Part 5 - Unlocking Excellence: Delivery and Architectures for Analytics Modernization

Part 5 of 5 in the Series "Navigating the Future of Analytics Modernization"

In our final installment of the "Enterprise Analytics - Analytics Modernization" series, we delve into the critical aspects of delivering excellence in analytics modernization projects. From project governance to change strategy and reference architecture, we explore the key components that pave the way for success in the final leg of the journey.

In this concluding installment, we provide invaluable insights into navigating the final mile of the analytics modernization journey. By elucidating robust project governance strategies, outlining effective change strategies, and presenting sample reference architectures, we equip organizations with the tools and frameworks needed to achieve excellence in analytics modernization.

Join us as we embark on this transformative odyssey, where strategy meets architecture to redefine the possibilities of enterprise analytics. Embrace the future of analytics modernization and unlock unparalleled competitive advantage through data-driven excellence and innovation.

Delivery Governance

Project governance is a critical aspect of ensuring the success of any initiative, especially in complex endeavors like analytics modernization. Let's delve deeper into 1) roles and responsibilities, 2) program governance framework, 3) change management and 4) communication.

Roles and Responsibilities?

Clearly defining roles and responsibilities ensures accountability and clarity throughout the delivery lifecycle and sets the foundation for effective delivery execution. By assigning specific tasks and areas of accountability to each team member, the project team can operate with clarity and focus. This clarity helps in avoiding confusion, streamlining decision-making processes, and ensuring that tasks are completed efficiently. In the context of delivery governance within the final installment of our series, "Unlocking Excellence: Strategies and Architectures for Analytics Modernization," the roles and responsibilities play a pivotal role in ensuring the success of the modernization journey.?

Here's an elaboration on each key role:

Program Leadership / Steering Committee: This leadership body provides strategic guidance, priorities, and authorizes key decisions regarding project initiation, resource allocation, milestones, and change requests. They serve as advocates for the project and ensure alignment with organizational objectives.

Business Function Leads: These individuals are responsible for identifying and empowering workstream leads and project teams within their respective functions. They take ownership of their workstream's approach, solutions, and results, serving as the main point of communication with the Program Leadership.

PMO (Project Management Office): The PMO orchestrates the day-to-day delivery of the project, facilitating communication between leadership and project teams, managing project quality, and overseeing change requests. They develop and manage the project plan to ensure objectives are met efficiently.

Data and Application Governance Lead: This role is tasked with creating and implementing an enterprise-wide Data Governance Framework, establishing governance processes, controls, policies, and standards. They ensure that new systems and applications adhere to existing data management practices.

Integration Architect: Drives the design for a secure, efficient, and adaptable future state model, while analyzing the impact of new technologies on IT infrastructure and defining architecture standards. Manages communication with IT and business managers to ensure alignment.

Integration Lead: Reviews solutions to ensure adherence to standards, coordinates seamless integration with application owners, and verifies coding standards. Works with stakeholders to understand data architecture objectives and defines data modeling standards.

Data Architect: Administers data source mapping, defines logical and physical data models, and coordinates with data modelers. Documents application requirements, provides source connectivity details, and offers data definitions as needed.

Functional/Source Data SME: Provides source connectivity details and data definitions, and defines standards and best practices for business and technical teams. Maintains technical documents and recommends strategies for reporting tool performance.

Reporting Architect: Improves implementation practices to minimize user interruptions and reviews analytic solutions for scalability and alignment with best practices.

Technology Architecture Lead: The Technology Architecture Lead oversees the system design of the technical solution, procures and configures technical infrastructure components, and validates technical components. They establish standards, frameworks, and release management processes for the project.

Validation Lead: Responsible for leading defect meetings, coordinating defect resolution, developing and executing test scripts, and providing summary reports. They also develop validation plans and summary reports to ensure the quality and integrity of the project deliverables.

Change Management / Business Readiness: This role (or team) defines change management plans and procedures, coordinates change activities, and ensures that change status, progress, and issues are communicated effectively. They educate stakeholders on the change management process to ensure smooth transitions.

Production Support: This team provides round-the-clock support to production processes, monitoring alerts, escalating issues, and coordinating with various teams for efficient issue resolution. They also track and understand changes deployed to production environments. In the landscape of project delivery, especially in the context of analytics modernization, the presence of production support representation is paramount. Here's why:

• Continuous Support and Maintenance: The production support team is tasked with ensuring the smooth operation and continuous functioning of systems and applications in the production environment. Their expertise in troubleshooting issues, managing incidents, and maintaining system health is invaluable throughout the project lifecycle.
• Early Identification of Operational Challenges: Having production support representation in the project delivery phase allows for early identification of potential operational challenges or system limitations. Their insights into production environment dynamics, performance metrics, and user feedback can help project teams anticipate and address issues proactively.
• Alignment of Project Deliverables with Operational Needs: By involving production support early on, project teams can ensure that the solutions being developed align with operational requirements and constraints. This alignment helps in building systems that are not only technically sound but also practical and sustainable in real-world production scenarios.
• Smooth Transition to Operations: Production support team members play a crucial role in facilitating the transition of project deliverables from development to production environments. Their involvement ensures that deployment processes are well-coordinated, and necessary support mechanisms are in place to address any issues that may arise post-deployment.
• Faster Incident Resolution: With production support representation embedded in the project delivery process, the turnaround time for incident resolution can be significantly reduced. Their familiarity with the system architecture and operational workflows enables them to diagnose and address issues more efficiently, minimizing downtime and impact on business operations.
• Feedback Loop for Continuous Improvement: Production support teams can provide valuable feedback to the project team based on their experiences in managing and supporting the deployed solutions. This feedback loop fosters a culture of continuous improvement, allowing project teams to refine and optimize their deliverables based on real-world operational insights.

Each role contributes a crucial piece to the delivery governance puzzle, ensuring that the project progresses smoothly, aligns with organizational goals, maintains data integrity, and meets quality standards. Integrating production support representation into project delivery ensures that the developed solutions are not only technically robust but also operationally viable and resilient. Their proactive involvement contributes to smoother deployments, faster incident resolution, and ongoing optimization, ultimately driving the success of analytics modernization initiatives.

These percentages are approximate and may vary based on the specific project requirements, organizational structure, and the level of involvement needed from each role. Additionally, ongoing operations may require continuous effort from all roles involved to ensure the system's stability, performance, and alignment with business goals.

Program Governance Framework

A robust program governance framework provides the overarching structure and guidelines for managing the project. It defines the governance structure, decision-making processes, escalation paths, and communication protocols. This framework ensures that the project is aligned with organizational objectives, adheres to standards and best practices, and effectively manages risks and issues.?

In the context of our analytics modernization journey, establishing an effective program governance framework is paramount to ensure alignment with strategic objectives and successful project delivery. This framework encompasses three tiers of governance: strategic, directional, and operational, each serving a distinct purpose in overseeing the program.

Strategic Tier: Steering Committee and Executive Sponsor

At the strategic level, the Steering Committee and Executive Sponsor provide overarching guidance and direction, focusing on enterprise impacts, budget, timelines, and major project risks. The Steering Committee manages resource conflicts, evaluates budget and timeline options, and authorizes significant project decisions, while the Executive Sponsor provides strategic direction and ensures alignment with organizational goals.

Directional Tier: Project Leadership and PMO

The directional tier involves project leadership and the Project Management Office (PMO), which oversee cross-functional decisions and ensure alignment with the operating model. Project leadership, including Technology, Architecture, and Functional Leads, drive decision-making across workstreams, while the PMO facilitates communication, integrates activities, manages project quality, and rationalizes decisions related to time and budget trade-offs.

Operational Tier: Project Teams and Implementation Teams

At the operational level, project teams, including workstream and project leads, are responsible for process, workflow, and configuration decisions within their respective workstreams. Implementation teams provide specialized expertise and input to ensure the successful execution of project activities.

Program & Project Management Services?

Leveraging program management services brings in expertise and resources dedicated to overseeing the project at a program level. Program managers play a crucial role in coordinating activities across different project teams, managing dependencies, and ensuring that the project stays on track in terms of scope, schedule, and budget. They also provide leadership and strategic guidance to navigate challenges and complexities.

In addition to the governance framework, implementing project management services is essential for executing the modernization program and achieving business objectives. These services encompass key activities aimed at managing overall timelines, resolving project issues and risks, coordinating activities across workstreams, aligning resource needs, maintaining quality standards, and measuring progress. Integration management, scope and delivery management, quality management, and risk and issue management are among the critical areas addressed by these services, ensuring a structured and proactive approach to project execution.

1. Integration Management - Integration Management is a critical process that enables seamless coordination between various workstreams, projects, and end users within the modernization program. It involves defining project timelines, considering priorities and dependencies to ensure the successful execution of multiple workstreams like Catalog, Collect Layer , Translate Layer, and Curate Layer (Self-Service data accel layer). The expected value includes better planning and increased efficiency leading to smoother execution of multiple workstreams, early adoption of the modernized platform reducing rework during migration, and minimization of impact on the overall program by integrating critical initiatives. Additionally, it focuses on regulatory compliance by identifying ongoing projects within various regions or business functions and developing a plan to onboard them onto the modernized platform, ensuring smoother transitions and alignment with overarching program goals. Key deliverables include coordination with upstream and downstream initiatives to mitigate risks and the development of a comprehensive roadmap outlining the impact assessment for both upstream and downstream applications, providing clarity on the program's direction and milestones.

1. Scope Management - Scope Management ensures the effective management of planned deliverables, schedule, budget, and additional demand and changes to continuously provide business value. The expected value includes focusing on critical business milestones and early user adoption, finalizing project scope with stakeholders, identifying new requirements, and fostering collaboration between teams to manage scope effectively. Managing program scope to demonstrate early business value by enabling analytics for users through data ingestion into the Collect layer and Translate layer is essential. Planning all program activities for Go-Live by a specified date and facilitating early adoption of the modernized platform by other business functions are key objectives. Scope is categorized by functional, technical, infrastructure, and change management tasks, with activity and milestone tracking defined to consistently measure performance. Key deliverables include defining functional and technical requirements for the future state, cutover and deployment plans, activity trackers, and a detailed project plan.

1. Time Management? - Time Management is crucial to providing value to the business according to the agreed-upon schedule and project plan. The expected value includes the realization of project milestone goals within specified timelines, identifying necessary ramp-up and ramp-down periods to meet project deadlines, and demonstrating value to the business. Key activities and objectives involve ensuring the successful implementation of projects within designated timeframes, preparing and tracking project timelines for implementation on the modernized platform following the SDLC process, and managing timelines for parallel execution of multiple business functions while maintaining dependencies and quality. Key deliverables include Team Velocity Reports and a Project Plan with Milestones/Releases, facilitating tracking and reporting to ensure closer monitoring of deliverables, milestones, and early detection of impediments to key activities through consistent PMO activities.

1. Quality Management - Quality Management is essential for ensuring the excellence of deliverables through meticulous measurement, monitoring, and enforcement of corrective actions. The expected value includes heightened efficiency and quality deliverables achieved through continuous reviews, consistency in solution design and development across the program, and ensuring data accuracy and completeness through reconciliation processes. Key activities and objectives involve thorough reviews by Functional and Technical Subject Matter Experts (SMEs), adherence to industry standards and best practices for architecture and development, and periodic reviews by vendor QA partners focused on people, processes, and technology. Key deliverables encompass QA Partner Reviews, Quality Review Reports, Testing Strategy and Plans, and Data Reconciliation reports, ensuring adherence to design and development best practices, standards, and a detailed testing strategy for various testing phases.

1. Resource Management - Resource Management involves defining a resource model that fosters effective collaboration between internal and external resources, ensuring seamless execution of design, development, transition, and application support activities within the modernization program. The expected value includes improved collaboration and upscaling of resources, assembling the right team with the required skills, achieving flexibility in team scalability aligned with demand fluctuations, and enhancing productivity and accountability. Key activities and objectives encompass leveraging the organization’s resources for code development, facilitating continuous learning and upscaling of resources, providing resources with the necessary skills and experiences, and cross-skilling teams for enhanced efficiency and utilization across multiple projects. Key deliverables include defining the operating model, project staffing plan, and delineating roles and responsibilities across teams, alongside capacity management to handle capacity fluctuations through seamless access to shared resource pools and leveraging functional and technical subject matter experts from both internal and external sources.

1. Risk and Issue Management - Risk and Issue Management ensures the timely identification of potential risks and issues, allowing for the formulation of appropriate mitigation strategies and actions to minimize their impact on the project. The expected value includes proactive resolution of key risks, keeping program leadership informed of project status, and reducing the impact on timelines by addressing risks early. Key activities involve establishing trackers for risk and issue management to proactively identify, notify, assess, and mitigate risks, as well as defining and documenting risks and issues while clearly articulating their impact on scope, time, budget, and ownership. Key deliverables include project status reports, RAID (Risks, Assumptions, Issues, Dependencies) logs, and regular review meetings with leadership to discuss mitigation strategies and escalate as necessary. Additionally, risks and issues are categorized based on functional, technical, infrastructure, and security aspects, with a defined governance model to track and resolve them effectively.

By adhering to a robust program governance framework and leveraging comprehensive project management services, organizations can navigate the complexities of analytics modernization with clarity, accountability, and efficiency, ultimately unlocking the full potential of their data-driven initiatives.

Communication

Implementing a regular meeting cadence is essential for maintaining alignment, communication, and accountability within the project team. Regular project meetings, such as status updates, progress reviews, and issue resolution sessions, provide opportunities for stakeholders to share updates, discuss challenges, make decisions, and track progress against milestones. A well-defined meeting cadence ensures that stakeholders are informed, engaged, and involved in key project activities, leading to better collaboration and decision-making.

Communication Cadence ensures structured reviews and updates to provide seamless communication to all stakeholders throughout the engagement. Operational updates, including status on incidents, risks, and issues, are communicated daily during the transition phase via Morning Calls led by the Engagement Operations Manager and attended by IT Operations Team & Leads, Application Leads, and BI Leads. Weekly Operational Team Meetings by workstream review open issues, status on defects, and root cause analysis, led by Application Workstream Lead and attended by respective teams. Additionally, change control meetings, engagement financials reviews, and management reviews are held biweekly or monthly, addressing various aspects such as code changes, financial forecasts, and key engagement level issues. Monthly Engagement Reviews, facilitated by the Engagement Manager and attended by IT Leadership, focus on metrics, SLA compliance, accomplishments, risks, and feedback from the business. Communication also extends to project milestones, with a structured cadence from design to deployment and go-live, ensuring alignment and support across downstream systems. Meetings are scheduled at the beginning, during, and end of each project/application, with weekly touchpoints throughout.

Delivery Governance Wrap Up

In essence, effective project governance establishes the framework, structures, and processes necessary to drive successful project outcomes. By defining roles and responsibilities, establishing a governance framework, leveraging program management services, and maintaining a regular meeting cadence, organizations can enhance project visibility, alignment, and ultimately, deliver results that meet stakeholder expectations.

Change Strategy

Effective change planning involves assessing impacts, defining change objectives, and developing strategies to mitigate resistance and foster adoption.

Change Strategy is a fundamental aspect of any organizational transformation, guiding the process of implementing new technologies and systems while minimizing disruption and maximizing adoption. At its core, the strategy involves several key phases.

The Path to Change encompasses various elements, including defining the program purpose and scope, planning the rollout strategy, managing stakeholders, implementing training programs, developing communication strategies, managing status and risks, and coordinating workstreams effectively.

Change Planning?

Change Planning begins with understanding the need for change and assessing its impact on people, processes, and technology. This phase defines the change and evaluates the environment to lay the groundwork for subsequent actions.

Approach and Design focus on gathering and assessing change impacts, understanding integration pain points, and designing solutions that adapt to the new technology landscape.

This plan represents a structured approach aimed at modernizing technology infrastructure and establishing a Global Analytics Platform. This journey involves several crucial steps to ensure successful implementation and adoption of change.

Initially, the plan entails understanding and documenting the compelling need for change, defining the change, and assessing its impact on people, processes, and technology. This phase sets the foundation for the entire transformation process.

Effective communication and engagement are paramount. Key stakeholders are identified, and a communication strategy and plan are developed to keep stakeholders informed and engaged. This includes establishing a two-way feedback process to address concerns and insights promptly.

Approach and design involve gathering and assessing change impacts, understanding different integration pain points, and designing solutions that can adapt to the new technology landscape. This phase focuses on developing strategies and solutions aligned with organizational objectives and capabilities.

Transition involves executing the plan with the support of all relevant stakeholders. Regular progress reviews and readiness assessments are conducted to ensure a smooth transition. This phase also includes retiring legacy systems and ensuring a seamless transition to the new system.

A Systematic Approach

This plan outlines a systematic approach to prepare for and execute a transformative initiative within an organization.

• Firstly, it involves defining the program's purpose and end-to-end scope of activities to establish clear objectives. This includes delineating the scope, approach, and detailed phase-wise plan to ensure successful execution.
• Program planning entails defining a rollout strategy that outlines interdependencies across various systems and applications, ensuring a coordinated implementation process.
• Stakeholder management is crucial, involving the identification, engagement, and alignment of key stakeholders with the overall program plan to garner support and involvement.
• Training initiatives are essential to equip intended users with the necessary skills and knowledge to adapt to the changes effectively. This includes defining training mechanisms and frequencies tailored to the needs of the audience.
• Scope of activities encompasses the development of a communication strategy and plan to ensure timely and transparent dissemination of information to stakeholders. Additionally, it includes establishing mechanisms for status and risk management to monitor progress and mitigate potential challenges.
• Process management involves implementing mechanisms to measure and report status while identifying, assessing, and mitigating key risks throughout the program execution. This includes defining a remediation process aligned with business objectives to address any issues that may arise.
• Lastly, workstream coordination is vital for facilitating support across teams to ensure a cohesive and successful execution of the program. This involves fostering collaboration and coordination among different teams to achieve the desired outcomes.

Overall, a thorough and frequent touchpoint remediation approach is emphasized throughout the process. This approach aims to deliver business value, drive sustainable results, and enable the organization to successfully transition to the new technology landscape, ensuring continued growth and success.

Communicate and Engage?

Communication and engagement strategies are essential for keeping stakeholders informed, addressing concerns, and building support for change initiatives.

Communication and Engagement are vital components of change management, involving the identification of key stakeholders, the development of a communication strategy, and engaging stakeholders in a two-way feedback process to ensure alignment and understanding. For an example of robust communication strategy for a complex delivery environment see the LinkedIn article Navigating Complexity: The Agile Approach to Overcoming Project Challenges.

A Robust Communication Strategy

In the "Path to Change – Communicate & Engage," a robust communication strategy is highlighted as pivotal for successful change management, emphasizing the importance of meeting the communication needs of all key stakeholders to keep them informed and engaged.

• The communication process begins with "Communication Inception," laying the groundwork for effective engagement by initiating clear communication channels and setting expectations for ongoing dialogue.
• "Action & Accountability" underscores the importance of translating communication into actionable steps, ensuring accountability among team members to execute tasks effectively.
• "Continuous Engagement" emphasizes the need for sustained communication and interaction with stakeholders throughout the change process, fostering transparency and trust.
• "Collaboration" stresses the importance of working closely with key stakeholders to assess the impact of change across downstream systems, encouraging collaboration to address challenges and capitalize on opportunities.
• Key activities include sharing and reviewing high-level plans, defining roles and responsibilities, aligning on deliverables across different phases of the software development lifecycle (SDLC), and establishing a two-way feedback mechanism to solicit input and address concerns.
• Regular reviews of change readiness at predefined intervals are essential to gauge progress and ensure a smooth transition to the new system.
• Identifying key stakeholders across different downstream systems and initiating change conversations through kickoff calls help to ensure broad participation and alignment with organizational goals.

Communication and Engagement within the Path to Change involve establishing robust communication strategies to keep stakeholders informed and engaged, fostering collaboration, setting up feedback mechanisms, and ensuring continuous engagement throughout the change process.

Change Cadence: Alignment and Collaboration Across Teams?

Establishing a consistent change cadence ensures timely updates, feedback loops, and adjustments to change management activities.

Transition involves executing the plan with support from relevant stakeholders, reviewing progress regularly, assessing readiness, and ensuring a smooth transition from legacy systems to new ones. For an example of techniques and methods for driving alignment and collaboration across teams see the article Navigating Large-Scale Agile Projects: A Comparison of Scrum of Scrums and Agile Release Train.

Communication Plan for Seamless Alignment and Collaboration

To maintain effective communication throughout the project lifecycle, a structured cadence is essential. The outlined communication plan ensures alignment and collaboration across teams at every stage:

• Project Initiation:
• Weekly Touch Points:
• Parallel Testing Support:

By incorporating SIT within the parallel testing phase, teams can verify the integration of various system components and functionalities. This ensures that all integrated systems work cohesively and meet the desired performance and functionality criteria. With regular communication touch points, teams can address any issues promptly, maintain alignment, and facilitate a successful transition to the new MDM solution. A comprehensive remediation approach, characterized by thorough planning, effective communication, stakeholder engagement, and targeted training, is essential for delivering business value, driving sustainable results, and facilitating a successful transition to the new landscape.

Summary of Syncing Strategies: Ensuring Alignment and Collaboration Across Teams

A well-defined communication plan is crucial for ensuring alignment and collaboration across teams at every stage of the project lifecycle. By establishing clear channels of communication and regular touch points, teams can stay informed, address challenges promptly, and maintain cohesion throughout the project. Effective communication fosters synergy, mitigates risks, and ensures successful project outcomes.

Training Approach

A comprehensive training approach equips stakeholders with the knowledge and skills needed to embrace new technologies and processes. A comprehensive training strategy plays a pivotal role in ensuring the successful integration of a new technology platform across the entire organization. Here's how we plan to implement our training strategy:

Whom To Train:

Identify the teams to be trained, including business users, data scientists, and IT support teams, to ensure comprehensive coverage across all relevant departments.

Training Methods:

Utilize a variety of training methods to cater to diverse learning preferences, including virtual sessions led by instructors, in-person classroom sessions (employing a train-the-trainer approach), and offline resources such as videos and presentation decks.

Identify Trainers:

Identify trainers based on their expertise, drawing from functional subject matter experts within the organization and technology vendors specializing in the selected tools and platforms.

Training Content:

Develop a range of training materials, including videos, PowerPoint presentations, and applications, to provide comprehensive and engaging learning experiences for trainees.

Training Scope: Divide training sessions into two primary categories:

1. Functional Training: Cover topics such as self-service analytics, understanding information, and navigating through the platform's user interface to empower users to leverage the platform effectively in their day-to-day operations.

1. Technical Training: Focus on specific tools and technologies essential for the successful operation of the new platform. This includes training on various storage solutions like Amazon S3, Azure Data Warehouse (ADW), and Snowflake, enabling users to efficiently manage and store data. Additionally, provide training on reporting tools such as IBM Cognos and Tableau, allowing users to create insightful visualizations and reports. Finally, offer training on analytics platforms like Python, R, and Alteryx, empowering users to conduct advanced data analysis, modeling, and automation tasks.

Fostering Effective Adoption of New Technology Wrap Up

Training Approach and Strategy are crucial for ensuring successful adoption of the new technology platform across the organization. It involves identifying the teams to be trained, selecting appropriate training methods, identifying trainers, developing training content, and defining the scope of training to cover both functional and technical aspects.

Reference Architecture

Technology Options?

Finalizing the technology options for data lake storage and compute involves evaluating combinations of technologies based on criteria such as ease of development, complexity, performance, connectivity, and pricing. Various short-listed technology combinations for data lake storage and compute, various combinations of technologies are evaluated based on several key criteria.?

Here's an elaboration on the shortlist of options among many others and how they align with the evaluation criteria:

Option 1 - Collect Layer: S3, Translate Layer: S3, Curate Layer: Oracle ADW, Compute: Data Stage:

• Ease of Development: Utilizing S3 for data storage and Oracle ADW for compute offers a familiar environment for development and maintenance, especially if the organization is already accustomed to using Oracle technologies.
• Complexity: Oracle ADW provides robust features for implementing complex business rules and accommodating future functionalities, making it suitable for data curation tasks.
• Performance: Oracle ADW can handle the processing of incremental data streams every 15 minutes efficiently and improve the performance of regular batch loads due to its optimized architecture.
• Connectivity: Oracle ADW offers seamless connectivity with upstream and downstream applications, ensuring smooth data flow across the ecosystem.
• Pricing: While Oracle ADW may require upfront investment, its scalability and performance improvements can lead to cost savings over time, especially when considering reduced human resources needed for maintenance and administration.

Option 2 - Collect Layer: S3, Translate Layer: S3, Curate Layer: Snowflake, Compute: Data Stage:

• Ease of Development: Snowflake's user-friendly interface and SQL-based querying capabilities contribute to ease of development and maintenance, especially for teams familiar with SQL databases.
• Complexity: Snowflake's ability to handle complex data structures and execute advanced analytics makes it well-suited for implementing business rules and supporting future functionalities.
• Performance: Snowflake can efficiently process incremental data streams and improve the performance of regular batch loads, ensuring optimal performance for data curation tasks.
• Connectivity: Snowflake offers robust connectivity options, enabling seamless integration with upstream and downstream applications for efficient data exchange.
• Pricing: Snowflake's pay-as-you-go pricing model and scalability make it a cost-effective solution, especially considering reduced human resource requirements for maintenance and administration.

Option 3 - Collect Layer: S3, Translate Layer: S3, Curate Layer: Oracle ADW, Compute: EMR (Spark):

• Ease of Development: Using EMR (Spark) for compute offers familiarity to teams accustomed to Apache Spark, contributing to ease of development and maintenance.
• Complexity: Oracle ADW's capabilities for handling complex data structures and executing advanced analytics complement EMR (Spark) for supporting complex business rules and future functionalities.
• Performance: EMR (Spark) can efficiently process incremental data streams and improve the performance of regular batch loads, ensuring optimal performance for data processing tasks.
• Connectivity: Oracle ADW's seamless connectivity with upstream and downstream applications ensures smooth data flow, complementing EMR (Spark)'s processing capabilities.
• Pricing: While Oracle ADW may require upfront investment, EMR (Spark)'s cost-effective pricing model and scalability contribute to overall cost savings, especially considering reduced human resource requirements for maintenance and administration.

Option 4 - Collect Layer: S3, Translate Layer: S3, Curate Layer: Snowflake, Compute: EMR (Spark):

• Ease of Development: Snowflake's user-friendly interface and SQL-based querying capabilities make it easy to develop and maintain, especially for teams familiar with SQL databases.
• Complexity: Snowflake's robust features for managing complex data structures and executing advanced analytics support the implementation of complex business rules and future functionalities.
• Performance: Snowflake can efficiently process incremental data streams and improve the performance of regular batch loads, ensuring optimal performance for data curation tasks.
• Connectivity: Snowflake offers seamless connectivity with upstream and downstream applications, facilitating smooth data flow across the ecosystem.
• Pricing: Snowflake's pay-as-you-go pricing model and scalability, combined with EMR (Spark)'s cost-effective pricing, contribute to overall cost savings, especially considering reduced human resource requirements for maintenance and administration.

Data Catalog Comparison: Collibra vs. Alation

Collibra and Alation offer robust data catalog solutions, each with its unique strengths and capabilities. Collibra excels in providing a user-friendly interface, making it easy for users to navigate, search, and trace records. It offers comprehensive views of data objects, including attributes, hierarchies, and historical changes. Additionally, Collibra's advanced workflow and stewardship features facilitate efficient data governance, with capabilities for creating and approving glossary changes. On the other hand, Alation provides similar functionality but lacks the mature workflow capabilities of Collibra. Both tools support business glossary management and offer strong security controls. However, Collibra stands out in data quality monitoring and profiling, providing detailed insights and visual representations. It also offers superior collaboration features, allowing users to start conversations, tag others, and receive email notifications. Collibra's extensive connectivity with external BI tools further enhances its appeal. While Alation provides lineage at the table level, Collibra offers detailed lineage up to the column level, along with comprehensive metadata management. Alation excels in data analytics and querying, allowing users to query datasets, save result sets, and analyze query logs.?

One method for maintaining catalog information using automated methods involves leveraging AWS crawlers combined with custom scripts to identify and synthesize data metadata changes in Parquet tables and applying updates to the data catalog software.

Setting up AWS Crawlers: Begin by setting up AWS Glue crawlers to automatically discover and catalog metadata from your data sources, including Parquet tables stored in Amazon S3. AWS Glue crawlers can be scheduled to run at regular intervals to ensure that any changes in the data are promptly detected.

Custom Scripting for Metadata Synthesis: Develop custom scripts or AWS Lambda functions to process the metadata extracted by the AWS Glue crawlers. These scripts should compare the current metadata with the previously stored metadata to identify any changes, such as new columns, schema modifications, or data location updates, in the Parquet tables.

Metadata Change Detection: Implement logic within the scripts to detect metadata changes. This can involve comparing attributes such as column names, data types, partitioning information, and file locations. Any variances between the current and previous metadata versions should be flagged as potential changes.

Applying Updates to Data Catalog Software: Integrate the custom scripts with your data catalog software, such as Collibra or Alation. Utilize the APIs or SDKs provided by the data catalog software to programmatically update the catalog with the detected metadata changes. This may involve adding new tables, updating table schemas, or refreshing metadata for existing tables.

Testing and Deployment: Thoroughly test the automated process in a staging environment to ensure its accuracy and reliability. Validate that the scripts correctly identify metadata changes and apply updates to the data catalog software without errors. Once validated, deploy the automated solution to your production environment.

Example Using AWS Crawlers: For instance, suppose a new column is added to a Parquet table stored in Amazon S3. The AWS Glue crawler automatically detects this change during its next scheduled run. Custom scripts analyze the extracted metadata and identify the new column. The scripts then utilize the AWS Glue Data Catalog API to update the corresponding table metadata in the catalog software, reflecting the addition of the new column. This automated process ensures that the data catalog remains up-to-date with the latest metadata changes in the Parquet tables.

Overall, the choice between data catalog options such as but not limited to Collibra and Alation depends on specific requirements and preferences, with Collibra offering a more comprehensive solution for data governance and management.

Storage and Compute: Snowflake, EMR/Redshift, Oracle ADW

In evaluating storage and compute solutions, critical features such as scalability, decoupled storage and compute, row-level security, maintenance requirements, integration capabilities, and cost considerations were assessed across Snowflake, EMR + Redshift, and Oracle ADW. For a comparison/contrast between cloud data approaches as opposed to a leading legacy data approach see the article Transforming Global Data and Analytics: Snowflake and Redshift vs. Oracle RDBMS.

Scalability: Snowflake offers instant scaling without data redistribution or downtime, while Oracle ADW supports autoscaling with no downtime. Resizing Redshift clusters requires manual intervention and triggers downtime.

Decoupled Storage and Compute: Snowflake separates computation and storage, leveraging cost-effective storage using S3. In contrast, Redshift and Oracle ADW do not offer decoupled storage and compute.

Row-level Security: Snowflake allows for row-level security implementation through custom views and runtime user configuration. Redshift and Oracle ADW require separate object creation to enable row-level security.

Maintenance: Snowflake is fully automated, requiring no maintenance from end users, while Redshift and Oracle ADW may require users to execute data manipulation-related housekeeping activities or perform routine maintenance tasks automatically.

Integration with Other Technologies: Redshift and Snowflake can integrate with data cataloging tools like Collibra, Alation, and data warehousing tools like Athena and Glue.

Cost: Snowflake offers better pricing compared to Redshift for minimal and scattered query usage across larger time windows, while Oracle ADW has high licensing costs.

The options considered are Snowflake, EMR with Snowflake/Redshift, and Oracle ADW. Each option offers distinct advantages and considerations, with Snowflake standing out for its scalability, decoupled storage and compute, and cost-effectiveness, making it a comprehensive choice for storage and compute needs.

Data Processing: Various Tools Offer Critical Features That Cater to Specific Needs

Here's a comparison of some prominent tools:

SnapLogic: It boasts elasticity, dynamically expanding and contracting based on data traffic, ensuring efficient resource utilization.

DataStage for Big Data: Leveraging parallel platforms like clusters or MPP architectures, it optimally utilizes resources for processing large volumes of data.

Talend for Big Data: Known for scalability, it allows the building of scalable solutions and provides tools like Talend Data Quality for data profiling.

PySpark: Offering scalability through parallel processing across multiple CPUs, it supports scalable and incremental data profiling.

Informatica BDM: Designed for scalability, it starts with basic deployment types and can be upgraded over time for increased computational resources.

Data Quality / Data Profiling:

• Talend and PySpark offer data profiling capabilities, with Talend providing an open-source tool and PySpark enabling scalable and incremental profiling.
• SnapLogic facilitates seamless data cleansing, enrichment, and governance on integration pipelines.
• Informatica BDM supports building data integration, data quality, and data governance processes for big data platforms.

Metadata driven & Code reusability:

• Talend and PySpark support metadata-driven approaches and offer reusable components for data processing.
• DataStage provides support for reusable components and extended metadata management.
• SnapLogic offers some metadata-driven capabilities but may require extensive configuration for complex solutions.

Developers can import metadata from Hadoop clusters and configure tools like PySpark and Talend easily, facilitating smoother integration and processing workflows.

Data Lake Batch Processing Technologies

Various critical features play a significant role in decision-making:

Cloud Integration:

• Talend, PySpark, DataStage, SnapLogic, and Informatica BDM all offer integration with multi-cloud platforms like Azure, AWS, and Google, facilitating seamless data integration across different cloud environments.

Support for Structured/Unstructured Data Formats:

• Talend provides extensive connectors supporting various data formats, while PySpark simplifies data pipelines through a metadata-driven approach.
• DataStage offers integration with heterogeneous data, and SnapLogic utilizes the Spark framework for big data solutions.
• Informatica BDM supports integration with structured, semi-structured, and unstructured data formats.

User-Friendliness:

• Talend and DataStage offer GUI interfaces with drag-and-drop functionality, making them user-friendly. PySpark is configuration-driven and requires knowledge of the Spark engine and Python coding.
• SnapLogic employs a web-based, drag-and-drop interface, while Informatica BDM is GUI-based.

Integration with AWS Tool Stack:

• Talend, PySpark, DataStage, SnapLogic, and Informatica BDM support integration with various AWS services like S3, Redshift, RDS, DynamoDB, and EMR, enabling seamless data processing and integration within the AWS ecosystem.

Integration with Cataloging Tools (Collibra):

• Talend supports integration with Collibra DGC using the Collibra DGC Connector, while PySpark integrates with Collibra DGC through the Collibra Catalog profiler.
• DataStage also supports integration with Collibra, whereas SnapLogic offers its own data governance tool.
• Informatica BDM provides its own data cataloging tool – EDC (Enterprise Data Catalogue).

Vendor Support & Product Stability:

• Talend, SnapLogic, and Informatica BDM offer vendor support for their respective products.
• IBM provides vendor support through PMR for DataStage.
• PySpark, being based on a programming language, can rely on various vendors for the Spark platform, such as Cloudera and Hortonworks.

Considering these critical features and technical requirements, organizations can make informed decisions about the most suitable data lake batch processing tool for their specific needs.

Summary of Architectural Options Evaluation

In summary, the reference architecture for data lake batch processing involves evaluating various technology options based on criteria such as ease of development, complexity, performance, connectivity, and pricing. This ensures the selection of the most suitable combination of storage and compute technologies to meet the organization's needs.

Although multiple other options exist, for the purpose of brevity and to provide a focus on example guidelines, principles, and best practices, four options were short-listed, each offering distinct advantages:

• S3 for collection, S3 for translation, Oracle ADW for curation, and Data Stage for computation.
• S3 for collection, S3 for translation, Snowflake for curation, and Data Stage for computation.
• S3 for collection, S3 for translation, Oracle ADW for curation, and EMR (Spark) for computation.
• S3 for collection, S3 for translation, Snowflake for curation, and EMR (Spark) for computation.

Each option was assessed based on its suitability across critical criteria, highlighting strengths such as ease of development, scalability, performance, and integration capabilities.

Furthermore, a comparison of data cataloging tools Collibra and Alation revealed their strengths in various areas, such as user experience, data governance workflows, metadata and lineage capabilities, and integration with external tools.

Additionally, methods for maintaining catalog information using automated approaches, such as leveraging AWS crawlers and custom scripts, were discussed, ensuring the catalog remains updated with metadata changes in Parquet tables.

Lastly, comparisons of data processing tools such as SnapLogic, DataStage, Talend, PySpark, and Informatica BDM emphasized their capabilities in cloud integration, support for structured/unstructured data formats, user-friendliness, and integration with AWS tool stacks.

Overall, the reference architecture provides a framework for organizations to select the most suitable technologies for their data lake batch processing needs, considering critical features, technical requirements, and organizational objectives. There are many other options; the intent is not to provide a comprehensive list of options but instead to offer a framework that can be applied to the vast set of technology options that exist. Organizations should carefully evaluate their options and choose the architecture that best aligns with their needs and objectives.

Article Wrap Up - Driving Excellence in Analytics Modernization: A Comprehensive Approach

Navigating the complexities of modernizing analytics and data processing requires a strategic blend of governance, change management, effective communication, and robust technology architectures. By establishing clear roles, governance frameworks, and communication strategies, organizations can drive successful project outcomes and ensure stakeholder alignment. Embracing systematic change planning and fostering engagement across teams are vital for navigating transformative initiatives effectively. Additionally, selecting the right technology options, based on thorough evaluation criteria, ensures the development of scalable, efficient, and cost-effective solutions. Ultimately, by leveraging these strategies and frameworks, organizations can unlock excellence in their analytics modernization journey, driving sustainable results and positioning themselves for continued success in an ever-evolving digital landscape.

Series Conclusion: Driving Excellence in Analytics Modernization

In this comprehensive five-part series, "Navigating the Future of Analytics Modernization," we've embarked on a transformative journey into the realm of analytics strategy, architecture, and execution. From envisioning a data-driven future to unraveling the intricacies of solution architectures and streamlining data pipelines, each installment has provided invaluable insights and actionable strategies for organizations seeking to modernize their analytics capabilities.

As we conclude this series, it becomes evident that excellence in analytics modernization requires a multifaceted approach. By establishing robust governance frameworks, clear roles and responsibilities, and effective communication channels, organizations can drive successful project outcomes and ensure alignment with stakeholder expectations. Embracing systematic change planning and fostering engagement across teams are essential for navigating transformative initiatives effectively, fostering a culture of continuous improvement and innovation.

Moreover, the selection of appropriate technology options, guided by thorough evaluation criteria, plays a pivotal role in developing scalable, efficient, and cost-effective solutions. Whether it's leveraging cloud-based platforms, implementing advanced data processing tools, or adopting modern data cataloging solutions, organizations must carefully assess their options to align with their unique needs and objectives.

In essence, excellence in analytics modernization is not merely about implementing new technologies or processes—it's about driving sustainable results, fostering a culture of innovation, and positioning organizations for continued success in an ever-evolving digital landscape. By embracing the strategies and frameworks outlined in this series, organizations can unlock the full potential of their data assets, driving transformative change and achieving excellence in analytics modernization.

Thank you for joining us on this journey through the world of analytics modernization. We're grateful for the opportunity to share insights and knowledge with you. Here's to continued success and innovation in your analytics endeavors!

A Look Back at the Series...

Part 1: Crafting the Vision and Blueprint

In our inaugural installment, we laid the groundwork by articulating the Program Vision, elucidating the Future State Objectives, and unveiling the Illustrative Features of a Modernized Platform. From envisioning a data-driven enterprise culture to fostering increased data accessibility and analytics agility, we delved into the foundational principles driving this evolutionary journey.

Part 2: Architecting the Future

Diving deeper into the architectural underpinnings, Part 2 unveiled the Solution Architecture trifecta - Capability, Information, and Technical Architectures. Through a meticulous examination, we dissected the structural frameworks empowering organizations to seamlessly navigate the complexities of data management, analytics provisioning, and technological integration.

Part 3: Streamlining Data Pipelines for Efficiency

Efficiency lays at the heart of every successful endeavor. Part 3 shined a spotlight on Process Improvements, illuminating the pathways to streamlined operations, enhanced workflows, and optimized resource utilization. From reimagining data ingestion protocols to fortifying data quality initiatives, we explored the transformative potential of process optimization within Data Pipelines.

Part 4: Frameworks and Best Practices

Guided by a commitment to excellence, Part 4 unveiled a treasure trove of Frameworks and Best Practices meticulously curated to propel organizations towards analytics prowess. From orchestrating seamless data workflows to upholding stringent audit, balance, and control mechanisms, we unveiled the blueprint for sustained success in the data-driven landscape.

Part 5: Delivering Excellence

In our final installment, we traversed the terrain of Delivery Best Practices and Sample Reference Architectures. From elucidating robust project governance strategies to charting comprehensive program roadmaps, we provided invaluable insights into navigating the final mile of the analytics modernization journey.