Big Data vs Data Science: What they are and why they matter
Let’s dive deep into the important concepts around data and technology. In this post, I will cover definitions, tools, and examples of possible applications, as well how Big Data and Data Science relate to each other.
Why Big data and Data Science are important
Big Data and Data Science are the two concepts visible in all discussions about the potential benefits of enabling data-driven decision making. It’s been measured that 90% of the world's data has been created in the last two years alone, which gives us an incredible 2.5 quintillion bytes of data being created every day. There are zettabytes of information available that we all leave behind when buying things, selling things, and leaving digital footprints of our modern daily life, so it’s only natural that the data-driven approach rules everything from business automation to social interaction.
Learning from digital data and getting a broader and more comprehensive perspective on processes and the future gives early adopters of data technologies a chance to seize strategic opportunities and drive full speed ahead. Advances in cloud computing and machine learning additionally allow the extraction of coherent, strategic insights from digital residues, while IT engineers and scientists come together to help businesses make sense out of complex data and drive profits.
What is Big Data?
Big Data refers to an ever-growing volume of information of various formats that belongs to the same context. But when we say Big Data, how big exactly are we talking? We usually refer to data residues bigger than terabytes or petabytes, but Big Data is not only about large amounts of data.
Current definitions of Big Data are dependent on the methods and technologies used to collect, store, process, and analyse available data. The most popular definition of Big Data focuses on data itself by differentiating Big Data from “normal” data based on the 7 Vs characteristics:
- Volume - is about the size and amounts of Big Data that is being created. It is usually greater than terabytes and petabytes.
- Velocity - is about the speed at which data is being generated and processed. Big Data is often available in real-time.
- Value - is about the importance of insights generated from Big Data. It may also refer to profitability of information that is retrieved from Big Data analysis.
- Variety - is about the diversity and range of different data formats and types (images, text, video, xml, etc.). Big Data technologies evolved with the prime intention to leverage semi-structured and unstructured data, but Big Data can include all types: structured, unstructured, or combinations of structured and unstructured data.
- Variability - is about how the meaning of different types of data is constantly changing when being processed. Big Data often operates on raw data and involves transformations of unstructured data to structured data.
- Veracity - is about making sure that data is accurate and guaranteeing the truthfulness and reliability of data, which refers to data quality and data value.
- Visualisation - is about using charts and graphs to visualise large amounts of complex data in order to both present it and understand it better.
Big Data definition
Big Data is an umbrella term combining all the processes, technologies, and tools related to utilising and managing large and complex data sets that are the fuel for today's analytics applications.
The true value of Big Data lies in the potential to extract meaningful insights in the process of analysing rich amounts of data while identifying patterns to design smart solutions.
Big Data tools
Apache Kafka - Kafka is a stream processing platform that ingests huge real-time data feeds and publishes them to subscribers in a distributed manner. The tool allows you to take up the challenge posed by Big Data when broker technologies based on other standards have failed.
Real-time stream processing has been gaining momentum in recent years, and major tools that enable it include Apache Spark and Apache Flink. Stream processing is a big data technology that focuses on the real-time processing of continuous streams of data in motion. It is important because data can be used as frequently as necessary for a particular use case.
Apache Cassandra - Cassandra is used by many companies with large active data sets of online transactional data. It is a NoSQL database that offers fault-tolerance as well as great performance and scalability without sacrificing availability.
Apache Hadoop - a big data analytics system that focuses on data warehousing and data lake use cases. It uses the Hadoop Distributed File System (HDFS) that provides high throughput access to application data and is suitable for applications that have large data sets.
Scala Programming - a prime language for tinkering with data that software developers, data engineers, and data scientists choose.
What is Data Science?
Big Data is useful, but it’s Data Science that makes it powerful. This discipline helps businesses and organisations draw informed insights from data by recognising meaningful patterns and predicting outcomes based on chosen scenarios.
Data Science makes the picture of the world more comprehensive and detailed. The key processes that data scientists and ML engineers perform to derive insights are: cleansing, aggregating, and manipulating data to perform advanced qualitative and quantitative research. Based on these findings, business leaders can optimise actions and achieve the best possible outcomes faster and in an automated fashion.
Data scientists need a cross-disciplinary set of skills, and according to Python Data Science Handbook by Jake VanderPlas, Data Science comprises three distinct and overlapping areas:
- the skills of a statistician who knows how to model datasets,
- the skills of a computer scientist who can design and use data algorithms,
- and the domain expertise necessary both to formulate the right questions and to put the answers in context.
(Source: Drew Conway)
Data Science definition
Data science is a domain of study in which information and knowledge are extracted from data by using various scientific methods, algorithms, and processes. A combination of various mathematical tools, algorithms, statistics, and machine learning techniques which are thus used to find the hidden patterns and insights from data can support organisations in the decision making process.
Data Science tools
R programming - R is the language for data scientists due to its flexibility and built-in statistical capabilities.
Python - is a general-purpose interpreted, interactive, object-oriented, and high-level programming language often used in Machine Learning and Data Science projects. It’s popular, relatively easy to learn, thus ideal for prototyping.
Statistics - data scientists use statistics to gather, review, analyse, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables.
Machine Learning - Data Scientists use machine learning algorithms in the data science lifecycle. They can automate the data analysis process and make informed data predictions in real-time without requiring any human intervention.
How do Big Data and Data Science show up in everyday life?
The Big Data market is expected to top $229.4 billion by 2025 (a measure of how much companies are investing, not how much value they are deriving from Big Data).
Nearly every industry uses Big Data for future planning and growth. Here are 5 industry verticals that are adopting Big Data and Data Science strategies like pros.
Netflix - Media and Entertainment
This is probably the easiest to explain example of how Big Data and Data Science enhance a customer-focused and data-driven business. Data Science and Engineering people at Netflix are members of different business units, like content or product development, and they are responsible for implementing analytics at scale.
- personalised movie and TV show recommendations, thumbnails, and trailers,
- content popularity prediction before it goes live (or not),
- enhanced technical and business decision making,
- enabling innovation.
Walmart - Foodchain and Retail
Big Data is an essential part of Walmart's strategy. The company uses Big Data to discover patterns in point of sales data. They’re thus able to provide customer-centric personalised experiences on their global websites. Walmart has a broad data ecosystem stored on Microsoft Azure cloud. The company processes multiple terabytes of new data and petabytes of historical data every day.
- product recommendations brought to users based on which products were bought together or which products were bought before the purchase of a particular item,
- higher customer conversion rates,
- better predictive analysis of new product launches,
- cost-effective inventory management.
Uber - Transportation Mobility
The company is serving millions of rides and food deliveries worldwide. Uber gathers information about all rides, car requests, they even track drivers going through the city without passengers to study traffic patterns. That data is later leveraged to determine the prices, how long it will take for the driver to arrive, and where to position the car within the city. On the product front, Uber’s data team is behind all the predictive models powering the ride sharing cab service, making the company the leader in the industry.
- Uber’s biggest use of data is surge pricing during certain times and days,
- a frictionless experience to the users,
- making informed decisions.
Mercedes Benz - Automotive
The company recognizes necessary repairs right away through data matching with past vehicles. Its “Big Data Diagnosis” reads error codes and interprets them right away.
- saving the customers’ precious time in the workshops.
SoftwareMill and the University of Lodz - Earth Sciences
Generative adversarial networks (GANs) have great potential to support aerial and satellite imagery interpretation activities. Carefully crafting a GAN and applying it to a high-quality data set can result in nontrivial feature enrichment. In this study, we have designed and tested an unsupervised procedure capable of engineering new features by shifting real orthophotos into the GAN’s underlying latent space.
- It is possible to describe the whole research area as a set of latent vectors and perform further spatial analysis not on RGB images but on their lower-dimensional representation.
- Production of a segmentation map of the orthophoto.
Big Data vs Data science - key differences
What are the major differences between Big Data vs Data Science?
Big Data refers more to technologies in computer science like cloud computing, stream processing tools, and distributed data platforms (Apache Kafka, Apache Spark, etc.) that are used to manage extremely large data sets that require specialised techniques in order to efficiently “use” the data.
Data Science, on the other hand, is a field of study, an umbrella term that encompasses all of the techniques and tools focusing on data analysis. Strategies for business decisions, data dissemination with the use of mathematics, statistics, and algorithms. Data Science makes Big Data powerful and both go hand in hand in real-world applications.
Big Data is essentially a special application of Data Science, in which the data sets are enormous and require Big Data specialists: engineers and analysts to extract valuable information.
Need Big Data and Data Science skills in your team?
Data professionals and ML or software engineers help businesses venture into the unknown and unseen in order to learn from data. Big Data and Data Science talents are scarce, especially when combined with the need for industry-relevant knowledge.
The most popular roles that will help your business leverage data-drive strategies are:
Data Scientist utilises data to extract meaningful information and develop insights. A Data Scientist’s core skills include: programming, developing statistical models, and cleaning data to get insights from it.
ML engineer/software engineer
ML engineer/software engineer creates a Big Data environment by designing, building, and managing data pipelines and analytic applications that connect information from several source systems. An Engineer’s core skills include: database systems, data pipelining and warehousing, Big Data tools such as Apache Kafka, ML frameworks.
Data Analyst works directly with the business team, prepares and analyses raw data to answer queries for the business and product teams. An Analyst’s core skills include: SQL, Microsoft Excel, statistics, knowledge of data visualisation tools.
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