Technology and data integration

Clear Channel: Building an enterprise data lake and pipeline

Technology and data integration

Clear Channel: Building an enterprise data lake and pipeline


Clear Channel Outdoor is one of the world’s leading Out of Home media owners. Across their large, diverse out-of-home portfolio of half a million sites in 22 countries throughout Europe, Asia and Latin America, they boost brands by connecting them with the people they want to reach, with media and ideas that enlighten, entertain, charm, challenge and influence. In the UK alone, they operate more than 35,000 sites nationwide, from Inverness in Scotland to Truro in Cornwall and in every major urban area in between.


The company had an initiative to make internal data sources available to the entire organization to monitor digital campaign execution and to use data for performing data analytics by Sales and Operations teams. It also had a compliance requirement as part of Clear Channel’s ongoing commitment to ensuring full transparency and accountability for their digital outdoor campaigns, as well as a contribution to further industry commitment towards brand transparency and benefit to advertisers.

The approach the company took historically was to implement local solutions that solve individual business needs or manual efforts by each independent team, resulting in a fragmented approach and a large number of siloed data ponds. Implementation of a data strategy began a few years prior centred around an on-premise approach to centralizing in-house data around a third-party product. Subsequently, a canvassing of data related needs across different countries and departments and core services groups identified a need for a technology capability that provided visibility and timely access to all existing campaign data and allowing for introduction of new datasets with relative ease, along with a data operating model and governance that supported the sharing of data across the organisation as well as with external parties – Buyers, Agencies, Specialists.


The Riverflex team’s solution was to create a data ecosystem (data lake and data pipeline) that would be central to providing Sales and operations teams across the organisation with access to near real-time campaign performance data. Based upon the technical needs to enable the data lake, we envisioned a cloud-based platform that would accommodate different types of data and compute needs that were most relevant to the business – a data pipeline that ingests and normalizes data to a data lake which acts as a central repository, discovery through a data catalog, data access methods to support disparate needs integrated with a cloud compute environment for applications and analytics. Azure was chosen as the cloud provider. Riverflex was engaged to drive the process to build and support the Azure technology foundation, security implementation, continuous integration/continuous delivery (CI/CD) pipeline and create the application stack to support the data lake and ETL ingestion pipeline.

The key elements of the cloud infrastructure, data and compute platform that we created are described below.

Cloud-based Account / Project Structure

The proper root account / sub-account / project structure was implemented to achieve huge gains in productivity, innovation, and cost reduction as the workload moved to the Azure cloud. There are a variety of services and features that allow for flexible control of cloud computing resources and also of the Azure AD account(s)

managing those resources. On the account level, these options are designed to help provide proper cost allocation, agility, and security. A project-based mapping oneto-one to a sub-account structure was implemented. Creating a security relationship between sub-accounts was a key element added to assess the security of cloudbased deployments, centralize security monitoring and management, manage identity and access, and provide audit and compliance monitoring services.

Project-based Implementation with Infrastructure as Code (IaC)

Infrastructure as Code (IaC) was implemented as a method to provision and manage IT infrastructure through the use of source code, rather than through standard operating procedures and manual processes. IaC helps the devops team to automate the infrastructure deployment process in a repeatable, consistent manner, also providing the benefit to easily deploy standard infrastructure environments in other regions where the cloud provider operates so they can be used for backup and disaster recovery.

Serverless Compute and Storage

By employing cloud serverless compute and storage, such as Blob and object stores, the organisation leverages the ability to build and run applications and services and with infinite elasticity without using physical hardware. In addition, all existing costs associated with managing servers and containers (operating system updates, maintenance updates, image snapshots, backups, restarts, etc.) largely disappeared.

ETL and the Data Pipeline

The data pipeline acts as a utility – a standard suite of data tools that enabled the develops team to automate the sourcing, processing, and entitlement of data. Automation of these processes allows data sources to be quickly added and the approach for the cloud data lake then extracted, transformed, combined, validated and loaded (ETL) for further use. The data pipeline is able to simultaneously process multiple data sources at once.

Enterprise Data Lake

The introduction of an enterprise data lake provided a central data repository and access to analytics tools that maximized the value of the data. The enterprise data lake is a centralized repository that allows storage of structured and unstructured data at any scale. Data can be stored as-is, without having to first structure the data, and run different types of analytics – from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Data Catalog and Discover-ability

The introduction of a data catalog provided a single searchable glossary of data that is available to the organization, including the data source, definition and entitlements. The data catalog built on top of the data lake allows users to find the data they need and then use it in the tools that they prefer along with ensuring information boundaries and data contracts are not violated.

Application Programming Interface

An Application Program Interface (API) is a set of software routines that allow programs to interact. The use of an API at the organisation allowed data from the enterprise data lake to be accessed by upstream applications that rely on it. Additionally, the API enabled end users (both internal & external) access to data for their individual analytics and modelling needs. External parties were able to integrate the APIs with their inhouse sales systems, to better improve programmatic buying and monitoring of campaign delivery.

Analyze and Visualize Data

Through self-discovery of data resident in the enterprise data lake through the data catalog, individuals are able to access data based on their entitlements. For low technology use cases, end users are able to upload datasets into Excel or tools such as PowerBI, or alternatively, the API allows data to be integrated with programs coded in Python, Scala, Java, R, etc. For heavy big-data workloads, data engineers are able to use data bricks clusters and Azure synapse to achieve limitless analytics.

Sandbox for Experimentation

To support innovation, the data lake includes a sandbox environment that provides the functionality of the enterprise data lake but allows one to easily introduce new datasets and technologies for experimentation.

Flexibility of Architecture

Unlike traditional technical approaches for data warehousing which are inflexible in terms of data schemas, technical capabilities and tools, the cloud-based approach allows flexibility on all of these fronts. Fit for purpose cloud-based data tools and technologies can be incorporated with relative ease as needs get identified.


The creation of an enterprise data lake had substantial benefits. Historically, the organisation had data that existed in individual business teams or systems. Providing access to this data required point-to-point solutions and significant time was spent preparing and reconciling data by each team who uses it. Additionally, teams were not aware of data that existed within the organization. The enterprise data lake enabled the organisation’s Sales and Operations teams to capitalize on the value in data, by bringing together internal and external datasets in a single place, in near real-time, as well as eliminating redundant reconciliations by using the same dataset. This value grows as new data sets are added. This easy access to a broad set of data empowers users to innovate the way the organisation sells and manages its digital campaign delivery and the overall performance.

As a direct result of Riverflex’s work, Clear Channel have now been independently audited by professional audit firm PwC. The audit covers Clear Channel’s delivery and reporting of selected UK digital campaigns, meeting advertisers’ requirement for transparency in digital media. It also covers Clear Channel’s delivery and reporting of UK digital campaigns over three months. The audit is part of Clear Channel’s ongoing commitment to ensuring full transparency and accountability for their digital outdoor campaigns. 

By leveraging the data catalog, the internal users can now easily discover, access and perform analytics on hundreds of datasets. After discovery, analysts can access data directly via an API into their environment, or they can leverage sophisticated scalable cloud-based analytics software, such as Spark based services, to perform intense algorithms against multiple datasets. This greatly speeds analytics and increases its use in digital campaign performance.

The implementation of a Continuous Integration/Continuous Deliver (CI/CD) approach to cloud development and deployment, the organisation can deliver new software features in hours or days instead of months. Smaller code changes are simpler (more atomic) and have fewer unintended consequences. Upgrades introduce smaller units of change and are less disruptive. The products improve rapidly through fast feature introduction and fast turn-around on feature changes. End-user involvement and feedback during continuous development leads to usability improvements. You can add new requirements based on customer’s needs on a daily basis. 

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