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Accelerating innovation with data-driven insights
Developing a data analytics strategy to move from optimization to innovation
Technology, media, and telecom (TMT) executives are using data-driven insights more than their counterparts in most other industries. But should they put a higher priority on harnessing data to drive innovation?
How TMT executives rank their use of data-driven insights:
The second annual Deloitte Global Industry 4.0 readiness report, “Success personified in the Fourth Industrial Revolution,” surveyed 2,042 global executives and public-sector leaders about their readiness for the convergence of big data, the Internet of Things (IoT), artificial intelligence (AI), and robotics. Seven out of 10 executives in TMT businesses reported that their organizations use data-driven insights—more than companies in other industries.1
However, their usage is mostly defensive. TMT executives stated that they harness data-driven insights primarily to optimize their operations, prepare for the impact of new solutions in the marketplace, and to defend against disruptive competitors. Notably, the same executives reported significantly less use of data-driven insights to drive innovation.
So, why isn’t there much data-driven innovation? Often, TMT companies prefer to take what appears to be a “safer” route. Optimization requires fine-tuning and little risk. Enterprise resource planning and business-intelligence tools are common, and operations generate considerable data. Defense against competitors can leverage market intelligence and competitive intelligence capabilities. Furthermore, defense is about protecting what you already have, while innovation is about creating something novel, often without much information. Effective innovation typically requires discovery, development, delivery, and market impact. It can be expensive and inherently risky. However, data-driven insights can accelerate the hardest parts: discovery and market impact.
Netflix has been a data-driven company since its early days using recommendation algorithms to match customers with content.2 As it has grown into a studio, it has extended the reach and sensing capabilities of its content-delivery solution to inform the development of new content, and where it should market that content for highest engagement.3 By using anonymized and aggregate customer data, Netflix can ask about which user persona is most likely to watch an action adventure show if it’s 28 minutes long and includes a regional actor in the main role.
It can further inquire about which personas are most likely to start the show if the placeholder icon features an image of that actor in front of an explosion. From there, Netflix can estimate the size of that audience and whether it’s valuable enough to fund the project, lowering the risk of failure. Once deployed, the company can measure the results of its engagement hypothesis, and then update the models accordingly.
Netflix may seem like a hard example to follow, but its focus is simple: Use data to get to know the customer better, and then apply that knowledge to accelerate innovation and drive market impact. Savvy business leaders could do well to invest in the data they already have—not just for defense, but to better understand how to deliver value to their customers.
This charticle authored by Chris Arkenberg on June 12, 2019.
1 “Success personified in the Fourth Industrial Revolution,” Deloitte, 2019.
2 Chmielewski, Dawn C., “Netflix will emerge from battle with Blockbuster as a powerful mass-market force, CEO says,” San Jose Mercury News, February 27, 2005.
3 Bernard Marr, “Netflix Used Big Data to Identify the Movies that Are Too Scary to Finish,” Forbes, April 18, 2018.