Check Point® Software Technologies Ltd.(link is external) announced that its Check Point CloudGuard solution has been recognized as a Leader across three key GigaOm Radar reports: Application & API Security, Cloud Network Security, and Cloud Workload Security.
By now, the concept of experimentation in software development is fairly well known. Most development teams understand at a high level the benefits that can be achieved through experimentation. Perhaps the most important of those is the ability to identify positive or negative impacts of a feature — in terms of both app performance and customer experience — earlier in the development process.
But many companies simply are not getting the most out of their efforts around experimentation or are reluctant to fully embrace it. Why? For starters, there's risk. And the degree of risk a company is willing to tolerate varies depending on their business.
Earlier this year, for example, Instagram decided to make a widespread change to the swipe button feature in its interface. The company was immediately flooded with negative feedback from many of its app users. In truth, the risk was relatively inconsequential to the ubiquitous social app because, despite user backlash, the change was rolled back and barely registered a blip on the company's radar. In other words, it is unlikely that Instagram lost money over the hiccup and in media interviews spokespeople were able to simply brush it off as a "bug."
For another, less innovative company however, taking that same risk with a major feature of a product or app could prove far costlier and more detrimental. Nearly every software feature release is designed to make improvements to the software itself. The reality is that not every feature release will result in positive improvement and the consequences for this could be severe: loss of users, revenue, or worse — both.
The goal is being able to run worthwhile experiments without disrupting the things you need to do to run your business. And that's where many companies attempting experimentation run into problems.
Experimenting the Hard Way
Companies understand that protecting their user experience is important and that releasing faster is important. That's the reason they have invested in monitoring tools, alerting systems and continuous delivery pipelines. When it comes to experimentation though, they often find that it is hard to do at scale in a repeatable fashion. The lack of tooling designed specifically for the experimentation problem set means someone has to step up and do a lot of ad-hoc data science work to make sense of results. The need and interest are there, as most teams know intuitively that better data will help them learn what works and does not work, earlier in the development process.
Your business may be doing many of the things one would associate with experimentation, and even reaping some benefits. Maybe you are performing canary rollouts or a/b tests, which have allowed you to accelerate feature releases or measure the impact of features. The problem is, the operational cost of doing those things can be high. You may be able to run a few experiments, but you will not be able to run very many because it's simply too difficult a process to do ad-hoc. As a result, features are actually being released more slowly because of the operational cost or dependence on scarce data science resources for one-off study of the results. The rate of innovation has slowed, reducing the value of experimentation.
Data is the Key
If instead businesses approach experimentation in a way that controls risk and streamlines ingestion and analysis of results data, they can do it in a much more effective way. And the key to doing that is access to data: how easily can you observe changes to key metrics when you conduct experiments? When data is siloed or must be manually curated during every experiment, it is less valuable and actionable. You also run greater risk of different teams drawing entirely different conclusions from the same data, because there's no common point of reference from which to make decisions. Lots of companies collect data today, but the data relevance — its breadth and scope — is not what it needs to be in order to be able to make actionable decisions.
Companies must remove the roadblocks separating them from their data. After all, if you are going to make decisions about anything, you want to be able to do it with the strength of relevant information. By making data ubiquitous, rather than scarce, you can establish a common language for measurement, which is the first step to being able to do meaningful experiments that positively impact both the user and your business.
Purpose-built experimentation platforms marry actionable data to changes that multiple teams make, eliminating the overhead and inconsistency of ad-hoc data analysis. With reliable tooling in hand and a repeatable process for making contextual decisions, companies can more easily embrace experimentation at scale. As the cost of "turning the crank" and making sense of the data for each experiment goes down and the number of experiments goes up, these companies give themselves more opportunities to unlock innovation and course-correct quickly in the face of failing ideas.
Industry News
LaunchDarkly announced the private preview of Warehouse Native Experimentation, its Snowflake Native App, to offer Data Warehouse Native Experimentation.
SingleStore announced the launch of SingleStore Flow, a no-code solution designed to greatly simplify data migration and Change Data Capture (CDC).
ActiveState launched its Vulnerability Management as a Service (VMaas) offering to help organizations manage open source and accelerate secure software delivery.
Genkit for Node.js is now at version 1.0 and ready for production use.
JFrog signed a strategic collaboration agreement (SCA) with Amazon Web Services (AWS).
mabl launched of two new innovations, mabl Tools for Playwright and mabl GenAI Test Creation, expanding testing capabilities beyond the bounds of traditional QA teams.
Check Point® Software Technologies Ltd.(link is external) announced a strategic partnership with leading cloud security provider Wiz to address the growing challenges enterprises face securing hybrid cloud environments.
Jitterbit announced its latest AI-infused capabilities within the Harmony platform, advancing AI from low-code development to natural language processing (NLP).
Rancher Government Solutions (RGS) and Sequoia Holdings announced a strategic partnership to enhance software supply chain security, classified workload deployments, and Kubernetes management for the Department of Defense (DOD), Intelligence Community (IC), and federal civilian agencies.
Harness and Traceable have entered into a definitive merger agreement, creating an advanced AI-native DevSecOps platform.
Endor Labs announced a partnership with GitHub that makes it easier than ever for application security teams and developers to accurately identify and remediate the most serious security vulnerabilities—all without leaving GitHub.
GitHub announced a wave of new features and enhancements to GitHub Copilot to streamline coding tasks based on an organization’s specific ways of working.
Mirantis launched k0rdent, an open-source Distributed Container Management Environment (DCME) that provides a single control point for cloud native applications – on-premises, on public clouds, at the edge – on any infrastructure, anywhere.
Hitachi Vantara announced a new co-engineered solution with Cisco designed for Red Hat OpenShift, a hybrid cloud application platform powered by Kubernetes.