Big Data Write For Us
Big Data Write For Us – Big Data is a term that defines the large amounts of difficult-to-manage data, both structured and unstructured, that inundate organizations every day. And Big data can remain analyzed for insights that improve decisions and inspire confidence to make strategic business moves. But it’s not just the kind or amount of data that matters, but what companies do with the data. For Submitting Your Articles, you can email us at contact@techiesline.com
History of Big Data
Big data mentions to data that is so large, fast, or complex that it is difficult or impossible to process using traditional methods. Of course, accessing and storing large amounts of information for analysis has been around for a long time. But the idea of big data gained momentum in the early 2000s when manufacturing 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 Net of Things, data is being transmitted to businesses at unprecedented rates and needs to remain processed on time. RFID tags, sensors, and intelligent meters increase the need to manage this flood of data in nearreal–time.
Diversity. Data comes in various formats, from structured numeric data in outdated databases to unstructured text documents, email, video, audio, stock price data, and financial transactions.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 significant 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 remains 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 vast data. Therefore, when developing a plan, it is vital to consider existing and future professional and technology goals and initiatives. In addition, it requires treating big data like any other valuable business asset and not just a by-product of applications.
2) Identify significant 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 decide which data to keep and which to explore further.
- Social media data comes from interactions on Facebook, YouTube, Instagram, etc. It includes large amounts of big data in images, videos, voice, text, and sound, which is helpful for marketing, sales, and support functions. The data is, however, then unstructured or semi-str
- 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 access vast amounts and types of massive data quickly. Some big data can be stored on-premises in a traditional data warehouse. Still, there are 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 increasingly drive 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. 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 total value of big data and be data-driven, making decisions based on big data, not instincts. The advantages of data control are apparent. Data-driven organizations are more efficient, operationally predictable, and profitable.
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