Today, our data management capabilities are increasing, but our data analysis capabilities are relatively backward. Despite the tools and processes, there is still a shortage of data scientists.
Early adopters, big data indicate interesting possibility of cross-sectoral development <br> <br> 2012, according to "Forbes" magazine published the article, the early adopters, big data mainly from the financial services, telecommunications, manufacturing (especially It is a consumer product) and a government sector.
Early adopters will use their newly installed big data infrastructure (such as HDFS, MapReduce, and NoSQL databases, etc.) to experiment with new applications in their infancy. According to data collected by Pacific Crest, early adopters often want to use these solutions to process data center log information (including servers, routers, and various IoT sensors) for network analysis and IT system performance monitoring. On this basis, a considerable number of companies have tried to use big data technology to analyze financial data (fraud detection) and Web data (sentiment analysis to achieve personalized experience).
Preliminary experiments are important to understand the benefits, potential, and deficiencies of big data infrastructure. However, according to a report released by CapGemini in 2014, the success rate of water testing experiments is not high. There are three main reasons for failure: 1) Dispersing the data across multiple different teams, so access is more difficult than the original design. 2) Data is placed in legacy systems, making it very difficult to export them to big data infrastructure. 3) Lack of a unified and clear global data management and data analysis solution, which makes it difficult for staff to extract information from the data. With the overcoming of such problems, we have recently found that success stories have become more common. In fact, interest in big data infrastructure and its add-on solutions around the world is rapidly increasing.
Corporate customers continue to invest in start-up companies to bring considerable venture support initiative <br> <br> enterprises showing growth potential to the capital investment in big data technology. According to NewVantage, 27% of respondents said they will invest more than $50 million in big data projects by 2017. In the survey of the same corporate objects in 2014, this ratio was only 5.4%.
At present, the trillion-dollar industry, including health care, insurance, agriculture, energy, medicine, education, automotive, transportation, and logistics, is actively exploring how to use big data tools to solve the real problems they face. For example, automakers want to analyze consumer infotainment options to provide a better in-vehicle infotainment experience, while also analyzing vehicle performance data to provide preventive maintenance advice. In addition, wireless operators also want to understand how consumers use their data to better deliver content and achieve revenue.
Considering the above-mentioned corporate investment and change, venture capitalists are more active in funding big data startups. In 2015 alone, the total amount of funds provided by venture capitalists for big data start-ups was $6.7 billion, up from $6 billion in 2014.
For the same reason, big data startups are beginning to wave a wave of acquisitions. These include AOL's acquisition of Convertro, Google's acquisition of Adometry, Apple's acquisition of Topsy, Teradata's acquisition of Aster Data and Think Big AnalyTIcs, Salesforce's acquisition of Edgespring and more. Recently, we also witnessed Microsoft's acquisition of RevoluTIon AnalyTIcs, HDS acquisition of Pentaho and Advance's acquisition of 1010Data. In addition, small-scale acquisitions have emerged, including Amazon's acquisition of Amiato.
At present, the main focus of VCs is on big data infrastructure and tools. While infrastructure and tool deployments also show strong vitality, here we focus on big data applications.
Early adopters, big data indicate interesting possibility of cross-sectoral development <br> <br> 2012, according to "Forbes" magazine published the article, the early adopters, big data mainly from the financial services, telecommunications, manufacturing (especially It is a consumer product) and a government sector.
Early adopters will use their newly installed big data infrastructure (such as HDFS, MapReduce, and NoSQL databases, etc.) to experiment with new applications in their infancy. According to data collected by Pacific Crest, early adopters often want to use these solutions to process data center log information (including servers, routers, and various IoT sensors) for network analysis and IT system performance monitoring. On this basis, a considerable number of companies have tried to use big data technology to analyze financial data (fraud detection) and Web data (sentiment analysis to achieve personalized experience).
Preliminary experiments are important to understand the benefits, potential, and deficiencies of big data infrastructure. However, according to a report released by CapGemini in 2014, the success rate of water testing experiments is not high. There are three main reasons for failure: 1) Dispersing the data across multiple different teams, so access is more difficult than the original design. 2) Data is placed in legacy systems, making it very difficult to export them to big data infrastructure. 3) Lack of a unified and clear global data management and data analysis solution, which makes it difficult for staff to extract information from the data. With the overcoming of such problems, we have recently found that success stories have become more common. In fact, interest in big data infrastructure and its add-on solutions around the world is rapidly increasing.
Where is the next phase of the rise of big data applications?
Corporate customers continue to invest in start-up companies to bring considerable venture support initiative <br> <br> enterprises showing growth potential to the capital investment in big data technology. According to NewVantage, 27% of respondents said they will invest more than $50 million in big data projects by 2017. In the survey of the same corporate objects in 2014, this ratio was only 5.4%.
At present, the trillion-dollar industry, including health care, insurance, agriculture, energy, medicine, education, automotive, transportation, and logistics, is actively exploring how to use big data tools to solve the real problems they face. For example, automakers want to analyze consumer infotainment options to provide a better in-vehicle infotainment experience, while also analyzing vehicle performance data to provide preventive maintenance advice. In addition, wireless operators also want to understand how consumers use their data to better deliver content and achieve revenue.
Considering the above-mentioned corporate investment and change, venture capitalists are more active in funding big data startups. In 2015 alone, the total amount of funds provided by venture capitalists for big data start-ups was $6.7 billion, up from $6 billion in 2014.
For the same reason, big data startups are beginning to wave a wave of acquisitions. These include AOL's acquisition of Convertro, Google's acquisition of Adometry, Apple's acquisition of Topsy, Teradata's acquisition of Aster Data and Think Big AnalyTIcs, Salesforce's acquisition of Edgespring and more. Recently, we also witnessed Microsoft's acquisition of RevoluTIon AnalyTIcs, HDS acquisition of Pentaho and Advance's acquisition of 1010Data. In addition, small-scale acquisitions have emerged, including Amazon's acquisition of Amiato.
At present, the main focus of VCs is on big data infrastructure and tools. While infrastructure and tool deployments also show strong vitality, here we focus on big data applications.
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