4 Hot Startups Using Big Data in Local Contexts

data-funnel-150In the data industry, sometimes its good to be small. Innovations in cloud computing have reduced the cost and expertise needed to work with “big data,” opening the door for entrepreneurs to turn the deluge of data streaming from our devices into the next big thing.

Data is at the core of innovation in the local technology industry and a new batch of startups are using local data to help consumer better understand the world around them and business more efficiently run their businesses in the real world.

At the Street Fight’s Local Data Summit in Denver next week, we will take a look at how technology companies, both big and small, are creating new ways for marketers and consumers to connect locally. These four startups, who will be speaking at the event, are using local data to reinvent the way we travel, market and even park.

Who uses a travel agent anymore? These days, searching, booking and arranging a trip is easy enough that paying someone to do it for you doesn’t make sense for most people. But what if that person was a machine? Native has developed an application that uses machine learning to create an automated travel assistant that can do everything from book a ticket to find a sushi place nearby. Text a request, and within a few minutes Native will have an answer.

Parking is one of the most notorious information problems in big cities. Thousands of spots whose occupancy is constantly changing means that filling every spot is nearly impossible. To wit: Parkifi has developed a system that uses sensors to help parking lot operators know which spots are open in real-time without having to send employees to search blindly. Better information could help reduce the time it takes for valets to parks cars, and in doing so could cut the cost of parking lots as a whole.

These days, every business is a technology company. And Hyprloco, a Denver-based startup, wants to help businesses build the type of location intelligence used by Google and other tech giants into its own applications. The year-old company has developed an API that allows developers to integrate often-complex location services (such as turn-by-turn navigation and asset tracking) into their own software. That means a retailer could turn the dwell data collected by a third-party into a heat map that store employees can use to manage peak times.

Online, brands have one critical advantage over small businesses: a marketing department. The rate of innovation in digital marketing technology has left small business struggling to understand where to invest, keeping a lid on a huge portion of the advertising market. Enter Choozle, a Denver-based startup that has developed a system that automates the digital marketing process for small businesses. The company has developed software the analyzes a businesses web visits data, and then creates custom audiences identifying key customer segments businesses can target with ads.

Learn more about these local data startups next week at Street Fight’s Local Data Summit on March 5th in Denver — click here for more info and tickets.

  1. February 25, 2015

    Hey guys! Great post. Just a head’s up though…

    The link to Choozle is set to http://choosle.com/ instead of http://choozle.com.

  2. Ilya Geller
    March 27, 2015

    There is no Big Data. Language has its
    own Internal parsing, indexing and statistics. For instance, there are two

    a) ‘Fire!’

    b) ‘In this amazing city of Rome some people sometimes may cry in agony:

    Evidently, that the phrase ‘Fire!’ has different importance into both
    sentences, in regard to extra information in both. This distinction is
    reflected as the phrase weights: the first has 1, the second –0.12; the greater
    weight signifies stronger emotional ‘acuteness’.

    First you need to parse obtaining phrases from clauses, for sentences and
    paragraphs. Next, you calculate Internal statistics, weights; where the weight
    refers to the frequency that a context phrase occurs in relation to other
    context phrases.

    After that, you index each word from each phrase by dictionary, annotate it by

    These startups are obsolete

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