Our employees strive every day to make a difference and we see for real that our strong relationships do make an important difference. So we want to introduce you to some of our employees. In a series of podcasts, you will get to know a little more about what drives our people to achieve, accomplish and accelerate!

#Arcgatian Prashant:

New York in known for being the top choice as headquarters for many of the most successful companies in the world and is rapidly gaining traction as a tech hub. And Techstars’ NYC program, which is part of the Techstars’ global ecosystem and aims to empower entrepreneurs to bring new technologies to market, produced these significantly successful startups. 

Real estate is not behind in adapting to modern technology. But there were times when the tools to properly analyze and draw relevant conclusions from data were reserved for bigger real estate companies and investments firms. However, thanks to the boom in personal computing (mobile, tablets, wearables etc.) anybody can find information on a property within a few minutes. Not only that, today mining listing of homes for sale and rent, geographic information systems data, results of consumer surveys, census information etc. and then further analyzing this enormous swathes of information – much of it aggregated from disparate places and formats can reveal actionable trends, patterns and associations when analyzed computationally. It’s time for real estate agents to start applying the insights big data provides to enhance business, attract more buyers and sellers and generate more leads. 

Every restaurant, supermarket, convenience store, food truck, cafeteria, manufacturer, distributor – every food business – generates a huge amount of data. The data generated is in form of customers’ orders, delivery locations, social media engagements, loyalty card usage, product inventory, average wait times, experience with delivery, taste of the food, menu availability etc. And as the on-demand marketplace grows, along with advancements in technology, the food industry will have to adapt big data analytics and technology effectively in order to stay competitive, enhance their market reach, increase customer satisfaction and provide better services. 

As one of the most active startups communities in the entire US, Austin has entrepreneurs coming to the city every day to try and make their entrepreneurial dreams come true. And Techstars’ Austin program, which is part of the Techstars’ global ecosystem and aims to empower entrepreneurs to bring new technologies to market, produced these significantly successful startups.

The global online food delivery market is expected to increase due to increase in urban population, increase in spending of global middle class population, increasing technological innovations, increase in smartphone usage, increase in internet penetration, etc. 

As more and more advertising technology companies are jumping on the bandwagon of all things programmatic; programmatic methods such as real-time bidding are becoming immensely popular. It is well-known fact in the ad-tech space that if the rate of supply growth is higher than the rate of demand growth, then the ad prices go down and display inventory becomes a commodity. Thus, by attaching data to inventory – publishers and other players can deliver better content in their ads, helping advertisers reach their customers more precisely, and customers themselves enjoy a user experience tailored to them. However, truly meaningful data in not valuable in quantity alone; it should be enriched, analyzed and combined with other sources such as location, behavioral and senor-related data. The virtual reality and augmented reality will also make it possible to get the most out of mobile advertising – as it is location based, active, interactive and social in one. Whether it’s desktop, tablets or mobile, consumer ad blindness is becoming an ever greater challenge and thus native ads have now firmly established themselves as an alternative and will continue their triumph with new formats. 

The sharing economy has been disrupting and transforming industries, with companies like Uber disrupting the taxi industry and Airbnb disrupting the hospitality industry. The sharing economy has shown tremendous value in delivering incremental revenues for asset owners and improving the options for customers. In fact this collaborative creation, distribution and consumption of peer-to-peer resources have become a defining part of the consumer markets. Sharing economy actually has a shot at disrupting any industry in which products can be shared for use. If we think about it the industries already disrupted by sharing economy can said to be equipment rentals, hostels and bed and breakfast, book rentals, car rentals, DVD rentals etc. And those are just a few, there are probably hundreds of industries, both big and small, that the sharing economy could into to disrupt and probably will. 

Sharing is caring – or is it? Airbnb and Uber have engaged private house and cars in collaborative consumption and made it available to many. Technology has redefined the concept of sharing and renting. A sizable sharing economy is opening up on apps and mobile allowing users to pick and share a mind boggling array of stuff. We look at some stories from around the web on what’s happening in the sharing economy space as almost everyone’s onto sharing these days – and from bikes to sugar – there seems to a huge array of shareable good services out there. Hear it from us on few such startups taking advantage of the rise of sharing economy. 

AI is fascinating and life-changing on its own. And big data, too, has incredible potential power on-its-own. But together, these are poised to change many industries and businesses forever. As the barrier to implementing these technologies disappear, more and more industries will put AI technologies into use; and more and more AI startups will emerge with new ideas and a lot of funding. However, big data is essential to AI’s success. AI itself doesn’t reason and deduce the way human minds do. Instead it learns through trial and error. That’s why having large amounts of data is more important than ever; and the more accurate data AI is trained on, the more accurate it will become.