

It’s no secret that the growth of publicly available data, or open data, is more prevalent than ever. From government databases, such as those aggregated by Data.gov, to data made available via tools such as Google Places API, the public has access to an overwhelming amount of information.
And this doesn’t even account for the large number of unstructured data sets that emerge from social networks, search engines and so on. In an age where 6,000 tweets are posted per second, people are constantly serving up information on themselves.
Open data can make life easier in many ways, but it isn’t just about commuting home on today’s traffic-optimized route while stopping by “the right restaurant at the right time” to pick up dinner. Organizations around the globe, including big players like Uber and Airbnb, are leveraging all this data to inform their business strategies.
In fact, according to a recent study from Accenture, 89 percent of business executives cited Big Data as a highly important component in bringing their companies into the digital age. While data can help an organization better engage its target customer base, the challenge becomes how to effectively navigate the vast sea of information to determine what is useful and what is not — an often time-consuming and costly task.
Many organizations don’t know how to identify and extract the value embedded within the heaps of data available to them. Consequently, they find themselves stuck in the “data hoarding” trap — capturing every available piece of information rather than focusing on what can provide the best business value.
In navigating the Big Data landscape, and combating the pitfalls of data overload, the companies that are able to find the most success are those that adopt a Deep Data framework. This framework focuses on identifying and maximizing a small number of information-rich data streams rather than analyzing vast volumes of stats and figures.
When presented with a bewildering array of numbers and statistics, it can often be a company’s first instinct to gather as much information as possible and figure out the purpose of it all later.
Instead, by starting with the goal of what they want to gain from the data, companies can better determine which data will lead to better personalized content to reach key audiences and offer the most value.
One industry being transformed by smart use of public data is transportation. Transportation apps, including Uber, use a number of public record sources, like street and traffic data, to inform their algorithms and business models. However, many government and public databases can often be overwhelming in terms of the amount of information available.
For example, Data.gov includes more than 135,000 datasets from more than 75 government agencies — much of which would be irrelevant to Uber. To retrieve valuable insights, Uber has to know exactly what it’s looking for going into the evaluation process.
For that organization, information on traffic, customer demographics and crime rates can help build a business strategy when entering a new market,as well as allow for a more personalized approach to recruiting customers to their service.
Once a company has gathered the most relevant data streams to meet its business goals, it must factor in time. Businesses need to ensure the data they are reviewing fits within time parameters, and makes logical sense within their business model. This is especially true if a company is trying to use historical data for predictive analytics. For history to offer valuable predictive information, organizations need to ensure the timing aligns.
In the real estate sector, up-to-date information is crucial for ensuring consumer trust. Sites like Trulia need data to be in near real-time. If, for instance, a consumer wants to view the value of homes in his or her neighborhood, Trulia can display the recent sale prices of homes rather than values from two or three years ago. Similarly, in the energy sector, companies are using real-time information, including weather and mapping data.
If a customer were looking for advice from their utility company on how they can save on energy costs this summer, it wouldn’t make sense to look at weather patterns for the last three months. Instead, the utility needs to assess weather patterns from the previous summer to properly evaluate and provide an actionable savings recommendation.
Public data is also a valuable resource for businesses when positioning themselves in the market. By zeroing in on competitor information — both what is available in the public domain and what customers and prospects are sharing on public forums — businesses can hone in on valuable data such as customer satisfaction, partner relations and so on, to make sure they are stacking up, while simultaneously looking for opportunities to get ahead.
For a small business such as a deli or pizza shop, public sources like Yelp and Twitter can provide relevant information to help gauge what competitors in the area are doing — from hours of operation, to promotions and even general customer satisfaction.
Larger businesses are also able to leverage open data to better position themselves both in general and against the competition — sites like TripAdvisor and Amazon that aggregate user information can provide a snapshot into the marketplace. By analyzing customer action, user reviews and competitive positioning, companies can better shape business strategies when targeting specific consumers and markets.
In today’s Big Data era, businesses are using public data to get personal with consumers in more ways than ever before. And the companies that are using data successfully are the ones looking for strategic ways to leverage specific data sets, rather than amassing the maximum amount of data.
This strategic approach lets companies effectively leverage publicly available data to improve their businesses and better target consumers.
