Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Cohesity‘s Greg Statton offers commentary on upcoming and new AI regulations and how enterprises can help write the rules.

The calls for regulating artificial intelligence (AI) are getting stronger. Developers, business leaders, academics and politicians on both sides of the aisle are calling for new rules aimed at making AI more accountable. This summer, Senate Majority Leader Chuck Schumer unveiled a legislative framework for regulating the new technology and announced a series of “AI Insight Forums” with top AI experts to explore how these regulations would work. Even self-described libertarian business leader Elon Musk has called for regulations around AI.

As questions surrounding AI regulation move from ‘if’ to ‘when’ and ‘how’, business leaders need to be proactive with helping the federal government formulate and roll out these new rules. A failure to participate in the process could threaten businesses’ ability to use AI to innovate, create new products and compete with foreign entities. AI is poised to radically transform the world around us, and the time to act is now. Business leaders simply can’t watch the situation play out from the sidelines.

Restoring Trust by Building a Culture of Responsible AI

As AI continues to evolve, the federal government is concerned about safety, security and trust – including potential risks associated with misuse of the technology such as biosecurity, cybersecurity and broader societal effects like protecting Americans’ rights and physical safety. Data plays a crucial role in creating these concerns (AI is only as good as the data you feed into it, of course), and the quality and management of this data directly influences the outcomes and potential risks that come with AI.

Business leaders can partner with the government to address these concerns by committing to common-sense, responsible AI development and deployment. This includes ensuring products are safe before launching them into the marketplace, building security and privacy safeguards directly into AI engines and earning the public’s trust by building a culture of transparency and accountability throughout their organizations. In parallel, businesses should prioritize research on the societal risks posed by AI systems – collaborating with governments, civil society and academia to share information and best practices for managing AI risks.

Here are three ways businesses can proactively address these concerns and create a culture of responsible AI in their organizations:

Develop & Implement Internal AI Ethics Guidelines

The business community can offer the government a set of best practices and guidelines by developing its own set of internal rules and regulations. This requires establishing a clear set of ethical principles that focus on transparency, fairness, privacy, security and accountability. Most companies today already adhere to clear policies around risk and bias, so it wouldn’t be much of a stretch to extend these policies across all data in the organization.

As policies change over time, it’s important to look back and curate older data sets that are still relevant to make sure new, evolving principles are being applied. As models learn, even small, imperceivable biases can create huge problems down the road – just ask Apple. Building and applying these guidelines internally ensures that government regulations will be means tested when rolled out on a grander scale.

Encourage Collaboration & Knowledge Sharing

As AI democratizes, organizations should foster a culture of collaboration and knowledge sharing within the organization and with key stakeholders – such as employees, partners, customers and the public as a whole. We’re seeing a rapid pace of innovation the world has never seen before, but this agility has expanded threat surfaces beyond traditional perimeters and threatens to create information silos within organizations. Knowledge workers across disciplines have different ideas and use cases for AI that engineers or developers haven’t considered. Openly encouraging cross-functional collaboration makes it easier to monitor and control AI development and use throughout the organization while breaking down silos.

Provide AI Ethics Training and Education

AI has the power to make employees’ lives easier, but it comes with great responsibility. Businesses need to make sure their workers understand the risks associated with using public and proprietary AI tools – especially the risks surrounding adversarial attacks, privacy preservation and bias mitigation. Clear guidelines around the kind of data they can input into AI engines protect personally-identifiable information (PII), intellectual property (IP) and other trade secrets. Consent is also important – making sure customers, employees and other stakeholders are comfortable with their data being used to train AI models. The last thing businesses want is for employees to go rogue and use AI on their own without the guidelines set out by the organization. The lack of visibility and control would be a recipe for disaster and set a dangerous precedent at a time when federal regulations are being formed.

Get Proactive to Get Ahead

Public trust is eroding across the board, according to the Edelman Trust Barometer, and governments are looking to increase regulations to get back on track. AI is firmly in their crosshairs, and business leaders need to get out in front of the process to provide clear ethical and procedural guidelines around the new technology’s development and deployment. This includes developing their own responsible AI principles internally, encouraging collaboration across the organization and ensuring all stakeholders are up to date on clear guidelines around protecting PII, IP and other critical data points. AI is going to change the world, and businesses have an opportunity to show regulators that they are serious about taking responsibility for using the technology in a safe, ethical manner.

The post AI Regulations Are Coming – here are 3 ways business leaders can help to formulate the new rules appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Reltio‘s SVP of Product Management Venki Subramanian offers commentary on data transformation amid economic and global uncertainty.

One thing remains crystal clear amid today’s uncertain economic climate: enterprises recognize the undeniable need to invest in their own data. It’s a treasure trove that holds immense potential and, at the same time, poses a formidable risk—the ultimate double-edged sword. Data has evolved into the make-or-break factor for countless organizations. But often, they are left grappling with its sheer volume, fragmented nature, and frustratingly low quality. Data has transformed into the unruly beast that keeps many companies up at night.

Amidst this chaos and frustration, however, there is a glimmer of hope. There’s a light at the end of the data tunnel, and it comes in the form of innovative solutions that breathe fresh life into the art of extracting value from information. These cutting-edge solutions defy the odds and accelerate the entire process by leaps and bounds. With game-changing tools in their arsenal, companies can unlock the true potential of their data and harness it to drive success like never before.

Putting Data First is the Goal

The challenge, however, is for organizations that have not yet begun their modern data management journeys. Many have yet to start, as evidenced by the potential market growth ahead. In fact, according to Polaris Market Research’s “Master Data Management Market Size Global Report, 2022-2030,” the market will more than triple in growth, from $17 billion in 2022 to $54 billion by 2030. The figures show that companies recognize the need for MDM. Many, however, have experienced MDM in a previous life and usually not too fondly.

Implementing MDM and generating results — including eliminating duplicate data, improving data quality, streamlining processes, enhancing customer experiences, helping lower IT costs, and enabling better decision-making across an enterprise – is as it sounds: complex. It takes time. That’s why MDM software is known for being a long journey rather than a short one.

An organization embarking on a digital transformation must put data at the heart of its efforts. That’s yet another challenge because enterprises today have so many different sources for their data. Moreover, the data is often siloed in various company locations, on edge devices, in data lakes and data warehouses, and more.

Unifying so many types of data from so many locations, whether on-premises or in the cloud, is the holy grail promised by modern data management, but that’s also what makes it complex and time-consuming. Much data management software has earned a lackluster reputation in many circles because of long implementation processes, scope creep, and project failures.

Modern MDM Implementations Speed up the Process

It’s time to overhaul outdated data management, including MDM approaches. The technology has been around for more than two decades. Many of the solutions available today are not cloud-native and have been retrofitted for the cloud. Modernizing and looking at ways to speed implementations should be a pressing concern for all organizations that need clean, reliable data available for operational and analytical systems in real time.

Implementing cloud-based SaaS MDM software can greatly accelerate the value businesses gain. By utilizing industry-specific and out-of-the-box solutions, time-to-value is expedited. These modern MDM velocity packs have pre-built connectors, prepackaged automation workflows, flexible embedding options, and powerful APIs. They efficiently handle structured data exchange between clients and external systems. When customers start using an MDM velocity pack, they immediately receive essential components:

• Unified, standardized core data domain configurations: A cloud-native, unified, and standardized data model acts as a common language and structure for data. This enables easy aggregation of data from various sources with different data models. The unified data can be effortlessly distributed to downstream destinations.
• Simple data onboarding: Internal first-party systems and external third-party data sources can be easily integrated for smooth data onboarding.
• Advanced entity resolution and data mastering: The software incorporates sophisticated entity resolution techniques, ensuring accurate and reliable data mastering.
• Automated Universal ID: The system automatically assigns a unique identifier to each party involved, streamlining data management.
• Comprehensive data sharing at scale: The software facilitates extensive data sharing across the organization, enabling collaboration and insight generation.

With these modern MDM starter pack solutions, there is no longer a need to build complex, bespoke MDM solutions from scratch. By installing preconfigured software, businesses can swiftly unify their core data and reap the benefits of a cloud-based SaaS offering in months.

Industry-Specific, Real-Time Cloud MDM

Delivering MDM in a consumable, less labor-intensive way is via pre-packaged solutions to help users in specific industries get a jump-start on an MDM journey. This is finally happening in MDM today. A Lifesciences MDM solution, therefore, is pre-populated with industry-specific data models, pre-built connectors for third-party data enrichments, such as the U.S. Drug Enforcement Agency (DEA) and the National Provider Identifier (NPI) registry, along with pre-built integrations with the leading database tools, leading applications, and data warehouses in use for that industry. A prepackaged solution significantly reduces implementation time and speeds up the process of getting trusted, reliable data into the hands of users much faster. And the

By having predefined universal data models for core data domains, and aligning them with leading industry standards for interoperability, MDM vendors can make it easier for customers in specific industries to collect, unify, and activate trusted, high-quality data in real-time. Customers can more easily and quickly unify data from disparate sources, enrich data from third-party sources, improve data quality, and create a single source of truth for key data domains.

It’s much like how geneticists have created building blocks to facilitate solutions like the COVID-19 vaccine more quickly than other vaccines. With MDM, these “building blocks” ease the start-up time companies normally take in figuring out how to connect their MDM software to every system and database relevant to their industry.

The result is faster time to value with their data. And when time is money, looking into solutions like quick-start MDM solutions makes sense.

Moving to the Cloud? Modern Data Management

In the legacy, on-premises world of data management software, companies have waited years for implementation and to realize value. In today’s environment, that doesn’t cut it. Digital transformation is a do-or-die proposition for incumbent companies as nimble upstarts with less baggage seek to disrupt slower, more entrenched players. Moving information to the cloud simply doesn’t cut it. You can move information to the cloud but the cloud alone doesn’t solve your existing data problems.

Organizations implementing modern data management software can make better-informed decisions while delivering a best-in-class customer experience because they have accurate and up-to-date information, and their data is unified, accurate, and trustworthy. Investing in modern data management software generates cost savings and strengthens data management capabilities. Modern data management solutions integrate data quality, governance, and data unification solutions. And the best part is that implementations are completed in weeks or months versus years.

The post Rapid Data Transformation Amid Economic and Global Uncertainty appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Zesty‘s Principal Engineer Omer Hamerman offers commentary on container rightsizing and how to balance performance and financial risk.

In today’s digital landscape, Kubernetes has emerged as the preferred solution for organizations seeking to deploy containerized applications at scale. Its ability to dynamically manage and scale applications in a distributed environment has revolutionized the way businesses operate. However, like any technology, there are challenges that come with harnessing the power of Kubernetes, and one critical aspect that demands attention is container rightsizing.

Container rightsizing is the process of optimizing the size of containers running on Kubernetes to ensure efficient utilization of resources. This optimization involves fine-tuning CPU and memory allocations for each container while considering factors such as network bandwidth and storage. For businesses leveraging Kubernetes, container rightsizing plays a pivotal role in driving cost reduction, improving resource utilization, and enhancing overall application performance.

The significance of container rightsizing cannot be overstated. By carefully managing resource allocation, organizations can achieve substantial cost savings while maximizing the potential of their Kubernetes deployments. Moreover, efficient resource utilization translates into enhanced application performance, ensuring smooth operations and an exceptional user experience.

In this article, we will delve deeper into the world of container rightsizing in Kubernetes, exploring its benefits, challenges, and actionable strategies. We will examine the importance of visibility and configuration in cost optimization, discuss the risks associated with resource provisioning, and present best practices for achieving the delicate balance between performance and financial considerations. Additionally, we will explore how container rightsizing contributes to streamlining operations and delivering a seamless user experience in the Kubernetes ecosystem.

Container Rightsizing: Why So Important?

To properly understand the critical role of container rightsizing, it’s good to first have a basic understanding of how Kubernetes distributes pods and containers across nodes. 

Kubernetes architecture involves a cluster of nodes, each running a set of pods with one or more containers, as illustrated below:

Figure 1: Kubernetes Node Overview

Kubernetes dynamically schedules pods to run on nodes based on resource availability and other factors, like affinity and anti-affinity rules. This enables Kubernetes to automatically scale applications up and down in line with changing demand, which is great. Still, there are various considerations companies have to factor in when using Kubernetes, such as cost.

The Cost of Kubernetes: Challenges

Despite the benefits of Kubernetes, cost optimization can be a significant issue for organizations using it. Several reasons for this exist, including the lack of built-in tools for cost visibility, the static configuration of workloads, and the tendency to overprovision resources at both the pod and node levels.

Visibility

By default, Kubernetes does not provide granular visibility into the expense associated with individual pods or nodes. This can make it difficult for developers to understand the cost implications of their applications and result in inefficient resource usage.

Configuration

Kubernetes workloads are configured using static CPU, memory, and storage configurations that are based on rough estimates of previous usage. This can result in overprovisioning resources to ensure performance and availability, leading to wasted resources and unnecessary spend.

Provisioning

More nodes than necessary are typically provisioned at the node level in Kubernetes to ensure application performance and availability. But this can lead to waste since each additional node increases the total cost.

There’s also the issue of provisioning the wrong instance type. For example, if you provision an instance type with higher CPU or memory than the workload requires, it will incur unnecessary costs.

Balancing Performance and Financial Risks in Kubernetes

Resource allocation in Kubernetes can be a complex and challenging task. The goal is to allocate resources in a way that optimizes app performance while minimizing total spend. However, there are several risks associated with both overprovisioning and underprovisioning resources.

Overprovisioning vs. Underprovisioning

Overprovisioning resources can lead to higher costs than necessary, as resources that no longer need to be used are still being paid for. It can also lead to resource conflict, which, in turn, can negatively impact app performance. Then there’s the false sense of security, as organizations may assume that their apps are always performing at their best when, in fact, they may be underutilizing resources.

In the case of underprovisioning, apps may experience performance issues, which can result in user dissatisfaction. Underprovisioning can also lead to operational risks since critical applications may lose access to the resources they need to function properly.

To mitigate these concerns, companies must adopt a balanced approach to resource allocation in Kubernetes. This involves accurately assessing each workload’s resource requirements and providing the appropriate amount of resources to meet those needs.

Rightsizing Containers in Kubernetes

The ideal solution for balancing performance and financial risks is rightsizing: determining the appropriate size of a container to optimize resource utilization and support app performance.

There are several Kubernetes-native solutions organizations can leverage to rightsize containers. Vertical Pod Autoscaler (VPA automatically adjusts container resource requests and limits based on historical usage patterns. Horizontal Pod Autoscaler (HPA) automatically alters the number of replicas of a container based on CPU utilization. And finally, Cluster Autoscaler can add or remove nodes based on demand.

When rightsizing containers in Kubernetes, there are several factors to consider. These include workload characteristics, scaling needs, and available resources. For example, you have to consider the CPU, memory, and storage requirements of each workload, as well as the potential impact of workload spikes on resource utilization. You also have to take into account the scaling needs of each workload and use this to determine the appropriate size for each container.

Best practices for container rightsizing in Kubernetes include:

• Conducting regular assessments of resource utilization
• Monitoring resource usage in real-time
• Leveraging Kubernetes-native solutions for better resource allocation
• Establishing clear policies and guidelines to ensure consistency and accuracy

Container rightsizing’s role in optimizing spend for the best possible performance brings another benefit: enhanced user experience.

Seamless UX in Kubernetes

A seamless user experience (UX) is critical for organizations implementing Kubernetes, as it can help streamline operations, reduce complexity, and improve overall productivity. However, Kubernetes is a challenging platform with a steep learning curve, and many struggle to achieve an intuitive UX.

Container rightsizing is key here. By optimizing resource allocation and supporting app performance, it ensures that critical applications are available and responsive when users need them. This improves overall productivity and reduces frustration and delays caused by slow or unresponsive applications.

Organizations looking to achieve container rightsizing for a seamless UX in Kubernetes are streamlining operations and continuous monitoring.

Streamlining Operations 

Companies must reduce complexity and offer users proper guidance and support; the clear policies and guidelines mentioned above for container rightsizing, as well as training and support, come into play here. 

Be  sure to leverage Kubernetes-native solutions to automate and optimize resource allocation. 

Continuous Monitoring

Making monitoring and analytics a priority is also a must to gain insight into resource utilization and to identify opportunities for improvement. By monitoring resource usage in real time and conducting regular assessments of resource utilization, organizations can spot potential bottlenecks and proactively address issues before they impact app performance. 

Bottlenecks and potential improvements are critical inputs to rightsizing since they give insights into actual resource usage.

Conclusion

Kubernetes has undoubtedly established itself as a powerful platform for managing containerized applications, providing organizations with the flexibility and scalability they need. However, as applications become more complex and dynamic, the task of container rightsizing can become increasingly challenging. It is essential to recognize that container rightsizing is not a one-time activity but rather an ongoing commitment to continuous monitoring, assessment, and adjustment.

To alleviate the burden of manual resource allocation management, cloud optimization solutions emerge as invaluable allies. These solutions streamline the container rightsizing process, allowing organizations to focus on what they do best—delivering high-quality applications—without the constant worry of resource allocation. By leveraging proper tools, businesses can improve operational efficiency, reduce unnecessary costs, and maintain a competitive edge in the ever-changing application landscape.

Continuous monitoring and assessment of resource utilization are crucial for optimizing container rightsizing. Proactive identification of potential bottlenecks and performance issues enables organizations to make informed adjustments and maintain optimal resource allocation. By embracing the power of cloud optimization solutions and implementing best practices for container rightsizing, businesses can ensure that their Kubernetes deployments are cost-effective, resource-efficient, and capable of delivering seamless user experiences.

In summary, container rightsizing in Kubernetes is an ongoing process that demands diligence and adaptability. With the right tools and a commitment to continuous improvement, organizations can unlock the full potential of Kubernetes, achieve optimal resource utilization, and stay cost efficient at the same time.

The post Container Rightsizing: Balancing Performance and Financial Risks in Kubernetes appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Ontotext‘s Doug Kimball offers commentary on how to properly interface with generative AI.

Developments in generative AI (GenAI) have reached a crescendo at what feels like hyper-speed. It has captivated our minds, imagination, and conversations over the last several months with its seemingly magical superpowers. Enterprises worldwide are analyzing Generative AI capabilities and seeking ways to leverage them for a variety of use cases to improve their competitive edge and incorporate automation and efficiency.

Terms related to GenAI such as hallucinations and Large Language Models (LLMs) have become lingua-franca for any and every business conversation. As a result, students, business professionals, developers, marketers, and others have begun exploring these “chatty robots” and discovered there is a lot to like, and some things to be concerned about. LLMs in particular have remarkable capabilities to comprehend and generate human-like text by learning intricate patterns from vast volumes of training data; however, under the hood, they are just statistical approximations.

So, What Exactly are Generative AI and LLMs?

Generative AI refers to computational models that are trained on massive amounts of text data and output in the form of text, images, video, audio, new data, and even code. An LLM, on the other hand, is a neural network model built by processing text data. Analyzing how this data relates to other text-based data allows the data to be associated with similar text.

In simple terms, these models predict which word best follows previous words by taking a broader context of the words before it. LLMs can even take tone and style into account where responses can be modified by incorporating personas such as asking ChatGPT (powered by an LLM) to explain the concept of data governance through a Taylor Swift style lyric.

Challenges & Limitations

So while innovation in AI technologies may introduce new capabilities and uncover new opportunities, it quickly runs into problems associated with data governance and quality, trust, bias, and ethics. For example, if input training data is of bad quality, the results from AI algorithms will be substandard too. LLMs routinely generate superficial and inaccurate information, are non-deterministic, unreliable, and suffer from being trained using stale data. They are also incapable of providing provenance or pointers to data sets that let users know how the results were obtained.

As a result, and even if they sound realistic, these LLMs routinely produce bad responses based on outdated training data, exhibit random hallucinations, create bias, and lack real-world context. This is because they don’t care about conflicting or ambiguous information, they often make up an answer randomly based on certain parameters. But as one could imagine, this can produce catastrophic results with buggy pipeline code, bad and/or suboptimal implementation logic, inappropriate answers, or just plain toxic information. They run the risk of using trademarked, copyrighted, or protected data as they scour public data and can be easily exploited and manipulated to ignore previous instructions. Worse, LLMS can be made to execute malicious code, commands, or unintended actions by creative prompting.

Additionally, data is the fulcrum of AI, and the data used to train LLMs must be properly governed and controlled. Otherwise, any LLM deployed in production would run the risk of basing their decisions on poor quality data, exposing privacy, intellectual property, bias, and ethical issues. Evaluating the trustworthiness of LLMs is notoriously difficult as well as they don’t have any ground truth label. This causes organizations to struggle to identify and benchmark as to when the model can be trusted.

Building Governance, Security, and Trust with LLMs

Before jumping on the generative AI bandwagon, organizations should do their homework and clearly understand the risks, challenges, and negative consequences of leveraging LLMs. Otherwise, they are like a black box, where very little is known as to how they arrive at answers and responses and organizations can lose control of private data, GenAI pipelines can get compromised, or applications can be attacked in subtle ways by hackers. To avoid this, enterprises should consider:

• A comprehensive data strategy: to align data and AI initiatives with business objectives. In industries and domains where compliance and regulations are mandatory, generative AI techniques need to be cross-pollinated with complementary capabilities to ensure transparency, and its responses must be verified before using it in production systems. Organizations should establish effective ways to refer to trusted heterogeneous data from disparate sources to effectively support LLMs and their associated applications and minimize errors.
• A centralized, cross-functional platform team and adoption framework: This should include governance and risk mitigation, guidelines, guardrails, and an organization-wide consensus on how LLMs can be used for business processes. The framework will also ensure inputs/outputs have context, and are reliable, trustworthy, and understandable.
• Creating a governance team: This will add specific policy guidelines, train data engineers, data scientists, and data quality teams accordingly, and make sure data stewards enforce them. Leveraging the adoption framework, this team will help ensure proper data quality, security, and compliance.
• Building a center of excellence for best practices: For LLM-assisted data management and analytics. This should include up-skilling programs and talent management strategies to foster a culture of continuous learning about changing data and architecture patterns.

LLMs with Knowledge Graphs

Knowledge graphs (KGs) are becoming increasingly important when it comes to making generative AI initiatives and LLMs more successful. Created by integrating heterogeneous datasets across diverse sources, KGs provide a structured representation of data that models entities, relationships, and attributes in the data in a graph-like structure. With interlinked descriptions of concepts and entities, KGs provide context which helps improve comprehension.

So how do organizations address these needs from a data perspective? They accomplish this by implementing a semantic KG that is based on factual data, has the ability to inject and enrich context to the prompts given to LLMs, and can direct the LLM engine towards higher accuracy and relevance. Additionally, KGs are very effective in validating the integrity and consistency of LLM responses. These responses can be represented as a graph of connected nodes, which can be further validated by an organization’s domain-specific KG. This addresses bias, consistency, and the integrity of the data and/or facts and ensures regulations and compliance are adhered to.

The synergy between a KG-enhanced or supplemented LLM can go a long way to mitigating incorrect information and to enhancing its accuracy. KGs help identify sensitive information, compliance errors, and ethical violations which minimizes associated risks. More importantly, a KG-based data model provides transparency to the responses generated by LLMs and allows answers that can be trusted.

In a nutshell, generative AI cannot be a standalone tool in a toolbox. There needs to be a strong synergy and collaborative partnership between LLMs and KGs with a feedback loop of continuous improvement. KGs offer comprehensive context that continually improves the performance of LLMs. They also provide guardrails to prohibit and prevent hallucinations and from having them give inconsistent answers to critical enterprise questions.

Final Thoughts

With its magic and pitfalls, GenAI has simultaneously opened both a box of jewels and Pandora’s Box. Almost every organization is asking the same question, “Do we go fast and adopt this technology, or do we leverage it in the right and responsible way and, if the latter, what is it?”

Leaders need to balance the adoption of generative AI with the risks involved, but it is a true joint effort. Teams need to outline ethical principles and guidelines, factoring in the specific risks of each use case and organizations must balance innovation with risk management by establishing robust governance frameworks to mitigate associated risks.

KGs are proving to be a highly effective tool to navigate the challenges and complex landscape of risk and governance management. Knowledge graphs help to establish a clear understanding and explain effective ways to access and use trusted data from disparate sources to effectively support LLMs and their associated applications, More importantly, they enable organizations to confidently leverage the true power and promise of generative AI and LLMs.

The post Generative AI – How to Care For, and Properly Feed, Chatty Robots appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Veeam CIO Nate Kurtz offers a commentary on key steps enterprises need to take after they’ve suffered a ransomware attack.

The infamous MoveIt tool threatening enterprises everywhere has, of late, begun breaching companies that don’t even use it, simply because their business partners do. Cyberattacks are proliferating with concerning ease and speed, and not everyone is prepared for it.

As a CIO myself, I’m keenly aware of the pressures CIO’s face, and have worked alongside Veeam’s own CISO to develop a strategic, targeted response to cyberattacks. What I’ve found is: there are four crucial measures to an effective post-attack response.

Observe

When faced with a ransomware attack, our first instinct from a security perspective is to eliminate the threat and resolve the issue. Truthfully, this isn’t the best move.

Instead, a CIO should first focus on quickly isolating the bad actor within the environment. Sequestering them without removal is helpful because 1) it prevents the bad actor from harming other parts of the environment, and 2) it allows you to observe their actions. Eliminating or resolving the threat is tempting but it often prevents the opportunity to analyze the threat actor’s actions, which can reveal a lot about their intent, target, and strategy, as well as the company’s own vulnerabilities. It is also critical to understand the extent of the compromise both from a systems and data perspective.

Critical observation will help CIOs gain a better understanding of how the threat actor operated, and down the line, this knowledge will also help develop a proactive approach for the next ransomware attack.

Correct

Now that you have a comprehensive understanding of how the attacker infiltrated your company, you can take corrective measures.

What do ‘corrective measures’ entail? Namely, removing the threat, patching up the attack vector, recovering systems and data, and addressing any other damage the attacker may have caused. Once a CIO has done the necessary footwork to obtain valuable data on attacker intent, behavior patterns, knowledge, and impact, it’s high time the attacker be eliminated. In the observation stage, the attack is siloed off to prevent them from accessing and harming more of the company’s data processes. Pull the necessary tools required for removal and do so with the knowledge that they will not be able to

immediately return through their original breach, or any other potential vulnerability visible to the artificial eye.

Once the attacker’s presence has been removed, a CIO can review the damage done in full, checking through valuable data, backups, logs, and what seems to be missing and if it can be recovered or has a copy, and what may require further action.

Prevent

With the threat actor removed and the breach secured, CIOs can kick off preventative measures to avoid undergoing such an attack again. Scanning security measures will help identify any immediate gaps or vulnerabilities in your attack surface.

While an attacker may not return to the scene of the crime for another go, knowing their point of attack can help patch the vulnerability and protect against another threat. In reviewing the criminal profile stemming from the attack, as a CIO, you must focus on the key variables at play: the target, the attacker’s identity, the actions they took, and the impact they caused. These factors are crucial to determining next steps to reduce future risks. Identify the pattern of behavior to determine if similar activity could cause another, or wider, breach.

Security vulnerabilities are often seen as technical issues, but the biggest risk is the people working within the organization. Most attackers enter companies through human engineering – phishing scams or the like, preying on the distracted employee. In such cases that lead to an attack, you could immediately restrict or lock down access for employees to avoid further harm.

Only when you have taken all the precautionary measures above to reduce or eliminate further threats can you move on to stage four: relaying the news.

Notify

It’s never fun breaking the news of a ransomware attack to your stakeholders. But transparency is valuable to retaining trust and loyalty while keeping the industry informed about emerging threats.

You must be purposeful in your notification. Sharing everything without a plan not only risks the company reputation, but also leaves you vulnerable to future attacks. Instead, start by reaching out to key parties – the board, the company’s legal team, and business stakeholders. If there has been a loss or theft of customer data, this can open the door to legal repercussions. Coordinate with your legal team and board to align on messaging and what information on the attack can be shared, with whom, and when.

It can take days to weeks to address an attack sequentially and thoughtfully. By this time, you will likely have the information to provide and be able to reassure customers of your company’s commitment to protecting their data, and the actionable steps taken to prevent more attacks. Doing so demonstrates customer value, helps retain customer loyalty and trust.

What Comes Next?

While ransomware attackers don’t normally target the same gap twice, they can, and likely will, strike again. Taking a backward approach and securing already-breached zones is not going to be effective for long. Instead, CIOs should consider the potential vulnerabilities and targets to get in front of before an attack can occur.

In the end, CIOs that follow the post-ransomware attack procedure, in whatever capacity, should operate with a primary goal in mind: To secure the future of the company.

The post Key Steps a CIO Should Take after a Ransomware Attack appeared first on Solutions Review Technology News and Vendor Reviews.

As part of Solutions Review’s Contributed Content Series—a collection of articles written by industry thought leaders in maturing software categories—Bernd Ruecker, the Co-Founder and Chief Technologist at Camunda, identifies the four steps needed to develop and maintain a successful process automation journey.

While surveys show more than nine in ten enterprises already deploy process automation and consider it a mission-critical force in their operations, many are only scratching the surface when it comes to generating value from the practice. Most are only automating single tasks. Few are taking full advantage of all that automation has to offer. 

To either get started with automation completely or improve the end-to-end perspective, you generally need to follow four steps: Discover, Design, Automate, and Improve your processes—and then repeat.  

Discover 

Let’s start with the obvious. Before implementing automated processes, you must know what you’re working with. What processes do you perform, and how are they working? What tasks need to be done in what sequence? Which people are involved, and what systems are used? You need to discover and understand the status quo first. 

Part of the discovery process is to mobilize your audience to gather all the information you can. Identify key stakeholders and set up meetings. Pull together a list of critical tasks and determine at a holistic level what’s going right, what’s going wrong, and what appears to be the most impactful changes you can make through process improvement. 

However, the discovery process should go beyond collecting anecdotes. Ideally, you can gather data to back up the assumptions you make. Real-life data can also point you to insights nobody in your organization had. One way to use existing data to gather process insights is through process mining. Process mining tools help users better understand how a process is automated within core systems like ERP or CRM. Typically, this requires loading and analyzing log files from these and other systems to discover correlations and process flows. Process mining tools can discover a process model and display it graphically. They also can turn up data that can identify bottlenecks or optimization opportunities. 

Design 

Once you’ve gathered the data and developed some conceptual plans, you can start on the design. A critical step is getting business and IT stakeholders to communicate as one. That’s not always easy. End-to-end business processes are complex and often span people from various departments, systems, and devices. The complexity of the process flow and the diversity of technology running in the background can make it difficult for stakeholders to visualize exactly what’s happening and communicate with each other as they work. 

Modeling languages ​​like Business Process Model and Notation (BPMN) can help. They visually represent process flows and dependencies through a flowchart, and a workflow engine can execute these BPMN models directly. This means the visual diagram is also a piece of program code simultaneously. 

Visually representing a complex process helps to break down communication barriers between different roles to better discuss what’s needed from the business and what’s technically feasible. It allows teams to rethink processes in general and agree on a chosen design before writing code. Also, later in production, generated audit data connected directly to the process model helps teams to iteratively improve a process. In this regard, BPMN can help teams take a more agile approach to solving problems. You can quickly create a minimum viable product (MVP) solution that addresses the issue you’re trying to fix. From there, you can make data-backed improvements iteratively and deploy the newest version during the next development cycle.  

Designing a process forces teams to take a closer look at it. Bringing all the stakeholders involved together on one table offers the chance to improve processes in general and to do a better job creating processes from scratch and to improve processes in general. Combining the visual nature of BPMN with a user-friendly way to model processes speeds up the creation of innovative solutions. 

Automate 

The next stage is where an organization starts actually automating tasks and processes. This is where process orchestration comes in. It often happens that only a task is automated locally with a single endpoint. But real business processes follow a much more complex logic, which isn’t easy to automate. Process orchestration software connects and coordinates the endpoints (systems, people, devices) of all business process tasks and allows for end-to-end automation. 

The workflow language is of importance once again. The above-mentioned BPMN, for example, can express many common workflow patterns out of the box, bringing a lot of clarity and understanding to every process detail. ​​These patterns handle complex business process logic, such as executing process flows in parallel, message correlation, escalating events, or dealing with a fatal error. 

One good example is dynamic parallel execution. BPMN makes it easy to coordinate many concurrent tasks. Imagine a retail setting where customers purchase multiple items in an online store. As the customer orders, the back-end system checks product availability and updates information in other systems, including finance, logistics, and CRM, for every ordered item in parallel. 

In a second pattern—message correlation and abortion—BPMN sorts out confusion in a process where someone is canceling a task. Picture a situation where a customer cancels their order via the web portal. Interrupting a workflow with many tasks spread across multiple distributed systems can be challenging at scale, especially as the handling might need to be different depending on the order’s current status. This gets much easier to master using BPMN and a workflow engine. 

In a third example, with the help of BPMN and a workflow engine, a process instance can be escalated if it’s not completed in an agreed-upon period. Essentially, if a bill isn’t paid on time, a process can be triggered to remind the customer with an automated email. Using BPMN, the process is coordinated across the firm’s business email and accounting system. 

Improve 

The work doesn’t end once a process is automated end-to-end. The automated functions are an excellent basis for continuous improvements. For example, it can make processes more efficient, less taxing, and more cost-effective. So, there should be a mechanism in the process improvement loop where the organization learns how to perform processes better. 

This needs to incorporate live data. How many transactions went through smoothly? What were typical cycle times? Are there outliers, and what do they have in common? Analytical tools and dashboards can help designers spot bottlenecks in the system and recommend ongoing improvements in process flows. 

Conclusion

Process automation is a mission-critical function for organizations across geographies and industries. Those who have put off implementing end-to-end automation plans are missing out on opportunities for ongoing improvement. Starting slow, planning ahead, and executing a strategy based on four basic steps—Discover, Design, Automate, and Improve—can get them on track for success.    

The post The Four Steps in a Successful Process Automation Journey appeared first on Solutions Review Technology News and Vendor Reviews.

As part of Solutions Review’s Contributed Content Series—a collection of contributed columns written by industry experts in maturing software categories—Josh Wetzel, the Chief Revenue Officer at OneSignal, explores how customer engagement and retention strategies can help companies maximize ROI during economic uncertainties.

In today’s challenging economic landscape, many companies face increasing pressure to cut costs and maximize return on investment (ROI). As businesses reevaluate their budgets, marketing spending is often one of the first to be reduced or scrutinized. However, studies suggest that focusing solely on cost-cutting measures may not be the most advantageous approach during economic challenges. Research conducted during the 2008 financial crisis revealed that companies prioritizing customer retention over acquisition experienced higher growth rates. OneSignal recently released the findings of its 2023 State of Customer Messaging Report that further proved this and showed the true value of investing in customer retention in a down economy.  

The True Value of Customer Retention  

While many businesses understand the value of building strong customer relationships, they often fail to take advantage of these opportunities by focusing primarily on customer acquisition. Acquiring a new customer is typically more expensive than retaining an existing one, and building and maintaining customer loyalty is more cost-effective in the long run. Selling to existing customers also yields higher success rates than selling to new customers. Engaged and valued customers are more likely to recommend a brand, leading to organic growth.   

So, why are so many businesses focusing on acquisition and struggling to shift their strategies? Some may lack the necessary resources and engagement tools to retain customers effectively. These businesses need to understand that just a small shift in resources from acquisition to retention can significantly impact ROI. The 2023 State of Customer Messaging Report showed that increasing customer retention rates by just 5 percent can substantially increase overall profits, ranging from 25 percent to 95 percent.   

To understand the actual value of user retention, businesses must evaluate current spending on customer acquisition and determine its ROI. This involves calculating customer acquisition costs by dividing total marketing and sales expenses by the number of new customers acquired. When evaluating the budget, it is important to consider both direct expenses, such as paid advertising campaigns, and indirect expenses, including marketing and sales team salaries, agency fees, and software costs. Ideally, the cost of acquiring a customer should be lower than the revenue generated from that customer over their lifetime, but this is not typically the case. Companies can substantially impact customer lifetime value (LTV) and enhance profitability during challenging economic periods by slightly reducing customer acquisition spending and reallocating savings toward customer engagement.  

How to Drive Engagement and Boost Long-Term Retention  

To increase customer engagement and retention, businesses must implement effective engagement strategies and invest in the right solutions. These tools play a vital role in retaining users by enabling automated, personalized communication, customized multichannel engagement strategies, and data-driven insights.   

Personalization plays a crucial role in customer engagement by using real-time data to customize recommendations, target offers, and tailor messaging to specific user segments. User segmentation plays a key role in personalization, enabling companies to create dynamic groups of users with shared characteristics, behaviors, and preferences. This approach leads to higher click-through rates and allows efficient resource allocation by identifying high-value users with higher ROI potential.  

Embracing a multichannel approach in customer engagement is essential as it allows businesses to extend their reach, cater to user preferences, enhance the user experience, and foster stronger brand loyalty. People actively use more devices daily than ever, and they expect companies to adapt their customer experience in tandem with their real-time engagement. Aligning messaging with the specific characteristics of each engagement channel optimizes their effectiveness. For example, push notifications and SMS are ideal for time-sensitive content, while email provides more space for comprehensive information. Companies with a personalized, omnichannel engagement strategy experience over three times higher click-through rates than those relying on a single channel.  

Adopting these engagement strategies can help businesses remain competitive during uncertain economic times. While cost-cutting measures may be tempting, prioritizing customer engagement and retention proves to be more advantageous. By shifting budgets from customer acquisition and investing in effective customer engagement solutions, businesses can build stronger customer relationships through relevant, timely, and personalized communication. With a stronger focus on customer engagement and retention, companies can maximize returns and build a sustainable future.

The post Maximizing Returns in an Economic Downturn: The Power of Customer Engagement and Retention appeared first on Solutions Review Technology News and Vendor Reviews.

As part of Solutions Review’s Contributed Content Series—a collection of articles written by industry thought leaders in maturing software categories—Rick Rider, the Vice President of Product Management at Infor, explains why enterprise simulations can help your company onboard emerging technologies (like generative AI).

Whether pursuing early adoption or taking a measured approach, the way companies react to new generative AI tools, such as ChatGPT and others, tells a lot about their approach to innovation. As we experience the AI renaissance and businesses turn their attention to modernizing processes, new enterprise software solutions powered by generative AI will become a large part of how companies embrace change. Generative AI is increasingly seen as a solution that can help enterprises make large-scale decisions to reduce inefficiencies and costs while driving growth. 

However, the promise of emerging technologies must be taken with a degree of caution and consideration. Today, it is paramount for companies to critically analyze their approach to innovation and draw in team members and stakeholders from various roles and disciplines to help inform their path forward.  

Taking a Methodical, Strategic, and Creative Approach to Innovation  

Companies must allow for creative team approaches and new ideas without impacting their businesses. According to Gartner, fusion team models are becoming increasingly important to enterprises to design, deliver, and improve digital offerings. For background, a fusion team is defined as a multidisciplinary team that blends technology or analytics and business domain expertise and shares accountability for business and technology outcomes. To effectively transform to a fusion team model and democratize digital delivery by design, Gartner points to the importance of composable technology, which enables businesses to create custom software applications composed of freely interchangeable components to support rapid and secure digital capability growth.  

With these findings in mind, companies poised for success will partner with various teams within their organization and their customers to help create new ideas, products, and solutions. For example, companies that embrace creative team approaches and prioritize agility have likely welcomed the possibilities of ChatGPT and other new and emerging technologies shaping the future of how enterprises work.  

Trying Before Buying: The Enterprise Simulation Solution  

An advantageous way that businesses are enabling creative and holistic approaches to innovation is by leveraging the practice of enterprise simulations. Today, companies often need to simulate decision-making with large amounts of contributing sources and data. In the case of generative AI, the technology will add to the available data pools for connected patterns and data.  

Similarly, generative AI models can be formidable allies to the industry-specific machine learning and deep learning models used for simulations. Large Language Models (LLMs) can aid data scientists by generating the test data quickly and with a low barrier of natural language prompts. As the demand for enterprise simulations grows, combining these new technologies is an efficient mix that empowers businesses, encourages them to experiment with new practices at a significantly lower cost and time, and delivers solutions to meet the demands of transforming industries.  

For example, businesses can leverage an enterprise simulation strategy to create a “digital twin” on business segments to properly test theories and ultimately make more informed decisions, such as changing production levels. Business simulations have been around for a while to coach and teach students. Still, this application focuses on building simulation environments specific to one’s organization and all the details and intricacies. By leveraging a business simulation, organizations can take a deeper look into the details and intricacies of their business. Furthermore, organizations can utilize these insights to improve processes. 

When businesses move from storing large volumes of data loads to extracting key and meaningful insights from simulations, they are well on their way to better-informed decision-making and growth. Generative AI tools such as ChatGPT now introduce additional opportunities to interact and create enterprise simulations to inform decisions quickly and easily. 

Proceed with Caution 

Generative AI and recent cases involving ChatGPT remind businesses to proceed with adopting newer technologies with a degree of caution. Inaccuracies, biases, and privacy concerns require keen human oversight.  

To help organizations safely leverage generative AI models, it will behoove leaders and teams alike to leverage safety filers and attributes to promote the responsible use of AI. Users turn to filters to mitigate risks using new AI models to inform more reliable outputs. For example, Vertex AI, a machine learning platform that enables users to customize Large Language Models for use in AI-powered applications, processes and assesses content, filtering sensitive and potentially “harmful” outputs.  

An additional approach is using training models using enterprises’ unbiased data. As the widespread adoption of AI tools unfolds, users will be introduced to more ways to overcome the challenges of accuracy, data privacy, and biases associated with using the technology. Enterprises that adopt the new technology earlier on improve the literacy and capabilities of generative AI to drive digital growth. 

A Step Forward 

Over the past decade, the technology industry has witnessed many emerging technologies that have shifted the course of business as we know it—from the notion of “Big Data” to the Internet of Things (IoT). It’s no secret that generative AI is the transformative technology of the year (and possibly many years). However, it is essential not to get swept up in the promise of how emerging technologies can uplevel businesses. Moreover, it is important to approach disruptive technologies strategically.

As the pace of innovation increases and businesses continue to evaluate their agility to embrace new technologies, enterprises that leave room for creative team approaches will adapt to the changing technology landscape ahead of competitors.

Widget not in any sidebars

The post The Value of Using Enterprise Simulations When Onboarding Emerging Technologies appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Executive Editor Tim King offers this guide to the essential AWS FinOps best practices to know right now.

FinOps, short for Financial Operations, is a set of practices and methodologies that brings together the worlds of technology and finance to manage cloud costs effectively. Think of it like applying engineering principles to the financial aspect of cloud computing. FinOps encourages you to approach cloud costs with the same engineering mindset.

FinOps employs data-driven insights to understand how your applications and services are utilizing cloud resources. In FinOps, you “tag” or “label” cloud resources with metadata that provides context about their purpose, project, or team. This acts like annotations for your cloud infrastructure, making it easier to track who’s using what and for what purpose in terms of costs.

Remember that the FinOps best practices you adopt should be tailored to your organization’s specific goals, cloud usage patterns, and business requirements. Experts recommend regularly reassessing and adapting your FinOps strategies as your cloud environment evolves. However, and with all that in mind, we thought it’d be worthwhile to compile the essential list of AWS FinOps best practices to know right now:

AWS FinOps Best Practices

Tagging & Labeling

Tagging and labeling serve as powerful organizational tools that facilitate precise financial management of cloud resources. By strategically implementing a robust tagging and labeling system, you’re effectively annotating your cloud infrastructure, allowing you to trace costs back to specific projects, departments, or teams.

This fine-grained visibility empowers your organization to allocate expenses accurately, identify areas of potential overspending or underutilization, and ultimately make informed decisions that harmonize technological innovation with financial prudence.

Right-Sizing

Right-sizing offers a strategic pathway to optimize cloud costs while maintaining performance excellence. This involves carefully assessing the computing power, memory, and storage needed for each application or service, avoiding the common pitfalls of over-provisioning (allocating more resources than necessary) or underutilization (wasting resources).

This not only leads to substantial cost savings but also enhances the efficiency of your cloud environment, driving higher value from your investments.

Reserved Instances & Savings Plans

RIs enable you to commit to a specific instance configuration for a defined period, securing substantial cost savings compared to on-demand pricing. Savings Plans, on the other hand, offer more flexibility by granting a discount on usage across a variety of instance types, providing greater adaptability as your workloads evolve.

Both mechanisms essentially act as forward-looking investments, aligning your cloud expenses with your organization’s anticipated usage. This strategic approach ensures that your cloud resources are allocated intelligently, striking a balance between innovation and fiscal prudence, ultimately contributing to the financial health of the organization.

Cost Visibility & Transparency

Cost visibility and transparency hold immense significance for underpinning effective cloud cost management. Imagine them as a powerful lens that grants unparalleled insight into your cloud ecosystem’s financial landscape. By embracing these principles, you’re equipped with the capability to illuminate the intricacies of your organization’s cloud spending, tracking costs with accuracy and granularity.

This knowledge empowers you to make informed decisions, align technology initiatives with budgetary constraints, and foster a culture of financial responsibility across your technical teams.

Budgeting & Forecasting

Budgeting and forecasting, as integral components of the FinOps framework, hold a crucial role for a CTO, acting as a compass that navigates the organization’s cloud expenditure with precision and foresight. Picture them as dynamic blueprints that guide your cloud-related decisions, seamlessly merging technological aspirations with fiscal realities.

With this strategic alignment of technology ambitions and financial constraints, you’re positioned to make proactive adjustments, seize cost-saving opportunities, and make well-informed choices that harmonize innovation with fiscal responsibility.

Cloud Governance

Cloud governance encompasses a spectrum of responsibilities, from defining who has access to cloud resources to establishing guidelines for resource provisioning, security protocols, and cost management practices. It empowers you to set up approval workflows for resource creation, ensure adherence to compliance standards, and manage risks associated with cloud costs.

By embracing cloud governance within the context of FinOps, you’re adopting a unified vision where cloud resources are provisioned judiciously, costs are transparent and controlled, and innovation is fortified by a framework that safeguards the organization’s finances.

Auto-scaling & Automation

Auto-scaling empowers your applications to automatically adjust resources in response to fluctuations in demand, ensuring you only use what’s needed at any given moment, thereby avoiding unnecessary expenses during periods of low activity. Paired with automation, which involves scripting and orchestrating various cloud operations, you’re streamlining processes, reducing manual intervention, and eliminating the risk of human errors that could lead to cost overruns.

Regular Review & Optimization

Regular reviews involve systematically examining cloud expenditures, resource utilization, and performance metrics, pinpointing areas of excess spending or underutilization. This proactive scrutiny not only keeps you attuned to emerging cost trends but also provides the insights needed to identify opportunities for optimization.

Optimization, in turn, involves making strategic adjustments to your cloud environment, ranging from resizing instances to modifying storage configurations or adopting serverless architectures. This process is rooted in the principle of extracting the maximum value from every cloud dollar spent.

Use of Well-Architected Frameworks

By embracing well-architected frameworks, you’re essentially adopting proven design principles that encompass operational excellence, cost optimization, security, reliability, and performance efficiency. This approach not only ensures that your cloud resources are provisioned optimally but also safeguards against unnecessary expenses that might arise from overprovisioning or suboptimal configurations.

Continuous Improvement

By embracing continuous improvement within the context of FinOps, you’re committing to regular assessments of your cloud environment, cost patterns, and optimization strategies. This entails gathering feedback from teams, analyzing data-driven insights, and leveraging lessons learned to iteratively enhance your cloud operations.

This approach not only ensures that your organization remains responsive to changing demands but also cultivates a culture of innovation and fiscal responsibility.

The post Cloud Costs: 10 Essential AWS FinOps Best Practices to Know appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review’s Executive Editor Tim King offers this guide to the essential Azure FinOps best practices to know right now.

FinOps, short for Financial Operations, is a set of practices and methodologies that brings together the worlds of technology and finance to manage cloud costs effectively. Think of it like applying engineering principles to the financial aspect of cloud computing. FinOps encourages you to approach cloud costs with the same engineering mindset.

FinOps employs data-driven insights to understand how your applications and services are utilizing cloud resources. In FinOps, you “tag” or “label” cloud resources with metadata that provides context about their purpose, project, or team. This acts like annotations for your cloud infrastructure, making it easier to track who’s using what and for what purpose in terms of costs.

Remember that the FinOps best practices you adopt should be tailored to your organization’s specific goals, cloud usage patterns, and business requirements. Experts recommend regularly reassessing and adapting your FinOps strategies as your cloud environment evolves. However, and with all that in mind, we thought it’d be worthwhile to compile the essential list of Azure FinOps best practices to know right now:

Azure FinOps Best Practices

Tagging & Labeling

Tagging and labeling serve as powerful organizational tools that facilitate precise financial management of cloud resources. By strategically implementing a robust tagging and labeling system, you’re effectively annotating your cloud infrastructure, allowing you to trace costs back to specific projects, departments, or teams.

This fine-grained visibility empowers your organization to allocate expenses accurately, identify areas of potential overspending or underutilization, and ultimately make informed decisions that harmonize technological innovation with financial prudence.

Right-Sizing

Right-sizing offers a strategic pathway to optimize cloud costs while maintaining performance excellence. This involves carefully assessing the computing power, memory, and storage needed for each application or service, avoiding the common pitfalls of over-provisioning (allocating more resources than necessary) or underutilization (wasting resources).

This not only leads to substantial cost savings but also enhances the efficiency of your cloud environment, driving higher value from your investments.

Reserved Instances & Savings Plans

RIs enable you to commit to a specific instance configuration for a defined period, securing substantial cost savings compared to on-demand pricing. Savings Plans, on the other hand, offer more flexibility by granting a discount on usage across a variety of instance types, providing greater adaptability as your workloads evolve.

Both mechanisms essentially act as forward-looking investments, aligning your cloud expenses with your organization’s anticipated usage. This strategic approach ensures that your cloud resources are allocated intelligently, striking a balance between innovation and fiscal prudence, ultimately contributing to the financial health of the organization.

Cost Visibility & Transparency

Cost visibility and transparency hold immense significance for underpinning effective cloud cost management. Imagine them as a powerful lens that grants unparalleled insight into your cloud ecosystem’s financial landscape. By embracing these principles, you’re equipped with the capability to illuminate the intricacies of your organization’s cloud spending, tracking costs with accuracy and granularity.

This knowledge empowers you to make informed decisions, align technology initiatives with budgetary constraints, and foster a culture of financial responsibility across your technical teams.

Budgeting & Forecasting

Budgeting and forecasting, as integral components of the FinOps framework, hold a crucial role for a CTO, acting as a compass that navigates the organization’s cloud expenditure with precision and foresight. Picture them as dynamic blueprints that guide your cloud-related decisions, seamlessly merging technological aspirations with fiscal realities.

With this strategic alignment of technology ambitions and financial constraints, you’re positioned to make proactive adjustments, seize cost-saving opportunities, and make well-informed choices that harmonize innovation with fiscal responsibility.

Cloud Governance

Cloud governance encompasses a spectrum of responsibilities, from defining who has access to cloud resources to establishing guidelines for resource provisioning, security protocols, and cost management practices. It empowers you to set up approval workflows for resource creation, ensure adherence to compliance standards, and manage risks associated with cloud costs.

By embracing cloud governance within the context of FinOps, you’re adopting a unified vision where cloud resources are provisioned judiciously, costs are transparent and controlled, and innovation is fortified by a framework that safeguards the organization’s finances.

Auto-scaling & Automation

Auto-scaling empowers your applications to automatically adjust resources in response to fluctuations in demand, ensuring you only use what’s needed at any given moment, thereby avoiding unnecessary expenses during periods of low activity. Paired with automation, which involves scripting and orchestrating various cloud operations, you’re streamlining processes, reducing manual intervention, and eliminating the risk of human errors that could lead to cost overruns.

Regular Review & Optimization

Regular reviews involve systematically examining cloud expenditures, resource utilization, and performance metrics, pinpointing areas of excess spending or underutilization. This proactive scrutiny not only keeps you attuned to emerging cost trends but also provides the insights needed to identify opportunities for optimization.

Optimization, in turn, involves making strategic adjustments to your cloud environment, ranging from resizing instances to modifying storage configurations or adopting serverless architectures. This process is rooted in the principle of extracting the maximum value from every cloud dollar spent.

Use of Well-Architected Frameworks

By embracing well-architected frameworks, you’re essentially adopting proven design principles that encompass operational excellence, cost optimization, security, reliability, and performance efficiency. This approach not only ensures that your cloud resources are provisioned optimally but also safeguards against unnecessary expenses that might arise from overprovisioning or suboptimal configurations.

Continuous Improvement

By embracing continuous improvement within the context of FinOps, you’re committing to regular assessments of your cloud environment, cost patterns, and optimization strategies. This entails gathering feedback from teams, analyzing data-driven insights, and leveraging lessons learned to iteratively enhance your cloud operations.

This approach not only ensures that your organization remains responsive to changing demands but also cultivates a culture of innovation and fiscal responsibility.

The post Cloud Costs: 10 Essential Azure FinOps Best Practices to Know appeared first on Solutions Review Technology News and Vendor Reviews.