Data Catalog Business Intelligence: Integrate in 7 Easy Steps!

Updated November 20th, 2023
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A data catalog and business intelligence (BI) are both crucial components of a comprehensive data strategy. They interact closely with each other, supporting businesses to make better, data-driven decisions.

Today, businesses rely heavily on Business Intelligence tools to extract insights from their vast data assets. However, these data assets become complex and diversity diverse in nature. So, organizations are grappling to make sense of the available data assets.

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This is where data catalog solutions come into play. When you integrate data catalogs with BI platforms, you’re setting the stage for streamlined workflows, improved data governance, and enhanced collaboration.

In this page, we will explore the factors to consider while choosing data catalogs, the steps to connect them with BI tools, and how to train your employees to improve the adoption of these tools.

Let’s dive in!

Table of contents #

  1. Data catalog and business intelligence: Understanding their interrelationship
  2. Key factors to remember
  3. Practical illustrations
  4. How to connect a data catalog to business intelligence
  5. Integrating data catalog with business intelligence
  6. Bringing it all together
  7. Related reads

Data catalog and business intelligence: Understanding their interrelationship #

A data catalog enables organizations manage large amounts of data. It organizes information about a company’s data assets, providing a way for data users to find and understand data that is relevant to their work. The data catalog holds metadata, which includes details about the data source, who can access it, the data’s structure, and relationships between different datasets, among other things.

Business Intelligence (BI), on the other hand, refers to the technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of BI is to support better business decision-making, offering a way to sift through data to find information that is useful for the business.

Here’s how a data catalog and business intelligence are inter-related:

  1. Data discovery
  2. Data understanding
  3. Data governance
  4. Data quality assurance
  5. Collaboration and communication

Now, let us understand each of the above aspects of their interrelationship in detail.

1. Data discovery #


A data catalog assists in discovering the right data for analysis. It helps BI users understand what data is available, where it’s from, and how it might be relevant to their needs.

Today, organizations collect and store vast amounts of data. Finding the right data for specific analyses can be challenging. A data catalog is a crucial tool in this scenario.

It serves as a single reference point for all data assets across the organization, aiding BI users in finding the data they need. With features like search functionality, categorization, and tagging, the data catalog simplifies the process of data discovery.

2. Data understanding #


A data catalog provides context about data, including descriptions, lineage, and relationships, which enhances the ability of BI to deliver meaningful insights.

A data catalog doesn’t only list data assets; it provides context about them. It includes metadata, such as data lineage (where the data comes from), relationships with other data, and usage history. These insights enhance data understanding, allowing BI users to analyze the data more effectively and derive meaningful conclusions.

3. Data governance #


A data catalog supports data governance by tracking who uses the data, when, and why. This assists in maintaining the quality and security of data used in BI.

A data catalog plays a significant role in data governance, tracking who accesses the data, when, and why. This ability ensures that only authorized individuals are using the data and helps maintain compliance with privacy and security regulations.

Since BI relies on data for all its operations, effective data governance supported by a data catalog is essential.

4. Data quality assurance #


A data catalog provides metadata that can help identify and rectify quality issues in the data, contributing to more accurate BI analysis.

High-quality data is vital for accurate, reliable BI analysis. A data catalog provides metadata that helps in identifying quality issues in data, such as missing data, inconsistent data, or outdated data.

It can help locate the source of these issues, making it possible to rectify them and thereby improving the quality of the data feeding into BI systems.

5. Collaboration and communication #


A data catalog promotes collaboration and communication among data users, which is key to effective BI.

Lastly, a data catalog facilitates collaboration and communication among data users. It provides a platform for users to share their insights about data, such as annotations or usage notes.

This communal knowledge can be invaluable for BI, as it allows users to learn from each other’s experiences and insights, promoting more effective use of data across the organization.

Integrating data catalog with BI tools: Key factors to remember #

Integrating a data catalog with Business Intelligence (BI) tools involves a process that ensures efficient access, analysis, and visualization of data for business decision-making. Here are some key factors to remember during this integration:

  1. Understanding the data catalog and BI tools
  2. Compatibility and integration capabilities
  3. Data governance and quality
  4. User access and permissions
  5. Scalability and performance
  6. Metadata management
  7. Search functionality and discovery
  8. Training and support
  9. Continuous monitoring and feedback loop

Let us understand each of these factors in detail.

1. Understanding the data catalog and BI tools #


A data catalog is a centralized repository where an organization’s metadata is stored, making it easier to find and manage data. BI tools, on the other hand, are software applications used to analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps.

They provide users with detailed intelligence about the state of the business.

2. Compatibility and integration capabilities #


Ensure that the data catalog and BI tools are compatible with each other. This includes checking for software compatibility, data format support, and integration interfaces (like APIs). Seamless integration allows for the automatic transfer and synchronization of data between the catalog and BI tools.

3. Data governance and quality #


A key aspect of integrating these tools is maintaining high data quality and governance standards. This includes ensuring data accuracy, consistency, and security.

The data catalog should have mechanisms to enforce data governance policies and manage data lineage, enabling users to trust and rely on the data used in BI analyses.

4. User access and permissions #


Managing user access and permissions is crucial. Different users may require different levels of access to the data catalog and BI tools. Implementing role-based access control ensures that sensitive data is protected and only accessible to authorized personnel.

5. Scalability and performance #


The integration should support scalability to accommodate growing data volumes and user demand. It’s important that the performance of BI tools isn’t hindered by large data sets or complex queries.

Efficient indexing, query optimization, and caching strategies can help in maintaining high performance.

6. Metadata management #


Effective metadata management is central to this integration. The data catalog should provide rich metadata that includes not just technical details, like data types and sources, but also business context, like data ownership, usage policies, and data quality metrics.

This enriches the BI analyses and makes them more actionable.

7. Search functionality and discovery #


The integration should enhance the searchability and discoverability of data. Users should be able to easily locate relevant datasets in the data catalog through the BI tools, using search functionalities that include filters, tags, and keywords.

8. Training and support #


Providing adequate training and support to the users of the BI tools and data catalog is essential. This ensures that they can effectively use the integrated system and leverage its full potential for better data-driven decision-making.

9. Continuous monitoring and feedback loop #


Finally, continuously monitor the integration for performance issues, user feedback, and changing business needs. Regularly update and fine-tune the integration to ensure it remains effective and aligned with organizational goals.

Integrating a data catalog with BI tools can significantly enhance an organization’s data analytics capabilities. It requires careful planning and consideration of the factors mentioned to ensure it meets the business requirements and adds value to the decision-making process.

Data catalog + business intelligence: A few practical illustrations #

Here are a few practical examples that illustrate how data catalogs can be integrated with business intelligence tools.

Example 1: Data discovery and understanding #


Imagine you have a data analyst, Sarah, who is working on a project related to patient readmission rates. She needs data about patient demographics, diagnoses, treatments, and readmission dates. Normally, she would have to ask around, search through multiple databases, or rely on tribal knowledge to find the right datasets.

With a data catalog integrated with your BI tool, Sarah can simply search for “patient readmission” in the catalog. The catalog shows her all the relevant datasets, tables, and reports available, along with their descriptions, data lineage, and any user reviews or comments. She can see which BI reports have used these datasets before and understand how the data has been transformed or calculated.

Example 2: Data lineage and impact analysis #


Now, let’s say your IT team needs to make some changes to the database schema for performance tuning. But they’re worried about how these changes might impact your BI reports.

With data lineage information from the catalog, they can easily see which datasets and reports will be affected by the changes. They can proactively communicate with the relevant stakeholders and make sure necessary updates are made to the BI reports.

Example 3: Data governance and compliance #


Consider a situation where a new privacy regulation comes into effect, and you need to ensure that sensitive patient data is not exposed in your BI reports.

The data catalog can help you identify which datasets contain sensitive data, who has access to them, and where they’re used. You can then review the access controls and masking rules in your BI tool to ensure compliance with the new regulation.

Example 4: Collaboration and knowledge sharing #


Finally, suppose a new data scientist, John, joins your team. He’s working on a predictive model for patient outcomes and wants to learn from past work.

With the data catalog, John can browse the catalog to find relevant datasets and BI reports. He can see who the data stewards are and reach out to them for questions. He can also read reviews and discussions about the data to gain insights and avoid past mistakes.

In all these examples, the data catalog serves as the bridge between your BI tool and your data assets, enhancing data discovery, understanding, governance, and collaboration. It helps you get more value out of your data and your BI tool, while reducing risks and inefficiencies.

How to connect a data catalog to business intelligence: A step-by-step guide #

The steps to connect a data catalog tool to a business intelligence (BI) tool can vary based on the specific tools you’re using. However, here are some general steps that you might follow:

  1. Choose a data catalog tool
  2. Setup and configuration
  3. Connect to data sources
  4. Connect to BI tool
  5. Metadata extraction
  6. Validation and testing
  7. User training and adoption

Let us look into each of the above steps in detail:

1. Choose a data catalog tool #


The first step is to choose a data catalog tool that suits your needs and integrates well with your BI tool. You’ll want to consider factors such as compatibility, scalability, security, user experience, and vendor support.

2. Setup and configuration #


Once you’ve chosen a tool, you’ll need to set it up and configure it according to your organization’s needs. This might involve defining user roles and permissions, setting up data classifications, and customizing the catalog’s look and feel.

3. Connect to data sources #


Next, you’ll need to connect the catalog to your data sources. These might include databases, data warehouses, and data lakes. The catalog tool will then scan these sources and extract metadata, such as table names, column names, data types, and data lineage information.

4. Connect to BI tool #


After your data sources are connected, you can then connect the catalog to your BI tool. This might involve installing a plugin or extension, or configuring an API connection. The catalog tool will then index metadata from the BI tool, such as report names, descriptions, and data fields used.

5. Metadata extraction #


Once connected, the data catalog will extract and index the metadata from the BI tool. This metadata may include information about the datasets used, transformations applied, and the outputs generated. The catalog tool may also extract information about the usage and popularity of different reports and dashboards.

6. Validation and testing #


After the integration is complete, you should test the setup to ensure everything is working as expected. This might involve performing a few searches in the catalog, checking the data lineage and impact analysis features, and validating the data access controls.

7. User training and adoption #


Finally, you’ll need to train your users on how to use the data catalog with the BI tool. This might involve creating user guides, hosting training sessions, and providing ongoing support. It’s also important to promote the benefits of the catalog to encourage user adoption.

Remember that successful integration is not just about technology, but also about people and processes. It’s essential to have clear data governance policies, collaborative workflows, and a culture of data literacy to fully leverage the benefits of a data catalog.

Integrating data catalog with business intelligence: Strategies to train your employees and and drive adoption #

User training and adoption are critical steps in the successful deployment of a data catalog. The tool’s effectiveness largely depends on whether users can comfortably and consistently use it. Here are some challenges and strategies to address them.

Strategies to train your employees for integrating data catalog with business intelligence #


Here are few strategies for training employees:

  1. Diverse user skill levels
  2. Resistance to change
  3. Data literacy
  4. Usage consistency
  5. Overwhelming Features

Let us look into each of the above training strategies in brief:

1. Diverse user skill levels #


Your users range from data analysts who are likely comfortable with technical tools, to department heads and executives who may not have the same level of technical expertise. It’s essential to tailor your training to these different audiences.

For example, data analysts may need in-depth tutorials on advanced features, while executives might benefit more from a high-level overview of key functionalities and benefits.

2. Resistance to change #


Like any new tool or process, a data catalog might be met with resistance from users who are comfortable with the current way of doing things. It’s important to communicate the benefits of the catalog clearly and show how it can make users’ jobs easier.

Early involvement of users in the selection and implementation process can also help increase their buy-in.

3. Data literacy #


A data catalog can help users discover and understand data, but it still requires a certain level of data literacy to use effectively. Consider providing training on data literacy concepts, like understanding data types, data quality, and data governance.

4. Usage consistency #


For a data catalog to stay useful, it must be consistently used and updated. Encourage users to contribute to the catalog by adding descriptions, tags, and reviews. You might also consider implementing a data stewardship program, where certain users are responsible for maintaining and enhancing the catalog.

5. Overwhelming features #


A data catalog comes with a plethora of features. Users might feel overwhelmed and unsure where to start. Begin with basic training on key features and gradually introduce more complex functionalities.

Strategies to promote user adoption of data catalog with business intelligence integration #


Here are a few strategies to promote user adoption:

  1. Champion network
  2. Hands-on training
  3. Support materials
  4. Ongoing support
  5. Feedback loop

Let us look into each of the above adoption strategies in brief:

1. Champion network #


Identify and leverage champions within each user group who can advocate for the tool’s usage. These champions can be superusers who provide peer support and share success stories.

2. Hands-on training #


Provide hands-on training sessions where users can try out the tool in a safe environment.

3. Support materials #


Create user guides, FAQs, and video tutorials that users can refer to at their own pace.

4. Ongoing support #


Provide ongoing support to answer questions, resolve issues, and gather feedback for future improvements. This could be a dedicated support team, a helpdesk ticket system, or community forums.

5. Feedback loop #


Regularly solicit feedback from users and show that their input is valued and acted upon. This can encourage active engagement and help you continuously improve the tool and training materials.

Remember, the ultimate goal is to create a self-service data culture where users feel empowered to find, understand, and use data independently. The data catalog is just a tool to facilitate this culture; user training and adoption are key to its success.

Bringing it all together #

In summary, a data catalog can greatly enhance the value you get from your BI tools and data assets. It can help you create a more efficient, collaborative, and self-service data culture. However, successful implementation requires careful selection of the tool, proper integration with your BI tool, and effective user training and adoption strategies.

Are you looking for a data catalog for your organization — you might want to check out Atlan.

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