The next step in big data: Grasp the prospects of big data
Due to the rapid development of the Internet of Things and mobile devices, human society has generated 90% of the world's data in the past two years. The cost of data collection, storage and analysis has plummeted.
Today, various industries are relying on data-driven industry insights to gain competitive advantages.
The future prospects of big data are even greater: broadening the horizons of the largest industries and solving some of the world's most complex problems.
From what macro perspective should entrepreneurs and investors grasp the prospects of big data?
The data in this article refer to global and US market conditions, but I believe it has the same reference significance for the Chinese market. The PPT in this article comes from the latest analysis report "The Next Step of Big Data: Grasping the Prospects of Big Data" by the Silicon Valley Bank Analytics team (SVB Analytics), provided by Shanghai Pudong Development Silicon Valley Bank. The text part is explained by NetEase Entrepreneurship Club.
Part One: Data Surge
Due to the significant drop in processing costs and storage costs, and the substantial enhancement of network transmission capabilities, the amount of data generated, processed and collected is increasing exponentially. growth trend.
The demand for data talents has tripled in four years. It shows that there are more business scenarios that require data collection and analysis. This basically coincides with the global popularity trend of mobile terminals that began around 2010. Considering the rise of enterprise-level services, the demand for data talents will be even stronger in the future.
Part 2: Big data business has become the focus of American VCs
Venture investment in big data companies increased from US$1 billion in 2010 to US$5 billion in 2014. The number of transactions increased from 150 to 500 during the year.
Although everyone is beginning to say that B2B is coming, in fact we can see from the data that the amount of investment in big data analysis companies by the US venture capital community has increased by about 17 times in the past five years. The amount of investment in B2B service companies has only increased three times.
Of course, due to the large volume of venture capital investment in US B2B services, this is not a particularly direct comparison.
However, this can also reflect the development momentum of big data business from one aspect.
In different financing stages represented by different financing scales, the valuation levels of big data companies are significantly higher than the average valuation of technology companies.
This shows that investors are very optimistic about the big data field and can tolerate higher entry prices.
It should be noted that the valuations of big data companies at all financing stages are higher than the average valuation of technology companies.
Part 3: Big Data 2.0, a larger funnel model
The picture shows a funnel model. I believe in products, operations, sales, and strategy. The students are no stranger to this.
As IoT (Internet of Things) gradually becomes a reality, the data sources of vulnerability entrances are and will show explosive growth.
The rapid development of physical hardware performance and computing power has greatly reduced the cost of data collection, storage and processing, and greatly improved the data processing methods and speed. This has led to unimaginable changes in the amount and type of data that can be processed. Growth and mutation.
Due to the above-mentioned series of capability improvement backgrounds, the scope and application scenarios of data analysis in "traditional" industries have become more diverse, and the value of analysis is also increasing.
Examples of big data application industries: retail, network security, advertising, financial services, agriculture, tourism and accommodation, medical health, energy, and financial services.
It can be seen that the industries where big data can be applied cover many or even all important fields of 2B and 2C.
As examples of usage scenarios, Silicon Valley Bank cited three examples here: precise advertising delivery, online fraud security, and sensor-operation optimization. We can already see big data and SaaS service startups in China that are outstanding in several aspects.
Part 4: Cross-industry application of big data, where are the opportunities for venture capital investment?
Silicon Valley Bank has calculated the maturity index of big data in three dimensions for different industries.
The three dimensions are: the degree of data supervision; the difficulty of data capture; and the degree of technology integration.
The first two dimensions reflect the richness and depth of data sources. If they are too difficult, their application will be limited.
For large-scale industries, the lower the maturity of current big data applications, the greater the room for future development.
Relatively mature markets:
In comparison, the network security, advertising, and tourism accommodation industries are "smaller" markets (200-300 billion U.S. dollars). The data penetration rate is relatively high.
As online retail has been developing for many years, the retail industry is a huge market (US$900 billion) with sophisticated big data analysis accumulation.
More potential markets:
Although agriculture is a "small market", it is still in a relatively early stage due to the difficulty of data collection and limitations of analysis technology.
Big markets such as financial services and medical care are obviously big data application markets that everyone will pay attention to. However, due to the strong supervision of data and the difficulty of obtaining data, it is still a big data market that is far from perfect.
Here, the more mature early-stage big data companies in the advertising industry are getting less and less favored by venture capital, while early-stage big data companies in the medical and health category are beginning to get more venture capital.
This trend is closely related to the maturity of big data applications in various industries.
When considering trends, venture capital investors will pay close attention to whether the potential development space is large enough and whether the constraints can be solved.
Part 5: Summary, cloud and machine learning are the future of big data
The so-called "cloud" depends on whether the big data company's cloud can put target customers in the public cloud The data on the Internet are linked together to form an ecosystem.
The so-called "machine learning" depends on whether the machine analysis capabilities of big data companies will become more insightful as the amount and type of data increase and hardware performance improves.
The above is what the editor has shared with you about the next step in big data and grasping the prospects of big data. For more information, you can follow Global Ivy to share more information