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Is entertainment ready for data science?

Analytics Community Manager, IBM

A data science craze is sweeping the world—but what is data science, and how can it revolutionize the entertainment industry? In spite of the massive amounts of data that have been collected for years, without data analysis, valuable insights remain undiscovered. As the field of data science rapidly evolves and the developing digital world churns out more data, media and entertainment companies must take the necessary steps to discover and learn from these insights.

What is data science?

All the hype around data science is being fueled by a virtual tsunami of data. It is estimated that more than 90 percent of the data in the world today has been created in the past two years. The concept of data science isn’t really new, but it is the next step in the evolution of both data mining and predictive analytics. It is also clear that this is no fad; as veteran IBM big data evangelist James Kobielus puts it, “The catch-all term ‘data scientist’ has been around for years and the various advanced analytics specialties that fall under it—statistical analysis, data miner, predictive modeler and others—are even older…[S]teady growth in data scientist job listings, professional forums and academic curricula in the past several years is undeniable.”

Shelly Palmer, a world-renowned strategic advisor, defines data science as “the analysis of data using the scientific method with the primary goal of turning information into action.” He says data science exists at the intersection of these three foundational skills; domain expertise, mathematics and computer science. Palmer also argues that now is the time to start transitioning to a data-driven business strategy (if you haven’t already) or you will be left behind like so many others who did not innovate soon enough. He is a key driver of market, technology and strategic direction for the C-suite of leading digital media, content, broadcasting and technology firms. If you’d like to hear more of his advice on this topic, check out his session at IBM Insight 2015.

What will next-generation data science look like?

Data science tools are being rapidly iterated and increasingly automated without any indication of slowing down. Meanwhile, data scientists are becoming core application developers, building and maintaining big data clusters, statistical models, machine-learning algorithms and so on. Data science will be seamlessly woven into the fabric of your life before you even realize it.

James Kobielus predicts that “Persuasion will be so embedded in the experience of shopping, buying and using products that we consumers won’t even perceive it as marketing or sales…The entire marketing and sales process won’t involve any direct human contact—not even an outbound call center—but rather will be driven by back-end predictive recommendation engines. These will chug away constantly and silently behind the scenes, presenting us with continuously and algorithmically personalized options so ‘in the ballpark’ that we’ll follow those recommendations more often than not.”

How can data science revolutionize the entertainment industry?

Best-in-class companies realize the magnitude of the benefits data analytics can deliver to their organizations. In the era of the connected consumer, media and entertainment businesses must go beyond simply being digital in order to compete. Data science is already allowing organizations to truly understand their audience on an individual level, and to tap into the bidirectional flow of data and communication that was nonexistent just a few years ago.

Dynamic market forces are transforming a once content-centric model into a consumer-centric one. The entertainment industry is prepared to capitalize on this new digital marketplace by converting information into insight that will boost production, cross-channel distribution and value creation. Building these smarter operations will ultimately lower costs and improve agility.

It is paramount to ensure a sustainable consumer experience is at the center of your entertainment organization if you want to compete and stay agile in the ever-changing technological landscape. Moving forward, audience value and growth will be attained only by those who provide a differentiated audience experience. Fortunately, there are solutions available that let you continuously monitor and capture consumer feedback from sources like social media, blogs, websites and transactional systems to deliver the right content at the right time to the right person. Imagine a time when each individual has a unique customer experience tailored to his or her wants and needs, leading to a satisfied audience, increased revenues and better overall viewer market share. That day is here.

What steps should entertainment companies take to prepare for the next generation of data science?

Your goal is to develop a sustainable data-driven business strategy with a cross-functional operating model that facilitates effortless collaboration, governance, analysis, and agility management. As Shelly Palmer notes, “You’ll have to create methodologies to empower ongoing data scientific research. You will need to build or buy appropriate infrastructure, including analytics platforms, visualization tools and big data environments.  You will find ways to manage data from third-party partnerships, enforce data governance and develop best practices for data munging and wrangling.”

What does this tall order entail?

  • Identify and ask the right questions: Get your key players together and determine the questions that are relevant for your business. Come up with questions you can ask of your data and some solid hypotheses, and then think about what you can do with those valuable answers.
  • Audit your data assets: Perform a thorough audit of your existing data assets and systems. Don’t skip this important step because data is the most important resource in the insight economy. Consider how data sources such as online search volume, social sentiment or weather patterns can benefit your business.
  • Craft a roadmap to a data-driven future: Figure out where you are on the data science readiness spectrum, and then identify how you can surpass your competition. Make a list of the moves you must make to get where you want to go, making sure each step is a small, achievable objective, and then check them off one by one. Remember to be patient and to keep your eye on the finish line.