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Big Data Write For Us

Big data Big Data is a term that describes the large amounts of difficult-to-manage data, both structured and unstructured, that inundate organizations every day. And Big data can be analyzed for insights that improve decisions and inspire confidence to take strategic business moves. But it’s not just the type or amount of data that matters, but what companies do with the data. For Submitting Your Articles, you can email us at contact@newyorkersblog.com

History of Big Data

Big data refers to data that is so large, fast, or complex that it is difficult or impossible to process using traditional methods. Accessing and storing large amounts of information for analysis has been around for a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated what is now the standard definition of big data as the three Vs:

Volume. Businesses collect data from various sources including transactions, intelligent devices (IoT), industrial assets, video, images, audio, social media and more. In the past, storing all this data would have been prohibitively expensive, but cheaper storage from data lakes, Hadoop, and the cloud has eased the burden.

Speed. With the growth of the Internet of Things, data is being transmitted to businesses at unprecedented rates and needs to be processed on time. RFID tags, sensors and intelligent meters increase the need to manage this flood of data in near realtime.

Diversity. Data comes in various formats, from structured numeric data in traditional databases to unstructured text documents, email, video, audio, stock price data and financial transactions.

How Big Data Works

Before organizations can leverage big data, they must consider how it flows across various locations, sources, systems, owners, and users. There are five critical steps to adopting this “Fabric of Big Data” that spans traditional structured data alongside unstructured and semi-structured data:

  • Establish a big data strategy.
  • Identify big data sources.
  • Retrieve, manage and store data.
  • Analyze the data.
  • Make intelligent decisions based on data.

1) Establish a big data strategy

At a high level, a big data strategy is a plan designed to help you oversee and improve how you collect, store, manage, share, and use data inside and outside your organization. A big data strategy creates the conditions for business success amidst huge amounts of data. When developing a strategy, it is vital to consider existing and future business and technology goals and initiatives. It requires treating big data like any other valuable business asset and not just a by-product of applications.

2) Identify big data sources

  • Data transmission occurs over the Internet of Things (IoT) and other connected devices, flowing into IT systems from wearable devices, smart cars, medical devices, industrial equipment and more. You can analyze these large amounts of data as it comes in and decides which data to keep or not and which to explore further.
  • Social media data comes from interactions on Facebook, YouTube, Instagram, etc. It includes large amounts of big data in the form of images, videos, voice, text and sound, which is useful for marketing, sales and support functions. It data is often unstructured or semi-structured, presenting a unique challenge for exploitation and analysis.
  • Publicly available data comes from vast amounts of open data sources such as the US government’s data.gov, the CIA’s World Factbook, or the European Union’s Open Data Portal.
  • Other big data can come from data lakes, cloud data sources, providers, and customers.

3) Retrieve, manage and store big data

Modern computer systems offer the speed, power, and flexibility to quickly access vast amounts and types of massive data. Some big data can be stored on-premises in a traditional data warehouse, but there are also inexpensive and flexible options for storing and managing big data via cloud solutions, data lakes, data pipelines, and Hadoop. In addition to trusted access, organizations also need methods to integrate data, create data pipelines, ensure data quality, provide data management and storage, and prepare data for analysis.

4) Analyze the data

Big data is increasingly driving today’s advanced analytics efforts such as artificial intelligence (AI) and machine learning. With powerful technologies such as grid computing or in-memory analysis, companies can use all of their big data for analysis. Another approach is to predetermine what data is relevant before analyzing it. Anyway, big data analytics is how companies derive value and insights from data.

5) Make intelligent decisions based on data

Trusted, well-managed data leads to trusted analysis and trusted decisions. To remain competitive, companies need to harness the full value of big data and be data-driven, making decisions based on big data, not instincts. The advantages of data control are obvious. Data-driven organizations are more efficient, operationally predictable and profitable.

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