User Stories

This page provides a collection of common scenarios that illustrate how Aurelius Atlas can help users address common data governance challenges. Each user story highlights a specific use case and includes a walkthrough to demonstrate how the platform can be used to solve the problem.

Building the Data Governance Model

Establishing a data governance model is a critical step for organizations to ensure data quality, compliance, and security. However, creating and maintaining a data governance model can be complex and time-consuming. Aurelius Atlas simplifies this process by providing a centralized platform for defining and managing the data catalog, data quality rules, and data lineage.

Challenges

1. Complexity: Contributing to the data governance model can be challenging for non-technical users.
2. Scalability: Managing data governance across multiple systems and environments.

Benefits and Features

• User-Friendly Interface: Simplifies the process of defining and managing data governance rules through an easy-to-use editor.
• Automated Data Lineage: Integrate your DevOps and DataOps processes with Aurelius Atlas to automatically generate data lineage from your data pipelines.

Walkthrough

Consider a scenario where a data governance manager needs to define a new data domain in the data governance model. The manager uses Aurelius Atlas to create the new domain, define some of its attributes, and assign a domain owner.

Manual Entity Creation

Start by navigating to the Aurelius Atlas landing page. Here, you can access the editor to create new entities in the data governance model. Click on the plus button next to the search bar to open the editor.

The editor provides a user-friendly interface for defining new entities in the data governance model. Start by selecting the entity type, in this case, a Data Domain. Enter the name of the new domain, such as Equipment, and add a description to provide additional context. Finally, assign a domain owner to take responsibility for the new domain.

Once you have defined all the necessary attributes for the new domain, click the Save button to add the domain to the data governance model. You are automatically redirected to the details page of the new domain, where you can review the information you have entered.

Automated Data Lineage

In addition to manually creating entities in the data governance model, Aurelius Atlas also supports automated data lineage generation. By integrating your DevOps and DataOps pipelines with Aurelius Atlas, you can automatically register data producers, consumers, and transformations to generate comprehensive data lineage.

Read more about the Lineage API to learn how to integrate your pipelines with Aurelius Atlas.

Cross-Platform Data Governance

Organizations often use a mix of solutions from different vendors, incorporating various clouds and on-premise systems. Data governance solutions from different cloud providers focus on their specific platforms, making it difficult for organizations to get a comprehensive overview of available data and data flows across all solutions.

Challenges

1. Fragmented Data Governance: Different platforms have isolated governance solutions.
2. Lack of Unified Data Catalog: No central repository to find and manage data across platforms.
3. Data Flow Visibility: Difficult to trace data lineage across multiple systems.

Benefits and Features

• Unified Data Governance: Aurelius Atlas collects governance information from various systems.
• Comprehensive Data Catalog: Serves as a central repository for organization-wide data.
• Cross-Platform Data Lineage: Visualizes data flows across different environments.

Walkthrough

Consider that we want to update the schema of a particular table in our data pipeline. We need to know who is using the data and where it is being used to ensure all schemas and pipelines are adjusted accordingly.

Step-by-Step Guide

Start by searching for the table NL1-HR-001 within the technical context.

Navigate through the search results to locate and click on the entity with type Dataset for more detailed information.

Once on the entity page of NL1-HR-001, you will see an overview providing essential details such as the entity's name and type, its hierarchical breadcrumb, a description, and a summary of all elements available on the page. Additionally, you can check the availability of the lineage model and find a navigation button for quick access to related sections.

To explore the data lineage of NL1-HR-001, locate and click on the "Lineage Model" option provided either in segment 5 or via the navigation button in segment 6.

The lineage analysis reveals that the data originates from a relational database, providing critical insights into its storage location and other pertinent details, as depicted in the image below:

From here, follow the data flow as it progresses into a Kafka topic. This step ensures effective communication of change events through Change Data Capture (CDC). You can examine the source code and specifics of how the data is stored and converted into a Kafka topic:

Next, explore the final destination of the data, which is stored in an Elastic Search index. Understand the source code and detailed attributes associated with this storage, including the transformation process into an Elastic index:

In the lineage graph of NL1-HR-001, visualize the comprehensive data flow across different systems, including SQL server, Kafka, and Elastic. Aurelius Atlas ensures consistent data recording and visibility across these varied environments:

For more specific details, such as those related to the hr entity, click to uncover additional insights about this particular component:

Finally, review the available data in a Kibana dashboard. Click on the last event depicted in the lineage model to access and explore this comprehensive dashboard, which provides detailed information about the entity and its usage:

By following these steps, you can effectively trace the data lineage across multiple cloud solutions and ensure all subsequent processing is adjusted as required. As a result, we can update our schema accordingly.

Data Discovery

In a data analytics project, data scientists or data engineers often need to find specific data to answer critical business questions. However, identifying relevant data, understanding where it is stored, and knowing who to contact for access can be challenging, especially in organizations lacking data governance tools. This process typically involves reaching out to multiple people and can be time-consuming and inefficient.

Challenges

1. Lack of visibility into available data.
2. Uncertainty about data storage locations.
3. Difficulty identifying the accountable individuals for data access.
4. Time-consuming processes to understand data attributes and their meanings.
5. Inefficiency for engineers, particularly those with smaller networks within the organization.

Benefits and Features

Aurelius Atlas provides a centralized solution that makes information about data availability, storage locations, and accountable contacts easily accessible. The tool facilitates quicker and more efficient data discovery and access, reducing the time and effort required to find and understand relevant data.

Business Context

Users can search for data by entering relevant terms related to their business context. The platform shows which data sets are available that match the search criteria. Comprehensive explanations of individual atomic attributes help users understand the meaning and relevance of the data.

Storage Locations

Information about where the data is stored is readily accessible.

Accountability

Users can see who is responsible for the data, making it easier to request access.

Walkthrough

Let's consider a scenario where a data scientist needs to calculate the number of people who currently work for their company as part of the annual report. Therefore, the data scientist needs to know:

• Where is the data stored?
• Who do I need to ask for permission to access the data?

Watch the video walkthrough below or read the step-by-step instructions.

Step-by-Step Guide

Let's start on the Aurelius Atlas landing page. Here, the data scientist can enter search terms like employee, staff, or workforce. See the screenshot below.

Here's an explanation of the highlighted elements:

1. Business Context: The landing page has a dedicated section to help you get started exploring the business context of the data. You can enter search terms related to your business question to find relevant data sets.
2. Info Panel: Click the question mark icon for an explanation of the business context meta model.
3. Search Bar: Queries made through this search bar will have business context filters pre-applied.

To search for employees, click the search icon and enter the term employee. This will generate a list of results related to employees.

As a data scientist seeking entities representing employees, use the sidebar to drill down and filter the results by data entities. Applying this filter will narrow the results to 4 entities.

With this smaller set of results, review each entity individually. Compare the entities Personnel and Internal by examining their breadcrumbs. The breadcrumbs indicate that Internal is a child entity of Personnel, showing a hierarchical relationship between these concepts.

We are only interested in permanent employees, therefore select the entity Internal. That will take us to the details of the entity.

In this overview, we can find the following segments:

1. Name and Type of the Entity
2. Business Hierarchy: Shows which domain this entity is related to.
3. Description
4. People Responsible for this Data
5. Summary of All the Elements of the Page
6. Button to Navigate Through Each Section Quickly

From this overview, we can effectively answer one of our questions: Who do I need to ask for permission to access the data? Merel Hofman ✔.

However, we still need to find out where this data is stored. Therefore, click on datasets in segment (5) or use the navigation button (6).

As we scroll down to the dataset section, we find a new set of descriptions and functionalities such as:

1. Results of the Datasets
2. Name of the Dataset and Where It Is Located in the Hierarchy
3. Filter Down the Datasets
4. Look for the Entities Inside This Dataset
5. Type of Storage

As we zoom in on the dataset's breadcrumbs, we can answer our remaining questions. Where is the data stored? N1L -> NL1-HR -> NL1-HR-001 ✔.

In summary, streamlining data discovery is crucial for data scientists and engineers who need quick access to relevant data for critical business questions. Aurelius Atlas addresses the common challenges in this process by providing a centralized platform that enhances visibility into available data, its storage locations, and the responsible contacts

Data Governance Quality

Implementing data management in an organization involves multiple facets: setting up the data governance organization, establishing business and technical data governance models, and monitoring data quality. Keeping track of progress across these activities can be challenging. Aurelius Atlas provides a comprehensive tool for key stakeholders to monitor implementation progress and data governance quality, aiding better decision-making for future improvements.

Challenges

1. Comprehensive Oversight: Difficulty in monitoring progress across various data governance activities.
2. Quality Assurance: Ensuring data governance rules are followed across the organization.
3. Prioritization: Identifying areas needing immediate attention for improvement.
4. Transparency: Communicating the status of data governance compliance to stakeholders.

Benefits and Features

• Holistic Monitoring: Track implementation progress and data governance quality.
• Rule Compliance: Assess compliance with data governance rules for each entity.
• Quality Scores: Provide overall data governance quality scores for entities.
• Actionable Insights: Identify potential issues and focus on necessary improvements.

Walkthrough

Consider a business data steward responsible for maintaining data governance quality in their domain. The steward uses Aurelius Atlas to review potential issues and delve into the details.

Watch the video walkthrough below or read the step-by-step instructions.

Step-by-Step Guide

Let's begin by searching for the "Logistics" domain within Aurelius Atlas to assess its data governance quality. Click on the button as shown in the image below:

Icons such as data type indicators, check marks for fully populated entities, and warning symbols for incomplete ones help us quickly identify areas needing attention.

Next, click on the "Logistics" entity flagged with a warning symbol to investigate further:

This section provides a detailed view of each field's quality, helping you identify areas that may require improvement. For instance, you might notice that fields like "FTE" and "location" meet your quality standards, while others, such as "HIER ORGANIZATION" exhibit lower precision.

To gain deeper insights into the specific data quality issues affecting "HIER ORGANIZATION" navigate to the data quality rules section.

Here, you can assess the compliance status of the "Logistics" entity with the applied data governance rules:

Check if all required data entities are complete or if there are any missing elements that require attention.

By following these steps, you can effectively monitor and improve data governance quality, ensuring compliance with established rules and standards across your organization.

Data Lineage

In organizations, data from various sources undergoes transformation using specific technologies, involving multiple steps where changes can impact downstream processes. Data scientists and integration experts rely on impact analysis to assess these effects. They face challenges such as dependency management, unclear data lineage, complex impact assessment, and identifying ownership.

Challenges

1. Dependency Management: Overlooking dependent steps can lead to data processing failures.
2. Data Lineage Clarity: Lack of visibility into data flow complicates tracing origins.
3. Impact Assessment: Assessing changes across interconnected systems is time-consuming.
4. Ownership Identification: Identifying responsible parties for data usage is challenging.

Benefits and Features

• Data Integrity: Visualizing data lineage ensures reliability.
• Change Management: Tools assess schema or transformation impacts.
• Data Governance: Tracks data usage and responsibilities.
• Efficiency: Centralized access accelerates decision-making.

Walkthrough

Consider an implementer extending a table schema to ensure subsequent processing adjustments.

Step-by-Step Guide

The table we want to extend is NL1-HR-001. Since we're interested in the technical specifications of the table, use the Technical Context search input and search for NL1-HR-001.

We will receive a list of all results related to NL1-HR-001. We are interested in the table, so click on the entity NL1-HR-001 with type Dataset. 0

In this overview, we find the following segments:

1. Name and type of the entity.
2. Breadcrumb showing the entity hierarchy.
3. Description of the entity.
4. Summary of all page elements.
5. Lineage model availability.
6. Navigation button for quick access.

We are looking for the data lineage. Therefore, click on Lineage Model in segment (5) or use the navigation button (6).

The lineage model shows the position of the entity in the data flow. It clarifies where the data comes from and where it flows to. In this case, we see that the entity NL1-HR-001 is a source dataset used and processed in the organization.

To get details of each entity in the lineage model, click on the icon in the image to open the detail panel on the right-hand side.

In the details, we find:

1. Name and type of the entity.
2. Summary of data governance metrics.
3. Properties of the entity.

Let’s follow the flow of the data. If you click on the next entity called Change-event, you can see how the data changes to a Kafka topic. Thereafter, you can see that this Kafka topic converts into an Elastic index.

With the lineage graph, you can see where the data is going and where it comes from. This is not limited to a particular system but spans across different applications and environments tracking governance information. Following these steps, the implementer can learn where the data is used and what subsequent processing can be affected by extending the schema.

Data Quality Management

Data quality is a crucial aspect of data management, ensuring data is useful and reliable, leading to better business outcomes. Poor data quality, on the other hand, can result in misleading indicators, ineffective decision-making, and wasted resources.

Challenges

1. Error-Prone Data: Inaccurate data can lead to wrong business decisions.
2. Incomplete Data: Missing values reduce the usability of datasets.
3. Outdated Data: Data that is not current can mislead analysis.
4. Lack of Uniqueness: Duplicate entries can cause inconsistencies.
5. Invalid Data: Data that does not fit predefined formats can lead to processing errors.

Benefits and Features

• Comprehensive Data Quality Metrics: Measures accuracy, completeness, timeliness, uniqueness, and validity.
• Data Quality Scoring: Provides a percentage score for each quality rule, highlighting areas of improvement.
• Root Cause Analysis: Helps identify and address the origins of data quality issues.
• Cross-Platform Data Quality Insights: Tracks data quality across different systems and transformations.

Walkthrough

Consider a data engineer who needs high-quality data for a new analysis. The engineer uses Aurelius Atlas to discover and evaluate the data quality of a candidate dataset.

Watch the video walkthrough below or read the step-by-step instructions.

Step-by-Step Guide

Imagine you're a data engineer tasked with ensuring high-quality data for a new analysis using Aurelius Atlas. Start by navigating directly to the details page of the dataset you intend to use.

Here, you'll find an overview of its data quality metrics, which include measures such as accuracy, completeness, timeliness, uniqueness, and validity. This initial view allows you to understand the overall quality status of the dataset.

Next, assess the quality of each field by navigating to the field details section.

This section provides a detailed view of each field's quality, helping you identify areas that may require improvement. For instance, you might notice that fields like "FTE" and "location" meet your quality standards, while others, such as "HIER ORGANIZATION," exhibit lower precision.

To gain deeper insights into the specific data quality issues affecting "HIER ORGANIZATION," navigate to the data quality rules section.

Here, you can review all applied rules and their respective scores. For example, you might discover that inconsistencies in the syntax of the "HIER ORGANIZATION" field contribute to its lower quality score.

To understand the implications of these findings and identify who within your organization is responsible for addressing these quality issues, navigate to the data attributes section.

This section provides detailed information about each attribute, including contact details for individuals accountable for the data. It helps you understand what each field represents and enables you to collaborate effectively to enhance data accuracy, completeness, and reliability for your analysis.

By following these steps in Aurelius Atlas, you gain clarity on the quality measures applied to your dataset and can take actionable steps to ensure high-quality data for your analysis.