Data Platform Operational Maturity Framework

Many growth-oriented organizations are undergoing data transformation and modernization efforts but few can truly build and sustain the platform of their vision. This is due to several reasons, but the most common culprit in preventing data transformation is a lack of a holistic operational strategy that’s implemented from the onset.

What typically occurs is that businesses will make operations the top priority only after experiencing a string of high-profile incidents. Hastily bolting on the operations at this stage usually causes the platform development to screech to a halt. Once incident and change management controls overwhelm the platform, organizational trust is lost and things begin to deteriorate quickly.

To remedy this, phData has put together an Operational Maturity Framework (OMF) that incorporates modern cloud-based technologies and vetted best practices to ensure your data operations do not slow down or encumber your data platform objectives.

This whitepaper is intended to provide you with a clear blueprint for developing a high-performing data operations capability.

Who Are the Primary Audiences of This Guide?

  1. Executives and data platform leaders responsible for implementing modern data platforms that are looking for guidance and perspective on industry best practices.
  2. Data platform operations teams curious about what to expect throughout a platform life cycle and what needs to be done to effectively run modern data platforms.

Table of Contents

What is the Operational Maturity Framework?

The phData Operational Maturity Framework (OMF) leverages phData’s experience with hundreds of customers and proven best practices to help enterprises accelerate the adoption and scalability of their data platforms. The OMF is divided into two central components: Operations Pillars and The Modern Platform Life Cycle.

The Operations Pillars

The OMF defines four operations pillars:

  • Strategy – Ensures that your data platform operations team delivers on core business objectives and helps accelerate your data transformation ambitions.
  • Center of Excellence – Codifies how you build and execute your operations strategy across the organization. This pillar focuses on establishing and promoting best practices, providing thought leadership and direction on new technologies/tools, and supporting platform optimization through automation and education.
  • Core Operations – Focuses on core operations capabilities and processes necessary to manage any data platform throughout its lifecycle.
  • Team – Explores the organizational structure, roles, responsibilities, skills, people capacity, and the planning required when scaling modern data platforms.

These four pillars are the foundation for your operational maturity that needs to evolve as your data platform goes through its life cycle. We’ll cover each of these pillars in greater detail in the subsequent sections. 

The Modern Platform Life Cycle

As modern data platforms are adopted and scaled, the platform and organization around them go through a similar sequence. At each stage of this cycle, your operational maturity enables your organization to accelerate the realization of the business value. The pillars covered below can be customized to fit your unique business needs.

Stages of Platform Life Cycle 

Data Strategy and Platform Architecture – Aligning data strategy with business strategy and making enterprise technology decisions.

Platform and Use Case Foundation – Implementation requirements planning.

Build and Operate – Platform implementation.

Use Case Development  – Business application development using foundational platform assets.

Support Platform and Applications – Ongoing platform application maintenance, support, and minor optimization.

Optimization and Reinvention – Platform and use case enhancements and platform capability expansion.

How They Tie Together

The data platform adoption curve shows that business outcomes are accelerated through organizational change (transformation), which is enabled by a set of foundational operational pillars. 

The operational pillars represent a value chain where strategy enables CoE, which enables core capabilities, which enables people. 

Each of the framework pillars described in the preceding section is enabled by a set of foundational capabilities shown in the following figure. A capability is an organizational ability to leverage processes to deploy resources (people, technology, and any other tangible or intangible assets) to achieve a particular outcome. 

phData OMF capabilities provide best practice guidance that helps you improve your operational maturity (your ability to effectively leverage data and analytics to modernize your organization). 

Not every organization is the same and approaches to modernization can differ. Consider tailoring the suggested sequence shown in the following figure to your particular needs.

Strategy Pillar Deep Dive

The operations strategy pillar helps ensure that your data platform operations team delivers on core business objectives and helps accelerate your data transformation ambitions and business outcomes. The operations strategy pillar consists of nine capabilities that you need to have for clear platform objects and organizational alignment. 

A screenshot of the Strategy section of the OMF. This image shows several boxes depicting different stages.

For the remainder of this section, we’ll explore each of the nine capabilities. It’s important to note that the common stakeholders include the CEO, CFO, COO, CIO, and CTO. 

Organizational Alignment

Assess organization alignment with platform objectives to ensure data platform operations are in support of goals. Establish a case for change, and assess if your organization reflects the desired behaviors, roles, and culture that you have determined are key elements to your success.

Data and Analytics Organization Model 

Many enterprises are made up of various business units. In a federated approach, each business unit’s data and analytics function operate as separate entities and own the full spectrum of data and analytics capabilities. 

In a centralized model, a single central organization controls and manages all the data and analytics for the enterprise. The distinction greatly influences how organizations adopt and scale data and analytics capabilities and technologies.

Data Platform Objectives

Data platforms without clear business objectives struggle to gain the support and resources needed to scale.

Analytics Delivery Sourcing Strategy

Every business has a different strategy for whether its data and analytics capability should be delivered in-house, be fully outsourced, or be a hybrid model. Each has tradeoffs that should be weighed.

Adaptability

Establishing flexibility as a desired goal of the platform operations model allows for the ability to adapt to new or changing business requirements. This is key to long-term success. 

Analytics Maturity

Understanding how mature each business and business unit is on the analytics maturity model helps you understand where to invest and what new data and analytics capabilities can be implemented to drive incremental business value. 

A diagram showing the analytics maturity curve, starting at "Descriptive analytics" and ending with "prescriptive analytics"

Value Management

Modern data platforms provide value in four important ways, increased revenue through new products, increased efficiency of staff, decreased technology costs, and increased speed to market/business agility. Understanding how a data platform creates and provides business value and actively measuring that value is critical to platform success.

Risk Management

The level of risk that your organization is willing or able to accept should be understood, documented and communicated at an organization-wide level. This helps minimize the potential for compliance failures and allows for metrics to be tracked and reported to validate adherence.

Cost Management

This covers consideration of all costs associated with operating modern data platforms. Examples include staffing costs, technology costs, and supplier costs. This also covers the reduction of Total Cost of Ownership (TCO) and liquidation of capital assets by downsizing the IT infrastructure, which is a key element of realizing the benefits of cloud transformation. 

Additionally, accurate tracking and reporting of costs allow for a true understanding of the value created by the product and subsequent business decisions on whether it is worth continuing to develop and maintain. 

Center of Excellence Pillar Deep Dive

This pillar codifies how we build and execute our operations strategy across the organization. It focuses on establishing and promoting best practices, providing thought leadership and direction on new technologies and tools, and support for platform optimization through automation and developer education. 

A screenshot of the CoE pillar in the OMF model

In this deep dive, we’ll visit 14 components that will ensure your center of excellence is positioned to thrive.

Technical Leadership, Guidance, and Coaching

Providing technical leadership and coaching are critical to the adoption and scalability of modern data technologies and platforms. Examples include guidance on core reference architectures, application architecture, and user and developer guidance on how to achieve desired outcomes.  

Solution architecture provides a blueprint for Application Onboarding Guidelines and promotes the optimal use of platform features.

Architecture

Establish and maintain guidelines, principles, patterns, and guardrails for your data platform. Create consensus within your organization for enterprise standards that will drive platform adoption. By defining best practices, each new data product will not have to reinvent proper authentication, security, networking, and logging and monitoring implementations. This allows the focus on product delivery. 

Data Architecture

Design and evolve a fit-for-purpose data and analytics architecture. A well-designed data and analytics architecture can help you reduce complexity, cost, and technical debt while enabling you to gain actionable insights from exponentially growing data volumes. 

Adopt a layered and modular architecture that will allow you to use the right tool for the right job as well as iteratively and incrementally evolve your architecture to meet emerging requirements and use cases.

Platform Best Practices

Adhering to best practices allows organizations to leverage lessons learned, avoid common pitfalls, and not reinvent the wheel when going through the platform life cycle.

Data Governance

Exercise authority and control over your data to meet stakeholder expectations. Your business processes and analytics capabilities depend on accurate, complete, timely, and relevant data. Define and assign key roles, including data owners, stewards, and custodians.

Consider adopting a federated (data mesh) approach to governance. Specify standards, including data dictionaries, taxonomies, and business glossaries. Lastly, identify what datasets need to be referenced and model the relationships between reference data entities.

A high-quality data governance practice is both integral to and partially delivered by your operations capability. The core components of a data governance program are found below.

Data governance capabilities are:

  • Authentication & Authorization: Ensuring the right users have the right access to the right data.
  • Information Architecture: Focuses on organizing, structuring, and labeling data effectively and sustainably.
  • Provisioning and Rights Management: Preparing, supplying, and managing the access of data and data and analytics technologies to users.
  • Data Stewards: Responsible for ensuring the quality and fitness for purpose of the organization’s data assets, including the metadata for those data assets.
  • Data Asset Definitions: Clear definition, description, and scope of both data and metadata of data assets.
  • Data Catalog and Classification: Providing data summaries and metadata for easy access and understanding.
  • Data Lineage: Understanding the impacts of changing source systems and the downstream effects.
  • Data Mastering: Set of defined activities to specify a single source of truth across the enterprise for all data required to run the business.

Identity and Access Management  

Manage identities and permissions at scale. You can connect your identity source, and then grant users the necessary permissions, so they can sign in, access, and provision resources. Effective identity and access management helps validate that the right people and machines have access to the right resources under the right conditions.

Communication

Name the platform as that will allow your organization to rally around a brand and get excited about the potential impact. Create a getting started guide for different personas that will be stakeholders of the platform and develop a communication channel for sharing best practices.  Provide timely updates on platform enhancements and availability.

Automation

A key principle for modern data platforms is an “automate everything” approach. Automation provides value from efficiency by reducing the amount of time spent managing infrastructure resources that can be repurposed toward product development. 

By automating repeated tasks such as operating system patches, application deployments, firewall changes, and monitoring/alerting configurations, organizations can devote more cycles to the development of features.

Below are examples of common automation features and the benefits they provide:

  • Elastic scaling capacity based on triggers can ensure resources are properly allocated for workloads. 
  • Repeatability allows for systems to be reliably reproduced and consistency is built into the automation. 
  • Automating good security practices into your systems makes it much more likely that your system will be uniform as it scales. This removes the element of human error and creates an exposure that allows you to roll out security improvements (like any feature) quickly and with confidence. 
  • Systems built through automation scripts tend to behave more predictably and have more informative logging. The sequence of actions taken for anything automated is by its very nature documented by the code that performs those actions. This makes it much easier to describe to others  ( your team, business stakeholders, auditors, and regulators)  who performed what action and when in your systems.
  • Automating health checks and common QA actions greatly increases platform quality and scalability. Automation can be used to roll back systems in flight that doesn’t pass these checks. Additionally, as the friction to make changes to an environment is reduced to zero, you can become more aggressive with how you push out changes.

Documentation & Runbook

Runbooks enable consistent and prompt responses to well-understood events by documenting procedures in runbooks. Runbooks are the predefined procedures to achieve a specific outcome. Runbooks should contain the minimum information necessary to successfully perform the procedure.

Start with a valid effective manual process, implement it in code and trigger automated execution where appropriate. This ensures consistency, speedy responses, and a reduction in errors caused by manual processes.

SLAs

Service Level Agreements sets the expectations between the service provider and the customer and describes the products or services to be delivered. This serves as the single point of contact for end-user problems and the metrics by which the effectiveness of the process is monitored and approved.

Security Assurance

Continuously monitor, evaluate, manage, and improve the effectiveness of your security and privacy programs. Your organization, and the customers you serve, need trust and confidence that the controls you have implemented will enable you to meet regulatory requirements, and effectively and efficiently manage security and privacy risks in line with your business objectives and risk tolerance.

Bug Fixes, Features, and Process Engineering

Throughout a platform’s life cycle, the environment, data, and technology landscape are in a constant state of flux. Inevitably, there will be a need to debug and develop feature enhancements, process engineering, and reengineering. 

Proper planning prior to changes to upstream data sources will reduce and eliminate the impact on users and dependent systems. To successfully adopt and scale modern data platforms, it is critical to have accounted for and deliver these improvements.

Governance and Security Policy Enforcement

Develop, maintain, and effectively communicate security roles, responsibilities, accountabilities, policies, processes, and procedures. Ensuring clear lines of accountability is critical to the effectiveness of your security program. 

Understanding your assets, security risks, and compliance requirements that apply to your industry and/or organization will help you prioritize your security efforts. Providing ongoing direction and advice will help accelerate your transformation by allowing your teams to move faster.

Platform Enhancements & Optimization 

The CoE provides leadership and guidance on how best to create platform enhancements and optimization that lower cost, improve user experience, and overall platform performance.  Maintain a backlog for platform enhancements.

Core Operations Pillar Deep Dive

This pillar focuses on core operations capabilities and processes necessary to manage any data platform throughout its lifecycle. With the 15 components covered in this section, you’ll have a blueprint to future-proof your data platform operations.

A screenshot of the core operations pillar from the OMF

Proactive Monitoring, Alerting, and Logging

Gain actionable insights from your infrastructure and application data through observability. When you are operating at cloud speed and scale, you need to be able to spot problems as they arise, ideally before they disrupt the customer experience. 

Develop the telemetry (logs, metrics, and traces) necessary to understand the internal state and health of your workloads. Monitor application endpoints, assess the impact on the end-users and generate alerts when measurements exceed thresholds.

Configuration and Provisioning

Maintaining consistent infrastructure provisioning in a scalable and repeatable manner becomes more complex as your organization grows. Streamlined provisioning and orchestration will help you achieve consistent governance and meet your compliance requirements. Adopt best practices for Infrastructure as Code (IaC) to execute faster, improve quality, and ultimately drive down costs.

Change and Release Management

Introduce and modify infrastructure and applications while minimizing the risk to production environments. Establish change processes that allow for automated approval workflows that align with the agility of modern technologies. Use deployment management systems to track and implement changes. 

Use frequent, small, and reversible changes to reduce the scope of a change. Test changes and validate the results at all lifecycle stages to minimize the risk and impact of failed deployments. Automate rollback to previously known good state when outcomes are not achieved to minimize recovery time and reduce errors caused by manual processes.

User Onboarding and Audit

Having clear paths for user onboarding and audits allow for increased adoption and scalability as well as the capacity to compile with security and platform governance requirements. Having the ability to automate both of these foundational capabilities greatly increase scalability. 

User Support

Providing prompt responses to requests, issues, and questions from users is critical to the success of any data platform. Business users, Developers, InfoSec teams, Executives, etc. will interact and have different responsibilities as it relates to the platform. Additionally, these folks will have a variety of needs and requests. 

The support team must triage these requests, prioritize, address, delegate, and/or escalate to the appropriate team. Building proper runbooks, knowledge base articles, and proper ongoing training of this team is key to success. 

User Notifications

Having the ability to notify the user community of changes, feature enhancements, outages, and maintenance to the data platform is integral to change management and user adoption. 

Incident Management  

A mature incident management process is one of the most important capabilities of a high-functioning operations team. The ability to identify, debug, and restore service as quickly as possible provides trust in the platform. Even more important is ensuring that communication throughout the incident is timely, clear, and gives stakeholders confidence in the incident process. 

With cloud adoption, processes for responding to service and application health issues can be highly automated, resulting in greater service uptime. As you move to a more distributed operating model, streamlining interactions between relevant teams, tools, and processes will help you accelerate the resolution of critical and/or complex incidents. Define escalation paths in your runbooks, including what triggers escalation and the procedures for escalation.

Continuous Integration and Continuous Delivery (CI/CD)

Evolve and improve applications and services at a faster pace than organizations using traditional software development and infrastructure management processes. Adopting DevOps practices with continuous integration, testing, and deployment will help you to become more agile, allowing you to innovate faster, adapt to changing markets better, and grow more efficiently at driving business results. 

We highly recommend implementing continuous integration and continuous delivery (CI/CD) for your data pipelines.

Availability and Continuity Management  

Ensure availability of business-critical information, applications, and services. Building cloud-enabled backup solutions requires careful consideration of existing technology investments, recovery objectives, and available resources. 

Timely restoration after disaster and security events will help you maintain system availability and business continuity. Back up your data and documentation according to a defined schedule.

Disaster Recovery

Develop a disaster recovery plan as a subset of your business continuity plan. Identify the threat, risk, impact, and cost of different disaster scenarios for each workload and specify Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs) accordingly. 

Implement your chosen disaster recovery strategy leveraging multi-AZ or multi-region architecture. Review and test your plans regularly and adjust your approach based on lessons learned.

Event Management 

Detect events, assess their potential impact, and determine the appropriate control action. Being able to filter the noise and focus on priority events will help you predict impending resource exhaustion, automatically generate alerts and incidents, and identify likely causes and remediation actions to improve incident detection and response times.

Integrate with cloud services and third-party tools, including with your incident management system and process. Automate responses to events to reduce errors caused by manual processes and ensure prompt and consistent responses.

Environment Usage Dashboards and Chargebacks

Monitor workload performance and ensure that capacity meets current and future demands. Although the capacity of the cloud is virtually unlimited, service quotas, capacity reservations, and resource constraints restrict the actual capacity of your workloads. Such capacity constraints need to be understood and effectively managed. 

Introduce standards for tagging resources. Identify key stakeholders and agree on the objectives, scope, goals, and metrics. Collect and process performance data and regularly review and report performance against targets. Periodically evaluate new technologies to improve performance and recommend changes to the goals and metrics as appropriate. 

Monitor the utilization of your workloads, create baselines for future comparison, and identify thresholds to expand capacity as required. Analyze demand over time to ensure capacity matches seasonal trends and fluctuating operating conditions.

Problem Management  

Identify and resolve underlying issues that can lead to incidents. Initially, the problem management will react to recurring or major incidents by investigating the root cause. The outcome includes proposing changes to minimize impact and prevents the incidents from happening again.

Additionally, introducing monitoring to notify in advance of the issue and educating the user community where necessary can help prevent those issues in the first place. Over time, problem management must mature to become proactive and predict similar incidents using trend analysis.

Data Pipeline Management  

Data analytics workloads often involve multiple systems and processes working in coordination. You must monitor not only individual components but also the interaction of dependent processes to ensure a healthy data analytics workload. Data validations should be performed on a subset of data. 

Runtime metrics should be collected, analyzed, and monitored to ensure jobs are being completed within the expected thresholds and are meeting business SLAs.

Incorporate modern development practices, such as CI/CD to ensure that changes are rolled out in a controlled and repeatable way. Use test automation to verify infrastructure, code, and data changes in stages to ensure the operational stability of the entire analytics pipeline.

System Maintenance and Updates 

Systematically distribute and apply software system and service updates. Software updates address emerging security vulnerabilities, fix bugs, and introduce new features. A systematic approach to patch management will ensure that you benefit from the latest updates while minimizing risks to production environments. 

Apply important updates during your specified maintenance window and critical security updates as soon as possible. Notify users in advance with the details of the upcoming updates and allow them to defer patches when other mitigating controls are available. 

Team Pillar Deep Dive

The Team pillar focuses on organizational structure, roles, responsibilities, skills, and people capacity planning required when scaling modern data platforms. In this section, we’ll take a closer look at seven critical components that help businesses create and sustain a data-driven culture.

A screenshot of the team pillar from the OMF

Leadership

Strengthen your leadership capability and mobilize leaders to drive transformational change and enable outcome-focused, cross-functional decision-making. To succeed with cloud transformation, your leaders must put as much focus on the people side of change as they do on technology.  Without an effective blend of technical and business leadership, your transformation may slow down or stall. 

Gain active and visible executive sponsorship from both technology and business functions who will make critical decisions on strategy, vision, scope, and resources while taking action in communication, coalition building, and holding teams accountable for results.

Culture

Evaluate, incrementally evolve and codify organizational culture with digital transformation aspirations, and best practices for agility, autonomy, clarity, and scalability. To succeed in digital transformation, you’ll need to leverage your heritage and core values while incorporating new behaviors and mindsets that attract, retain, and empower a workforce that’s invested in continuously improving and innovating on behalf of your customers. 

Maintain a long-term focus, obsess over customers, and boldly innovate to meet their needs. Institute an organization-wide approach to recognizing behaviors and goals for all roles that help shape your desired culture. 

Consider rapid experimentation, agile methodologies, and cross-functional teams to drive ownership and autonomy. This enables rapid decision-making and minimizes the need for excessive approvals or bureaucracy.

Organization Design

Establish an ongoing partnership between organizational structures, business operations, processes, talent, and culture to enable your enterprise’s rapid adaptation to market conditions and the ability to capitalize on new opportunities. 

To augment cloud value realization, organizational alignment serves as a bridge between technology and business strategy so that technology changes are embraced by the business units that produce business outcomes.

Roles, Responsibilities, and Expectations

Having a clear separation of duties as well as common definitions for responsibilities allows for more efficient operations and higher-performing teams. 

Skill Requirements and Competencies

Build digital acumen to confidently and effectively leverage cloud technologies to accelerate business outcomes. The requirement for an exceptional workforce goes beyond adapting to a digital environment. The greatest challenge is not the technology itself, but, rather, the ability to hire, develop, retain and motivate a talented, knowledgeable, proficient, and high-performing workforce.

Capacity Requirements and Planning

Having clear metrics and processes for understanding the capacity of the team creates better morale, increased retention, and more efficient operations. Resources that are not performing tasks that are suited for their skill level are bad for the organization and at risk of leaving.

Identifying the off-hours support model reduces people working weekends or evenings which often leads to burnout.  

Workforce Transformation

Enable talent and modernize roles to attract, develop, and retain a digitally fluent high-performing, and adaptable workforce that can autonomously drive key capabilities. 

To succeed in your cloud transformation, take a proactive approach to talent enablement planning beyond traditional HR to include C-suite leadership, and modernize your approaches to leadership, learning, rewards, inclusion, performance management, career mobility, and hiring.

Training is important not only for the core technologies of the platform but also for processes and procedures. With proper training, employees can become more productive and reduce incidents. Creating materials for future team members solidifies understanding and mastery of the platform.

Conclusion 

As technological innovation continues to accelerate, the need for continuous data modernization will become even more pressing. The OMF leverages phData experience and best practices to help you accelerate platform adoption and scalability. Use the OMF to identify and prioritize transformation opportunities, evaluate and improve your operational maturity, and iteratively evolve your operations.

To begin maturing your operations capability, start with an assessment of your current state.  You can use our questionnaire to understand your overall maturity, and identify gaps and challenges that you should focus on. With a clear understanding of how mature you are combined with clear business objectives, you can map out a roadmap to operational maturity. 

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