The promise of big data is that companies will have far more intelligence at their disposal to make accurate decisions and predictions on how their business is operating. Big Data not only provides the information necessary for analyzing and improving business results, but it also provides the necessary fuel for AI algorithms to learn and make predictions or decisions. In turn, ML can help make sense of complex, diverse, and large-scale datasets that are challenging to process and analyze using traditional methods.
What is Big Data?
Big data is a term used to describe the collection, processing and availability of huge volumes of streaming data in real-time. Companies are combining marketing, sales, customer data, transactional data, social conversations and even external data like stock prices, weather and news to identify correlation and causation statistically valid models to help them make more accurate decisions.Gartner
Big Data is Characterized by the 5 Vs:
- Volume: Large amounts of data are generated from various sources, such as social media, IoT devices, and business transactions.
- Velocity: The speed at which data is generated, processed, and analyzed.
- Variety: The different types of data, including structured, semi-structured, and unstructured data, come from diverse sources.
- Veracity: The quality and accuracy of data, which can be affected by inconsistencies, ambiguities, or even misinformation.
- Value: The usefulness and potential to extract insights from data that can drive better decision-making and innovation.
Big Data Statistics
Here is a summary of key statistics from TechJury on Big Data trends and predictions:
- Data volume growth: By 2025, the global datasphere is expected to reach 175 zettabytes, showcasing the exponential growth of data.
- Increasing IoT devices: The number of IoT devices is projected to reach 64 billion by 2025, further contributing to the growth of Big Data.
- Big Data market growth: The global Big Data market size was anticipated to grow to $229.4 billion by 2025.
- Rising demand for data scientists: By 2026, the demand for data scientists was projected to grow by 16%.
- Adoption of AI and ML: By 2025, the AI market size was predicted to reach $190.61 billion, driven by the increasing adoption of AI and ML technologies for Big Data analysis.
- Cloud-based Big Data solutions: Cloud computing was expected to account for 94% of the total workload by 2021, emphasizing the growing importance of cloud-based solutions for data storage and analytics.
- Retail industry and Big Data: Retailers using Big Data were expected to increase their profit margins by 60%.
- Growing usage of Big Data in healthcare: The healthcare analytics market was projected to reach $50.5 billion by 2024.
- Social media and Big Data: Social media users generate 4 petabytes of data daily, highlighting the impact of social media on Big Data growth.
Big Data is also Great Band
It’s not what we’re talking about here, but you might as well listen to a great song while you’re reading about Big Data. I’m not including the actual music video… it’s not really safe for work. PS: I wonder if they chose the name to take catch the wave of popularity big data was building up.
Why Is Big Data Different?
In the old days… you know… a few years ago, we would utilize systems to extract, transform, and load data (ETL) into giant data warehouses that had business intelligence solutions built over them for reporting. Periodically, all the systems would back up and combine the data into a database where reports could be run and everyone could get insight into what was going on.
The problem was that the database technology simply couldn’t handle multiple, continuous streams of data. It couldn’t handle the volume of data. It couldn’t modify the incoming data in real-time. And reporting tools were lacking that couldn’t handle anything but a relational query on the back end. Big Data solutions offer cloud hosting, highly indexed and optimized data structures, automatic archival and extraction capabilities, and reporting interfaces that have been designed to provide more accurate analyses that enable businesses to make better decisions.
Better business decisions mean that companies can reduce the risk of their decisions, and make better decisions that reduce costs and increase marketing and sales effectiveness.
What Are the Benefits of Big Data?
Informatica walks through the risks and opportunities associated with leveraging big data in corporations.
- Big Data is Timely – 60% of each workday, knowledge workers spend attempting to find and manage data.
- Big Data is Accessible – Half of senior executives report that accessing the right data is difficult.
- Big Data is Holistic – Information is currently kept in silos within the organization. Marketing data, for example, might be found in web analytics, mobile analytics, social analytics, CRMs, A/B Testing tools, email marketing systems, and more… each with a focus on its silo.
- Big Data is Trustworthy – 29% of companies measure the monetary cost of poor data quality. Things as simple as monitoring multiple systems for customer contact information updates can save millions of dollars.
- Big Data is Relevant – 43% of companies are dissatisfied with their tools ability to filter out irrelevant data. Something as simple as filtering customers from your web analytics can provide a ton of insight into your acquisition efforts.
- Big Data is Secure – The average data security breach costs $214 per customer. The secure infrastructures being built by big data hosting and technology partners can save the average company 1.6% of annual revenues.
- Big Data is Authoritive – 80% of organizations struggle with multiple versions of the truth depending on the source of their data. By combining multiple, vetted sources, more companies can produce highly accurate intelligence sources.
- Big Data is Actionable – Outdated or bad data results in 46% of companies making bad decisions that can cost billions.
Big Data Technologies
In order to process big data, there have been significant advancements in storage, archiving, and querying technologies:
- Distributed file systems: Systems like Hadoop Distributed File System (HDFS) enable storing and managing large volumes of data across multiple nodes. This approach provides fault tolerance, scalability, and reliability when handling Big Data.
- NoSQL databases: Databases such as MongoDB, Cassandra, and Couchbase are designed to handle unstructured and semi-structured data. These databases offer flexibility in data modeling and provide horizontal scalability, making them suitable for Big Data applications.
- MapReduce: This programming model allows for processing large datasets in parallel across a distributed environment. MapReduce enables breaking down complex tasks into smaller subtasks, which are then processed independently and combined to produce the final result.
- Apache Spark: An open-source data processing engine, Spark can handle both batch and real-time processing. It offers improved performance compared to MapReduce and includes libraries for machine learning, graph processing, and stream processing, making it versatile for various Big Data use cases.
- SQL-like querying tools: Tools such as Hive, Impala, and Presto allow users to run queries on Big Data using familiar SQL syntax. These tools enable analysts to extract insights from Big Data without requiring expertise in more complex programming languages.
- Data lakes: These storage repositories can store raw data in its native format until it’s needed for analysis. Data lakes provide a scalable and cost-effective solution for storing large amounts of diverse data, which can later be processed and analyzed as required.
- Data warehousing solutions: Platforms like Snowflake, BigQuery, and Redshift offer scalable and performant environments for storing and querying large amounts of structured data. These solutions are designed to handle Big Data analytics and enable fast querying and reporting.
- Machine Learning frameworks: Frameworks such as TensorFlow, PyTorch, and scikit-learn enable training models on large datasets for tasks like classification, regression, and clustering. These tools help derive insights and predictions from Big Data using advanced AI techniques.
- Data Visualization tools: Tools like Tableau, Power BI, and D3.js help in analyzing and presenting insights from Big Data in a visual and interactive manner. These tools enable users to explore data, identify trends, and communicate results effectively.
- Data Integration and ETL: Tools such as Apache NiFi, Talend, and Informatica allow for the extraction, transformation, and loading of data from various sources into a central storage system. These tools facilitate data consolidation, enabling organizations to build a unified view of their data for analysis and reporting.
Big Data And AI
The overlap of AI and Big Data lies in the fact that AI techniques, particularly machine learning and deep learning (DL), can be used to analyze and extract insights from large volumes of data. Big Data provides the necessary fuel for AI algorithms to learn and make predictions or decisions. In turn, AI can help make sense of complex, diverse, and large-scale datasets that are challenging to process and analyze using traditional methods. Here are some key areas where AI and Big Data intersect:
- Data processing: AI-powered algorithms can be employed to clean, preprocess, and transform raw data from Big Data sources, helping to improve data quality and ensure that it is ready for analysis.
- Feature extraction: AI techniques can be used to automatically extract relevant features and patterns from Big Data, reducing the dimensionality of the data and making it more manageable for analysis.
- Predictive analytics: Machine learning and deep learning algorithms can be trained on large datasets to build predictive models. These models can be used to make accurate predictions or identify trends, leading to better decision-making and improved business outcomes.
- Anomaly detection: AI can help identify unusual patterns or outliers in Big Data, enabling early detection of potential issues such as fraud, network intrusions, or equipment failures.
- Natural language processing (NLP): AI-powered NLP techniques can be applied to process and analyze unstructured textual data from Big Data sources, such as social media, customer reviews, or news articles, to gain valuable insights and sentiment analysis.
- Image and video analysis: Deep learning algorithms, particularly convolutional neural networks (CNNs), can be used to analyze and extract insights from large volumes of image and video data.
- Personalization and recommendation: AI can analyze vast amounts of data about users, their behavior, and preferences to provide personalized experiences, such as product recommendations or targeted advertising.
- Optimization: AI algorithms can analyze large datasets to identify optimal solutions to complex problems, such as optimizing supply chain operations, traffic management, or energy consumption.
The synergy between AI and Big Data enables organizations to leverage the power of AI algorithms to make sense of massive amounts of data, ultimately leading to more informed decision-making and better business outcomes.
This infographic from BBVA, Big Data Present And Future, chronicles the advancements in Big Data.