Infinite data: How the business can stand out with the crowd
By Hal Charnley
Welcome to the modern world — an age in which data is infinite and we are able to collect more information than we know what to do with. Every company with a digital footprint is searching for insights to propel them ahead of their competitors, only to be overwhelmed by the sheer volume of data collected on a daily basis.
Yet there are troves of valuable insights waiting for the right scientist to discover. As a recent Inc. article aptly put it, “it would almost be easier to ask who doesn’t need [a data scientist]” to help transform data into actionable insights for business decisions. When it comes to recruiting a data scientist, companies generally have three options: build, buy or partner. Companies with greater resources can recruit data scientists and build their own data-driven departments. Buying another company with expertise in data gathering, analytics and management is also an option. Forming a partnership with this kind of company is a good solution as well, particularly for those with budget constraints.
The Data Scientist
Whether companies choose to build, buy or collaborate, it is helpful to understand the role of the data scientist. The individual data scientist is a new breed of employee — a mashup of left brain quantitative analysis and right brain creativity who specializes in sifting through piles of raw data from all mediums and uncover gems for insightful decision-making. This could include data a company has collected on its products, online marketing tactics and campaigns, supply chain relationships and more. The sky is the limit, and data scientists aim high.
As a company of data scientists, we understand this mentality and the benefits data provides; however, we also recognize that insights derived from data are only as good as the data collected. A single data scientist or data-driven company can determine the type of questions to ask in collecting data, but it takes an army to do the legwork — or a crowd.
Crowdsourcing is one of the most exciting data collection tools available to data scientists today. Waze is revolutionizing navigation by allowing drivers to map and share real-time alerts about their commute. MyShake is crowdsourcing earthquake detection. Even the Smithsonian Institute has signed on, crowdsourcing history through its online Transcription Center.
Crowdsourcing also drives innovation. Emerging technologies that leverage the power of the crowd to deliver real-time insights about customer behavior are critical for understanding why customers want, love, hate or switch to another vendor for the things they do. Platform solutions that rely on mobile apps to gather crowdsourced data can also help businesses identify needs for products that do not yet exist. Clorox is using crowdsourced initiatives to direct portions of its R&D. Hershey’s is developing a new product based on crowdsourced data to create and launch a new summer treat. Major retailers are using crowdsourced data to measure, track and refine store operations and promotional compliance in real-time. The list goes on.
Retail is one of the most promising arenas for crowdsourcing. Many brands and retailers spend countless hours and resources trying to measure product placement, employee training and the “human condition” within their stores. Crowdsourcing allows these companies to collect data on the omnichannel health of their stores or product nationwide. Picture competitive analysis, and performance information on product displays, adjacencies, and more, delivered and layered on top of other data streams in real-time to create a 360-degree view of the shopping experience—from the shopper’s perspective.
In-store data is just as important as sales, revenue and other online data collected by a company, but integration is key. By layering data streams, brands and retailers can not only assess the current health of their stores, but also forecast trends for better business decisions in the long-term.
While most companies drown in a sea of ones and zeros, a few have recruited data scientists to turn data into insights, insights into predictive intelligence, and predictive intelligence into prescriptive actions for success. As the sea of data continues to expand, we predict that crowdsourcing will become a necessary tool for data scientists, allowing them to gather and analyze data at scale and in real-time to drive revenue and innovation.
For brands and retailers in particular, embracing the power of crowd is inevitable. The shopper is the common denominator, and crowdsourced data integrated with other data streams is the gateway to the ultimate shopping experience consumers are demanding (and expecting).