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Continuous Delivery vs Deployment: What Are Key Differences?

Posted in: automated continuous testing, continuous delivery, continuous delivery cycle, continuous delivery processes, continuous deployment, continuous development/continuous integration, IT Strategy - Sep 10, 2021

The terms continuous delivery and deployment can sometimes be used interchangeably, but they’re not the same thing. Understanding continuous delivery vs deployment is critical to building high-performing digital products faster.

Here’s what you need to know about continuous delivery and deployment, the differences between them, and how to pick the right model.

What Is Continuous Delivery?

Traditional software development methods deploy once every three to six months (often with delays). Continuous delivery takes an old concept — continuous integration — and applies it to deployment.

With continuous delivery, organizations can take several approaches. Typically, they release daily or weekly. Continuous delivery doesn’t always mean deploying daily — rather, it implies companies will be able to easily roll back faulty releases should problems arise. This way, no matter how large or small a release may be, users continue to experience consistently high quality and enhanced functionality over time.

In contrast to other approaches that simply address issues after they arise, continuous delivery tries to prevent problems from happening in the first place by using automated processes and test-driven development models.

When something does go wrong in production, there’s not much left to fix.

Many companies use continuous delivery in conjunction with continuous deployment, so their employees only have to commit code during normal business hours — as opposed to during core hours, when programmers are less likely to be available.

Automated testing tools enable developers and business users alike to submit changes into repositories, which then automatically process changes for regression tests, unit tests, and functional tests across all environments. This includes live operations for better efficiency, accuracy, and confidence in shipping. When something does go wrong in production, there’s not much left to fix.

Delivering software continuously has numerous benefits for IT departments and end users. Developers save time because they’re not waiting for code reviews or handoffs between teams. Continuous delivery lets developers automate workflows to expedite deployments even further.

Continuous delivery allows developers and IT teams more flexibility when rolling out products and features to end-users. This flexibility allows customers to receive critical improvements within days rather than months, enabling businesses that adopt these strategies to keep pace with evolving customer needs more quickly than those that don’t.

Overall, continuous delivery helps IT teams deliver high-quality software faster and with less risk. Finally, continuous delivery provides faster access to bug fixes because new versions are automatically tested before being deployed into production environments.

What Is Continuous Deployment?

Continuous deployment is a software engineering approach in which teams push code to production multiple times per day. Proponents of continuous deployment say it allows them to build better software more quickly by delivering working code fast and often.

In a continuous deployment workflow, each check-in is verified by an automated build and/or test script, then automatically deployed into production. Each time new code is deployed, it is immediately available for use by users.

Each time new code is deployed, it is immediately available for use by users.

From a developer perspective, changes are committed to version control and then immediately moved into testing. Deployments often occur – usually more than once per day – but not so often that they become routine. With continuous deployment, developers can see how their changes will function in a live environment as soon as they make them.

This rapid feedback cycle means they can refine their solutions more quickly without introducing bugs, or risk impacting business operations. Users see only stable versions of software running between deployments, with no unintended behavior from earlier versions being run undercover.

Let’s take a look at some examples of continuous delivery vs continuous deployment.

Examples of Continuous Delivery and Deployment

A number of organizations have already adopted continuous delivery and deployment strategies. While each company has its own unique processes for getting code from developers to production environments, they all utilize some element of continuous delivery and in their process.

Etsy used Jenkins to set up a workflow that could automatically merge code into their main branch after successful unit tests were completed. Any developer can then deploy directly from that branch into production at any time.

Spotify also uses an automated build pipeline, with several deployments triggering off commits made to specific branches. Automated testing is then run on every commit before it’s deployed.

Companies like Facebook use continuous deployment extensively, because they make major changes several times a day, with small patches throughout each day. They do a rollback to ensure no regressions are introduced but rely on automated checks to do so.

Continuous deployment and continuous delivery allow developers to deploy code whenever it meets certain standards, instead of on a set schedule.

Traditional Deployment vs Continuous Deployment

One of the main differences between traditional deployment and continuous deployment is how you go about creating a deployable artifact. With traditional deployments, your code gets deployed to production at set intervals, such as once per week or once per month.

Continuous deployment isn’t appropriate for everyone or every situation.

Continuous deployment pushes code into production as soon as it’s ready, whereas traditional deployment requires an entire interval to lapse before deploying new code into production.

That said, continuous deployment isn’t appropriate for everyone or every situation. It requires close monitoring from engineers, who need to be on call regularly in case something goes wrong. If any problems occur, those engineers must put out fires quickly and seamlessly restore service to customers.

Why Opt for a Continuous Model?

It comes down to speed and predictability. With traditional deployment, you typically get a large batch of changes deployed all at once. That can lead to big issues if there’s a critical failure in any of those changes. Your whole system is broken until you can isolate and fix that one component. With the continuous model, you push smaller batches of change out more frequently, so there are always working pieces for your users.

Traditional deployments are not meant for agile development methods. Traditional deployments only allow you to fix one problem at a time. However, with continuous deployment, several issues can be fixed simultaneously, resulting in a better overall product over time.

The goal with continuous delivery and continuous deployment processes is rapid feedback. Continuous delivery aims to get changes into production rapidly while maintaining stability through practices like automated testing and built-in monitoring. Continuous deployment happens every time there are changes made to your code that are approved by QA.

The difference between continuous delivery vs deployment — and why developers might want to consider one or both techniques — is primarily found in how quickly your team gets new features into users’ hands.

The post Continuous Delivery vs Deployment: What Are Key Differences? appeared first on CIO Insight.

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AI Equity in Business Technology: An Interview With Marshall Choy of SambaNova Systems

Posted in: AI, AI equity, artificial intelligence, Covid-19, Digital Transformation, Innovation, Leadership, machine learning, Marshall Choy, ML, News & Trends, SambaNova Systems - Sep 10, 2021

Artificial intelligence (AI) and machine learning (ML) are quickly transforming business operations and customer experiences, but not all enterprises are keeping up with the AI trend. Some businesses lag behind because they lack the financial and human resources to develop these tools. Others haven’t yet developed a vision of the AI future, and the extent to which it could benefit their business models.

Regardless of where different businesses currently fall on the AI development spectrum, trends point to a future where businesses will need to accept AI innovations in order to survive. The concept of AI equity has recently entered into greater business technology conversations, with major tech companies discussing how AI can be made more equitable, accessible, and easily understood by the business community at large.

SambaNova Systems is a company that specializes in AI innovations. Marshall Choy, VP of Product at SambaNova Systems, recently shared his thoughts with CIO Insight about how AI development looks now, and how a drive toward AI equity can improve the business technology landscape.

Also Read: AI vs. Machine Learning: Their Differences and Impacts

AI Equity Q&A with SambaNova Systems Executive

The Current State of Enterprise AI Development

CIO Insight: What is the number one mistake you see companies make when they first try to integrate AI into their business model or product offerings?

Choy: A lot of companies should actually look at AI as a strategic initiative across the organization. Usually, they just dip their toes in with a pilot project, then all teams decide to do their own pilot projects, and then there are several disjointed projects.

AI is less effective with these silos of heterogeneity. So it’s important to develop a strategic initiative across departments in an organization.

CIO Insight: How is AI currently changing the world of business? What efficiencies and new solutions come from AI in business?

Choy: AI is revolutionizing business in much the same magnitude we saw the internet do in business technology nearly two decades ago. The nature of software development and IT has changed significantly with AI.

We’re crossing into this new world of machine learning and AI-driven technology. Enterprise resource planning, CRMs, all of those other systems will continue to exist, but there’s a whole new growth wave of tech that’s being driven by AI, like computer vision and natural language processing.

Also Read: CRM vs ERP: What Are the Key Differences?

Larger vs. Smaller Business AI Adoption

CIO Insight: Traditionally, larger enterprises have adopted AI technologies before smaller companies. Why is that the case, and what makes them more successful and quick to adopt these new technologies?

Choy: The tech giants, the industry leaders, the hyperscalers have all the resources from the financial and human resources perspective. And they also have the motivation to keep up with the latest technologies in their hyper-competitive scenario, because of the sheer volume of what they’re trying to accomplish. Their size also gives them a large amount of purchasing power, so they’re able to experiment with new technology and get more purchasing power as a result.

CIO Insight: Can you think of a specific example/use case in which a smaller company developed a successful AI solution? What do you think made them successful?

Choy: In many cases, we hear smaller companies saying ‘I’m a small player, let me pick out a smaller pilot project.’ The successful smaller companies I’ve seen continue to think like big players, and don’t necessarily accept the stratification of AI relative to their size.

A smaller manufacturing customer that we worked with had a particular problem in QA image detection. We worked with them to develop a high-resolution solution with a greater scope, and without manual intervention and additional know-how, their defect detection accuracy increased. This project also improved their end-user safety.

CIO Insight: What industries have traditionally been slow to adopt AI technologies? How do you think it hurts their business and/or their customers?

Choy: The reality is we’re in the early days of AI rollout, so we don’t totally have the data. The thing that I’ve seen that’s slowed down AI adoption is actually less tied to industry and more tied to the digital maturity of the organization. It’s going to be really hard to embrace AI if you’re still doing ERP on an Excel spreadsheet.

Banking and financial services are typically ahead of the curve, but it’s still not uniform across the industry … I think the main point I want to make here is that it’s not too late for anybody in any industry to get on this now, and maintain or gain a leadership position, or become a leader in an industry that has not yet arrived here.

What Is AI Equity?

CIO Insight: What is AI equity? What does it mean for the future of the business technology world, and what does it mean to you specifically?

Choy: It’s really about leveling that playing field. To me, it describes the end state of making AI more accessible to a broader user base; offering the AI capabilities of the tech giants without being a tech giant. AI equity means that AI access is not just for the Fortune 10, but for the Fortune ‘everybody else,’ and without the need for the same infrastructure and people resources.

Tech Vendors Driving Toward AI Equity

CIO Insight: How are you and SambaNova Systems currently working to create greater AI equity across industries and businesses?

Choy: The reality is that I can think of few industries that build things in piece parts and whose customers expect to self-integrate the solutions and build the end products themselves. Car companies don’t do that, other companies don’t do that, but for whatever reason, that has become the status quo in IT.

At SambaNova Systems, we offer Dataflow-as-a-Service so that organizations of all shapes and sizes can quickly use AI and machine learning services while reducing technical staffing requirements for the solution. We’re automating and integrating things into a single package.

One of the big areas of inequity is in the AI models themselves. The big tech giants have the stacks of PhDs and technical experience to understand these models, but most teams don’t. SambaNova Systems is staffed similarly and does that research, selection, and optimization on behalf of our customers.

CIO Insight: How can other technology vendors encourage AI adoption and greater equity within their customer base?

Choy: It’s all about making the technology easier to use, and maybe more importantly, easier to integrate into what they already have. Not just what’s in the data center, but what’s in the heads of their staff. Companies need to offer solutions that are less vendor-specific, helping their customers to avoid vendor lock-in. Open standards yield more flexibility and choice.

The Future of Business Tech With AI Equity

CIO Insight: How do you think that more widespread AI development could create positive global change? How has the pandemic affected AI and business technology?

Choy: AI is here to oversimplify and automate. Automation is efficiency … let’s take a look at the current pandemic situation. Many pandemic problems are being made slightly less devastating with AI and other patient outcome technologies realized in less time.

I think digital transformation has accelerated, and AI is a big part of that. We’ve talked about digital transformation for a decade now, and a lot of companies who were thinking about it achieved it in less than a year because of the pandemic.

More on COVID and Business Efficiencies: COVID’s Impact on Agile Project Management

CIO Insight: What do you think the consequences will be for companies that don’t begin to develop AI solutions in the next few years?

Choy: Just like with the onset of the internet, the companies that adopt AI correctly will be the kings and queens of their industry. And those that don’t could be left behind. The internet completely refactored how apps were written and run and connected people across the globe. Many major enterprises fell off because they were not quick to adopt the internet, and I think AI is going to have the same refactoring effect on future business successes.

Conclusions

CIO Insight: Anything else you’d like to add?

Choy: When [your company] is considering its AI strategy, think beyond the pilots and test drafts. Think beyond the short term toward the long term. Pilots should be experiments on how to use the tech in a broader sense. They should move beyond cross-organizational and application boundaries to get true enterprise-scale benefits … How can you really have a successful pilot if you don’t know what you’re trying to achieve?

Note: This interview has been edited for clarity.

Read Next: AI Software Trends for 2021

About Marshall Choy

Marshall Choy, SambaNova Systems' VP of Product

Marshall Choy is Vice President of Product at SambaNova Systems, responsible for product management and go to market. Mr. Choy brings extensive experience leading global organizations to bring breakthrough products to market, establish new market presences, and grow new and existing lines of business.

Mr. Choy was previously Vice President of Product Management at Oracle until 2018. There, Mr. Choy was responsible for the portfolio and strategy for Oracle Systems products and solutions. He led teams that help deliver comprehensive end-to-end hardware and software solutions and product management operations. Prior to joining Oracle in 2010 when it acquired Sun Microsystems, Mr. Choy served as Director of Engineered Solutions at Sun. During his 11 years there, Mr. Choy held various positions in development, information technology, and marketing.

The post AI Equity in Business Technology: An Interview With Marshall Choy of SambaNova Systems appeared first on CIO Insight.

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Biggest Challenges & Rewards of Enterprise SaaS

Posted in: Big Data, data managment, Enterprise Apps, Infrastructure, IT Management, SaaS, SaaS applications, SaaS security, Security, software security - Sep 10, 2021

Major enterprises are increasingly turning to Software-as-a-Service (SaaS) solutions to drive greater agility and cost efficiency throughout the business. However, the path to results has been paved with more than a few challenges, including systems integration, data migration, and most significantly, new security concerns.

Adaptive Shield’s SaaS Security Survey Report 2021 examines a variety of issues related to SaaS adoption, but primarily focuses on the different types of security and the role stratifications enterprises often overlook in SaaS management. Read on to learn about the security challenges many organizations face in their SaaS development, as well as the rewards these organizations can reap when SaaS is handled with care.

Also Read: Why Is Risk Management Important?

Optimizing Your SaaS Experience

SaaS Security Survey Report Demographics

In May 2021, Adaptive Shield surveyed 300 InfoSecurity professionals from North America and Western Europe, focusing on companies with 500+ employees. Although there was some diversity in the roles of people surveyed, the majority of survey participants fell into one of the following job categories:

Cybersecurity/InfoSec
IT
SecOps
Cloud Security Architects
SaaS Security Architects
Security Engineers
Risk Assessment Vendors
Forensics Experts

Some other important metrics to note from the study:

Companies of all sizes, starting with at least 500 employees, were surveyed; however, smaller enterprises made up the majority of the surveyed population, with 41% of survey participants falling in the 500 to 1,000 employee count range.
Over half of all participants surveyed were from the United States, with additional participants from Canada and the United Kingdom.
The survey primarily targeted executives within these businesses, with most participants holding a manager position or higher.
The five industries most heavily represented by these results are financial services, technology, e-commerce and retail, energy and utilities, and industrials.

User-Based Security Ideas: Access Control Security Best Practices

SaaS Security Survey Report Findings

Adaptive Shield’s survey mostly discusses a newer security solution that many companies are looking to adopt: SaaS security posture management (SSPM). Cloud security posture management (CSPM) and cloud access security broker (CASB) tools have been a key part of cybersecurity models for many years. But SSPM is working to fill a gap in security directly at the SaaS application level, rather than at the greater cloud and cloud-to-application layer levels.

In this study, Adaptive Shield identifies SaaS application misconfiguration as one of the biggest problems organizations face. Consequently, SSPM is a solution many companies are selecting to help them better monitor and detect problems with application configurations.

An SSPM tool’s main goals are to assess security risks, identify misconfigurations across SaaS applications, and provide deep visibility and detection for security hygiene maintenance. Although SSPM solves many of the major misconfigurations that organizations face, mismanaged SaaS applications and company roles continue to be a problem for cloud and application security.

The Biggest Challenges of Enterprise SaaS

According to the report, 85% of surveyed companies believe SaaS misconfiguration is one of three top security threats to their organization. Interestingly though, only 27% of surveyed companies check for SaaS configurations on a weekly basis, while 73% check monthly or even less.

A trend found within this study: the more SaaS applications your organization manages (50+), the less likely you are to monitor their security status on a weekly basis. Although the infrequency of SaaS application monitoring in major enterprises seems paradoxical, there are several reasons for the seeming cognitive dissonance:

Large Companies and Stratified Roles

Companies with highly stratified specialties and roles may not feel the need to dedicate security personnel to SaaS maintenance specifically. They instead turn to sales, marketing, and product owners who are familiar with the SaaS tool. However, these personnel are likely unfamiliar with important security maintenance requirements for these apps.

The Speed of SaaS Development and Adoption

SaaS apps have grown dramatically in variety and functionality, and many companies have bought into them at an equally rapid rate. As the number of apps to manage grows, unless a focused SaaS security automation tool is in place, it becomes increasingly challenging for internal teams to audit a large portfolio of SaaS tools on a regular basis.

The Growing Attack Surface

Beyond the growth of actual SaaS tools in companies, there’s also the sprawl of users and company tools across the globe. The attack surface of enterprise networks has grown with remote work, turning more users into vulnerable access points for attacks as they move further away from company data centers and traditional protocols.

When users who aren’t security professionals receive unmitigated access to SaaS applications and their management, organizations run a high risk of application misconfiguration, as well as potential phishing and unauthorized access. In larger organizations with thousands of employees, it becomes nearly impossible for the security team to actively monitor vulnerabilities across all users and devices each week.

More on remote work and security: VPNs, Zero Trust Network Access, and the Evolution of Secure Remote Work

The Biggest Rewards of Enterprise SaaS

When SaaS applications are managed well, they offer a variety of benefits to the organizations that use them:

SaaS apps are typically easier for non-technical users to understand, which further democratizes IT across an organization.
Unlike many traditional applications, SaaS apps are hosted on the cloud, which offers all of the benefits of cloud access to company users — like real-time collaboration, updates, and cloud security offerings.
Third-party hosts typically manage SaaS application platforms. So even if your internal team lacks technical expertise, resources from the third-party company help your team use tools optimally.
Most software-based and on-premises applications require a lump-sum purchase model. But with the cloud-based structure of SaaS, companies can subscribe to the tools, often in a pay-as-you-go model.

The rewards associated with SaaS are numerous, but their consequences can be even greater if your organization and third-party providers don’t take the necessary steps to protect SaaS tools.

Talk to your SaaS providers about the security options they provide, ensure that internal SaaS users learn to work with security best practices, and consider investing in tools like SaaS security posture management to provide extra protection and support for your SaaS applications.

The post Biggest Challenges & Rewards of Enterprise SaaS appeared first on CIO Insight.

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What Is Fully Homomorphic Encryption (FHE)?

Posted in: big data security, data security, encryption, FHE, fully homomorphic encryption, IBM, News & Trends - Sep 10, 2021

Company leaders are continually looking for ways to keep data safe without compromising its usability. Fully homomorphic encryption (FHE) could be a step in the right direction.

What Is Fully Homomorphic Encryption?

Fully homomorphic encryption allows the analyzing and running of processes on data without needing a decryption method. For example, if someone wanted to process information in the cloud but did not trust the provider, FHE would allow sending the encrypting data for processing without providing a decryption key.

How Does Fully Homomorphic Encryption Work?

FHE is like other encryption methods that require using a public key to encrypt the data. Only the party with the correct private key can see the information in its unencrypted state. However, FHE uses an algebraic system that allows working with data without requiring decryption first. In many cases, information is represented as integers, while multiplication and addition replace the Boolean functions used in other kinds of encryption.

FHE uses an algebraic system that allows working with data without requiring decryption first.

Researchers first proposed FHE in the 1970s, and people became interested back then. However, it has taken substantial time to turn these concepts into feasible real-world applications.

A researcher showed it was plausible with his 2009 published study. However, working with even a tiny amount of data proved too time-intensive. Even now, FHE can require hundreds of times more computing power than an equivalent plaintext data operation.

What Advantages Does FHE Have Over Other Types of Encryption?

Data is at a higher risk of becoming compromised when it’s not encrypted. FHE keeps the information secure by not requiring decryption to occur for processing to happen.

In one recent example, Google released an FHE-based tool that allows developers to work with encrypted data without revealing any personally identifiable information (PII). Google’s blog post on the subject gave the example of FHE allowing medical researchers to examine the data of people with a particular condition without providing any personal details about them.

Encryption takes private information and makes it unreadable by unauthorized third parties. However, something that makes people particularly excited about FHE is that it eliminates the tradeoff between data privacy and usability, making both present at a high level.

Is Fully Homomorphic Encryption Safe?

Many people familiar with FHE and its potential applications agree that it seems safer than other methods of data protection, which require decrypting data for processing. It could be particularly widely embraced in certain sectors. After all, cloud computing brings in $250 billion per year.

Experts believe FHE will emerge as a compelling option in tightly regulated industries.

People are continually interested in how to keep their data safe when stored in the cloud. Some experts also believe FHE will emerge as a compelling option in tightly regulated industries because it could become a better safeguard against breaches.

“Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable patient data,” IBM notes on its site. “FHE in clinical research can improve the acceptance of data-sharing protocols, increase sample sizes, and accelerate learning from real-world data.”

What Are the Business Use Cases of FHE?

Fully homomorphic encryption could forever change how companies use data. That’s crucial, especially considering how many businesses collect it in vast quantities at a time where many consumers feel increasingly concerned about keeping their details safe.

For example, FHE allows keeping information in an encrypted database to make it less vulnerable to hacking — without restricting how owners can use it. That approach could limit an organization’s risk of regulatory fines due to data breaches and hacks.

It also permits secure data monetization efforts by protecting customers’ information and allowing services to process people’s information without invading privacy. In such cases, individuals may be more forthcoming about sharing their information, knowing in advance that business representatives cannot see certain private aspects of it.

Using an FHE-based solution also enables sharing data with third-party collaborators in ways that reduce threats and help the company providing the information comply with respective regulations. Thus, this kind of encryption could support research efforts where people across multiple organizations need to work with sensitive content.

Which Companies Offer FHE Products?

Fully homomorphic encryption is not widely available in commercial platforms yet. However, some companies offer products based on homomorphic encryption that could eventually work for the use cases discussed earlier.

For example, Intel has such a product that allows segmenting data into secure zones for processing. Similarly, Inpher offers a product with an FHE component. It primarily uses secure multiparty computation, but applies FHE to certain use cases.

IBM says FHE is now adequate for specific use cases.

Beyond those examples, IBM has a fully homomorphic encryption toolkit that it released for iOS in 2020. That progress primarily occurred after IBM’s experts took it upon themselves to make FHE more commercially feasible, addressing the time and computing power that it previously took to use this type of encryption.

The company’s representatives say FHE is now adequate for specific use cases and suggested the health care and finance industries as particularly well suited to it.

Fully Homomorphic Encryption Shows Potential

Since FHE is not widely available via commercial platforms yet, interested parties should not expect to start using it immediately. However, that could change as organizations become increasingly concerned about striking the right balance between data security and usability.

The ideal strategy for businesses to take now is to explore the options currently on the market. They can then determine if any of those options check the boxes for helping them explore fully homomorphic encryption, including what it might do in the future and what capabilities exist now.

The post What Is Fully Homomorphic Encryption (FHE)? appeared first on CIO Insight.

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Tips for Implementing Scrum Best Practices

Posted in: agile manifesto, agile scrum practices, best practices, IT Strategy, Project Management, Scrum, scrum definition - Sep 10, 2021

Agile development is all about continuous improvement, and scrum — an iterative and incremental agile software development framework — helps get it done. Here are 10 tips for implementing scrum best practices.

Agile Transformation Takes Time

Moving to an agile framework changes not only the way projects are implemented, but also an organization’s culture from managing contracts to delivering maximum business value in the shortest time. This shift will be gradual, but don’t let the time it takes discourage you from reaching your goal.

Principles Facilitate Scrum Best Practices

Successfully performing agile techniques means fully embracing agile principles and focusing on people, interactions and culture. This cultural shift will make the practices more sustainable in the long run.

Keep Your Rollout Simple

Agile tools shouldn’t be overemphasized. Don’t spend time getting a tool up and running instead of focusing on getting people to work together. The Agile Manifesto values individuals and interactions more than processes and tools.

Empower Your Scrum Team

Allow people on your team to make mistakes. They are more likely to learn from their errors if they have a sense of ownership over their work. Scrum masters exist to serve their team — not the other way around.

Maintain an Updated Backlog

Keep your product backlog up-to-date and filled with plenty of relevant work for your team. Because agile development is iterative, there is almost always something to improve or refine. More work will get done if your team has a robust list of high-value features to develop.

Don’t Hide Behind the Scrum Master

Your team members know their problems better than anyone else. Encourage them to articulate their issues directly to the Product Owner. This will help build a flatter, more efficient team structure and reduce miscommunications.

Designate a Product Owner

Ensure that your product owner is involved in the day-to-day activity of the project team early. The more engaged they are, the fewer changes and revisions will have to be made later.

Hold Consistent Daily Standups

Keep standups consistent, to-the-point, and respectful of your team’s time. When done correctly, a good standup will help increase transparency and communication — preventing issues from snowballing.

Encourage Transparency

Open communication is the key to quick issue resolution. To encourage transparency, build a “free to fail” atmosphere in which team members feel safe enough to ask when they need help.

Conduct Retrospectives

Sprint retrospectives are not optional. Agile is about continuous improvement. Progress cannot be realized without reflecting on how we work, what we do well, and what we can do better.

Learn more about Agile best practices: Agile Project Management Methodology & Principles

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