Big Data, IoT, and Machine Learning

Tools and Applications

Rashmi Agrawal, Marcin Paprzycki, Neha Gupta, Rashmi Agrawal, Marcin Paprzycki, Neha Gupta

• Publisher: CRC Press
• Year: 2020
• Print ISBN: 9780367336745
• eBook ISBN: 9781000098303
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools.

This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them.

Features

• Addresses the complete data science technologies workflow
• Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning
• Covers data processing and security solutions in IoT and Big Data applications
• Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems
• Presents security issues and data migration techniques of NoSQL databases

Section II

Big Data, Cloud and Internet of Things

7
Latest Data and Analytics Technology Trends That Will Change Business Perspectives

Kamal Gulati

Contents

7.1 Introduction

7.2 Strategic Planning Assumptions and Analysis

7.3 Driving Factors for Latest Data and Analytics Technology Trends

7.3.1 Trend 1: Augmented Analytics

7.3.1.1 What Does It Enable?

7.3.1.2 Use Cases

7.3.1.3 Recommendations

7.3.2 Trend 2: Augmented Data Management

7.3.2.1 What Does It Enable?

7.3.2.2 How Does This Impact Your Organisation and Skills?

7.3.2.3 Use Cases

7.3.2.4 Recommendations

7.3.3 Trend 3: NLP and Conversational Analytics

7.3.3.1 What Does It Enable?

7.3.3.2 How Does This Impact Your Organisation and Skills?

7.3.3.3 Use Cases

7.3.3.4 Recommendations

7.3.4 Trend 4: Graph Analytics

7.3.4.1 What Does It Enable?

7.3.4.2 How Does This Impact Your Organisation and Skills?

7.3.4.3 Use Cases

7.3.5 Trend 5: Commercial AI/ML Will Dominate the Market over Open Source

7.3.5.1 What Does It Enable?

7.3.5.2 How Does This Impact Your Organisation and Skills?

7.3.5.3 Use Cases

7.3.5.4 Recommendations

7.3.6 Trend 6: Data Fabric

7.3.6.1 What Does It Enable?

7.3.6.2 How Does This Impact Your Organisation and Skills?

7.3.6.3 Use Cases

7.3.6.4 Recommendations

7.3.7 Trend 7: Explainable AI

7.3.7.1 What Does It Enable?

7.3.7.2 How Does This Impact Your Organisation and Skills?

7.3.7.3 Use Cases

7.3.7.4 Recommendations

7.3.8 Trend 8: Blockchain in Data and Analytics

7.3.8.1 What Does It Enable?

7.3.8.2 How Does This Impact Your Organisation and Skills?

7.3.8.3 Use Cases

7.3.8.4 Recommendations

7.3.9 Trend 9: Continuous Intelligence

7.3.9.1 What Does It Enable?

7.3.9.2 How Does This Impact Your Organisation and Skills?

7.3.9.3 Use Cases

7.3.9.4 Recommendations

7.3.10 Trend 10: Persistent Memory Servers

7.3.10.1 What Does It Enable?

7.3.10.2 How Does This Impact Your Organisation and Skills?

7.3.10.3 Use Cases

7.3.10.4 Recommendations

References

7.1 Introduction

This is an era of big data. These data and analytics technology trends will have significant disruptive potential over the next three to five years. Data and analytics leaders must examine the impact on business and adjust their operating, business and strategy models accordingly.

The expanded and strategic role of data and analytics in digital transformation is increasing the complexity of data, the number of variables to analyze and the types of analyses required for success. This is pushing the limits of current capabilities and approaches.

Virtually every aspect of data management, analytics content, application development and sharing of insights uses machine learning (ML) and artificial intelligence (AI) techniques to automate or augment manual tasks, analytic processes and human insight to action.

Intelligent capabilities that enable emergent and agile data fabrics, and explainable, transparent insights and AI at scale, are necessary to meet the new demands and expand adoption.

7.2 Strategic Planning Assumptions and Analysis

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, and of embedded analytics.

Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management. By 2020, 50% of analytical queries will be generated via search, natural language processing or voice, or will be automatically generated.

By 2021, natural language processing 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. The application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more-complex and adaptive data science. By 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial instead of open-source platforms. By 2022, cloud-based ML services from the hyperscale cloud providers (Amazon, Google and Microsoft) will achieve the digital tipping point of a 20% share in the data science platform market.

By 2022, every personalised interaction between users and applications or devices will be adaptive.

Through 2022, custom-made data fabric designs will be deployed primarily as a static infrastructure, forcing organisations into a new wave of “cost to complete” redesigns for more dynamic data mesh approaches. By 2023, over 75% of large organisations will hire artificial intelligence specialists in behavior forensics, privacy and customer trust to reduce brand and reputation risk. By 2021, most permissioned blockchain uses will be replaced by ledger DBMS products. By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. By 2021, persistent memory will represent over 10% of in-memory computing memory GB consumption.

7.3 Driving Factors for Latest Data and Analytics Technology Trends

The expanded and strategic role of data and analytics in digital transformation is increasing the complexity of data, the number of variables to be analysed, the types of analysis and the speed of analysis required for success. With this increasing complexity comes ever more subtle and potentially damaging risks and challenges, such as the potential for bias and the need for transparency and trust in analytics and in ML and AI models.

The size, complexity and distributed nature of data needed for increasingly closer to real-time and optimised decision-making means that rigid architectures and tools are breaking down. This complexity is pushing the limits of current approaches, and is leading to unprecedented cycles of rapid innovation in data and analytics to meet the new requirements. At the same time, to have impact, data and analytics must be pervasive and scale across the enterprise and beyond to customers, partners and to the products themselves. The strategic technologies covered in this research in Figure 7.1 represent trends that you cannot afford to ignore. They have the potential to transform your business and will accelerate in their adoption over the next three to five years.

FIGURE 7.1
Latest data and analytics technology trends.

Many of the trends are interrelated as they are enabled by many of the same technology disruptions, but have an impact on different parts of the data and analytics technology stack (Agrawal, 2020). All have three attributes in common – they support intelligent, emergent data and analytics and are scalable for pervasive AI- and ML-driven insights and agile data-centric architectures:

• Intelligent: Advanced analytics – including AI and ML techniques – are at the core of future platforms, solutions and applications. We see the green shoots of this today. Virtually every aspect of data management, analytic content and application development, and sharing of insights incorporate ML and AI techniques. These are used to automate or augment manua...

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Contents
• Preface
• Acknowledgement
• Editors
• Contributors
• Section I Applications of Machine Learning
• Section II Big Data, Cloud and Internet of Things
• Index