New year, new features: Level up your Stack Overflow for Teams in 2025 - Related to teams, platform, new, options, buy:

New year, new features: Level up your Stack Overflow for Teams in 2025

The first release of the year is packed with aspects to make your knowledge-sharing community improved.

As we step into 2025, we’re kicking things off with a series of powerful updates designed to make your Stack Overflow for Teams experience even advanced. Whether you’re celebrating the milestones of the past year or gearing up to tackle new challenges, these enhancements are here to support your knowledge-sharing community in meaningful ways.

This release is packed with tools to help your community thrive in 2025 and beyond. Dive into the details below to explore everything we’ve rolled out!

Let’s take a moment to acknowledge the incredible contributions that kept your community thriving in 2024. Your 2024 Stacked, available for qualifying teams only, goes beyond the numbers, offering an interactive snapshot of engagement and impact. It’s more than a retrospective—it’s a chance to celebrate the collaboration, curiosity, and camaraderie that define your team.

Stay connected with improved weekly digests.

Our redesigned weekly digest emails bring actionable, personalized insights right to your inbox. These new, personalized digests keep you in the loop and empower individuals to contribute more effectively and include five key components:

Summary: Each user will see a persona-driven wrap-up of how they helped their community during the prior week. SME Progress: If SME auto-assign is enabled, customers will see the top two tags they are progressing on toward becoming a SME. Your Reminders: customers will see a list of service product nudges and reminders so they can follow up and take the primary actions to support a thriving community. Unanswered Questions: Leveraging the algorithm used on the homepage, the top unanswered questions will be surfaced to the user based on their activity and tag preferences. Account Configuration Nudges: customers will receive smart recommendations on account configurations—like setting up notifications for MS Teams/Slack or watching tags—based on where customers are in their journey with Teams.

Search smarter with OverflowAI enhancements.

We’ve fine-tuned OverflowAI to deliver precise, relevant summaries and to guide individuals toward the best possible answers—or help them craft enhanced questions when needed.

Prompting for OverflowAI Enhanced Search has been updated to ensure that results are both accurate and relevant. If relevant context is found, OverflowAI Enhanced Search will deliver a summary. However, if no relevant results are available, the system will prompt individuals to post their question.

This will create clarity in the search summary experience by indicating when OverflowAI is answering a question versus when it is summarizing and by encouraging them to post a new question if the summary doesn’t answer their question.

In addition, OverflowAI thread summarization in both Slack and Microsoft Teams integrations has been updated to be more personalized and focused, eliminating generic phrasing and unnecessary content. These updates will give people clearer, more concise outputs when asking questions and receiving summarized answers. Once summarization has been completed, an updated success message will unfurl the summary and encourage people to review, verify, and add tags to OverflowAI answers to ensure knowledge integrity.

Seamless integration with Microsoft 365 (public preview).

In this release, we’re bringing Stack Overflow for Teams to Microsoft 365. The Stack Overflow for Teams Microsoft Graph Connector allows organizations to bring trusted, team-validated knowledge from Stack Overflow for Teams directly into Microsoft 365, where it can be accessed seamlessly by development teams and other technical people.

With this connector, content such as questions, answers, and top answers from Stack Overflow for Teams is indexed and made searchable within Microsoft 365 Copilot. Developers can simply ask technical questions in natural language within Copilot and receive summarized responses sourced from their organization’s internal Stack Overflow knowledge base. Each answer includes links to the original Stack Overflow for Teams content, making it easy for clients to dive deeper into topics if needed. This setup excludes data from the public Stack Overflow platform, ensuring only internal, organization-approved knowledge is referenced.

For organizations using Microsoft 365, this integration improves the accuracy and accessibility of developer resources, enhancing the efficiency of internal technical support and knowledge sharing. Developers benefit from reduced context-switching, as they no longer need to jump between applications to find reliable, organization-specific insights. With Stack Overflow for Teams content readily available in the Microsoft 365 experience, teams can streamline workflows, access accurate knowledge, and boost productivity directly within the tools they use every day.

NOTE: The Stack Overflow for Teams Microsoft Graph Connector is currently in public preview. Please direct any questions or feedback to the Microsoft team.

For additional details on the improvements above and other updates with the latest release, view the [website] release notes.

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Build vs. Buy: Compare Your Kubernetes Platform Options

Kubernetes has emerged as the go-to orchestration tool for managing containerized applications. ’s 2024 Voice of Kubernetes Experts study, 58% of organizations are planning to move some of their virtual machine (VM) workloads to Kubernetes. Of this group, 65% of organizations are urgently moving their VM workloads to Kubernetes, and 80% plan to be completely using Kubernetes in the next five years.

However, when adopting Kubernetes, organizations face a critical decision: Should they build their own Kubernetes platform in house, or should they invest in a third-party Kubernetes native solution?

Let’s explore the pros and cons of both approaches to help you make an informed choice that best suits your organization’s needs.

Building a Kubernetes platform from scratch can be tempting, especially if your team has the necessary technical expertise and needs control over how the platform is configured. Let’s break down the pros and cons of building an in-house Kubernetes solution.

Here are some advantages of building a Kubernetes platform within your organization.

Building your Kubernetes platform allows you to tailor the environment to your specific use cases and business requirements. This means you can control every aspect — from custom integrations to fine-tuned configurations — allowing maximum flexibility to fit your development processes and workflows.

If your organization has specialized infrastructure needs, you may find that no off-the-shelf solution is an exact match. For example, if you need to deploy Hyperledger Fabric in Kubernetes, you might not find a lot of platforms catering to this use case. By building in house, you can align your Kubernetes platform with your unique workloads, data handling requirements and security policies.

Relying on third-party solutions can lead to vendor lock-in, especially if a platform becomes a key part of your CI/CD pipeline. By managing your own Kubernetes platform, you retain independence and avoid potential challenges when switching vendors down the road. Additionally, if your chosen platform vendor doesn’t have a lot of flexibility and support, it might be difficult to switch to a different one.

For organizations with dedicated DevOps and development teams, building a Kubernetes platform offers a great opportunity to cultivate deep expertise in Kubernetes, cloud native tooling and automation. This knowledge can benefit other areas of infrastructure and platform management, provided you can invest dedicated staff time to build the platform.

Here are a few disadvantages you might experience in building your own Kubernetes platform.

Investing Staff Time and Diverting Resources.

Building a Kubernetes platform is far from a plug-and-play solution. It requires time, effort and expertise across various domains, including security, monitoring, logging, scaling and networking. One of the biggest challenges of building an in-house platform is you must divert resources from projects designed to build a competitive advantage over competitors. The time your team spends building and maintaining the platform could be invested in other core business activities.

Managing a Kubernetes platform is not a one-time effort. The team must stay up to date with new releases, security patches and emerging best practices. Ongoing maintenance includes troubleshooting, scaling and ensuring the platform remains resilient and secure. Kubernetes is one of the most active CNCF projects, with three releases each year, and every release brings new enhancements, deprecations and attributes. Keeping your platform compatible with it is another big overhead.

While building a platform allows customization, it may delay its time to completion. Developing a robust platform that includes all necessary integrations and automation could take months or even years, which could affect how quickly your teams can deliver applications to production.

Kubernetes expertise is in high demand, so finding and retaining skilled professionals to manage your custom-built platform can be challenging. If key team members leave the organization, knowledge gaps may cause operational issues, especially during critical incidents.

Building a platform requires the right expertise: people who understand the Kubernetes infrastructure and can build on it. Initially, it can cost a lot of time and money to hire the right talent and dedicate them to work on the Kubernetes platform. There may also be high initial costs to maintain the platform, as well as to make sure it is compatible with the latest Kubernetes versions, aspects, and deprecations and caters to the organization’s requirements.

Purchasing a managed Kubernetes platform can provide a streamlined, out-of-the-box solution with many built-in elements for container orchestration, CI/CD, monitoring and more. Here’s a closer look at the pros and cons of buying a Kubernetes platform.

Here are some advantages of buying a Kubernetes platform.

Buying a platform enables a quick spin-up of the infrastructure and platform and helps ensure teams can get started as quickly as possible. This allows your team to focus on developing and deploying applications rather than spending time architecting and maintaining the underlying platform.

Managed platforms handle much of the heavy lifting associated with Kubernetes management, such as autoscaling; DecSecOps practices, policies and governance; setting up access management; and monitoring. Offloading these tasks to a vendor allows your team to focus on business-critical tasks rather than maintaining infrastructure.

Managed Kubernetes platform vendors have developed expertise in improving the developer experience and regularly integrate their learnings into their products. Buying a platform allows you to take advantage of the vendor’s DevEx knowledge to improve developer experience and collaboration among your teams.

Managed platforms incorporate industry best practices by default, which helps ensure that your Kubernetes environment is set up optimally for security, scalability and performance. For example, Devtron has CI/CD pipelines, GitOps workflows and multicluster management baked in.

Most managed platforms provide comprehensive support and documentation, which can be a significant advantage. They also conduct training sessions to make sure individuals benefit from support and robust documentation. This can make it easier to troubleshoot issues or onboard new team members.

Buying a Kubernetes platform can be more cost-effective than building one, particularly for smaller organizations. This can also be true for larger organizations that lack the staffing and expertise to manage a large-scale Kubernetes infrastructure.

Buying a Kubernetes platform also has some disadvantages. Here are some of them.

While third-party platforms offer a wide array of attributes, they may not allow the same level of customization as a homegrown solution. Some organizations may find that unique use cases are not fully addressed by a prebuilt solution. If the platform and the team are flexible enough to enable building custom options, it can be a huge advantage. But most platforms don’t offer this kind of flexibility.

Relying on a managed Kubernetes platform means you are dependent on the vendor’s roadmap, pricing structure and future availability. This might make things harder if you plan to switch things or re-engineer your architecture.

The cost of a subscription or license for a managed Kubernetes platform could increase over time as your infrastructure scales. For organizations with significant workloads, this could lead to higher operational expenses compared to an in-house solution, especially if you can manage scaling internally.

You can mitigate some of these disadvantages with careful research into potential platforms.

Choose a platform that is extendable, so as you grow and your requirements change over time, the platform can adapt to your requirements and you are not left fighting the platform.

No product is without vendor lock-in risk, and that includes home-grown platforms. But using a platform built on industry standards and an open source ethos gives you more flexibility and options to choose from compared to a DIY platform.

Organizations may benefit from building their own Kubernetes platform when they have:

Specialized industry requirements: Businesses that require highly specialized Kubernetes workflows that an off-the-shelf Kubernetes platform can’t accommodate. For example, financial trading firms that need custom integrations with market data feeds and proprietary risk management systems.

Technical capabilities: Building and maintaining an in-house Kubernetes platform requires strong in-house expertise in:

Kubernetes and other cloud-native tools.

Buying a Kubernetes platform offered as a self-service solution might be the ideal choice for organizations with the following characteristics:

Require rapid time to value: Organizations seeking to quickly enable their development teams with a functional platform can benefit from the faster time to value offered by prebuilt Kubernetes platforms. These platforms typically require minimal setup and configuration, allowing developers to get up and running quickly and minimizing disruption to existing workflows.

Limited in-house development resources: Organizations with limited in-house development resources or that lack the expertise required to build and maintain a complex platform can benefit from leveraging the expertise of established vendors. This also frees up internal teams to focus on core development activities while ensuring access to a robust and feature-rich platform.

This summary of the pros and cons should help you weigh your options.

Control Build: Complete control over security and data management. Buy: Some level of control, depending on the vendor’s policies.

Customization Build: Highly customizable . Buy: Limited options. Some vendors might offer customizations .

Effort and investments Build: Requires a lot of effort and heavy investments in teams. Buy: Comparatively requires less effort and investment.

Talent scarcity Build: Difficult to find and retain skilled developers to build and maintain the platform. Buy: Vendor expertise eliminates the requirement for specialized talent.

Scalability Build: Scalability completely depends on internal resources and infrastructure. Buy: Easy scalability that is managed by the vendor.

Security Build: Complete responsibility for security is on internal teams. Buy: Evaluate for organizational compliance and security considerations.

Maintenance overhead Build: Requires ongoing maintenance efforts by internal teams to ensure security patches and timely upgrades. Buy: The vendor performs all the heavy lifting.

The decision to build or buy a Kubernetes platform depends largely on your organization’s needs, budget and expertise.

If your organization has specialized requirements and a team capable of managing the complexities of Kubernetes, building in house may give you the customization and control you desire. However, for most organizations, especially those looking for faster deployment times and reduced operational complexity, buying a customizable solution like Devtron, which is built in the open with an open core philosophy using industry standards, can offer the best of both worlds.

Ultimately, the question boils down to your long-term business goals: Do you want full control at the cost of increased management, or are you looking to offload platform maintenance to a trusted vendor, freeing your team to focus on innovation? Whichever path you choose, Kubernetes is here to stay — and choosing the right platform can make all the difference in driving success for your cloud native applications.

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Understanding the Data Culture

A data culture is the collective behaviors and beliefs of people who value, practice, and encourage the use of data and AI to propel organizational transformation. It equips everyone in an organization with intuitive, productive insights for tackling complex business challenges.

Creating a data culture helps you accelerate the value of analytics and AI. It transforms the quality and speed of decision-making across an organization and forms a foundation of data trust and transparency.

Building a data culture requires time, investment, and an organization-wide commitment, but a robust data culture is critical to making your data and people AI-ready. This playbook will guide you through the key steps to achieve — and maintain — a data culture for your organization’s long-term success.

Establishing a strong data culture reaps the rewards across your organization — from rapid innovation, personalized customer experiences, and improved decision-making to reduced costs, higher employee retention, and increased revenue. And while the journey to build a data culture can seem daunting, with the right strategy, you can plan for data and AI success.

Figure 1. Core components of data governance with AI capabilities.

Data is the backbone of every AI strategy — and making high-quality, trusted data accessible to more people is key to unlocking the full potential of AI. And a strong data culture can help you do this by equipping more people with the right technology, processes, and insights to help your entire organization achieve data-driven success.

To bridge the AI trust gap and increase the broad use of data across your organization, you need to have a data strategy. Building a data strategy will equip you to increase your operational efficiency and revenue streams.

The secret to unleashing actionable insights is marrying trusted analytics with the power of AI. With the power of AI, the secret to unleashing actionable insights consumption at scale is bringing trusted generative AI to the entire platform. By combining analytics and AI with people equipped with data skills, you can maximize your technology investments and uncover opportunities that drive business strategy and strengthen customer trust.

Faster business decision-making Operational efficiencies Free up time for valuable work Automated workflows Improved customer satisfaction.

An effective change management plan details how you will engage people across your organization to promote ongoing awareness, education, and a strong data culture. Start by identifying members of a cross-functional team to form your steering community or center of excellence (CoE). Your team will:

Determine business goals or benchmarks where data and AI can help increase productivity, improve customer understanding, reduce manual efforts, or drive targeted business outcomes.

Set and align business goals and performance measures (OKRs).

After you’ve established this framework, you can create a change management plan that articulates the behaviors and beliefs you want to instill in people throughout the organization. First, determine that your stakeholders are engaged and know what to do. Then, address specific steps you will take to:

Train your community to build data and AI competency.

Create realistic data maturity model targets.

Learn and improve through a continual feedback loop.

2. Empower Teams to Deliver on Data’s Value.

To get the most out of your data, you need more than technology alone. Promoting data fluency at every level empowers people to use trusted data and AI tools effectively so they can apply actionable insights and improve their decision-making.

Does your entire workforce have the skills, tools, and curiosity to deliver on data’s value? Most likely not. Start by assessing the skills and gaps in your people’s knowledge that can impact their ability to make insight-driven decisions. Leadership agreement is critical in assessing current skills and identifying workforce needs to identify what is critical to using data effectively.

Audit and assess your workforce and organizational needs for data analytics and AI skills by aligning use cases to employee competency. For example, what data and AI skills do you expect a product manager to have versus a financial analyst? You may want to create a matrix that exhibits your current and future (ideal state) workforce skills based on data culture behaviors that drive data maturity.

Innovative solutions can also help you address the data skills gap more quickly and across all of your teams. You can use AI in Tableau solutions like Tableau Pulse to democratize data analysis and simplify insights consumption at scale. It accelerates time to value and reduces repetitive tasks for the data analyst with smart suggestions and in-product guidance.

Knowing our AI is built on the Einstein Trust Layer, your organization is enabled with trusted, ethical, and open AI-powered experiences without compromising data security and privacy — which is critical as you grow access to analytics and AI, and nurture data skills.

Change management is a key component that can promote your people’s skills and capabilities. After all, investing in data and AI tools will not ensure your people have the skills to use them.

That’s where training and development play an essential role. Continuous learning through training, education, and data community involvement ensures your workforce has the skills needed to use your tools. And it is an ongoing investment that should be fully aligned with your corporate strategy.

Consider this a two-step process. First, you need to upgrade your current workforce in terms of data fluency and AI proficiency. And second, you want to recruit and hire talent that aligns with your data and AI strategy.

Maximizing analytics investments and capitalizing on the transformative potential of data means that everyone who encounters data — regardless of their skill level — can find insights and take action. Rather than relying on instincts or feelings, your people actively seek to use data in the decision-making. Promote user education, measure adoption and engagement, and increase analytics use within your organizations to support insight-driven decisions.

AI capabilities empower your people by adding automated, plain-spoken explanations to your dashboards in seconds, helping you to discover the “why” behind insights with dynamic visualizations that allow deeper exploration and bring trust and transparent predictions and recommendations to everyone.

3. Advance Your Journey to Data Maturity.

To progress on the data maturity roadmap, here are these best practices:

Define what data maturity looks like and means to your organization.

Benchmark competency levels and capabilities across people, processes, and technology.

Measure your ROI using the following key performance indicators: business performance, analytics productivity, organizational alignment, community satisfaction, and adoption.

Build curated, analytics-ready data insights to address critical decision points.

Use a data lake to centralize, secure, process, and organize large amounts of data so that people across your corporation can access the unified data they need from a single location.

Promote a Culture of Data-Driven Decision-Making.

Use data discovery coupled with AI-infused analytics to improve productivity.

Get the commitment of executive stakeholders to behavior change and budget allocation for change management.

Automate analysis and increase data collaboration.

Elements of a successful data maturity roadmap include considerations around your analytics strategy, governance approach, having an agile or flexible deployment, and communicating the value of and facilitating a community that supports analytics.

Discuss the integration of AI to automate and enhance data processes.

Build or support a community that inspires and celebrates data-driven wins.

The data journey begins on the platform, which provides a single source of truth across your organization. Then, AI leverages the data, reveals trends and patterns, and uncovers actionable insights to drive accurate and rapid decision-making without impacting existing technology investments.

Finally, we can leverage the productivity gains of AI without compromising on data security and privacy.

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