Stay Nimble: Using Data to Adapt to Changing Marketplaces
It’s 2017, and disruption is the name of the game. No matter how big, brands and companies face a fast-changing landscape as new technology and business models overturn the status quo across all industries.
“From Data to Disruption,” a Harvard Business Review report, set the stage for the global challenges facing businesses:
A wave of disruption is threatening businesses around the world, as new entrants with structural cost advantages and a “digital first” culture spring up in a wide range of industries. The new competitors offer solutions that are often simpler, cheaper, or more convenient for customers (e.g., Uber, Airbnb, Alibaba, Netflix). Or they may provide services that address a market that previously couldn’t be served (e.g., mobile banking in developing markets). As different as these new disruptors are from one another, they all display the ability to leverage digital technologies to understand the customer, sense market shifts, and innovate faster than the competition.
Whether a 100-year-old company or a year-old startup, your business must be nimble enough to adapt. Hoping the status quo will hold is not a viable strategy.
The Power Of Data
Viewed as a whole, the pattern of disruption across industries teaches a valuable lesson: successful companies are able to capitalize upon emerging digital technologies to move faster than their competitors. By understanding customer needs, demographic shifts, and changing consumption patterns, your business is better as the disruptor — not the disrupted.
The key to being a nimble company? Capturing, collating, and creating actionable strategies from Big Data. The ability to respond to a constantly changing marketplace hinges on the ability to understand those changes.
At LMS, we are experts at developing innovative market solutions powered by collecting, managing, and integrating high-quality data. Contact us today to learn how partnering with LMS can transform your business by combining innovative methodologies with big data analysis.