Data is the new currency, that is an indisputable fact when it comes to customer-centric businesses. In this blog, we are discussing a few data and analytics technology trends that will have significant disruptive potential over the next three to five years. For now, we are keeping aside the fact that the digital world still needs to achieve more heights in the non-technological aspects of data analytics — data literacy and data ethics. Augmented analytics, Commercial AI, Blockchain, NLP conversation, and Continous Intelligence are some of the leading trends in data analytics.
All of them are emerging, scalable and intelligent technologies.
Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. According to Gartner, by 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms.
The availability of insights to all business roles demands data literacy in the organization. Augmented Analytics is capable of optimizing business decision making, and it reduces reliance on data scientists and machine learning experts to understand data. With technical skills in short supply and data growing exponentially, organizations need to automate data management tasks. Machine learning and artificial intelligence (AI) resources are scarce — by making data management processes self-configuring and self-tuning these highly skilled technical staff can focus on higher-value tasks.
Commercial AI and Machine Learning
Open-source platforms are the major service providers in AI-ML based solutions. The algorithms and development environments that the large scale open-source platforms offer have become the ground for innovation in this field. Commercial vendors are bound to step-in at this juncture and offer enterprise-grade solutions to scale AI and ML in large-scale businesses. Enterprises are in high need of automation when it comes to project and model management, reuse, transparency and integration — those capabilities that most open-source platforms lack.
Blockchain in data analytics tackle two challenges, it ensures transparency and provides a lineage of assets and transactions. The data networks are becoming more and more intricate as multiple device users are increasing across the world — especially with IoT based devices gaining a large user base at present. This demand data privacy and asset protection at a higher level — using blockchain. Blockchain-based systems can’t serve as e as a system of record, meaning a huge integration effort involving data, applications and business processes. Realistically, the technology hasn’t yet matured to real-world, production-level scalability for use cases beyond cryptocurrency.
NLP Conversational Analytics
NLP lets businesses to interact with data in an easier way, it lets you ask questions to data and receive an explanation of the insights. Conversational analytics is used to advance NLP by enabling questions to be posed and by answering them using sound instead of text. Gartner predicts- by 2021, NLP and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.
Continuous intelligence is a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events. Real-time intelligence and analytics systems are the future of data — at present, these are available for a limited set of tasks. With the growth in data from sensors in IoT devices and cloud-based infrastructure — advanced data software to predict minimum functions in real-time is the practical implementation of continuous intelligence.
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