Being part of the industry with responsibility to deliver a massive platform/product which is empowered by the latest technologies, It helps to envision and recognize some patterns or trends well in advance which would become mainstream in coming days.
In this post, I will note some of the Data and Analytics trends which are spread across industries and technologies.
According to Gartner, expanding DataOps to XOps (data, ML, model, platform), which facilitates collaboration between data science, machine learning, AI governance, and AI platform management, would be a more effective approach of dealing with the problem.
Businesses may achieve more comprehensive end-to-end AI automation by merging all of these processes, from edge computing and data architecture through AI management.
Data as a Service and Data as a Product
Software as a Service (SaaS) on cloud is what ruling the new age companies and in coming days DaaS (Data as a Service) will pick up the traction and any application or service would request for data on the fly and would be catered or customized to their needs.
Similarly Data as a Product would be really sought of as the raw data in various formats cannot be consumed as it is and would require whole lot of transformations which can be customized based on various filters so that the end data product would be of production ready quality for usage.
Quantum Computing and Edge Computing
Processing a large volume of data with present technology can take a long time. Quantum computers, on the other hand, calculate the probability of an object's state or occurrence before measuring it, implying that they can process more data than conventional computers. We can drastically cut processing time by compressing billions of data at once in only a few minutes, allowing enterprises to make more rapid decisions and achieve more desired outcomes. Quantum computing may be able to help in this procedure. Quantum computers being used to rectify functional and analytical research across multiple firms could make the sector more exact.
Edge Processing is the process of running processes on a local system, such as a user's computer, an IoT device, or a server. Edge computing moves computation to the network's edge, reducing the amount of long-distance communication required between a consumer and a server, making it one of the most recent big data analytics developments. It improves Data Streaming, including real-time data Streaming and processing with low latency. It enables the devices to respond very quickly. Edge computing is a low-cost method of processing huge amounts of data while consuming minimal bandwidth. It can assist a company in lowering development costs and enabling software to run in remote locations.
Predictive Analysis and NLP
Big data analytics has always been a critical component of a company's strategy for gaining a competitive advantage and achieving its objectives. They employ fundamental analytics tools to prepare massive data and figure out what's causing specific problems. Predictive methods are used to study current data and historical events in order to better understand customers and identify potential threats and events for a company. Big data predictive analytics can foresee what will happen in the future. This method is quite effective at correcting studied data and predicting customer response.This allows businesses to outline the steps they need to take by predicting a customer's next move before they take it.
Natural Language Processing (NLP) is a branch of artificial intelligence that aims to improve human-computer communication. NLP's goal is to read and decode the meaning of human language. Natural language processing is a type of machine learning that is used to create word processors and translation software. Algorithms are needed in Natural Language Processing Techniques to recognize and extract the required data from each sentence using grammatical rules. Syntactic and semantic analysis are the most common approaches used in natural language processing. Syntactic analysis deals with sentences and grammatical difficulties, whereas semantic analysis deals with the data.
With orchestration between two interfaces, a cloud computing system uses an on-premises private cloud and a third-party public cloud. By shifting operations across private and public clouds, hybrid cloud provides exceptional flexibility and more data deployment possibilities. To achieve flexibility with the targeted public cloud, an enterprise must have a private cloud. It will need to build a data centre, which will include servers, storage, a LAN, and a load balancer. To support the VMs and containers, the company must deploy a virtualization layer/hypervisor along with installation of private cloud.
Deep Reinforcement Learning
With the Digital IT Transformation every industry can solve their mundane routine patterns or process related issues through various software but still critical decisions which are to be made for various flow of the process remained a bottleneck and Machine Learning in last decade has helped to overcome it with enormous amount of data what we have at our disposal. So, the major area of concern in industry is more to do with Strategy or Policies which would determine the fate of the product which are to be launched where the major stakeholders would be in top of the pyramid like CEO/CTO/CXO. So, these are inversely related to the budget or cost involved i.e., to strategize and coming up with a policy is major differentiator in the industry which involves topmost people is a costly affair and then comes the decisions for various process flows of the business where the cost is moderate and finally the implementation of the process themselves with help of bunch of software which would be less relatively. Now that we have support system with Software and Machine Learning to address the process and decisions issue, industry is betting on ‘Reinforcement Learning’ to fill the void in the strategy space.
Below operation vs value diagram shows the same ( Image Ideation Courtesy: winderresearch.com)
Along with these keep an eye on Quantum Computing, Internet of Things(IoT), Augmented Reality which shapes the usage of the Big Data and Analytics in coming days.
Do leave your comments about how you envision future in data economy perspective.
Do Read Gartner Report for more details