A data analytics strategy guide for accelerated data governance with cataloging

1. Introduction

Research suggests 2.5 Quintilian bytes of data gets produced every day. The rapid proliferation of data has made it extremely difficult for enterprises to efficiently manage and find the right information when needed. Due to the generation of such large data volumes and the lack of a cohesive data analytics strategy, enterprises end up wasting a lot of time and effort on finding and accessing data from ‘data swamps’. Lack of a common business vocabulary, complex methods to assess provenance, quality, and trustworthiness, difficulty in understanding ‘dark data’ and numerous regulations to abide by have further turned data accessibility into a severely tangled knot.

With modern enterprises increasingly relying on big data and analytics for decision making, implementing an effective data governance process has evolved as a top priority. A well-designed data analytics strategy for an effective data governance program can help companies successfully capitalize on data and use it to drive tangible business outcomes. The need of the hour is to thus deploy a robust data governance framework that aligns with the future business objective and business models of the company.

The guidebook will explain the importance of data governance for enterprises, ways to achieve simpler and consolidated access to data assets, and how this can be facilitated by data catalogs.

2. What is data governance?

On an enterprise level, implementing data governance effectively translates to defining the authority of data control and utilization. In essence, it is about managing the roles, responsibilities and the overarching processes that ensure ownership and accountability of the organizational data assets.

3. Key objectives and advantages of data governance

  • Overall improved and more informed decision making with consistent and uniform data usage across the organization
  • Agility and scalability of business and IT with outlined processes for change
  • Central control mechanisms to help reduce costs in other areas of data management
  • Improvement in data quality and process documentation
  • Reuse of processes and data that increases efficiency
  • Compliance with data regulations such as GDPR, CCPA and PII

4. The data governance framework enabled by a robust data analytics strategy

A well-managed data governance framework can successfully underpin an organization’s journey towards operating on digital platforms. However, creating such a framework requires a process to deal with common imperatives surrounding data, such as:

data governance framework

Building a robust data governance framework

As such, data governance frameworks support an organization’s strategy to manage data. From data collection, management, security, and storage, data governance frameworks cover end-to-end enterprise data lifecycle. An effective data governance framework must account for:

Data governance framework

Fig 1: Sigmoid’s Approach to Data Governance

Data operations management

An effective data operations management framework with data modelling and designing capabilities for data analysis, pipeline building, testing, and maintenance.

Data risk & security management

Data privacy, confidentiality, and access control for data security and risk management along with deployment and management of structured data storage.

Data provenance & lineage

Data provenance and lineage for data source identification, re-enactment of data flow for updates and tracking of errors throughout the data lifecycle. This spans across data integration and extraction, transformation, replication and virtualization.

Data catalog management

Data discoverability, and search for complete data visibility and automatic data classification based on context using comprehensive metadata management.

Data quality management

Improving the fitness of data through, holistic definition, monitoring, and maintenance to achieve accurate, complete and consistent data for downstream analytics.

Regulatory compliance

Standardized definition and usage of shared data values to ensure regulatory compliance, data quality and analytical data processing management to foster Business Intelligence (BI).

Empower business teams with insights from high quality data

5. Enabling data governance with data catalog

A data catalog is a core component of data governance and an integral part of an organization's broader data analytics strategy. It makes use of metadata to provide organizations with a single, overarching view along with deeper visibility into their data assets. A data catalog is essentially a cluster of metadata combined with high-end data management and search tools. These search tools allow data users and analysts to locate specific data for intended use cases. This can help organizations efficiently manage their valuable data and get easy access to trusted data as and when required. Technologies like AI and machine learning have greatly diversified the use-cases of metadata. Technical, business, and operational metadata have undergone a mini-revolution and have found usage beyond audit, lineage, and reporting.

Today, metadata can augment data management in almost every possible way, be it for self-service data preparation, alerting anomalies, or auto-scaling resources. And the data catalogs leverage this metadata to enable data scientists to gain an edge.

6. Data cataloging principles

Data cataloging principles are codes or guiding rules that must be followed by the data catalog users. The following principles form the core of such directives set for the users and catalogers:

Data Cataloging Principles

Fig 2: Data Cataloging Principles

7. Best practices to adopt data catalog

Intelligent data catalog adoption can ensure faster data discovery, lesser time to generate insights, and reduced time-to-market only if companies know the way to generate the greatest value from it. Here is a step-by-step process developed on the basis of road-tested best practices to simplify the adoption of data catalog:

Fig 3: Best Practices to Adopt Data Catalog

8. How good data cataloging helps data scientists in better model development

In the past few years, cloud, big data analytics, AI, and machine learning have transformed how data scientists manage, leverage, and access data. Data scientists now rely on data quality to a greater extent. Good data cataloging can serve this purpose in so many ways because fundamentally it provides wider visibility and deeper access to quality data. Here are just a few use cases of the same:

Self-service analytics

Most data scientists struggle in finding the right data and then face trouble in understanding whether it’s useful. Data cataloging can help them understand the business context around data elements. This will ensure that data scientists have information about the data source, its relationship with other data assets, and other crucial information like whether it’s a managed resource or if it’s from the right data source. These elements can convey an understanding about something as simple as statistical information, to something as complex as personal information.

Compliance and change management

Data cataloging can help demonstrate the provenance of data and provide detailed data lineage. It can ensure that data artifacts come from the right source and get transformed before reaching the final target. This can also help data scientists understand how changes introduced in a particular data pipeline can impact other related ones.

Business glossaries

Most organizations adopt a specific vocabulary and a consistent understanding of business concepts. Having a data catalog can help store and manage this critical information. It links business terms to establish a taxonomy. This can help data scientists understand which business concepts correlate to which technical artifacts and then see everything related to their data.

9. Data catalog advantages

Organizations now aim to be more data-driven. Their need for better and faster analytics can be fulfilled by data catalogs, that too without sacrificing governance. A good data catalog can offer to its users:

Flexible searching

A data catalog provides companies with flexible searching and filtering options. This allows data teams to find the required data sets using technical information, user-defined tags, or business terms in reduced time.

Wider access

Data catalogs harvest multiple technical metadata from diverse connected sets of data. This facilitates deeper visibility and wider access.

Business knowledge

Metadata curation provides a way for data scientists to contribute business knowledge in the form of their business glossaries, classifications, tags, and more.

Data automation

Data catalog utilizes AI and machine learning to automate manual repetitive tasks. This AI-backed metadata can augment capabilities with data management.

Deeper visibility

Data catalog can help data scientists gain a holistic view of data assets using tags and business terms.

Secure access

Data catalogs can help companies easily monitor and secure access with groupbased policies

All these benefits result in better usage of data which ultimately contributes to:

Data catalog advantages

10. Data cataloging tools

Choosing the right data catalog solution and vendor can prove to be a daunting task as it requires thorough research. To make this easier for companies, here are a few data tools profiled by us:

Azure data catalog logo
  • Allows data teams to control access and discovery of registered data assets
  • Allows business glossary integration
  • Makes data discovery and search easy
  • Ensures better data governance and administration
  • Provides easier access to data from anywhere
  • Easily integrates into existing tools and processes with open REST APIs
collibra tool logo
  • Provides users with easy to understand data that can help them generate impactful business insights
  • Creates a unified view of data assets to ensure comprehensive visibility into relevant data with full business context
  • Ensures adherence to privacy policies so that users always have access to trusted data
  • Enables self-service data access to empower your organization with predefined data
  • Integrates with leading business tools like Tableau to deliver faster business insights
  • Easily registers your data sources using wide-ranging, native connectivity
  • Uses proprietary algorithm to automate the process of context addition to data assets
  • Embedded with data governance capabilities
atlan tool logo
  • Uses proprietary algorithm and is easy to set-up
  • Facilitates AI-powered metadata scanning
  • Uses intuitive UI for great user experience
  • Provides Google like search and AI powered metadata scanning to quickly find data assets
  • Embedded with security and access control for easy governance
  • Integrates with Slack and email
  • Searchable business glossary to correlate important business terms with data objects
  • Reveals data lineage and impact analysis
  • Allows easy collaboration with your team through in-line chats and annotations
  • Enables running Excel-like queries without any coding
Apache Atlas tool logo
  • Open-source solution
  • Provides predefined types for Hadoop and non-Hadoop data types
  • Easily defines new types for the metadata to be managed
  • Uses intuitive UI to view data lineage and REST API to access and update it
  • Embedded with security and data masking features to enable controls on access
Azure Preview tool logo
  • Automates discovery of data, identification of lineage and data classification across cloud, multi-cloud and on-premise sources
  • Creates a unified map of all the data assets and their relationships, thereby fostering robust governance
  • Allows semantic search which makes it easier for data teams to conveniently locate data using simple technical or business terms
  • Enables data teams to manage and automate metadata from hybrid sources
  • Allows classification of data with custom or built-in data classifiers and Microsoft Information Protection sensitivity labels
  • Facilitates seamless connection with Azure Data Factory to automatically set up data integration lineage
Alation tool logo
  • Leverages machine learning to index a range of data sources such as cloud data lakes, relational databases and file systems
  • Facilitates seamless collaboration on trusted data assets
  • Provides automated recommendations and suggests policies and flags based on the query logged in the intelligent SQL editor by the data consumer
  • Enables active data governance by closing the gap between topdown policy enforcement and policy setting
  • Facilitates seamless connection with other third party data source and business intelligence tools
Amazon Glue tool logo
  • The tool comes with job definitions, table definitions, schemas and several other types of control information that helps data teams efficiently manage data cataloging process
  • Allows data teams to run crawlers on-demand or by scheduling on the basis of an event to ensure that the metadata is updated
  • Enables data teams to validate and control streaming data through registered Apache Avro schemas
  • Provides a clean and interactive visual interface for data scientists and analysts where they can clean and normalize data without writing codes
  • Allows data teams to easily define ETL process through drag and drop job editor
  • Automatically generates code to extract, load and transform data


Managing data in the age of data lakes and self-service can be quite challenging. However, today’s enterprises must ensure they have an effective data governance framework in place, aligned with their overarching data analytics strategy, to gain the most out of business data. To that, every organization must look to simplify data management and governance with a strong data catalog. It helps data scientists to get more value from their enterprise data assets and empowers them to leverage data in the way they had always wanted.

By adopting a modern data catalog business are essentially taking their first step to creating self-service analytics ecosystems to democratize data, implement data governance, accelerate digital transformation, and reduce time to actionable insight.

About the author

Gunasekaran S is a Technical Consultant at Sigmoid and has over 20 years of experience. He is an advisory to customers on Data Strategy and Data Platform design and implementation using modern technology stack. He has experience working with customers on Retail, CPG, BFSI and Travel domain and help them drive towards becoming data-centric organization.

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