Today's big data analytics market is very different from the market a few years ago. It is precisely because of the massive increase in data that there will be changes, innovations and subversives in all industries around the world in the next decade.
According to a recent market research report released by Wikibon Analytics, the global big data analytics 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 platforms, tools and other solutions. The integration of the program. In addition, many companies are moving away from the experimental and validation phases through big data analytics and gaining higher business value from deployment.
Looking ahead, Wikibon predicts that the overall growth rate of the big data analytics market in 2027 will grow at an annual rate of 11%, reaching $103 billion worldwide. Its main market is derived from the use of big data analytics in the Internet of Things, mobile and edge computing.
The development trend of big data analysis in the next decadeAs the Wikibon Institute confirms, the main trends that will drive the big data analytics industry over the next decade are as follows:
Public cloud providers are expanding their influence. The big data industry is revolving around three major public cloud providers, AWS, Microsoft Azure and Google Cloud, and most software vendors are building solutions that can run on these platforms. In addition, database vendors are offering hosted IaaS and PaaS data lakes, encouraging customers and partners to develop new applications and migrate them to older applications. As a result, pure data platforms and NoSQL vendors are increasingly marginalized in the large data domains of increasingly diversified public cloud providers.
The advantages of public clouds over private clouds continue to expand. Public cloud is gradually becoming the preferred big data analysis platform for the customer base. This is because public cloud solutions are more mature than on-premises stacks, adding more features and increasing costs. In addition, public clouds are increasing their application-level programming interface ecosystem and speeding up the development of management tools.
Accelerate convergence to achieve business value in the enterprise. Users are beginning to accelerate the convergence 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 being integrated into integrated products 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.
Big data startups are bringing more and more sophisticated AI attention applications to market. Over the past few years, many new databases, 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 innovations are designed for public or hybrid cloud deployments.
Emerging solutions are gradually replacing traditional methods. More and more big data platform vendors will emerge the next generation of methods for converging the Internet of Things, blockchain and stream computing. These big data platforms are optimized for end-to-end devops management for machine learning, deep learning, and artificial intelligence management. In addition, many big data analytics platforms are designing edge devices for the AI ​​microservices architecture.
Hadoop status does not stand. Hadoop now has more indications that the market sees 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 in key use cases for users' IT organizations and still has a long life in many organizations. With this prospect in mind, vendors are continually improving product performance by achieving 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 continue to deliver the best business results. These services will be incorporated into a pre-trained model where customers can adjust and expand to their specific needs.
Deployment barriers for big data analyticsWhile the predictions used by big data analytics look good, there are still many obstacles:
The complexity is too high. Big data analytics environments and applications are still too complex. Therefore, vendors need to continue to simplify these environmental interfaces, architectures, functions, and tools. To apply sophisticated big data analytics to mainstream users and developers.
Costly and inefficient. For many IT professionals, big data analytics management and governance are still too isolated, costly, and inefficient. Suppliers need to build pre-packaged processes to help large professional teams manage data and analytics more efficiently, quickly, and prepared.
Lack of automation. The development and operation of big data analytics applications is still too time consuming and requires manual. Suppliers need to enhance their automation capabilities to ensure increased productivity for their technicians while ensuring that even low-skilled people can handle complex businesses.
For enterprise IT, Wikibon's main recommendation is to start migrating more big data analytics development work to the public cloud environment, which will also accelerate the ability of fast, mature and low-cost products provided by cloud vendors such as AWS, Microsoft, and Google. .
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