A Complementary Partnership

“Data is the new currency.”— has gained immense popularity in recent years as data is now a highly valuable and sought-after resource. Overtime data continues to be accumulated and is becoming increasingly abundant.​​ The focus has now shifted from acquiring data to effectively managing and protecting it. As a result, the design and structure of data systems have become a crucial area of interest, and research into the most effective methods for unlocking its potential is ongoing.

While innovation and new ways keep coming to the fore, the best of the ideas currently consists of two distinct approaches in the form of data mesh and data fabric. Although both aim to address the challenge of managing data in a decentralized and scalable manner, they have different approaches and benefits, and they differ in their philosophy, implementation, and focus.

Data Mesh

The architectural pattern was introduced by Zhamak Dehghani for data management platforms that emphasize decentralized data ownership, discovery, and governance. It is designed to help organizations achieve data autonomy by empowering teams to take ownership of their data and provide them with the tools to manage it effectively. Data mesh enables organizations to create and discover data faster through data autonomy. This contrasts with the more prevalent monolith and centralized approach where data creation, discovery, and governance are the responsibility of just one or a few domain-agnostic team(s). The goal of data mesh is to promote data-driven decision-making and increase transparency, break down data silos, and create a more agile and efficient data landscape while reducing the risk of data duplication.

Building Blocks of Data Mesh

Data Mesh Building Blocks

Data Mesh Architecture

Since data mesh involves a decentralized form of architecture and is heavily dependent on the various domains and stakeholders, the architecture is often customized and driven as per organizational needs. The technical design of a data mesh thus becomes specific to an organization’s team structure and its technology stack. The diagram below depicts a possible data mesh architecture.

Data Mesh Architecture

It is crucial that every organization designs its own roadmap to data mesh with conscious and collective involvement of all the teams, departments, and line of Business (LoBs), with a clear understanding of their own set of responsibilities in maintaining the data mesh.

Data Mesh Management Teams
Data mesh is primarily an organizational approach, and that's why you can't buy a data mesh from a vendor.

Data Fabric

Data Fabric is not an application or software package; it’s an architectural pattern that brings together diverse data sources and systems, regardless of location, for enabling data discovery and consumption for a variety of purposes while enforcing data governance. A data fabric does not require a change to the ownership structure of the diverse data sets like in a data mesh. It strives to increase data velocity by overlaying an intelligent semantic fabric of discoverability, consumption, and governance on a diverse set of data sources. Data sources can include on-prem or cloud databases, warehouses, and data lakes. The common denominator in all data fabric applications is the use of a unified information architecture, which provides a holistic view of operational and analytical data for better decision-making. As a unifying management layer, data fabric provides a flexible, secure, and intelligent solution for integrating and managing disparate data sources. The goal of a data fabric is to establish a unified data layer that hides the technical intricacies and variety of the data sources it encompasses.  

Data Fabric Architecture

It is an architectural approach that simplifies data access in an organization and facilitates self-service data consumption. Ultimately, this architecture facilitates the automation of data discovery, governance, and consumption through integrated end-to-end data management capabilities. Irrespective of the target audience and mission statement, a data fabric delivers the data needed for better decision-making.

Data Mesh

Principles of Data Fabric

Principles of Data Fabric
Parameters Data Mesh Data Fabric
Data Ownership
Decentralized
Agnostic
Focus
High data quality and ownership based on expertise
Accessibility and integration of data sources
Architecture
Domain-centric and customized as per organizational needs and structure
Agnostic to internal design with an intelligent semantic layer on top of existing diverse data sources
Scalability
Designed to scale horizontally, with each team having their own scalable data product stack
Supports unified layer across an enterprise with the scalability of the managed semantic layer abstracted away in the implementation

Both data mesh and data fabric aim to address the challenge of managing data in a decentralized and scalable manner. The choice between the two will depend on the specific needs of the organization, such as the level of data ownership, the focus on governance or accessibility, and the desired architecture.

It is important to consider both data mesh and data fabric as potential solutions when looking to manage data in a decentralized and scalable manner.

Enhancing Data Management: The Synergy of Data Mesh and Data Fabric

A common prevailing misunderstanding is that data mesh and data fabric infrastructures are exclusive to each other i.e., only one of the two can exist. However, fortunately, that is not the case. Data mesh and data fabric can be architected to complement each other in a way that the perquisites of both technologies are brought to the fore to the advantage of the organization. 

Organizations can implement data fabric as a semantic overlay to access data from diverse data sources while using data mesh principles to manage and govern distributed data creation at a more granular level. Thus, data mesh can be the architecture for the development of data products and act as the data source while data fabric can be the architecture for the data platform that seamlessly integrates the different data products from data mesh and makes it easily accessible within the organization. The combination of a data mesh and a data fabric can provide a flexible and scalable data management solution that balances accessibility and governance, enabling organizations to unlock the full potential of their data.

Data mesh and data fabric can complement each other by addressing different aspects of data management and working together to provide a comprehensive and effective data management solution.

In conclusion, both data mesh and data fabric have their own strengths but are complementary and thus can coexist synergistically. The choice between the two depends on the specific needs and goals of the organization. It’s important to carefully evaluate the trade-offs and consider the impact on the culture and operations of the organization before making a decision.

Incedo Lighthouse™ with Self-Serve AI is a cloud-based solution that is creating a significant business impact in  commercial effectiveness for clients in the pharmaceutical industry. Self-serve entails empowering them with actionable intelligence to serve their business needs by leveraging the low-code AI paradigm. This reduces dependency on data scientists and engineers and makes faster iterations of actionable decisions and monitoring their outcomes by business users.

As internal and external enterprise data continues to grow in size, frequency, and variety, the classical challenges such as sharing information across business units, lack of a single source of truth, accountability, and quality issues (missing data, stale data, etc.) increase.

For IT teams owning diverse data sources, it becomes an added workload to ensure the provisioning of the enterprise-scale data in requisite format, quality, and frequency.  This also impedes meeting the ever-growing analytics needs of various BU teams, each having its own request as a priority. Think of the several dashboards floating in the organizations created at the behest of various BU teams, and even if with great effort they are kept updated, it is still tough to extract the insights that will help take direct actions to address critical issues and measure their impact on the ground. Different teams have different interaction patterns, workflows, and unique output requirements – making the job of IT to provide canned solutions in a dynamic business environment very hard.

Self-service intelligence is therefore imperative for organizations to enable business users to make their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo LighthouseTM

Our AWS cloud-native platform Incedo LighthouseTM, a next-generation, AI-powered Decision Automation platform t arms business executives and decision-makers with actionable insights generation and their assimilation in daily workflows. It is developed as a cloud-native solution leveraging several services and tools from AWS that make the journey of executive decision-making highly efficient at scale. Key features of the platform include:

  • Customized workflow for each user role: Incedo LighthouseTM is able to cater to different needs  of enterprise users based on their role,  and address their specific needs:
    • Business Analysts: Define the KPIs as business logic from the raw data, and define the inherent relationships present within various KPIs as a tree structure for identifying interconnected issues at a granular level.
    • Data Scientists: Develop, train, test, implement, monitor, and retrain the ML models specific to the enterprise use cases on the platform in an end-to-end model management
    • Data Engineers: Identify the data quality issues and define remediation , feature extraction, and serving using online analytical processing as a connected process on the platform
    • Business Executives: Consume the actionable insights (anomalies, root causes) auto-generated by the platform, define action recommendations, test the actions via controlled experiments, and push confirmed actions into implementation
  • Autonomous data and model pipelines: One of the common pain points of the business users is the slow speed of data to insight delivery and share action recommendation, which may take even weeks at times for simple questions asked by a CXO. To address this, the process of insights generation from raw big data and then onto the action recommendation via controlled experimentation has been made autonomous in Incedo LighthouseTM using combined data and model pipelines that are configurable in the hands of the business users.
  • Integrable with external systems: Incedo LighthouseTM can be easily integrated with multiple Systems of Record (e.g. various DBs and cloud sources) and Systems of Execution (e.g. SFDC), based on client data source mapping.
  • Functional UX: The design of Incedo LighthouseTM is intuitive and easy to use. The workflows are structured and designed in a way that makes it commonsensical for users to click and navigate to the right features to supply inputs (e.g. drafting a KPI tree, publishing the trees, training the models, etc.) and consume the outputs (e.g. anomalies, customer cohorts, experimentation results, etc.). Visualization platforms such as Tableau and PowerBI are natively integrated with Incedo LighthouseTM thereby making it a one-stop shop for insights and actions.

Incedo LighthouseTM in Action: Pharmaceutical CRO use case:

In a recent deployment of Incedo LighthouseTM, the key users were the Commercial and Business Development team of a leading Pharma CRO.  The company had drug manufacturers as its customers. Their pain point revolved around the low conversion rates leading to the loss of revenue and added inefficiencies in the targeting process. A key reason behind this was the wrong prioritization of leads from conversion propensity and total lifetime value perspective. This was mainly due to manual, human-judgment-driven, ad-hoc,, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

Specific challenges that came in the way of the application of data science for lead generation and targeting were:

  • The raw data related to the prospects –  that was the foundation for f for e predictive lead generation modeling – was in silos inside the client’s tech infrastructure. This led to failure in developing high-accuracy predictive lead generation models in the absence of a common platform to bring the data and models together.
  • Even in a few exceptional cases, where the data was stitched together by hand and predictive models built, the team found it difficult to keep the models updated in the absence of integrated data and model pipelines working in tandem.

To overcome these challenges, the Incedo LighthouseTM platform was deployed. The deployment of Incedo LighthouseTM in the AWS cloud environment not only brought about real improvements in target conversions but also helped transform the workflow for the BDAs. By harnessing the power of AI and Data, as well as leveraging essential AWS native services, we achieved efficient deployments and sustained service improvements.

  • Combine the information from all data sources for a 360-degree customer view, enabling the BDAs to look at the bigger picture effortlessly. To do so effectively, Incedo LighthouseTM leveraged AWS Glue which provided a cost-effective, user-friendly data integration service. It helped in seamlessly connecting to various data sources, organizing data in a central catalog, and easily managing data pipeline tasks for loading data into a data lake.
  • Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo LighthouseTM platform, after developing the data engineering pipelines that create a ‘single-version-of-the-truth ’ every time raw data is refreshed. This was done by leveraging the pre-built model accelerators, helping the BDAs sort those prospects in the descending order of their conversion propensity, thereby maximizing the return on the time invested in developing them. The Data Science Workbench also helps with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations. Using the Amazon SageMaker Canvas, Incedo LighthouseTM enables machine learning model creation for non-technical users, offering access to pre-built models and enabling self-service insights, all while streamlining the delivery of compelling results without extensive technical expertise.
  • Deliver key insights in a targeted and attention-driving manner to enable BDAs to make the most of the information in a short span of time. Incedo LighthouseTM leverages Amazon QuickSight, a key element in delivering targeted insights, that provides well-designed dashboards, KPI Trees, and intuitive drill-downs to help BDAs and other users make the most of the information quickly. These tools allow leads to be ranked based on model-reported conversion propensity, time-based priority, and various custom filters such as geographies and areas of expertise. BDAs can double-click on individual targets to understand deviations from actuality, review comments from previous BDAs, and decide on the next best actions. QuickSight seamlessly integrates with Next Gen Stats apps, and offers cost-effective scalable BI solutions, interactive dashboards, and natural language queries for a comprehensive and efficient user experience.  This resulted in an increased prospect conversion rate due to data-driven automated decisions leveraging AI that are disseminated to BDA in a highly action-oriented way.

In the ever-evolving landscape of cloud computing, organizations strive to enhance operational efficiency, optimize costs, and deliver exceptional performance. One such standout player in the industry is “Incedo,” a pioneering force in the cloud domain. In this article, we delve into the comprehensive Cloud Operations capabilities particularly on AWS platform offered by Incedo and explore the diverse use cases that make them a frontrunner in the industry.

Understanding Cloud Operations

Cloud Operations is a crucial aspect of managing and maintaining cloud-based services, ensuring seamless performance, scalability, and reliability. Incedo with its specialization in AWS cloud computing platform, goes beyond conventional practices to provide a robust suite of services that are designed to streamline processes, enhance security, and drive innovation.

Key Cloud Operations Capabilities at Incedo

  1. Automated Infrastructure Management

    Incedo leverages advanced automation tools to manage and orchestrate infrastructure, minimizing manual interventions and optimizing resource utilization. Through automated scaling, provisioning, and configuration management, Incedo ensures a resilient and agile infrastructure.

    In combination to the Auto Scaling Groups, Incedo leverages AWS CloudFormation to automate the provisioning and management of infrastructure. Through Infrastructure as Code (IaC), Incedo ensures the consistent deployment of resources, reducing the risk of manual errors and enhancing scalability. Templates define AWS resources, and changes are tracked and versioned, ensuring reproducibility and traceability.

  2. Cloud Resource Management

    Manually managing hundreds of thousands of compute instances across environments is a tremendous challenge. Incedo set out to resolve this problem for its customers by building a solution on AWS Systems manager.

    Incedo utilizes the OpsCenter from the AWS Systems manager catalogue for a central location that operations engineers and IT professionals can use to view, investigate, and resolve operational issues related to any AWS resource. Utilizing AWS Incident Manager helps operations teams prepare for incidents with automated response plans. whereas AWS Change Manager provides a central location for operators and engineers to request operational changes (Path management and system upgrades) for their IT infrastructure and configuration.

  3. Continuous Monitoring and Performance Optimization

    Incedo’s state-of-the-art monitoring solutions provide real-time insights into the performance of cloud resources. By utilizing predictive analytics-based cloud solutions, Incedo identifies potential bottlenecks and proactively optimizes workloads for peak efficiency.

    Use of Amazon CloudWatch provides real-time monitoring of AWS resources. Alarms and events are configured to trigger automated responses, ensuring optimal performance and availability. With CloudWatch Metrics, Incedo gains insights into resource utilization, enabling proactive optimization for improved efficiency.

  4. Security and Compliance

    Security is a top priority for Incedo. Their Cloud Operations team implements robust security measures, including encryption of data at rest as well at transit, identity management, and access controls. Incedo ensures adherence to industry-specific compliance standards, instilling confidence in clients regarding the safety of their data.

    Incedo places a strong emphasis on security, utilizing AWS IAM to manage user access and permissions. IAM roles and policies are meticulously configured, ensuring the principle of least privilege. Incedo helps clients achieve and maintain compliance with industry standards by implementing security best practices within the AWS environment.

  5. Disaster Recovery and Business Continuity

    Incedo’s Cloud Operations extend to comprehensive disaster recovery and business continuity planning. With geographically distributed data centres and failover mechanisms, Incedo ensures minimal downtime in the face of unforeseen events.

    Incedo’s disaster recovery strategy involves leveraging AWS Backup for centralized backup management and AWS Elastic Disaster Recovery (CloudEndure) for seamless replication and failover. This combination ensures business continuity by minimizing downtime and data loss in the event of disruptions.

  6. Cost Optimization

    The Cloud Operations team at Incedo excels in cost management and optimization. Through effective budgeting, utilization tracking, and rightsizing of resources, Incedo helps clients achieve cost efficiencies without compromising on performance. 

    Incedo’s technical approach to cost optimization involves using AWS Cost Explorer to visualize, understand, and manage costs effectively. AWS Trusted Advisor is employed to analyse an organization’s AWS environment and provide recommendations for cost optimization, performance improvement, security, and fault tolerance.

    This is another area where Incedo is getting ahead of the field while developing its inhouse FinOps tools and solutions.

Use Cases Handled by Incedo

  1. Financial Services Scalability

    Incedo’s Cloud Operations have empowered numerous Financial Services businesses to scale effortlessly during peak seasons. Automated scaling ensures that resources align with fluctuating demand, providing a seamless experience for end customers.

    Incedo employs AWS Lambda for serverless computing to enhance scalability. By decoupling functions and executing code in response to events, Lambda allows Incedo to scale effortlessly during peak demand, ensuring a responsive and cost-effective solution.

  2. Data Analytics for Wealth Management Customers

    Incedo’s capabilities shine in handling complex Big Data workloads. By optimizing data storage, processing, and analytics, Incedo enables organizations to derive valuable insights from massive datasets efficiently.

    Incedo harnesses the power of Amazon Redshift for efficient Big Data analytics. With its fully managed, petabyte-scale data warehouse, Redshift enables Incedo to analyse vast datasets and derive actionable insights, empowering organizations to make data-driven decisions.

  3. DevOps Acceleration

    Incedo’s Cloud Operations facilitate DevOps practices, enabling organizations to achieve faster development cycles, continuous integration, and seamless delivery. Automation of deployment pipelines ensures rapid and reliable application releases while maintaining the fine grain access control and security using the cross-account CI/CD pipelines.

    Incedo accelerates DevOps practices using AWS CodePipeline for continuous integration and delivery. Automated build, test, and deployment pipelines using AWS CodeCommit, CodeBuild and CodeDeploy streamline development workflows, enabling organizations to achieve faster release cycles and maintain application reliability.

  4. Global Content Delivery for a wealth management client
    Incedo leverages Amazon CloudFront, AWS’s content delivery network (CDN), for low-latency global content delivery. By caching content at edge locations, Incedo ensures reduced latency and enhanced user experiences, catering to a diverse, worldwide audience.
  5. Zero Ops based State of the Art operation centre
    Incedo’s capability of designing and deploying advanced serverless solutions in combination with global AWS services and containers demonstrates a proven state of the art framework designed considering the ZeroOps capabilities.

Conclusion: Incedo Setting Industry Standards

In conclusion, Incedo stands as a beacon of excellence in Cloud Operations, offering a suite of capabilities that address the dynamic needs of modern businesses. With a focus on automation, security, and performance optimization, Incedo empowers organizations to navigate the complexities of the cloud landscape with confidence. As the cloud computing industry continues to evolve, Incedo sets the standard for operational excellence, making them a trusted partner for businesses embarking on their cloud journey.

In the fast-changing landscape of cloud computing, the efficient management of costs and resources has emerged as a paramount concern for businesses of all sizes. The concern is also shared by the majority of enterprise IT leaders. According to a 2020 survey of 750 senior business and IT professionals at large enterprises across 11 industries and 17 countries, only 37% of respondents say they are achieving the full value expected on their cloud investments[i].Moreover, this is becoming a rising board-level issue – According to CloudZero’s State of Cloud Cost Intelligence 2022 report 73% of respondents, cloud costs concern the board or C-suite[ii].

As organizations expand their cloud presence, there is a growing need for strategies and practices that can help optimize financial operations in the cloud. This is precisely where Cloud FinOps, or Cloud Financial Operations, plays a pivotal role. Organizations that use FinOps effectively can reduce cloud costs by as much as 20 to 30 percent[iii].

Cloud FinOps, encompasses a range of practices and principles aimed at optimizing and overseeing the financial aspects of cloud computing within an organization. Cloud FinOps is not merely about reducing costs; it is about achieving a delicate balance between controlling cloud expenses and maximizing the value that the cloud can deliver. Its primary focus is on cost control, ensuring cost-effectiveness, and aligning cloud expenditures with the organization’s broader business objectives.

One of its key attributes is the collaborative approach it fosters, uniting teams from finance, IT, and operations in the endeavour to collectively manage cloud expenses. This collaborative approach goes beyond cost management, ensuring that cloud expenditures are in harmony with the overarching business goals. In our blog,  we will talk about why Cloud FinOps matters and share the simple steps we took to set it up internally and help others do the same. Join us as we break down why it is important and how it can make cloud management easier and more efficient for everyone.

Why is Cloud FinOps needed?

By uniting diverse perspectives and skill sets, Cloud FinOps cultivates a synergistic environment that empowers organizations to confidently and efficiently navigate the financial complexities in the ever-changing landscape of cloud computing. Cloud FinOps is your reliable guide for a bunch of good reasons:

  1. Cost Control and Optimization: While cloud technology offers remarkable flexibility and scalability, it can pose a financial challenge if not handled with precision. Cloud FinOps empowers organizations with the strategies and tools needed to regain control over their cloud expenses, ensuring that resources are used efficiently and budgetary constraints are avoided. In essence, it is a methodical approach to enhance financial discipline and resource optimization in the cloud environment.
  2. Cost Visibility: Gaining a comprehensive understanding of cloud expenses can be a formidable challenge in the realm of cloud management. Cloud FinOps practices provide organizations with the tools and methods to meticulously track and analyze their cloud spending, offering a detailed, granular view of where financial resources are allocated. It is similar to having a precise financial roadmap for your cloud expenditures.
  3. Efficiency: Cloud FinOps focuses on enhancing the efficient use of cloud resources by optimizing the size of instances, capitalizing on reserved instances for cost savings, and exploring cost-effective pricing models. It is like fine-tuning the performance of your machinery to maximize productivity and minimize costs.
  4. Business Alignment: Ensuring that cloud expenditures directly contribute to the achievement of business objectives is of paramount importance. Cloud FinOps practices are instrumental in aligning cloud spending with the delivery of tangible value to the organization. In essence, it is about ensuring that every cloud investment is a purposeful step toward fulfilling your business goals, making financial decisions a strategic asset for your organization.
  5. Accountability: Cloud FinOps uses strategies like cost allocation and tagging to ensure that teams and individuals are responsible for how much cloud resources they use. This encourages a culture of financial prudence and careful spending.

The setup essentials for a FinOps practice

Setting up a Cloud FinOps practice means taking specific actions to make sure we spend our cloud budget wisely, manage costs, and make sure our cloud resources match our business goals. Below is a comprehensive guide that outlines the initial steps to get started:

  1. Objectives and Goals: Start by defining your organization’s financial objectives regarding cloud usage. Are you aiming to reduce expenses, enhance cost transparency, allocate costs to specific teams or projects, or pursue other goals? Your FinOps practice’s actions will be tailored to these objectives, so ensure they are clearly defined.
  2. Team Formation: Build a cross-functional team comprising members from finance and IT Operations. This team will oversee the implementation and management of the Cloud FinOps practice, analyzing spending trends and offering insights into optimizing costs. The selection of the right individuals for this team is critical.
  3.  Cloud Cost Visibility: Deploy tools and methodologies to gain visibility into your cloud expenditures. Utilize cloud cost management tools such as AWS Cost Explorer, Azure Cost Management, or Google Cloud Cost Management. AWS Trusted Advisor is especially valuable for rightsizing recommendations and other cost-related insights.
  4.  Tagging and Labelling: Develop a systematic tagging and labelling strategy to track resources and allocate costs to specific departments, projects, environments, or teams. Tags and labels are vital for precise cost attribution, so ensure you have an effective tagging mechanism in place.
  5. Budgeting and Forecasting: Establish cloud budgets and forecasts based on historical usage data. This allows you to set cost expectations and monitor your spending against these predefined targets.
  6.  Cost Allocation: Implement cost allocation methodologies that accurately distribute cloud costs to different departments or projects. This may involve creating custom scripts or employing third-party tools to streamline the process.
  7.  Cost Optimization: Identify opportunities for cost optimization, such as rightsizing instances, utilizing reserved instances, or leveraging serverless computing. Regularly assess and adjust your resources to maximize efficiency and minimize unnecessary expenses.
  8. Cost Monitoring and Alerts: Ensure vigilant cost monitoring by setting up alerts that notify you when expenses surpass predefined limits. This quick-response system helps address unexpected cost spikes promptly.
  9.  Education and Training: One of the key requirements to establish a robust Cloud FinOps practice is investing in the education and training of your employees. By providing targeted training, you empower your team to navigate the cloud landscape with financial acumen. Equipping your workforce with the knowledge and skills needed to make informed decisions contributes significantly to the success of your Cloud FinOps practice, fostering a culture of financial responsibility and efficiency.
  10. Monthly Reporting: Generate regular financial reports outlining cloud costs, allocations, and savings. These reports serve as a crucial tool for informed decision-making within your Cloud FinOps practice. Share these insights with relevant stakeholders to enhance transparency and foster strategic choices aligned with your organizational goals.
  11.  Continuous Improvement: It is imperative to consistently refine your Cloud FinOps approach. Stay vigilant for pricing changes from cloud providers and keep abreast of evolving technology trends. This commitment to continuous improvement ensures the ongoing optimization of your cloud financial operations, aligning them with the dynamic landscape of both technology and pricing structures.
  12.  Governance and Policies: Enforce governance policies to align cloud resource provisioning with organizational standards. This alignment not only fosters a structured and compliant approach but also lays the foundation for effective cost management within the Cloud FinOps framework.
  13.  Cost Accountability: Cultivate accountability by associating cloud spending with specific teams or individuals. This not only encourages a sense of ownership but also empowers teams to actively manage and optimize their cloud usage, fostering a more cost-conscious and efficient Cloud FinOps practice.
  14. External Assistance: In instances where internal assistance in Cloud FinOps is limited, consider external expertise, such as engaging  with a Cloud FinOps consulting firm. Their specialized knowledge can bridge the gap, offering invaluable insights, best practices, and hands-on guidance. This external collaboration ensures a smoother implementation of Cloud FinOps, even if your in-house proficiency is currently lacking.
  15. Feedback Loop: Establish a culture of continuous improvement. Gather feedback from teams and stakeholders to refine the Cloud FinOps practice. Remember, establishing a Cloud FinOps practice is an ongoing commitment. Regular monitoring, adaptation to organizational needs and dedication are key. It is a crucial element of cloud management, ensuring cost-effectiveness and alignment with business goals.

Incedo’s Cloud FinOps Success with AWS Optimization:

Incedo’s Cloud FinOps practice empowers clients to uncover hidden cost-saving opportunities in their cloud expenditures. Our innovative approach combines a swift 5-day Diagnostics process with the cutting-edge CloudXpert platform and powerful AWS tools like Cost Explorer, Trusted Advisor, and Performance Manager to guide clients through seamless cloud expense optimization.

In a recent success story, Incedo achieved a remarkable 20% cost reduction for a client by seamlessly transitioning to a serverless data ingestion architecture. This achievement shows our commitment to delivering real results and helping organizations get the most value from their cloud investments.

Conclusion: In today’s rapidly evolving cloud computing landscape, efficient cost and resource management are paramount for businesses. Cloud FinOps, or Cloud Financial Operations, is instrumental in optimizing cloud expenses and aligning them with overarching business objectives. It thrives on collaboration among finance, IT, and operations teams, ensuring seamless financial navigation.

To make Cloud FinOps work effectively in your organization, you need to establish clear objectives, assemble the right team, gain cost visibility, implement resource tagging and labeling, set budgets and forecasts, employ cost allocation strategies, and continuously optimize costs. These steps are essential for ensuring that Cloud FinOps becomes a valuable and impactful practice within your operations.

Source:

[i] – https://newsroom.accenture.com/news/most-companies-continue-to-struggle-to-realize-full-business-value-from-their-cloud-initiatives-accenture-report-finds.htm
[ii] – https://www.cloudzero.com/state-of-cloud-cost-intelligence/
[iii] – https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-finops-way-how-to-avoid-the-pitfalls-to-realizing-clouds-value

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