AI and IoT are still popular, but they still rely on big data analysis

Today's big data analysis market is completely different from the market a few years ago. It is precisely because of the explosion of massive data that all industries around the world will undergo changes, innovations and disruptions in the next decade.

According to a recent market research report released by Wikibon, the global big data analysis market grew by 24.5% in 2017 compared to the previous year, mainly due to stronger-than-expected deployment and application of public clouds, as well as accelerated integration of platforms, tools and other solutions. In addition, many companies are using big data analysis to get out of the experimental and verification phase faster and gain higher business value from deployment.

Looking ahead, Wikibon predicts that the overall big data analytics market will grow at an annual rate of 11% in 2027, reaching $103 billion globally. The main market comes from the use of big data analytics technology in the Internet of Things, mobile terminals, and edge computing.

The development trend of big data analysis in the next ten years

As confirmed by Wikibon’s research, the key trends that will drive the big data analytics industry over the next decade are as follows:

1. Public cloud vendors are expanding their influence. The big data industry is centered around three major public cloud vendors, AWS, Microsoft Azure, and Google Cloud Platform, and most software vendors are building solutions that can run on these platforms. In addition, database vendors are providing hosted IaaS and PaaS data lakes, encouraging customers and partners to develop new applications and migrate old applications to them. As a result, pure data platform, NoSQL vendors are gradually marginalized in the big data field of increasingly diversified public cloud vendors.
2. The advantages of public cloud over private cloud continue to expand. Public cloud is gradually becoming the preferred big data analytics platform for the customer base. This is because public cloud solutions are more mature than on-premises stacks, adding richer features and increasing costs. In addition, public clouds are increasing their application programming interface ecosystem and accelerating the development of management tools.
3. Accelerate integration to realize business value for enterprises. Users are beginning to accelerate the integration of isolated big data assets into public clouds. Public cloud vendors are also optimizing cross-business silos that plague private big data architectures. Equally important, cloud data and local data solutions are merging into integrated products designed to reduce complexity and accelerate business value. More solution providers are providing standardized APIs to simplify access, accelerate development, and enable more comprehensive management across the entire big data solution stack.
4. Big data startups are bringing increasingly complex AI applications to market. In the past few years, many new database, stream processing and data startups have joined the market. Many companies have also begun to join the market competition through AI solutions. Most of these innovative solutions are designed for public cloud or hybrid cloud deployment.

Emerging solutions are gradually replacing traditional methods. More and more big data platform vendors will emerge with next-generation methods that integrate IoT, blockchain, and stream computing. These big data platforms are mainly optimized for end-to-end devops management of machine learning, deep learning, and artificial intelligence management. In addition, many big data analysis platforms are designing edge devices for AI microservice architecture.

• Hadoop is here to stay. Today, there are more signs that the market views Hadoop as a traditional big data technology rather than a strategic platform for disruptive business applications. However, as a mature technology, Hadoop is widely used for key use cases in users' IT organizations and still has a long life in many organizations. With this prospect in mind, vendors continue to improve product performance by enabling smoother interoperability between independently developed hardware and software components.
• Packaged big data analytics applications are becoming more widespread. In the next decade, more services will automatically adjust their embedded machine learning, deep learning, and AI models to continuously provide the best business results. These services will include pre-trained models that customers can adjust and extend to their specific needs.

Big Data Analytics Deployment Barriers

While the predictions made possible by big data analytics look promising, there are still many obstacles:

• Too much complexity. Big data analytics environments and applications are still too complex. Therefore, vendors need to continue to simplify these environment interfaces, architectures, functions, and tools to make complex big data analytics capabilities available to mainstream users and developers.
• Costly and inefficient. For many IT professionals, big data analytics management and governance processes are still too siloed, costly, and inefficient. Vendors need to build pre-packaged processes that help large teams of professionals manage data and analytics more effectively, quickly, and preparedly.
• Lack of automation capabilities. The development and operation of big data analytics applications is still too time-consuming and manual. Vendors need to strengthen their automation capabilities to ensure that the productivity of user technicians is improved while ensuring that even low-skilled personnel can handle complex business.

The era of big data has arrived and has gradually penetrated into various industries. For enterprise IT, Wikibon's main suggestion is to start migrating more big data analysis and development work to public cloud environments, which will also accelerate the ability of cloud vendors such as AWS, Microsoft, and Google to provide fast-maturing and low-cost products.