Data Governance in Telecommunications: What You Need to Know
By Michelle Cloutier
Published on September 21, 2023
Telecommunications companies must attract (and retain) customers with the best customer experience. This is especially important in an industry where average revenue per user (ARPU) continues to decline. To compete effectively and boost revenue, modern telecom companies must offer top speeds, the latest features, and value-added 5G services. Yet offering such features require a deep understanding of customers, which can only be gleaned from data. Likewise, determining where and when to invest in network infrastructure upgrades requires tapping into network usage and IoT sensor data captured from a multitude of sources.
How can telecom leaders enable their teams to meet these demands? Communications service providers (CSPs) must ensure that the people who need data for analytics and decision-making can easily find, use, and trust that data. They also need to meet stringent privacy and regulatory guidelines related to the proper use of customer data.
What is data governance in telecommunication?
Data governance ensures that data is used properly within an organization. A data governance framework defines the policies, procedures, and standards that determine who has access to what data and what actions they can take with it. For telecommunications companies, governance means that the data used for everything from customer retention, predictive analytics, and churn modeling to decisions about 5G network buildout is clean, consistent, complete, and fit for purpose.
Good data governance helps maximize the value of customer and other data for operational effectiveness, decision-making, and regulatory requirements while minimizing the risks associated with poor data management.
Why is data governance critical for telecommunications companies?
While all industries need strong data governance, the regulatory complexities in the telecommunications industry may be second only to those in banking and finance. Telecom providers are under unique pressure to get their data governance house in order after decades of growth, acquisitions, and mergers — with an accompanying proliferation of data across disconnected systems.
Additionally, burgeoning data compliance requirements and privacy regulations have put added pressure on these companies to effectively govern customer data. As these regulations with their “right to delete” clauses become ever more stringent, operators need a single window of truth into their data to quickly access, amend, and remove customer data. Add to this the growing need to personalize customer experiences based on their usage histories, and the need for a data governance strategy that enables both defensive and offensive tactics begins to emerge.
A strong data governance framework helps CSPs:
Remain compliant with regulations through better data management
Boost operational efficiency by minimizing duplicative work around data management and reporting
Foster greater trust in data by improving data integrity and reducing data errors
Increase data quality by tagging trustworthy data and flagging deprecated or questionable data
Improve analytical and DataOps efficiency when analysts and engineers can quickly find the right data for their purpose
Respond to customer expectations around both privacy (the right to be forgotten) and personalization (media content tailored to tastes)
Ultimately, CSPs that leverage better data governance practices can expect to increase revenue, improve customer retention, and avoid fines and penalties. Data governance provides a deeper understanding and more effective use of customer and other critical data throughout the organization. To be successful, a data governance program must align people, processes, and technologies in service of the proper and effective use of data to achieve the organization's goals.
Strategies for effective telecom data governance
A “people-first” approach begins by helping people throughout the organization understand the value of data governance. Ultimately, a robust governance program ensures that data users can confidently use data to drive business outcomes, enhancing the capabilities and competitiveness of the business.
Promoting the business value of data governance
Implementing a successful data governance strategy requires an organization-wide understanding that data is the CSP’s most valuable asset. Communicating how data governance supports your business objectives creates a sense of responsibility for the proper use and protection of data among users.
Building cross-functional collaboration
Connecting the right people to the right data to drive business insights should be a key goal of any data governance program. Data users across your organization should be able to quickly find data, determine its trustworthiness, and understand the policies that apply to it.
Delegating stewardship responsibility to those closest to the data ensures accountability for data quality and integrity. CSPs should encourage collaboration between data stewards and data consumers and across business units. Shared reports, data models, and the like, accessible through a common repository, will accelerate time-to-insight and reduce the time needed for data search and discovery.
Fostering a data-driven culture
Culture makes technology investments and human changes “stick” as practices and processes become embedded in the organization. Companies with a strong data culture — one where people feel confident in their ability to find, use, and trust data — are more likely to meet or exceed their revenue targets. Harness the enthusiasm of your data stewards to evangelize your data governance program. Formal training, adapted to the specific business function and level of data understanding, and “data governance boot camps” will help bring your organization together around the goal of data governance.
Tools and technologies for telecom data governance
CSPs need to choose the right technology tools to support their data governance strategy. Many have turned to cloud data warehouses, data lakes, and data lakehouses to break down data silos and consolidate data across the business. But to be useful for decision-makers, the migrated data must be searchable, classified and tagged for appropriate use, and include access controls for sensitive data.
Metadata management tools
An intelligent metadata management platform like Alation allows you to capture, store, and organize metadata from multiple data sources in a single repository. This allows data stewards from across the organization to define policies, set data permissions, and control access to the data.
Policy management tools
Policies and standards are the foundation for any data governance framework. Organizations with a strong governance framework will catalog policies in a searchable policy center or similar repository. They can be divided into three types:
General policies: Guide the principles of the overall data program with a broad vision of how data will be managed throughout the organization.
Standards: Provide consistency around data management and use. They can include naming standards, guidance on what data types to use or not use, standard patterns for data pipelines, and rules for data retention, processing, and deletion. The latter is particularly critical for CSPs in light of privacy regulations.
Data policies: Specify the rules to be followed at the data column level and share what works and what doesn’t work based on feedback from data users.
Technical controls embed policies into the data platform, so people can work with data compliantly without having to understand every policy impacting that data. These include access controls and data masking for PII.
Data privacy tools
To execute data removal requests, data managers need a platform in which customer data is classified and categorized for easy discovery and deletion. When data is cataloged effectively, the data catalog can be a go-to source to identify what data an organization has about an individual and where it resides, allowing deletion on request. We expand on Alation’s suite of data privacy tools later in this piece.
Data lineage tools
Data lineage tools like those in Alation track the flow of data throughout an organization from its origin through use and consumption to deletion. Knowing when data is accessed, used, reported, or changed can help you prevent or respond effectively to data breaches.
Data quality tools
Poor quality data leads to incorrect insights and misguided decisions. Data quality tools ensure better analysis, consistent regulation compliance, and better data management overall. Because a data catalog is a central place where people discover, judge, and comprehend data, it is the natural place to expose data quality rules, metrics, and alerts. Today’s data quality landscape includes many data quality tools. Alation’s Open Data Quality Initiative permits CSPs to adopt the data quality tool of their choice and integrate it with their data catalog.
Using Alation for telecommunications governance
Alation includes key data governance features that protect data privacy and security while facilitating an organization-wide use of data for strategic decision-making. At the most fundamental level, Alation catalogs your entire data environment to create a single view of all data. Easy-to-use search tools enable data users to quickly find the data that they need, evaluate its fitness for their intended use, and access data stewards who can answer questions about the data. Alation classifies and categorizes personal data to determine the level of risk and access control needed, keeps track of data usage, and provides additional controls to help data governance efforts.
Alation and Spark NZ: Telecommunications governance in action
Spark NZ, New Zealand’s largest digital services company, provides mobile, fixed, broadband, and IT services to millions of individuals and businesses across New Zealand. The company uses Alation to catalog and govern their consolidated data on the Snowflake Data Cloud. Their data classification, sensitivity, PII, and data retention periods are all visible in Alation.
“In addition to the basic metadata that we’ve captured in Alation, we’ve also implemented an extensive tagging taxonomy on our Snowflake objects,” says Peter Langham, domain chapter lead for data engineering at Spark NZ. “It helps our data engineers and our DataOps teams understand the policies that need to be applied across those objects.”
Using Alation to bolster data privacy at telecom companies
Specific Alation data governance features relevant to data privacy for telecommunications companies include:
Classification and tagging: Allow you to organize your data by domain, tag sensitive or private data, and conceal PII from data users who do not have access permissions.
Policy center: Enables governance leaders to create policies that guide appropriate usage of private or sensitive data.
Stewardship workbench: Empowers stewards to curate data at scale with help from AI and ML and apply privacy settings across multiple datasets simultaneously.
With Alation data privacy and compliance, policies are transparently managed to protect sensitive data. Business users can create definitions of data types and categorize them according to compliance requirements. This allows you to apply data privacy controls, like assigning responsibility or data masking. Alation also allows you to leverage autonomous data stewardship, giving your teams the ability to use data without creating data security and privacy risks. With data risk audit and reporting capabilities, Alation gives you real-time visibility into compliance by tracking data usage to monitor for policy violations that may lead to potential fines and penalties.
Alation also boasts rigorous privacy and security certifications for our cloud platform, so your cloud migration is secure and protected.
Learn more about Alation for data governance in the telecommunications industry
Alation has also been awarded a telecom industry competency for our Snowflake solutions by creating a better understanding of audiences and using data to deliver solutions and experiences specifically in the media and telecommunications industry.
For more information on how effective data governance helps to improve data quality, data security, privacy management, DataOps, compliance, and more in telecommunications, check out these additional resources:
- What is data governance in telecommunication?
- Why is data governance critical for telecommunications companies?
- Strategies for effective telecom data governance
- Tools and technologies for telecom data governance
- Using Alation for telecommunications governance
- Learn more about Alation for data governance in the telecommunications industry