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How Broadcasters Can Stop Leaving Revenue on the Table

The broadcast and content production landscape has never been more competitive. Because viewers now have many viewing options, content delivering systems must be smarter. Data analytics driven decision-making can help any media company maximize both efficiency and revenues.

The use of data analytics to inform business strategy and decisions is paramount for any media organization looking to grow its revenues, both in terms of advertising and content utilization. Without the data and insights enabled by a well-architected data analytics system, the business undoubtedly will leave money on the table.

Rapid change in the technologies used to create, deliver, and consume media demands that media organizations be more agile and responsive than ever. The proliferation of new content distribution channels, new media formats, social media networking, and the continued advance of advertising technologies have contributed to audience fragmentation and the shift of linear spot revenue to other mediums.

Within this swiftly evolving landscape, strategic decisions based on intuition and tradition are no longer sufficient. Data has become the lifeblood of the media organization; it is an indispensable asset in staying competitive in today’s rapid-pace world. When properly aggregated, conformed, and analyzed, data delivers timely and accurate insights that can be critical to maximizing revenue streams.

Consider the platform

Enterprise-wide data analytics can help a broadcaster avoid leaving revenue on the table. However obtaining the data and analytics to support informed decision-making is difficult due to the complexities of a media business and the many diverse operational systems that manage them.

The systems that sustain core day-to-day transactional business processes — the routine workflow of sales, traffic, programming, research, production, accounting, and the associated reporting — are not decision support systems. They were built to solve daily transactional needs, not to support strategic analysis. As a result, it can take days or weeks for decision-makers to receive accurate strategic information from specific operational silos.

Data analytics is a significant competitive differentiator. It can yield information, statistics, and predictive modeling (based on historical facts) that allow a company to gain powerful insights and predict aspects of specific business situations in a way they simply couldn’t otherwise. The key to implementing effective data analytics rests in building a system based on deep knowledge of the industry and a proven technology base that has been applied successfully within the media business.

Useful data analytics require the creation and maintenance of a pristine data repository complemented by data analysts with the media industry experience. These experts can apply statistical algorithms and machine learning to dig deeper into the data for answers to specific questions.

Data sources

Companies adopting data analytics typically create their data repositories in one of two ways: building either a “data lake” or a tightly structured database. Refer to the following diagram for comparison.

A data lake collects the media organization’s data and puts it into a single unstructured repository. Because the complexity of data lake technology typically requires data science skills, the data analysts responsible for day-to-day business must formulate and forward questions to the data scientists if they wish to acquire specific information. The data scientist also plays a key role in keeping the data lake “clean” and capable of generating answers based on the proper context and common meaning. Accurate, useful results require pristine data and contextual comparisons (apples to apples) of data originating from different sources.

A highly structured database is an accessible repository that helps users understand what data means. In this scenario, more people are empowered to develop their own reports with confidence that they are working with accurate and properly contextualized data. Because data is effectively pre-processed when brought into the database, the results are essentially pre-calculated answers rather than freshly processed data. Costs are built into the transfer and formatting of data, which occur just once rather than many times (as with a data lake). The maturity of structured databases enables the media analytics system to become a part of normal business processes.

The debate over which approach is better has been vigorous. In truth, when backed by solid data engineering, either system will work. The assiduous data engineer will ensure that data, no matter what its source, is accurately collected and stored within the repository. This person will also see that the necessary data management and data warehouse tools are in place to allow smooth scaling of the repository as the volume of stored data increases.

Knowledge of multiple factors, with respect to the media organization’s business area, TV/radio/cable is essential to extracting and transforming the data from source operational systems into the right relationships so that end users can perform effective analysis. Once data and relationships are clearly understood and defined, only then does it become possible to institute the hundreds of audit controls that impose strict and formal procedures for data accuracy.

Data deployment

Figure 2 highlights considerations for the best deployment approaches. The resulting infrastructure can ensure a single accurate version of the truth, both from a fact and a formula perspective, across the enterprise. Moreover, when generated from accurate data, media key performance indicators (KPIs) can be used more effectively in aligning employee actions with the enterprise’s overall strategic goals. It becomes possible for every individual to monitor how well the results of their efforts support overall business success factors.

If the data repository supporting data analytics makes it easy for staff to publish their personal or departmental analysis, the enterprise can preserve that institutional knowledge as a formal part of its business assets. This model also encourages collaboration and best practice sharing. In both cases, the capture of collective wisdom can yield greater efficiencies and better-informed business decisions if the data analytics system has been engineered to support it. 

Deployment considerations.

Deployment considerations.

Fast access to information improves the intelligence behind decision-making while also improving productivity, client satisfaction, and stakeholder value. When data analytics is embedded into daily business activities, employees have the timely, accurate, and useful information they need to drive further corporate success.

Using data can drive success

Instant access to accurate, current, corporate-wide media data likewise can facilitate competitive innovation. Empowering employees to be more self-sufficient in acquiring the information they need for their jobs means the enterprise can free up its IT department for other tasks.

Corporate-wide reporting traditionally has been a time-consuming, and often, tedious task. A data analytics system can sit above such sub-systems simplifying the extraction of information to create comprehensive reports. A solidly engineered data analytics system ensures that data is collected and stored in a manner that yields timely and trustworthy business insights. When this fresh data is pushed back into transactional systems, the organization is able to tune and optimize its overall operations.

Taras Bugir is president of Decentrix.

Taras Bugir is president of Decentrix.

The use of data analytics to inform business strategy and decisions is paramount for any media organization looking to grow its revenues, both in terms of advertising and content utilization. Without the data and insights enabled by a well-architected data analytics system, the business undoubtedly will leave money on the table.

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