Big Data, AI and Machine Learning: Fueling the Future

Introduction

The fields of artificial intelligence (AI), machine learning (ML) and big data have seen explosive growth in recent years. These technologies are transforming industries, driving innovation and efficiency, and creating huge opportunities for businesses and professionals alike. In this comprehensive report, we‘ll take a deep dive into the world of big data and explore how it intersects with and enables AI and ML. We‘ll look at the latest trends, key players, high-impact use cases, and the skills needed to succeed in this dynamic space.

Understanding the Basics

Before we jump in, let‘s define some key terms:

Artificial Intelligence: AI refers to the development of computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Machine Learning: ML is a subset of AI that involves training computer systems to learn and improve from experience without being explicitly programmed. ML algorithms build mathematical models based on sample data in order to make predictions or decisions.

Big Data: Big data refers to extremely large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume, variety and velocity of data being generated today. Big data includes structured data like databases as well as unstructured data like social media posts, sensor readings, images, and video.

While AI, ML and big data are distinct concepts, they are closely intertwined. ML is one of the most important and widely used branches of AI today. And ML relies heavily on big data – the enormous volumes of raw information that can be mined for insights and used to train sophisticated models. Big data is the fuel that powers modern AI and ML.

The Big Data Boom

The big data industry has seen massive growth in recent years and shows no signs of slowing down. According to market research firm IDC, global revenues for big data and analytics products and services are forecasted to reach $274.3 billion by 2022, with a compound annual growth rate of 13.2%.

Companies are heavily investing in big data infrastructure and initiatives:

• Banking giant JPMorgan Chase has over 150 petabytes of data from sources like transactions, log files and market data.
• Retail behemoth Walmart handles over 2.5 petabytes of data every hour from its customer transactions.
• Social media powerhouse Facebook ingests over 500 terabytes of new data each day.

On the jobs front, a 2020 survey from Forrester found that 72% of companies are planning to hire data scientists and 69% are planning to hire data engineers in the coming year. Job site Indeed currently lists over 70,000 open big data jobs in the US alone. And per Glassdoor, the average data scientist makes over $113,000 per year, with top earners in the $150,000+ range.

The Big Data Toolbox

Processing, analyzing and deriving value from the massive volumes of big data being generated requires a powerful set of tools and technologies. Over the past decade, the big data ecosystem has rapidly evolved and matured. Some key components of the modern big data stack include:

Distributed Storage and Processing: Systems like Apache Hadoop allow large datasets to be stored and processed across clusters of commodity servers, providing cost-effective scalability. Hadoop is an open source framework managed by the Apache Software Foundation.

Real-time Processing: Platforms like Apache Spark and Apache Flink enable real-time and near real-time processing of streaming big data. Spark is known for its speed, ease of use and versatility for handling both batch and real-time workloads.

NoSQL Databases: Unlike traditional relational databases, NoSQL databases are designed to store and manage unstructured, semi-structured and highly variable data at scale. Popular options include MongoDB, Cassandra, HBase and Couchbase.

Data Warehouses: Enterprise data warehouses (EDWs) aggregate data from multiple sources into a central repository for reporting and analysis. Cloud-based data warehouses like Snowflake, Amazon Redshift, Google BigQuery and Microsoft Azure Synapse can elastically scale to petabytes.

Data Lakes: Data lakes serve as storage repositories for raw, unstructured and semi-structured data. This data is stored in its original format and can be processed later as needed. Amazon S3, Azure Data Lake and Google Cloud Storage are popular choices.

SQL Engines: SQL remains the lingua franca for working with structured and semi-structured data. Engines like Apache Hive, Apache Impala, Presto and Apache Drill provide a SQL interface for querying big data.

Business Intelligence & Visualization: BI and data visualization tools enable data exploration, dashboarding and reporting to derive business insights from big data. Tableau, Looker, Microsoft Power BI and ThoughtSpot are leading platforms.

Big Data Powers AI & ML

Big data and AI/ML enjoy a symbiotic relationship. The proliferation of big data has been a key enabler for the recent rapid advancements in AI and ML. Training ML models – especially large deep learning models – requires vast amounts of labeled data. The more quality data available to train a model, the better it performs.

At the same time, AI and ML are also being used to enhance big data platforms and tools. For example, database vendors like Oracle are integrating ML capabilities into their products to enable self-tuning, autonomous operation. And big data processing engines like Spark now support distributed ML libraries for running machine learning at scale.

Some interesting examples of big data and ML in action:

• American Express handles over $1 trillion in transactions per year and uses ML models trained on this big data to detect fraud in real-time with 99.9% accuracy.
• Researchers at Mount Sinai analyzed over 700,000 clinical notes to develop ML models that could predict disease onset 30-80 days in advance.
• Airbnb uses over 2 PBs of data from its 5+ million properties to train machine learning models that personalize search results and detect host fraud.

Big Data Across Industries

Big data technologies and analytics are being adopted across virtually every industry today. Let‘s look at some of the domains where big data is driving transformation:

Healthcare: The volume of healthcare data is expected to soar to over 2,314 exabytes by 2020. Leading use cases include personalized medicine, clinical trial analysis, drug discovery, hospital readmissions prediction, and medical imaging analysis.

Finance: Banks and financial services firms are using big data for fraud detection, customer segmentation, creditworthiness evaluation, algorithmic trading, and risk modeling. JPMorgan Chase estimates that it uses big data analytics to save $150 million annually in fraud detection.

Retail: Retailers rely on big data to optimize supply chains, personalize marketing, analyze customer behavior, set prices, and manage inventory. Walmart‘s 240 petabytes of data has yielded over $1 billion in annual incremental sales.

Manufacturing: Sensors on manufacturing equipment generate huge volumes of big data that can be analyzed to enable predictive maintenance, improve production quality, and optimize processes. GE has estimated that even a 1% productivity gain driven by big data could add $300 billion to the US economy over 15 years.

Transportation: Autonomous vehicles rely on big data from LIDAR, cameras, and other sensors to train the machine learning models that enable them to drive safely. It‘s estimated that each autonomous car will generate about 4,000 GB of data per day. Uber also applies big data and ML models to optimize rider matching, set prices, and route drivers.

Careers in Big Data

As companies seek to harness the power of big data, it has created a surge in demand for professionals with data skills. Let‘s examine some of the key big data job roles:

Data Engineer: Data engineers are responsible for building the infrastructure and data pipelines that enable the ingestion, processing and storage of big data. They work with tools like Hadoop, Spark, Kafka, and NoSQL databases. Strong programming skills, especially in Java/Scala and Python, are essential.

Data Architect: Data architects take a strategic, big picture view of an organization‘s data assets. They design overarching data architectures, encompassing data models, pipelines, storage systems, governance policies, and more. Deep knowledge of the big data ecosystem is required.

Data Analyst: Data analysts query datasets and use descriptive and diagnostic analysis techniques to answer business questions and inform decision-making. Strong SQL skills, data visualization acumen, and business domain knowledge are key.

Data Scientist: Data scientists apply advanced analytics and build machine learning models to derive forward-looking insights from big data. They use tools like Python, R, Apache Spark, and TensorFlow. Solid statistics and ML knowledge is a must, as are strong programming and communication skills.

Getting Started with Big Data

For professionals looking to gain big data skills and enter this high-growth field, there are many educational resources and options available:

• Online courses & MOOCs on platforms like Coursera, Udemy and edX
• Books like Hadoop: The Definitive Guide, Spark: The Definitive Guide, and Designing Data-Intensive Applications
• Certifications like Cloudera Certified Professional (CCP) Data Engineer, Google Cloud Professional Data Engineer, and Microsoft Certified Azure Data Engineer Associate
• Graduate programs in data science, business analytics and computer science
• Kaggle and other data science competition platforms to practice
• Open source projects to contribute to and learn from
• Conferences like Strata Data Conference, Hadoop Summit, Spark + AI Summit

Conclusion

As the world continues to digitize and more human activity moves online, the tsunami of big data being generated will only accelerate. At the same time, big data technologies will continue to advance to keep pace with the growing volume, variety and velocity of this data. Big data has already had a profound impact over the past decade, but in many ways we are still in the early innings.

Artificial intelligence and machine learning are the technologies that will enable us to actually capitalize on the value hidden in big data. AI/ML will increasingly be applied to make sense of the massive volumes of unstructured and streaming data to enable real-time insights and intelligent decision-making. Leading IT analyst Gartner envisions a future of "augmented analytics," where AI is applied throughout the data management and analytics process.

For companies, investing in a modern big data architecture and building your team‘s big data skills will be essential to compete in the 2020s. For individuals, gaining expertise in big data tools and platforms can lead to a rewarding, well-compensated career with high demand and job security. Now is the time to take the plunge into big data and ride the wave of this transformative technology trend.