Business Intelligence

Sylvain Perras

Sylvain Perras, BBA, MCS, MBA is CBC/Radio-Canada’s Director, Applications Management, since July 2011. Sylvain is responsible for the management of a portfolio comprised of more than 200 applications and systems in various domains, including the Corporation’s Business Intelligence (BI).

Djamel Djemaoun Hamidson

Djamel Djemaoun Hamidson, Eng., BSc., Ph.D. is an IT/Software Enterprise Architect at CBC/Radio-Canada since February 2009; he has over twenty-five years of relevant software, technology, and management experience. During his career, he has participated in major projects in a variety of sectors (administration, banking, insurance, high technology, etc.). He is business-focused, user-concerns-conscious, and result-driven.

Business Intelligence at Work for CBC/Radio-Canada Managers

What Is Business Intelligence?

Business Intelligence (BI) is a phrase we have been hearing a lot these past few years in the corporate and IT management worlds. This deceptively simple term refers to all disciplines, processes, and systems that help managers make decisions – a complex field indeed, given that managers base their decisions on a wide range of information sources. As such, BI encompasses:

  • All types of reports (statistical reports, dashboards or scorecards including performance indicators, etc.),
  • Predictive models based on historical data (time-series forecasting) or correlated interpretive data (regressive models),
  • Client segmentation or classification models widely used in target marketing,
  • Prospective models for determining long-term trends, and
  • Scenario analyses for determining potential cases/situations (with an eye to optimising the programming schedule, for instance).

One of the initial activities involved in implementing a Business Intelligence system is to create and maintain a foundation supported by a Data Warehouse[i] (DW) that is viable, scalable, and modelled after current and future corporate needs. A Data Warehouse is a prerequisite for providing managers with the information they need for decision-making. In more concrete terms, the data warehouse manages historical data in operational systems (OnAir,[ii] SAP, etc.), the data change history, as well as the quality and integrity of corporate data, with an eye to helping managers make decisions based on accurate, unbiased statistics and information.

The key BI players are the decision-makers from the various business units within the organisation. Without them, it is hard to establish the business rules and decision-making processes underpinning the BI system.

Strategic Thrusts

Introducing BI practices at CBC/Radio-Canada has helped enhance and increase analysis possibilities in many of the Corporation’s areas of activity. The main ones include sales and marketing, finance, human resources, programming, and contract management. The BI teams have focused their efforts on setting up data warehouses in these various areas to produce management reports.

This information was rolled out in DW format primarily on a departmental basis to support operational managers in their tasks with their teams. These tasks are still poorly integrated with one another, which limits the ability to perform tactical and strategic cross-sectional corporate analyses. CBC/Radio-Canada has therefore reached a point where it needs to strive for greater integration to enhance the value of BI.

This requires action on three levels:

  • Data management foundation,
  • Analysis tools, and
  • Governance model.

Data Management Foundation

The data foundation relies on the corporate DW, a corporate database that interrelates all CBC/Radio-Canada data, regardless of their origin. This foundation includes the Data Warehouse’s feed processes, as well as the processes for ensuring corporate data quality and integrity.

One of BI’s priorities is to continue building on this foundation by introducing greater flexibility into the CBC/Radio-Canada warehousing system. The goal is to complete and centralise the current structure in a single, primary DW, and increase the agility of the BI system through its reuse within the main corporate warehouse.

Incorporating financial data (currently underway) will make the corporate data warehouse even more integrated and enable a wider range of business areas to be included in BI initiatives (finance, media lines, etc.).

Data Analysis Tools

As a result of this effort, a data management structure capable of meeting tactical and strategic needs will be in place and able to support the evolution of the BI interface and tools. The system will evolve through more frequent use of dashboards, performance indicators, and ad-hoc multidimensional analytical queries (OLAP[iii]). However, initially, the biggest enhancements will come through incorporating statistical and mathematical analysis tools. These tools will more effectively support practices leading up to decision-making, especially in the area of performance management.

The priority at this stage will be implementing Business Process Discovery[iv] tools to facilitate access to internal/external structured and unstructured data, as well as enable greater self-serve capabilities. The need to analyse content from network sites or other varied sources (such as text files, videos, websites, etc.) will also require a rapid shift to technologies that process unstructured data (as opposed to the structured data stored in relational or hierarchical databases). Analyses of unstructured information (such as election “Tweets”) will allow us to track trends and content on a longer-term basis. Finally, BI mobile solutions should be rolled out to support managers and allow them to make decisions when and where they need to be made.


To support BI initiatives, good governance is essential for improving data quality by ensuring that each piece of data is given context and meaning through its natural owner (the department producing the information). Implementing a data dictionary (currently in progress) will allow business rules to be accessed by data stewards[v]. This will help establish a data governance system at CBC/Radio-Canada that could be based on a self-serve approach and agile, flexible change management to better meet media-line needs.

Business Initiatives

In 2012, we conducted a comprehensive inventory of the organisation’s performance indicators and BI analytical needs, allowing us to identify fifteen-odd business areas using similar performance metrics. This knowledge will assist the transition from a departmental approach to a more corporate-wide one, where all departments can access the various business areas and perform cross-area analyses.

To this end, the IT group is actively working with the media lines and Corporate Services to establish a road map for taking business intelligence to a higher level of maturity, where all corporate objectives give rise to BI and performance management strategies aligned with the corporate metrics framework (Report Card, Matrix, PPI, STIP, etc.) and supported by corporate-wide governance policies. The key objectives are as follows:

  • Support the transformation of CBC/Radio-Canada activities and operations by providing appropriate information for the decision-making process.
  • Allow users to be self-sufficient in their analytical and decision-making needs.
  • Reduce the number of manual tasks required to produce reports and analyses.
  • Evolve and enhance BI services, reports, and analyses (forecasts, scenario studies, text and data mining, etc.).

A large number of business initiatives are planned for the next few years and many will surely evolve over time.

For instance, we will be implementing a staff planning and analysis initiative that will provide a variety of ready-made dashboards and scorecards to support workforce planning and budgeting, as well as a more strategic vision for staff planning and HR metric analysis. This initiative could also evolve to incorporate staff management data (currently in progress) with an eye to optimising use of media-line resources.

Several other initiatives are underway or in the works, specifically in the areas of programming, record archiving, and the streamlining of financial reports.

To meet our growing business needs, IT has set up a BI expertise centre in charge of implementing technologies and solutions for gathering, consolidating, synthesising, and storing corporate data so that it can be made available for use in standardised and ad hoc reports, dashboards, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.


Getting components to work together toward an integrated business strategy within the various BI initiatives will take CBC/Radio-Canada business intelligence to a new level of maturity, helping expand the analysis methods and capabilities of the Corporation’s various business areas.

Within CBC/Radio-Canada, IT is proactively focusing on BI and analytics projects that will help the organisation to meet the goals set out in its strategic business plan. There is growing evidence that more pervasive BI and analytics have a direct impact on efficiency, productivity, and revenue by improving key decision-making business processes.

BI Trends Over the Next 5 Years

Even in spite of its maturity, the BI market keeps reinventing itself. Gartner’s 2012 predictions for business intelligence focus on the challenges around the Cloud, alignment with business metrics, and an organisational model that strikes the right balance between being centralised and scattered. In 2012, the BI market is shaped by many trends, such as:

  1. Big Data, which is mainly characterised by larger volume, higher frequency, and a diverse variety of sources. The scope of this domain is the analysis of new types of data: un-structured data (Word documents, PDF files, Web sites, network sites, blogs, etc.). The Big Data concept requires the implementation of a new type of database (a non-SQL [vi] database), featuring an object-oriented file system. It typically refers to the following types of data:
    1. Social data, which includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook.
    2. Machine-generated /sensor data, which includes Call Detail Records (CDR), weblogs, smart meters, manufacturing sensors, equipment logs (often referred to as digital exhaust), and trading systems data.
    3. Enterprise data, such as customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data.
  2. Collaborative BI is making the information available for employees to search, rate, comment on, and request enhancements to content as is usually seen every day in the Web 2.0 world of social communities such as Facebook, Wikipedia, Linkedln, and Xing.
  3. Media Engagement Metrics and Social Media Analytics: increasingly, analytical tools target engagement metrics to gauge the level of attention, interest, and relevance that consumer segments assign to media experience. It also tries to monitor aggregated views of significant attitudinal trends relative to a topic of interest (brand, product, or issue).
  4. Mobile BI, by offering huge advantages for staff and management to never be disconnected from the tools that help them make business decisions regardless of the time or location.

All these trends may mature within the next five years, and careful attention to them will be necessary to ensure the positive evolution and application of BI within CBC/Radio-Canada.

[i] Data Warehouse (DW or DWH) is a database used for reporting and analysis. The data stored in the warehouse are uploaded from the different operational systems (such as marketing, sales, etc.).

[ii] SintecMedia’s OnAir is a digital media industry’s broadcast management solution, combining sales, traffic, programming, content rights, etc.

[iii] OLAP (OnLine Analytical Processing) is an approach to swiftly answer multi-dimensional analytical (MDA) queries. OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.

[iv] Business Process Discovery (BPD) related to process mining is a set of techniques that automatically construct a representation of an organization’s current business processes and its major process variations.

[v] Data Stewards are commonly responsible for data content, context, and associated business rules. Data Custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules.

[vi] SQL (Structured Query Language) is a special-purpose programming language designed for managing data in relational database management systems (RDBMS).

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