Data and smart technology are transforming the scope of business intelligence in media and entertainment. But their power depends on making continuous insight accessible across the enterprise.
In the rapidly evolving media and entertainment market, insight is what separates leading companies from the rest. Across domains — customer experience, finance, engineering — success depends on how effectively teams can turn good data into good decisions.
Converting raw data into insight, and turning that insight into revenue, is complex. Organizations are stymied by the cumbersome, costly, and time-consuming processes involved in extracting value from disparate and massive data sets. Given the difficulty of simply wrangling data, leveraging it to improve business outcomes can seem all but impossible.
Consequently, organizations often rely on lower-quality insight than is needed to be compete. Slow, siloed, incremental data analysis grants limited benefits, but how can executives scale insight across the enterprise to achieve transformational impact?
Sludge or oil?
In a 2014 article on the future of business analytics, two Indiana University professors noted that data had gone from being called the “sludge of the information age” to “the new oil.” While the value of data has certainly skyrocketed, the truth is that it can be both sludge and oil, depending on what companies do with it.
Value does not come from the data itself, but rather the insight it yields. In the race for data-driven innovation, media companies need to do more than just collect data. They need systems that can manage, understand, derive and disseminate insights from it.
A crucial component of data-driven insight is what Gartner describes as “continuous intelligence,” in which “real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events.”
The systems that have dominated the sector for the past 20 years simply can’t deliver continuous intelligence; demand for insight far surpasses the supply of resources that can produce it. Through no fault of their own, teams of data scientists take too long designing and deploying their own tools to analyze, interpret, and visualize data. Business intelligence tools tend to focus on a singular data set or business challenge, leaving large blind spots.
Artificial intelligence (AI) delivers insight at extraordinary scale, but it only becomes a competitive advantage when stakeholders can easily access its output. In fact, in a recent Gartner survey, 56 percent of companies cited employees’ inability to use AI systems as a barrier to adoption.
QoI: A new metric
The greatest differentiator in 2021 is quality of insight (QoI): a comprehensive KPI that spans nine crucial components.
- Speed: Real-time intelligence
- Frequency: Continuous output
- Objectivity: Free of human bias
- Depth: Sufficient data analysis
- Accuracy: Error-free conclusions
- Relevance: Answering the right questions
- Accessibility: Availability to all stakeholders
- Explainability: Clarity on how decisions are reached
- Actionability: Drives measurable change
To achieve major gains in QoI, most companies need to revolutionize how they manage, process and analyze data so that entire enterprise, and its customers, can contribute to and benefit from it. AI, and any system designed to draw insight from data, should be evaluated based on QoI.
Building trust into the system
McKinsey recently reported a 25 percent year-over-year increase in AI spending in the business world. The company also analyzed how the top eight percent of companies were “breaking away” from the pack in terms of analytics adoption. Among the key drivers were a strong commitment from all levels of management, creating cross-functional agile teams and empowering the front lines to make analytics-driven decisions.
The higher an organization’s QoI, the more value its data can create. The fact is that humans make better decisions when AI is involved in data analysis. Consider accuracy; AI weeds out variables that don’t accurately predict outcomes and checks the assumptions driving decisions. As media and entertainment companies develop strategies to combat churn and increase ROI, enterprise AI ensures that executives and their teams are asking the right questions — and that the findings are free of error.
Information you can act on
Enterprise AI not only makes insight readily available throughout the company, it also creates a more transparent decision-making process. If stakeholders want to understand why certain content strategies worked – or why they didn’t – the data that led to those decisions is available for everyone to see.
McKinsey’s research on “breakaway” AI adopters found that companies excel when they are “delivering the right insights to the right people at the right time in a way that informs their decision making to drive better business outcomes.”
Getting to that point isn’t necessarily easy. Scaling insight requires a company-wide commitment to innovation, rethinking the role of technology in data analysis and, most importantly, an emphasis on the comprehensive quality of insight driving business decisions large and small.
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