Big Data Characteristics Explained
Big Data is a hot topic in business, and it's not just a buzzword – it's a part of our daily lives everywhere. It goes beyond just having heaps of data; it includes how the data is structured, how fast we can process it, and, most importantly, what we can achieve with it. Two major factors driving the surge in data are improved computer capacity and increased data generation. Nowadays, our hard drives are not only bigger but also faster, allowing us to handle more data at lightning speed. This has led to a significant rise in data from various sources over the past decade. The value of Big Data for businesses today is immense, as it allows for improvements in various departments by recognizing common patterns, analyzing data, and delving into artificial intelligence and machine learning.
Big data has four key characteristics, known as the 4 Vs:
- Volume:
Receiving large amounts of data from various sources, often posing
challenges when processed on personal computers.
- Variety:
It's not just numbers; there are all kinds of data like audio and video.
- Velocity:
The speed that we are receiving the data, often in milliseconds. Also, how
we quickly process and analyze the data for possible decision-making.
- Veracity:
Addresses the uncertainty associated with data sources, acknowledging
incomplete, low-quality, ambiguous, and inconsistent data that needs
careful consideration.
Skills for Big Data
Analyzing big data initiates with formulating broad set of
business questions. Subsequently, we navigate the data landscape, seeking out
patterns, correlations, and relationships to uncover valuable business insights
that lead to specific hypotheses. This exploration necessitates proficiency in
three fundamental skills: managing, comprehending, and acting upon the data.
- Managing
Data:
- Organize data systematically to facilitate efficient analysis.
- Possess expertise in data architecture, governance, and adherence to business policies.
- Understanding Data:
- Utilize knowledge in data science, statistics, data mining, and computer science.
- Demonstrate proficiency in data visualization to interpret and graphically represent data meaningfully.
- Acting on Data:
- Managers and executives typically handle business decision-making.
- To use data for informing managerial decisions, one needs an understanding of basic data analysis, a foundational grasp of basic data science, and domain expertise in the relevant area.
In addition to these skills, the use of tools is crucial for
achieving data-related goals. Here are some examples:
- Data
Warehouse:
- A
centralized database to store both new and historical data from various
sources.
- Serves
the purpose of providing a comprehensive view of organizational data.
- Examples
include Google BigQuery, Snowflake, Amazon Redshift, and Azure SQL Data
Warehouse.
- Open-Source
Big Data:
- These
tools facilitate the storage of data across multiple computers, employing
distributed systems due to the substantial volume of data.
- Examples
are Hadoop and Spark, which employe primarily for storing and processing
large volumes of data.
Data Management Infrastructure
When dealing with big data, it's important to build a solid data
management infrastructure, determining where and how we store and retrieve
data. Typically, businesses maintain two types of databases: transactional and
analytical. A transaction database stores data for quick and easy access,
primarily focusing on more recent data. In contrast, an analytical database
houses all data but operates at a slower pace than the transactional database.
Two prevalent concepts in data management are the data lake
and data marts. The concept of a data lake encompasses all data from various
sources, whether structured or not. Data marts, on the other hand, involve a
technique to extract data from the data lake, transform it, and re-store it for
more accessible retrieval later on. Once the data is stored, and retrieval
methods are established, the subsequent step is to analyze the data.
Data Mining
Analyzing data is vital for comprehending big data and
addressing business queries. Frequently, data mining is employed to explore
extensive datasets, identifying patterns and segments. Key techniques within
data mining include:
- Clustering:
A technique grouping data based on their similarities.
- Associate
Rule Mining: Searching for and identifying common co-occurrences in
the data.
- Predictive
Analytics: Utilizing data to propose outcomes based on observed
patterns. For instance, suggesting products a specific customer is likely
to purchase in their e-commerce shopping cart.
Artificial Intelligence
In addition to data analysis, incorporating Artificial Intelligence (AI) proves advantageous for enhancing decision-making processes. There are three primary types of intelligences within AI:
- Weak AI (Artificial Narrow Intelligence - ANI): Specialized in performing a specific task. For instance, an algorithm designed to detect potential fraudulent transactions.
- Strong AI (Artificial General Intelligence - AGI): A computer program capable of emulating all cognitive functions of the human mind. An example includes Artificial Neural Networks.
- Artificial Super Intelligence (ASI): A program that can rapidly improve itself to surpass human capabilities in performing various tasks, aiming to excel in any given domain.
The evolution of AI requires the translation of human tasks
into programmable systems. For instance, crafting software for disease
diagnosis involves interviewing multiple doctors, conducting research, and
identifying common symptoms leading to a diagnosis. Presently, AI has
limitations and cannot surpass all facets of human capabilities. Although
diagnostic software may manage basic diagnoses, accuracy is not guaranteed due
to the intricate complexities and specialized knowledge in medical fields. AI remains
a burgeoning technique with the potential to transform various aspects of our
lives and industries.
Machine Learning
An immense subfield of AI highly applicable in big data is
machine learning, characterized by its ability to learn from data without
explicit programming. This technology is frequently employed for making
predictions across various industries. There are three main types of machine
learning:
- Supervised Learning:
- Develops
predictive models based on historical input and output data to learn how
to classify future behavior.
- Utilizes classification and regression techniques. For example, using labeled email data to predict whether a new email is likely to be classified as spam.
- Unsupervised Learning:
- Groups and comprehends observations based solely on input data.
- Involves anomaly detection and clustering. For instance, clustering news from multiple sources based on topics like politics, war, economy, etc., without predefined output data.
- Reinforcement
Learning:
- Acquires
new data through actions and ad hoc feedback.
- Involves
Bandit algorithms and Q-learning, where algorithms learn from testing
multiple strategies to determine the most effective one. For example,
gaming AI playing against multiple users and learning the optimal
strategy to win.
In summary, Big Data
is powerful with various characteristics and many techniques available to us to
process, analyze, and act based on the input and output of the data.
Understanding and utilizing it involves a combination of skills, tools, and
techniques like data warehouses, open-source tools, data infrastructure
management, data analysis, and AI/machine learning.
