Part 4 - Frameworks and Best Practices: Towards Analytics Prowess

Part 4 of 5 in the Series "Navigating the Future of Analytics Modernization"

Guided by a commitment to excellence, Part 4 unveils 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 unveil the blueprint for sustained success in the data-driven landscape.

Orchestration: Streamlining Data Workflows for Efficiency

Efficiency lies at the core of every successful data operation. In this section, we delve into the critical importance of orchestrating seamless data workflows. By implementing robust orchestration frameworks such as Apache Airflow or Kubernetes, organizations can streamline data pipelines, automate repetitive tasks, and ensure timely data delivery. We explore best practices for workflow design, task scheduling, and error handling, empowering organizations to optimize resource utilization and accelerate time-to-insight.

In the modern data landscape, efficient data processing relies heavily on robust orchestration frameworks. An orchestration framework serves as the backbone for automated end-to-end data processing, encompassing essential functionalities such as dependency management, error handling, monitoring, notifications, scheduling, and restart ability. By providing a structured framework, process, and toolset, orchestration ensures seamless execution of data pipelines and refreshes for reports and dashboards within the data lake ecosystem.

Key Features of Orchestration:

1. Error Handling: An effective orchestration framework includes mechanisms for identifying, capturing, and managing errors encountered during data processing tasks. It ensures prompt resolution of issues to maintain data integrity and pipeline reliability.

2. Monitoring: Continuous monitoring of data processing jobs, applications, and services is essential for detecting anomalies, performance bottlenecks, and potential failures. Monitoring capabilities enable proactive intervention and optimization of data workflows.

3. Notifications: Automated notifications provide stakeholders with timely updates on job statuses, data availability, and critical events within the data processing pipeline. This facilitates informed decision-making and ensures alignment with business objectives.

4. Dependency Checks: Managing dependencies between data processing tasks is crucial for orchestrating complex workflows efficiently. Dependency checks ensure that tasks are executed in the correct sequence, minimizing errors and maximizing parallelism.

5. Scheduling: Orchestrating the scheduling of data processing tasks enables organizations to optimize resource utilization and meet SLAs effectively. Scheduling capabilities ensure timely execution of jobs based on predefined criteria and priorities.

6. Restart Ability: In the event of job failures or interruptions, the ability to restart data processing tasks from the point of failure is critical for maintaining data consistency and meeting operational requirements. Restart ability features enable seamless recovery and resumption of processing tasks.

Guiding Principles for Future State Orchestration:

1. Synergy between Micro Batch and Standard Batch Processing: The orchestration framework should accommodate and maintain synergy between micro batch and standard batch data processing methodologies. This ensures flexibility and scalability in handling diverse data processing requirements.

2. Decoupling Dependency: Decoupling dependencies between processes promotes maximum parallelism and simplifies data pipeline architectures. By minimizing interdependencies, organizations can enhance agility and scalability in data processing workflows.

3. Dependency Setup at Job and Cohort Level: Establishing dependencies at both job and cohort levels enables granular control and management of data processing tasks. This facilitates efficient coordination and execution of complex workflows with multiple interrelated components.

4. Restart Capability for High Volume Jobs: High-volume data processing jobs with multiple partitions should have robust restart capabilities to resume processing from the point of failure. This ensures resilience and continuity in data processing operations.

5. Automated Notification of Data Availability: Automated notifications for critical tables, data pipelines, and applications enable stakeholders to stay informed about data availability and processing status. This proactive communication ensures timely action and facilitates seamless decision-making based on up-to-date information.

Micro Batch Orchestration Flow: Optimizing Parallel Processing

For micro batch processing, orchestrating data workflows efficiently is essential for maximizing throughput and minimizing latency. This section outlines the design of a micro batch orchestration flow, focusing on identifying independent cohorts and defining dependencies for seamless execution.

Identifying Cohorts for Parallel Processing:

• The micro batch orchestration flow begins by identifying independent cohorts within defined subject areas. These cohorts represent groups of data processing tasks that can be executed in parallel.
• Jobs processing similar types of data are grouped together within each cohort to facilitate easy identification of data loads and restartability.

Defining Cohort Dependencies:

• Each individual cohort comprises a set of jobs with appropriate interdependencies. The dependencies between jobs are defined to ensure proper sequencing and execution.
• For example, the dependency chain might be defined as: ITEM collect -> ITEM translate temp -> ITEM curate job.

Managing Dependencies Across Cohorts:

• Dependencies across cohorts may also be necessary based on specific data requirements. For instance, sales order loads may depend on the completion of the item cost job from the item cohort.

Selective Loading of Data Pipelines:

• In some cases, data pipelines may only need to be loaded up to a certain processing layer in the micro batch mode.
• For instance, the inventory cohort may load data up to the translate layer in micro batch, while the curate layer is loaded in standard batch mode.

Example - Micro Batch Orchestration for Supply Chain Data:

• Consider a scenario where multiple subject areas are involved in the supply chain domain, such as item management, sales orders, and inventory management.
• Each subject area, represented by a cohort, undergoes processing through the Collect Layer, Translate Layer, and Curate Layer.
• The orchestration flow ensures smooth execution and coordination of data workflows across these related subject areas, optimizing parallel processing and enhancing overall efficiency.

The micro batch orchestration flow is designed to optimize parallel processing of data cohorts, ensuring efficient execution and management of dependencies. By strategically organizing data processing tasks and defining clear dependencies, organizations can achieve seamless orchestration of micro batch workflows, driving agility and scalability in their data operations.

Standard Batch Orchestration Flow: Managing Daily Data Changes

In the context of standard batch processing, orchestrating data workflows efficiently is crucial for capturing daily data changes, building end-of-day aggregate tables, and performing full refreshes for cohorts with less frequent updates. This section outlines the design of a standard batch orchestration flow, focusing on end-of-day processing and scheduling for certain data pipelines.

Processing Daily Data Changes and Aggregate Tables:

• Standard batch orchestration is utilized to capture daily data changes and construct end-of-day aggregate tables.
• It facilitates the execution of full refreshes for cohorts that do not require frequent intraday updates.

Triggering Standard Batch Jobs:

• Standard batch jobs are triggered after the last micro batch run for the day, ensuring sequential processing and data consistency.

Refreshing Historical Tables:

• Historical tables (Hist tables) are refreshed only once a day as part of the standard batch process. For example, while the Item dimension is loaded in micro batch mode, the Item Hist table is loaded in standard batch mode.

Loading Aggregated Facts:

• Aggregated facts are loaded as part of the standard batch process. For instance, while Sales Order Line data is loaded in micro batch mode, the consolidated version of the same data entity is loaded in standard batch mode.

Processing Data to Curate:

• Certain data pipelines process data up to the curate layer exclusively in standard batch mode, ensuring comprehensive data processing and refinement.

Managing Processing Stages:

• Data processing from the Item Location of an ERP to the inventory translate layer is conducted through micro batch processing. However, data processing from the translate layer to the curate layer occurs in standard batch mode.

Weekly/Monthly Schedules:

• Weekly and monthly schedules are maintained separately and executed after the standard batch execution for the current run.
• These schedules are designed to run only on the last day of the week or month, ensuring timely processing and alignment with business requirements.

The standard batch orchestration flow is designed to manage daily data changes, construct aggregate tables, and perform full refreshes for less frequently updated cohorts. By orchestrating end-of-day processing and scheduling weekly/monthly schedules, organizations can ensure efficient data processing and maintain data integrity in their analytics pipelines.

In summary, orchestration plays a pivotal role in streamlining data workflows, enhancing operational efficiency, and ensuring reliability in data processing operations. By adhering to guiding principles and leveraging key features of orchestration frameworks, organizations can optimize their data pipelines and unlock the full potential of their data assets.

Audit, Balance, and Control: Upholding Data Governance Standards

Maintaining trust and integrity in data is paramount for organizations operating in today's data-driven landscape. In this section, we delve into the significance of audit, balance, and control mechanisms. By implementing robust data governance frameworks and leveraging tools such as Apache Ranger or Collibra, organizations can enforce data access controls, monitor data usage, and ensure compliance with regulatory requirements. We discuss best practices for data auditing, logging, and compliance reporting, enabling organizations to mitigate risks and uphold data governance standards effectively.

Metadata-Driven Framework: Enhancing Data Pipeline Management

In the dynamic landscape of data management, a metadata-driven framework emerges as a pivotal tool for capturing operational metadata within the data lake. This framework meticulously records crucial statistics associated with each step of the data movement process, facilitating efficient management, auditing, and error resolution. Let's delve into the features and capabilities of this framework:

Features:

1. Metadata Capture:

• The framework adeptly stores and manages metadata pertaining to all ETL (Extract, Transform, Load) jobs. This includes essential details such as job start time, end time, number of records loaded, and instances of errors encountered.
• By maintaining a comprehensive record of job execution statistics, the framework empowers stakeholders with valuable insights for auditing purposes and error debugging.

2. Notifications Functionality:

• Embedded within the framework is a robust notifications system that promptly alerts individual stakeholders or workgroups when predefined events occur. These events may include exceptions during job execution or successful completion of jobs.
• The notifications functionality ensures proactive communication and enables timely intervention in the event of anomalies or critical occurrences.

3. Restart Ability:

• In cases where job failures occur, the framework's ABC (Audit, Balance, Control) module comes to the rescue. It systematically checks for jobs flagged as errors within a particular batch execution.
• By leveraging ABC tables, the framework enables seamless job restarts, triggering execution from the point of failure. This ensures continuity of operations and minimizes downtime associated with error resolution.

4. Reusability:

• A notable aspect of the framework lies in its inherent reusability across diverse job flows, eliminating the need for code changes or redeployment.
• This versatility is achieved through configurable input parameters, allowing customization of critical aspects such as file delimiter type, input path, database name, target table name, and audit tables.
• By promoting reusability while enhancing maintainability, the framework offers a scalable and adaptable solution for managing varied data pipeline requirements.

Audit, Balance, and Control (ABC) | Enhancing Operational Oversight

The Audit, Balance, and Control (ABC) mechanism within the data pipeline framework undergoes significant enhancements to bolster operational oversight and streamline data processing efficiency. Here's an overview of “plain-old” ABC challenges and the next generation improvements:

Ordinary ABC:

1. Scheduler Notifications:

• Notifications are solely triggered by the Control-M scheduler in the event of job aborts.
• Tracking multiple failures simultaneously poses challenges, potentially leading to oversight of aborted jobs.

2. ABC Model:

• The existing ABC model stores job processing status and run timings but lacks comprehensive data counts.
• Updates to failed entries upon job restarts may obscure status tracking and execution details.

Future State:

1. Enhanced Notifications:

• Notifications will be expanded to cover failure and restart events for all jobs, supplemented by regular notifications for failed jobs.
• This proactive approach ensures timely alerts and mitigates the risk of overlooking critical job failures.

2. Comprehensive Data Counts:

• The ABC metrics will capture source, target, and rejected record counts along with corresponding source file names at each processing layer.
• These detailed statistics ensure data completeness for critical tables and facilitate anomaly detection.

3. Detailed Execution Statistics:

• Instead of updating failed entries, the ABC metrics will maintain separate entries for each job execution.
• This granular approach enables precise tracking of job statuses, especially after restarts, eliminating ambiguity.

4. Operational Metadata Reporting:

• Dashboards will be developed to provide comprehensive reporting on operational metadata, including batch run timings and data load statistics.
• These dashboards offer insights into system health, facilitating proactive monitoring and anomaly detection.

By addressing the shortcomings of common ABC mechanism and implementing future state enhancements, organizations can achieve heightened operational oversight and efficiency within their data pipelines. The expanded notifications, comprehensive data counts, detailed execution statistics, and operational metadata reporting collectively empower stakeholders with the insights needed to ensure seamless data processing and maintain system integrity. With these improvements, the ABC mechanism becomes a cornerstone of operational excellence in the data-driven landscape.

Audit, Balance, and Control: Key Objects in Data Pipelines

To ensure seamless operation and robust oversight within data pipelines, a structured approach is adopted, organizing processes at three distinct levels: Batch, Process, and Job. At each level, metadata and audit information are meticulously captured, enabling efficient restartability from points of failure and facilitating troubleshooting of job failures. Let's delve into the key objects within this framework:

1. Schedule:

• Schedules are defined at regional or functional levels (e.g., APAC, EMEA) and dictate the execution of batches.
• Each schedule triggers batches in a predefined order, ensuring systematic data processing.

2. Batch:

• Batches represent collections of processes (e.g., Supply Chain, Manufacturing, Marketing, Finance, etc) that need to be executed on a selected date.
• They are triggered by schedules and initiate the execution of processes according to defined dependencies.

3. Process:

• Processes encapsulate specific data processing tasks (e.g., Sales Order, Purchase Order) within a batch.
• Triggered by batches, processes orchestrate the execution of individual jobs, ensuring orderly data transformation and loading.

4. Job:

• Jobs refer to individual ETL (Extract, Transform, Load) tasks, each with its logic for transformation, validation, and loading.
• They are executed within processes, contributing to the overall data processing flow.

High-Level Flow:

• Schedules, triggered by scheduling tools like Control M, initiate the execution of batches.
• Batches, in turn, trigger processes, ensuring the sequential or parallel execution of tasks within a batch.
• Processes orchestrate the execution of jobs, facilitating data transformation and loading according to defined dependencies.

By organizing data pipelines into structured levels and capturing metadata at each stage, organizations can enhance auditability, streamline troubleshooting efforts, and enable efficient restartability in the event of failures. This structured approach ensures the orderly execution of data processing tasks, promoting reliability and operational excellence within the data pipeline ecosystem.

Audit, Balance, and Control Framework: Process Flow

The Audit, Balance, and Control (ABC) framework serves as the backbone for maintaining job execution statistics, managing rejects and error records, and ensuring seamless data processing within the data pipeline. Let's delve into the detailed process flow facilitated by the ABC framework:

1. Data Reprocessing:

• ABC framework loads data into metadata tables, enabling comprehensive tracking of job execution and error details.
• This facilitates data reprocessing and assists in audits and debugging efforts.

2. Job Failure / Restartability:

• In the event of a job failure, error details are logged, and the respective job/process/batch is aborted.
• Upon restart, the ABC process identifies the failed job within the current batch/process and initiates it without manual intervention or data cleansing.

3. Process Flow:

• The process flow within the ABC framework spans from ingestion to processing data until batch closure, with notification or alerts shared in case of failure.

Detailed Process Steps…

Ingestion Phase:

• The batch is initiated, capturing start time, end time, and relevant statistics for each data source.
• Metadata is populated in configuration tables for ingestion sources, batches, processes, and jobs.
• Source statistics, including start and end time, record counts, and file formats, are captured for all applicable data sources.

Processing Phase:

• Each process within the batch is initiated, executing jobs sequentially.
• Process statistics, including start and end time, and execution status, are captured.
• Similarly, job statistics, start and end time, execution status, and record counts are logged.
• Email notifications are sent to selected groups upon job success/failure, ensuring timely updates and alerts.

The next generation ABC framework ensures robust oversight and control over the data processing pipeline, facilitating efficient reprocessing, error handling, and job restartability. With detailed tracking of job execution and comprehensive notifications, organizations can maintain operational excellence and ensure the integrity and reliability of their data pipelines.

ABC Wrap Up

In essence, the metadata-driven framework serves as a cornerstone for efficient data pipeline management, offering robust metadata capture, proactive notifications, seamless restart capabilities, and unparalleled reusability. By leveraging these features, organizations can navigate the complexities of data management with agility, resilience, and operational excellence.

Data Quality: Ensuring Accuracy and Reliability

High-quality data forms the foundation of successful analytics initiatives. In this section, we explore the importance of data quality management and best practices for ensuring accuracy and reliability. From implementing data profiling and cleansing techniques to establishing data quality metrics and monitoring processes, organizations can enhance data integrity and reliability throughout the data lifecycle. We delve into tools and methodologies such as Apache Nifi or Talend Data Quality, empowering organizations to proactively identify and rectify data quality issues, thereby maximizing the value of their analytics investments.

Data Quality Management: Supply Chain Sales Order Use Case

In the world of supply chain management, ensuring data quality is paramount to operational success. This use case delves into the intricacies of data quality checks, reference integrity validations, business validation criteria, and data completeness assessments within the sales order domain.

1. Data Checks:

• Invalid quantity values, such as "abc," will be defaulted to '0' and flagged for review.
• Records with key columns as NULL will be directed to an error table for further investigation.

2. Reference Integrity Checks:

• The absence of currency code references will be highlighted, ensuring adherence to data integrity standards.
• Hash keys will be populated in the fact table, with records flagged for referential integrity checks.

3. Business Validation Checks:

• Business-defined validation criteria will be applied to guarantee the accuracy and reliability of reported data.

4. Data Completeness Checks:

• Comprehensive data reconciliation will be performed between source and target systems for critical tables, ensuring data completeness and accuracy.

Process Flow:

Source: Raw data originating from various supply chain sources.

Collect Layer: Initial data collection phase, where raw data is ingested and stored.

Translate Layer: Data translation and transformation phase, preparing data for reporting and visualization.

Curate Layer: Data curation and refinement stage, ensuring data quality and integrity.

Reporting / Visualization: Final stage where curated data is utilized for reporting and visualization purposes.

Data Quality Dashboard:

• Provides an overview of data quality metrics, highlighting issues and discrepancies for further analysis and resolution.

Reconciliation Report:

• Detailed report showcasing discrepancies between source and target data, aiding in identifying and rectifying data inconsistencies.

Reference Integrity Report:

• Highlights instances where references to master data are missing, ensuring data integrity and coherence.

Data Quality Framework

Ensuring the quality and integrity of data is essential for making informed business decisions. The Data Quality Framework (DQF) serves as a comprehensive solution that connects to multiple/source target systems, enabling rigorous validation across the entire business intelligence (BI) platform.

Salient Features of the Data Quality Framework:

1. Compatibility: Easily compatible with heterogeneous data sources, accommodating diverse systems seamlessly.
2. Root Cause Categorization: Provides the ability to categorize data discrepancies based on their root causes, aiding in targeted resolution.
3. Layer Compatibility: Compatible across multiple data layers, ensuring consistent validation throughout the data pipeline.
4. Customizable Checks: Users have the flexibility to define and control rules, enabling customizable data quality checks tailored to specific business requirements.
5. Metadata Driven: Completely metadata-driven approach enhances flexibility, allowing for agile adjustments and configurations.
6. Custom SQL Execution: Supports custom source/target SQL execution and comparison of results, facilitating detailed data analysis.
7. Automated Reporting: Automatically generates validation reports at the end of each batch, with the option to email reports to targeted users.
8. Reporting Framework: Enables the creation of reporting frameworks to analyze collected statistics over time, facilitating trend analysis and insights.

Data Quality Checks:

1. Record present in source but missing in target, also not present in error table/file.
2. Record present in source but missing in target, captured in error table/file.
3. Record present in both source and target, with differing values in the latest transaction, not present in error table/file.
4. Record present in both source and target, with differing values in the latest transaction, captured in error table/file.
5. Record deleted in source but still present in target.
6. Duplicate data validation based on key fields.

Framework Components:

1. User Control: Users can control data sources, metadata, and define threshold limits for validation.
2. Configurability: Different data source/target systems can be configured for data quality/validation checks.
3. Algorithmic Validation: Checks and validations are performed using optimized algorithms implemented in Python.
4. Reporting Mechanism: Validation results are compiled into Excel sheets, with shared links emailed to users along with charts and trend lines.
5. Reporting Capabilities: Reporting capabilities can be enabled for in-depth data analytics and insights.

The Data Quality Framework empowers organizations to uphold the highest standards of data integrity, facilitating accurate decision-making and driving business excellence.

Data Quality Wrap Up

Effective data quality management within the supply chain sales order domain is critical for maintaining operational efficiency and accuracy. By implementing robust data quality checks and validation processes, organizations can mitigate risks, enhance decision-making, and drive business success.

Self-Service Analytics: Empowering Business Users with Data Insights

In today's fast-paced business environment, agility and empowerment are key drivers of success. In this section, we explore the transformative potential of self-service analytics. By leveraging user-friendly analytics platforms such as Tableau or Power BI, organizations can empower business users to explore data, create visualizations, and derive insights independently. We discuss best practices for implementing self-service analytics initiatives, including user training, data governance considerations, and collaboration strategies. By democratizing access to data and insights, organizations can foster a data-driven culture and drive innovation across the enterprise.

Self-Service Analytics

Self-service analytics has emerged as a pivotal component in the modern enterprise's data strategy, empowering users across various roles to derive insights and make informed decisions. In this section, we delve into the intricacies of self-service analytics, exploring its relevance, user personas, user experience considerations for data scientists, and future state considerations.

Self-Service Analytics – Personas

Understanding the diverse needs and capabilities of users is crucial for the successful implementation of self-service analytics. We delineate four key user personas—Data Scientists, Tech-Savvy IT and Business Users, Ad-hoc Data Analysts, and Executives/Decision Makers—each with distinct requirements and expectations. By comprehensively addressing the needs of these personas, organizations can ensure the effective adoption and utilization of self-service analytics tools and platforms. Self-service analytics caters to a diverse range of user personas, each with distinct roles, responsibilities, and requirements. By understanding these personas, organizations can tailor their self-service analytics solutions to meet the specific needs of different user groups. Here are the four primary user personas for self-service analytics:

1. Data Scientists - Data scientists are highly skilled professionals who leverage advanced analytics techniques to extract actionable insights from data. They possess expertise in statistical analysis, machine learning, and data mining, enabling them to uncover complex patterns and trends within large datasets. Data scientists often work closely with business stakeholders to develop predictive models, optimize processes, and drive strategic decision-making.
2. Ad-hoc Data Analysts - Ad-hoc data analysts are business users with a moderate level of technical expertise who require on-demand access to data for ad-hoc analysis and reporting. They rely on self-service analytics tools to explore data, generate reports, and derive insights without the need for extensive IT support. Ad-hoc data analysts often perform exploratory data analysis, identify trends, and communicate findings to stakeholders to support tactical decision-making.
3. Guided Data Analysts - Guided data analysts are business users who require more structured guidance and support in their analytics endeavors. While they possess some analytical skills, they may lack the expertise to independently navigate complex datasets or perform advanced analytics tasks. Guided data analysts benefit from predefined workflows, templates, and guided analytics tools that streamline the analysis process and provide step-by-step guidance.
4. Executives / Decision Makers - Executives and decision-makers are senior leaders within an organization who rely on data-driven insights to inform strategic decision-making. While they may not possess deep technical expertise, they require access to relevant and actionable insights to drive business outcomes. Executives often rely on intuitive dashboards, KPI reports, and summary analytics to monitor performance, track key metrics, and make informed decisions that align with organizational goals.

• Who Am I?? Executives and decision-makers rely on data insights to make strategic business decisions, driving organizational growth and success.
• What is expected from me?? Their role involves analyzing summarized analytics reports, tracking key performance indicators (KPIs), and leveraging data insights to inform strategic decision-making processes.
• What do I Need?? To effectively fulfill their responsibilities, executives require access to executive dashboards, summary analytics reports, and BI portals that offer concise, actionable insights to support strategic decision-making.

By catering to the unique needs of these user personas, organizations can empower users at all levels to leverage self-service analytics tools effectively. Whether it's enabling data scientists to build advanced predictive models or providing guided analytics capabilities to business users, self-service analytics plays a vital role in democratizing data and fostering a culture of data-driven decision-making across the organization.

User Experience for Data Scientists Persona

Data scientists play a pivotal role in leveraging advanced analytics techniques to extract insights and drive strategic decision-making. We explore the user experience considerations specific to data scientists, focusing on enhancing their productivity, flexibility, and collaboration capabilities. From providing access to raw data and robust tools for hypothesis creation to enabling seamless integration with analytical models and workflows, optimizing the user experience for data scientists is essential for maximizing the value derived from self-service analytics initiatives. Facilitating efficient data exploration, model development, and collaboration are key capabilities needed by the data scientist. Here's a breakdown of the user journey for a data scientist persona:

1. Initiate Request: The data scientist initiates a request to work on a specific use case aimed at solving a business problem.
2. Explore Catalog: They search for relevant data sources within the catalog to identify datasets from the data lake that are pertinent to their use case.
3. Get Access & Retrieve Data: The data scientist requests permission to access the identified data sources for exploration and analytics purposes.
4. Create Hypothesis: They begin by creating hypotheses and formulating analytical models based on the retrieved data.
5. Analyze Data: The data scientist analyzes the data by connecting to the required datasets and performing various data manipulation and analysis tasks.
6. Create Workflow: They create a workflow within the analytics platform, incorporating steps such as data joining, sorting, filtering, and aggregation to develop their analytical model.
7. Create Initial Output: The data scientist writes the output of their workflow to a file in their study area and generates visualizations to illustrate their findings.
8. Share & Collaborate: They collaborate with peers by sharing their workflow and visualization outputs for review and feedback.
9. Take Approvals: After receiving feedback, the data scientist revises and finalizes their workflow and visualizations, seeking approvals from relevant stakeholders.
10. Share & Collaborate: They publish the finalized workflow and visualizations to a Shared Gallery for broader review and collaboration.
11. Create Final Output: Once approved, the data scientist shares the final workflow and visualizations in the Public Gallery for broader dissemination and use.

Key Enablers:

• Change Management: Ensuring training and adoption of new platform and technology assets by data scientists.
• Governance: Implementing robust governance processes to ensure data security, quality, and adherence to best practices for analytical model development.

By providing a seamless and intuitive user experience tailored to the needs of data scientists, organizations can empower these professionals to unlock the full potential of their data and drive actionable insights for informed decision-making.

Self-Service Analytics – Future State Considerations

In envisioning the future state of self-service analytics within the modernized platform, several crucial capabilities must be established to empower users and streamline the analytics workflow.

1. Search (Data Catalog)

Technical and business metadata for all information assets, spanning tables (DB), data files (S3 for example), data pipelines (examples: DataStage / EMR), and reports and visualizations (such as Cognos and Tableau), should be meticulously stored in a common catalog (Alation / Collibra / Apache Atlas / Informatica Enterprise Data Catalog…more examples see below). This catalog serves as a centralized repository, enabling users to search and discover the right assets for their analytics purposes. In addition to the mentioned technologies, potential offerings such as Apache Atlas and Informatica Enterprise Data Catalog can further enhance metadata management and search capabilities.?

Here's a list of other examples of data catalog solutions:

• Apache Atlas
• Informatica Enterprise Data Catalog
• IBM Information Governance Catalog
• Collibra Catalog
• Alation Data Catalog
• Waterline Data Catalog
• AWS Glue Data Catalog
• Cloudera Navigator
• Azure Data Catalog
• Erwin Data Catalog
• TIBCO Data Virtualization
• SAP Data Intelligence
• Denodo Platform
• Talend Data Catalog
• Zaloni Arena Data Catalog

These solutions offer various features and functionalities tailored to different organizational needs, ranging from metadata management and search capabilities to data governance and compliance management.

2. Get Access (Governance Processes)

Access to information assets must be tightly controlled through robust governance processes tailored to align with user personas. Once users identify the desired assets, they can request access through the governance process and proceed with use case development upon approval. Beyond traditional governance tools, solutions like Collibra Governance or IBM Information Governance Catalog can offer advanced access control and compliance management functionalities.

3. Retrieve & Analyze

Self-service analytics should be enabled through a diverse range of tools, including Alteryx, R, Python, Cognos, Tableau, Power BI and potentially other offerings like KNIME and SAS Visual Analytics. These tools empower users to wrangle, aggregate data, create ad-hoc reports, analytical models, and visualizations seamlessly. Users should also have the capability to bring their own data files and integrate them with enterprise datasets within their allocated personal working areas.

4. Create Output & Take Approvals?

Following data retrieval and initial analysis, users finalize analytical objects such as data pipelines, models, reports, or visualizations. Output can be generated in various forms, including data files, tables, reports, or visualizations. Advanced analytics platforms like Alteryx, R, Python, Cognos, and Tableau facilitate this process. Once created, output is shared with peers or managers within closed groups to gather feedback and make necessary adjustments.

5. Share & Collaborate (Governance)

Upon finalizing the code and output data for a use case, it undergoes review and is published to a public gallery for consumption by other users, subject to data security restrictions. Users should be able to search the catalog and discover newly created assets, facilitating collaboration and knowledge sharing across the organization. Governance solutions like Collibra or Erwin Data Intelligence Suite can enhance collaboration capabilities and ensure compliance with data security policies.

In essence, by establishing these future state capabilities and leveraging a comprehensive array of technologies, organizations can cultivate a dynamic self-service analytics environment that empowers users to derive actionable insights from data and drive informed decision-making across the enterprise.

Self-Service Analytics Summary

In the dynamic landscape of enterprise analytics, self-service analytics stands out as a catalyst for democratizing data and fostering a culture of data-driven decision-making. By embracing the principles and best practices outlined in this section, organizations can unlock the full potential of self-service analytics and drive transformative outcomes across the enterprise.

Conclusion

In conclusion, Part 4 unveils a comprehensive framework of best practices and methodologies designed to propel organizations towards analytics prowess. By orchestrating seamless data workflows, upholding an ideal balance of audit and governance standards, ensuring data quality, and empowering business users with self-service analytics capabilities, organizations can unlock the full potential of their data assets and drive sustained success in today's competitive landscape.

Forward to Part 5 of 5: Delivering Excellence

As we approach the culmination of our series on modernized data analytics, we shift our focus to Delivery Best Practices and Sample Reference Architectures. In this final installment, we offer invaluable insights into navigating the complexities of executing analytics initiatives with excellence.

From unveiling robust project governance strategies to outlining comprehensive program roadmaps, we provide essential guidance for organizations aiming to achieve success in their analytics endeavors.

Join us as we explore the strategies and frameworks that will empower you to navigate the final stages of the analytics modernization journey with confidence and effectiveness.

Part 5: Delivering Excellence

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