Big Data
“Big data is at the foundation of all the megatrends that are happening.” — By Chris Lynch, American Writer of Books
The term “Big Data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.
Big data challenges include capturing data, data storage, data analysis, search, etc. The act of accessing and storing large amounts of information for analytics has been around a long time. Current usage of the term Big Data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the Three V’s:
Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more. In the past, storing it would have been a problem — but cheaper storage on platforms like data lakes and Hadoop have eased the burden.
Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time.
Variety: Data comes in all types of formats — from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.
Two more Vs have emerged over the past few years: value and veracity.
Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data — and how much can you rely on it?
Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.
Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.
Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.
History Of Big Data
Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ’70s when the world of data was just getting started with the first data centers and the development of the relational database.
Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.
The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data — but it’s not just humans who are doing it.
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.
While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data.
How Big Data Works ?
Before businesses can put big data to work for them, they should consider how it flows among a multitude of locations, sources, systems, owners and users. There are five key steps to taking charge of this big “data fabric” that includes traditional, structured data along with unstructured and semistructured data:
- Set a big data strategy
A big data strategy sets the stage for business success amid an abundance of data. When developing - Identify big data sources
Streaming data
Social media
Publicly available data from government sources
Other big data like Cloud Data sources, Users - Access, manage and store the data
Some data may be stored on-premises in a traditional data warehouse — but there are also flexible, low-cost options for storing and handling big data via cloud solutions, data lakes and Hadoop. - Analyze the data
Big data analytics is how companies gain value and insights from data. Increasingly, big data feeds today’s advanced analytics endeavors such as artificial intelligence. - Make data-driven decisions
Well-managed, trusted data leads to trusted analytics and trusted decisions. Data-driven organizations perform better, are operationally more predictable and are more profitable.
Big Data USE CASES
- Product Development
Companies like Netflix, Amazon use big data to anticipate customer demand. - Predictive Maintenance
Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime. - Customer Experience
Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively. - Fraud and Compliance
Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster. - Machine Learning
Machine learning is a hot topic right now. And data — specifically big data — is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible. - Operational Efficiency
Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. Big data can also be used to improve decision-making in line with current market demand. - Drive Innovation
Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services.
Big Data Challenges
While big data holds a lot of promise, it is not without its challenges.
First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.
But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their timecurating and preparing data before it can actually be used.
Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Author:
Rohan Sarkar
is part of CORE TEAM of HEXABERRY DATA SCIENCE COMMUNITY.
Authors can be reached at:
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