Large data Industry Ecological Atlas 3.0– Information map

2012, the Firstmark capital Mattturck plotted a large data eco-MAP version 2.0, covering a large number of 38 business models, by the industry as a big data venture investment in the Qingming River map. Two years later today, after a long wait, Turck finally launched a large data eco-map version 3.0. (Bloomberg launched a 2013-version large data ecological map)

In the large data eco-map version 3.0, Turck from the perspective of a risk investors in the two years of large data market, the latest development of in-depth analysis, and the future trend of interpretation, the following is the Turck eyes of the big data market, several of the most critical evolution trend:

Competition intensifies: Entrepreneurs are pouring into the big data market, followed by the VC is also spendthrift, leading to big data start-up market is already very crowded. For example, some entrepreneurial project categories, such as databases (whether NoSQL or newsql), or social media analytics, are now facing consolidation or a bubble (as Twitter buys Bluefin and Gnip, the integration of social analytics has begun)

Although big data startups are already crowded, there is still plenty of room for new startups, and the current stage of innovation in big data infrastructure and analytics tools attracts a lot of money, and of course, such big data start-ups are capital-intensive projects.

The big data market is in its infancy: Although the concept of big data has been sizzling for years, we are still in the early stages of the market, and while companies like drawn and scale have failed in the past few years, quite a few companies have seen the dawn of victory, such as Infochimps, Causata, Streambase, Paraccel, Aspera, Gnip, Bluefinlanbs, BlueKai, etc.

There are plenty of big data start-ups that have grown in size and climate, and have received huge amounts of financing, such as MongoDB has raised 230 million of dollars, PLALANTIR9 billion, and CLOUDERA1 billion. But in the case of successful IPOs or companies, the market is still in its early stages (though there are already successful IPOs such as Splunk and tableau).

In addition, at this stage some traditional it giants have launched a takeover battle, such as Oracle Acquisition BlueKai and IBM acquisition cloudant. In many big data start-ups, startups are still fighting for the position of market leaders.

From the hype back to reality: Although after several years of the hoarse upsurge, the media on large data has some aesthetic fatigue, but this is precisely the big data really landed important stage of the beginning. The next few years are the key period of big data market competition, the enterprise's big data application from the concept verification and experiment to the production environment, which means that the large data manufacturers ' revenue will grow rapidly. Of course, this is also a time to check whether big data really has "great value".

Large data infrastructure: Although Hadoop has established its position as the cornerstone of a large data ecosystem, there are still many competing and alternative products for Hadoop, but these products need time to evolve. Open source framework based on Hadoop distributed File System Spark has recently become a hot topic for discussion because spark can make up for the short boards of Hadoop, such as improving interaction speed and a better programming interface. Fast data (real-time) and memory computing are always the hottest topics in the Big data field. Some new hotspots are also emerging, such as data conversion finishing Tools Trifacta, Paxata and Datatamer.

A key debate is whether corporate data will move to the cloud (public or private), and if so, when? Some cloud-based Hadoop services startups such as qubole and mortar believe that in the long run all enterprise data will eventually move to the cloud.

Large data analysis tools: In terms of entrepreneurial and VC activity, large data analysis is the most active field in the big data market. From spreadsheets to timeline animations to 3D visualization, large data startups offer a wide variety of analytics tools and interfaces, some for data scientists, and some to bypass data scientists directly to business units, because different companies have different preferences for the types of analysis tools, So every start-up company has a chance in its own niche.

Large Data applications: the development of large data applications is relatively slow, but the current stage of large data has indeed entered the application layer. From the large data ecological Map 3.0, we can see that some startups have developed large data common applications, such as large data marketing tools, CRM tools or fraud prevention solutions. And some big data startups have developed vertical applications for industry users. Finance and advertising are the first industries to start large data applications, even before the advent of large data concepts. Future Big data will also be widely used in more industries, such as healthcare, biotechnology (especially genomics) and education.