Advanced anti DDoS server big data technology: outsourcing or outsourcing
For retailers, big data is a double-edged sword. These companies are trying to explore comprehensive market competition because they are trying to resist industry giants like Amazon. Some companies are deploying a lot of resources to develop their own big data solutions, Advanced anti DDoS server Try to compete with retail giants.
Big data technology
One of the problems faced by retailers is whether they need to build internally or should outsource to suppliers.
With the popularity of the software as a service (SaaS) model, it becomes easier and easier to deploy new solutions in enterprise environments. This will naturally lead to the continuous growth of innovation in the industry, because traditional solutions can easily become more innovative and effective solutions to replace them in just a few weeks.
At the same time, large retailers hope to develop solutions based on the company's wishes. For example, Amazon has invested a lot of money in internal technology and developed many products. However, it is important to realize that not all products and solutions can or should be built internally. Retailers should regard infrastructure as a data platform, and suppliers should innovate in the same way. MAC and Android platforms allow individual developers to innovate through applications
It is believed that cloud computing algorithm will become the most common SaaS application in the next few years. As the "core competitiveness" and its development is limited to the internal retail team, this algorithm will only stifle technological innovation and will fall behind in the long run. List the reasons here
cost
Great algorithm solutions need core talents. The competition for these talents is very fierce, especially in data science. Data scientists usually have doctorates. Computer science, statistics or mathematics, salary more than 150000 dollars.
Due to the limited supply of excellent engineers and data scientists in the market, these engineers are more likely to apply for start-ups or technical positions, such as Amazon, Google and Facebook. Unfortunately, most physical and online retailers are not destinations for top engineers. Therefore, retailers must make up for higher salaries.
A simple mathematical calculation shows that a team of 20 data scientists and engineers can cost retailers $4 million per year. This is just the cost of recruiting talent, not including investment in any infrastructure that supports solution development. In contrast, the annual cost of a typical SaaS solution will be less than $1 million (this may be the absolute upper limit, and the traditional cost will be less than $500000). By cooperating with suppliers, retailers can save a lot of money.
Fast time to market and flexibility
For any technology startup company, the rapid release of the market is the key to determine the overall success. This includes the development of internal technology. It may take 2-3 years from the beginning of the project to successfully create a large data solution. Although the immediate need for solutions is an urgent problem, the technology life cycle cannot be bypassed. Two years of waiting time may lead to one or two problems: the newly developed solutions of the company are outdated when they are launched, or they are trying to lead the rapid development of the technical environment to an endless redesign cycle.
At the same time, with the wide application of cloud SaaS mode, the integration and deployment of third-party solutions have never been so fast. Some can be integrated and deployed in just 20 days, which means that cutting-edge technologies are constantly improved (algorithms are constantly optimized and adjusted in the world's largest retailers) to meet immediate needs. More importantly, third-party manufacturers also provide internal systems without flexibility. Deleting and replacing third-party SaaS solutions is very simple, and there is no need to worry about high costs and internal struggles.
innovation
The technology and algorithm have made rapid progress. Throughout history, competition has played a vital role in innovation. SaaS mode makes deployment easy and easy to replace. Therefore, suppliers are constantly innovating and facing the pressure of improvement. When there is an internal team, the choice is made, so there is no competition. Once the solution is built and deployed, the team's goal is to maintain and improve the solution. However, people will never know whether the internal team's solution is competitive.
By cooperating with third-party SaaS providers, retailers can evaluate and deploy many cutting-edge solutions in a short time with less investment. Many other retailers are using these solutions, and suppliers get customer innovation and improvement through continuous review. Trying to build these solutions internally is not only costly, but also slow. But the most important thing is to limit innovation, so that enterprises are not so flexible in the long run.
This does not mean that retailers should completely outsource all technology to suppliers. When people are there in the context of big data talks about technology, they involve infrastructure for storing and processing data, as well as interpreting data and making predictions. The infrastructure includes storing a full range of customer data in a secure, privacy protected way, such as purchasing coupons and enabling applications to access data.
The algorithm is an effective application of infrastructure, which uses data to forecast demand, forecast losses, dynamic pricing or product personalization and positioning. They are built on data, the same as on the operating system. Therefore, retailers must invest in internal resources and spend time to establish a secure, efficient and scalable infrastructure.
The right infrastructure with external APIs and security (encryption of sensitive data) will enable companies to innovate using suppliers' cutting-edge technologies. This will enable companies to focus their attention and expertise on core business functions rather than trying to become experts in unrelated fields. For any business, capital, time and R&D capacity are limited. Successful companies know how to put these resources in the right place to succeed.
