When I first knew the concept of big data, it seemed that people thought that this industry was only for large enterprise users. Big data-related technologies are changing with each passing day, but their complexity and implementation cost will also surprise them. Many small business users seem to encounter big data problems, but in the end they find that the implementation cost has already exceeded their tolerance, and even the salary of data analysts can't afford it. However, big data, like many other technologies, is not a "patent" of large enterprises at all.
Larger enterprises often have larger data sets, which makes them easy to become "candidates" in the big data industry. However, the data of these large enterprises are mostly scattered on different systems or platforms, which need more budget and more powerful infrastructure to support them. Of course, there are also data quality problems. However, the data of small-scale companies are usually relatively centralized, which is convenient for unified management. Some growing enterprises even independently develop some information systems to capture and mine data.
If you want to get valuable information from data, you must first have ready-made data, which can even be said to be half the battle. Therefore, growth companies may not have to pay huge big data fees in the early stage. When those large and medium-sized enterprises are busy concentrating their scattered and huge data, the managers of small enterprises have been able to look for business value in the existing data calmly.
In addition, just like enterprise software platforms such as ERP and CRM, big data is rapidly developing towards commercialization. This is the same reason that anyone with a credit card can successfully order an enterprise-level cloud computing software platform or a Fortune 500 it service in just a few minutes. Big data analysis is gradually coming out of the enterprise server room.
The most intuitive embodiment of big data lies in large-scale storage array and in-memory database technology, and the investment of these technologies is usually considered as an inevitable and necessary expenditure in big data management. However, big data is also related to the speed of generation and real-time analysis. Speed is not only related to the performance of technical functions, but also closely related to the speed at which data improves business and the speed at which enterprise managers make decisions based on data analysis.