What are the new DataOps challenges in digital media?
The data landscape for digital media companies is becoming increasingly complex. Revenue is no longer driven by a single Google Ad Manager (GAM). And it is no longer the sole responsibility of the revenue operations team to manage the process.
Instead, business teams, revenue teams and tech teams are all dealing with a proliferation of new revenue channels to add into the data management mix. Programmatic sales, mobile, affiliate ecommerce, newsletter subscribers, subscription paywalls and acquisition funnels are just some of the channels these teams need now to consider.
But without the right DataOps processes in place, these new data sets can fragment your view of inventory, revenue and yield, which can present a number of challenges. For instance, if you’re a publisher, your advertising partners may be willing to pay a premium to reach specific audiences, but only if they can be assured that those demographic groups are actually viewing their campaigns.
Meanwhile, tech teams are often burdened with requests to pull raw data manually and painstakingly stitch it together into one-off reports. The intense pressure caused by the need for this operational visibility across data sets creates a dangerous stumbling block. While at Google, our co-founders worked with some of the world‘s largest publishers and ad agencies to enable real-time insights from a rich mix of media data. They saw firsthand how difficult these data challenges can be.
Forward-thinking digital media companies know they need timely data insights to effectively grow viewer loyalty, satisfy advertisers and optimize yield. Those that do not use data effectively today risk being outflanked by more nimble competitors tomorrow, as they drown in a sea of disparate data... unless they have the right strategy and the right data platform.
A scalable DataOps solution delivers continuous visibility into critical KPIs such as Sell-Through Rates, Campaign Delivery, and User Growth. These insights help you become aware of emerging trends, rapidly understand their causes, and use those insights to create new opportunities for growth and profitability.
But how can you use DataOps to empower your publishing business? In our next posts, we’ll dive into each the four steps to realizing the benefits of Data Operations:
Identify KPIs (that will help you measure and improve performance)
Normalize raw data (into foundational data)
Transform foundational data (to create distinct KPIs)
Automate (using the right tools for DataOps and real-time reporting)
Fundamentally, DataOps combines processes for collaborative data management, tools for automation and monitoring, and a scalable architecture to ensure that data growth is an asset, not a liability.
This post forms part of a series of bite-sized tips and advice on DataOps.
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