BI & Metadata Management: The Rhino and the Oxpecker

Last Updated on: June 20th, 2023, Published on: May 30th, 2023
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Metadata management capabilities are essential for Business Intelligence (BI) tools as they enhance data understanding, integration, quality, governance, report development, security, and collaboration. It enables organizations to effectively leverage their data assets for informed decision-making and business insights.

Before we further understand how metadata management is crucial for BI tools, let us understand a symbiotic partnership in the animal kingdom - that of a rhinoceros and the oxpecker.

The rhino goes back 14.2 million years, while the oxpecker has been around for a few centuries. The oxpecker depends on the rhino for food - it works in a comb-like fashion to pry out parasites like bugs and ticks on the rhino’s back, which helps the rhino prevent diseases. Oxpeckers also have sharp vision and can alert rhinos about potential threats lurking nearby.

Similar is the combination of BI tools and their metadata management capabilities - one helps the other become an integral part of the data ecosystem in an organization.

The true power and effectiveness of BI tools are unlocked through robust metadata management. Metadata holds the key to understanding, discovering, and utilizing data within BI environments.

Table of contents

  1. How metadata management tools boost BI tool functionality?
  2. The importance of metadata management in BI tools - Explained with examples
  3. The power of metadata: Enhancing business intelligence workflows for data practitioners
  4. Driving business outcomes: What to factor in while choosing a metadata management solution compatible with BI
  5. Summary
  6. Business intelligence and metadata management: Related reads

How metadata management tools boost BI tool functionality?

Today, BI tools play a pivotal role in empowering organizations to make informed decisions. These tools enable users to transform raw data into meaningful insights and actionable reports.

In this section, we will learn how metadata management is important for BI and how it integrates with BI tools.

Let’s dive in!

Metadata management is a critical component of BI due to four prime reasons, which are:

1. Increases data understanding

Metadata describes what data means, where it comes from, and how it relates to other data. This understanding is crucial for users to trust and correctly interpret data reports and analytics.

2. Enhances data discovery

Users can search and discover relevant data based on metadata attributes like data names, descriptions, and classifications.

3. Improves data quality

Metadata can help track data lineage (data’s life cycle), which can be used to identify and rectify data quality issues.

4. Ensures compliance

Metadata provides the information needed to maintain data privacy, security, and compliance with regulations like HIPAA.

Now that we’ve understood why metadata management is important for BI tools, let us understand how they integrate with each other.

Metadata management tools often integrate with BI tools by interfacing with the tool’s metadata repository, which contains the underlying definitions of the data being used within the BI tool. This integration allows metadata management tools to extract, store, and update metadata from these repositories automatically.

Metadata management tools can integrate with BI tools in several ways:

1. Direct integration

The metadata management tool directly integrates with the BI tool to automatically extract, import, or synchronize metadata. This could involve using an API provided by the BI tool or connecting directly to the tool’s metadata repository.

2. File-based integration

In some cases, the BI tool might allow exporting metadata to a file, which can then be imported into the metadata management tool.

3. Manual input

If automatic extraction is not possible, metadata can be manually entered into the metadata management tool.

The importance of metadata management in BI tools - Explained with examples

We saw above the importance of metadata management in BI tools. Now, let’s elaborate on each point a bit more to understand it better:

1. Increases data understanding

  • In BI tools, metadata provides users with the necessary context to understand and interpret the data.
  • For example, you may have a data field labeled “CXR” in your BI report. Without metadata, users might not know that “CXR” stands for “Chest X-Ray”.
  • Furthermore, without metadata describing the format of the data (e.g., date, number, or text), and the specifics about what’s measured (e.g., number of CXR procedures performed, the cost of CXR), users could misinterpret the data.

2. Enhances data discovery

  • Metadata can be used to improve data discovery in BI tools.
  • For instance, a user looking to analyze patient satisfaction might not know which data sets or tables contain relevant data.
  • If the metadata is well-managed and descriptive, the user can search for “patient satisfaction” within the metadata and quickly find all relevant data sources, fields, and reports.

3. Improves data quality

  • Metadata, particularly data lineage metadata, can improve data quality by providing visibility into the entire data journey.
  • For example, if a BI report shows an unusually high number of patient readmissions, data lineage metadata could be used to trace back the data to its original source(s) and transformation(s).
  • This might reveal that a data integration error caused outpatient visits to be incorrectly categorized as readmissions, leading to the apparent anomaly. Thus, metadata helps identify and rectify data quality issues.

4. Ensures compliance

  • Metadata is essential in demonstrating compliance with various data regulations.

  • For example, under GDPR, companies need to be able to show what personal data they have, where it came from, and who it’s shared with.

  • A BI tool could display customer data, and the associated metadata could provide information about:

    • Its source
    • Who has accessed it
    • How it’s been transformed, and
    • whether it’s been shared with any third parties.

    This way, metadata directly helps with regulatory compliance.

These examples underline the importance of metadata management in using BI tools effectively. The more comprehensive, accurate, and accessible your metadata is, the more value you can derive from your BI tool.

The power of metadata: Enhancing business intelligence workflows for data practitioners

Metadata management is particularly crucial in Business Intelligence (BI) workflows for a number of reasons. In a BI context, metadata not only describes data but also provides critical context that aids in understanding and using that data effectively.

Here’s how metadata management benefits different types of data practitioners in their BI workflows. Let’s delve deeper into each role with some examples:

1. Data engineers

  • Let’s say a data engineer is creating a new ETL process to incorporate data from a new vendor. The metadata might include information about the data source (e.g., the vendor’s system), data types, and the expected data structure.
  • With this metadata, the engineer can design a suitable ETL process and also set up checks for data quality issues, like unexpected null values or deviations in data types.

2. Data analysts

  • Suppose an analyst is working on a sales report and encounters a field called “ACV.” Without metadata, they might not know if ACV stands for “Annual Contract Value” or “Actual Cash Value,” which could lead to very different interpretations of the report.
  • But if metadata management is in place, they could easily check the definition of “ACV” and accurately complete their analysis.

3. Data scientists

  • Imagine a data scientist developing a predictive model for customer churn. Metadata about past models (like their performance metrics, training datasets, and hyperparameters) can help them avoid previous mistakes or build upon earlier successes.
  • Additionally, they might use metadata to understand the lineage of a specific feature (e.g., how the ‘days_since_last_purchase’ feature was calculated) to ensure it’s appropriate for their model.

4. Data stewards and governance professionals

  • For example, a data steward needs to ensure compliance with GDPR. With metadata, they can easily find all instances of personally identifiable information (PII), such as email addresses or IP addresses, across the organization’s databases.
  • Metadata might also provide information about who has accessed this data and when which is crucial for audits and data breach investigations.

5. Business users

  • A business user may want to understand the company’s quarterly sales performance using a BI dashboard.
  • Metadata about each metric on the dashboard—such as ‘total_sales’ or ‘new_customers’—can help them understand what each metric represents. (For example, total sales is the sum of all invoiced sales for the quarter, while new customers are those who made their first purchase in the quarter).

6. BI and reporting teams

  • A BI team might be tasked with creating a company-wide sales dashboard. Using metadata, they can ensure they’re using consistent definitions across different reports.
  • For instance, if the ‘customer_lifetime_value’ metric is calculated differently by the Sales and Marketing teams, metadata can help define a consistent calculation for use across all reports.

These examples illustrate how metadata management plays a crucial role in various aspects of data operations, ensuring consistency, aiding understanding, promoting collaboration, and facilitating regulatory compliance.

Driving business outcomes: What to factor in while choosing a metadata management solution compatible with BI

Here’s what you need to keep in mind while looking for a metadata management solution that combines well with BI tools:

  1. Integration
  2. Active metadata capabilities
  3. Support for all types of metadata
  4. Security and compliance
  5. Automation and intelligence capabilities
  6. User-friendly
  7. Vendor support and community
  8. Cost

Now, let us look into each of the above aspects in detail:

1. Integration

  • Ensure that the metadata management solution supports integration with your specific databases, data warehouses, and BI tools.
  • This integration is crucial for facilitating the flow of active metadata across your entire data stack.
  • Consider how well potential metadata management tools can integrate with your current BI tools, and how they can enhance your current data ecosystem.

2. Active metadata capabilities

  • When considering a metadata management tool, look for one that supports active metadata.
  • This means the tool should be capable of:
    • Continually collecting metadata across the data stack
    • Intelligently processing that metadata
    • Driving action based on metadata insights, and
    • Communicating metadata back into all the tools in your data stack through APIs

3. Support for all types of metadata

  • The metadata management tool should be capable of handling all types of metadata – technical, operational, business, and social.
  • Technical metadata includes information like table schemas, data types, and column names.
  • Operational metadata includes information about data processing operations, data lineage, and data quality.
  • Business metadata includes business terms and definitions, KPI formulas, and business rules.
  • Social metadata includes user comments, annotations, ratings, and usage statistics.

4. Security & compliance

  • Given the sensitive nature of healthcare data, security is a top priority. Pick a solution that supports encryption, access controls, and other security features.
  • It should also be able to help with demonstrating compliance with regulations like HIPAA, GDPR, etc.

5. Automation and intelligence capabilities

  • The tool should offer automation capabilities, such as the ability to purge stale or unused assets or allocate compute resources dynamically. These functionalities are made possible by active metadata.
  • Furthermore, intelligence capabilities that allow the tool to learn and adapt over time are important as well.

6. User-friendly

  • The tool should be user-friendly and promote a self-service data culture. It should ideally offer a good search interface, easy-to-understand data cataloging, and visual representations of data lineage.
  • Active metadata can help enhance the user experience by bringing context right into the BI tool.

7. Vendor support and community

  • Consider the reputation of the vendor, their track record of support, and their commitment to future development.
  • A responsive vendor and an active user community can be very helpful in resolving any issues that you might encounter. They can also be a good source of best practices and useful tips.

8. Cost

  • Beyond the initial purchase price, consider the total cost of ownership including implementation, training, maintenance, and upgrade costs.

Remember, no single solution may fulfill all your requirements. So it might be a good idea to prioritize your needs and select a solution that best fits your most critical needs.


In summary, metadata management is a vital aspect of any data strategy, playing a key role in ensuring the effective use of data assets and BI tools. In the age of big data, it’s increasingly important to move towards active metadata management, which offers more dynamic, automated, and integrated solutions.

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