources of information. You will likely find that data moves through a lot of concurrent layers. For example, your e-commerce platform might collect data about a customer’s purchase, submit that information to a CRM, put it in a database, and pass the information to business intelligence (BI) apps that can predict the customer’s future behaviors.
You might also find concurrent data layers that don’t interact with the rest of your organization’s data. For instance, your system admin could have information about network performance, user activity, and “shadow IT” assets that haven’t been authorized by management.
Create a detailed map of your data layers. Knowing more now will help you plan for any obstacles ahead.
The typical organization has more data silos than they know. If you haven’t made democratizing data a priority, your departments — and even small teams — probably have data silos that no one else can access. In other words, your business has data that it doesn’t know about. How can you use information when only a small number of people know it exists?
Democratizing data breaks down silos and puts information in a source every authorized user can access. Consider storing data in a data lake. We like that option because of its flexibility. You can put structured, semi-structured, and unstructured formats in a data lake, meaning it can hold everything from sales numbers to social media posts.
Democratized data can unlock enormous potential within your business. Still, you need to safeguard some data to prevent leaks and follow regulations.
Some important steps to embracing data democratisation without contributing to security issues include:
Your plan to embrace applied observability should include small steps that lead toward organization-wide adoption. We’ve seen companies try to adopt ambitious plans too quickly. It rarely goes well.
Applied observability is a complex strategy that you should take in incremental steps. For example, you might want to start by democratizing one department’s data. During the process, challenges will force your IT leaders to find solutions. Later, you can apply those solutions to a broader rollout of applied observability. For now, use a single department as a testing ground.
Similarly, you could group your data types into categories and move through each item on the list. This month, your team might work on making app data more observable. Next month, the team can concentrate on improving network observability.
Yes, you want a plan that leads to robust applied observability. Don’t feel that you need to follow the plan too strictly, though. You might discover that some steps take longer to complete than expected. Adjust your expectations and move forward.
Not sure how to make applied observability to your organisation’s data collection, sharing, and analytics? Companies have unique use cases that require personalized approaches.
Feel free to reach out to our team to talk about applied observability, your company’s needs, and related topics. We’re always happy to help groups embrace critical trends that will make them more successful.