When we talk abaut big data terms that needs to be analyze, we refers to data that is complex, fast, and so large that it is technically impossible to process it using the old methods. Although storing large amounts of data for analysis has been around for many years, the term Big Data became relevant in the 2000s when Doug Laney, an industry analyst, gave it relevance by articulating it with the famous slogan of the three Vs.
The “three Vs.” of Big Data
Volume: Organizations aggregate data from various sources, such as intelligent devices (IoT), business transactions, intelligent devices (IoT), videos, industrial equipment, social networks, etc. Previously, collecting all the data would have been a hassle; however, cheaper storage on platforms such as Hadoop and data lakes have made the job much more straightforward and low cost.
Speed: The immediacy of the network, the growth of the internet of things, and users, have generated data that reaches organizations at an unmatched rate in human history, so care must be taken to handle it promptly. Sensors, tags, and smart meters create the need for torrents of information to be managed in real-time and earlier to mitigate risks.
Variety: Data arrives in an interconnected manner and in different formats, such as numerical data that are housed in traditional database structures, as well as text documents, videos, audios, financial transactions, and emails.
Why is important?
It is vital to understand that in Big Data, it is not about having a large amount of data; it is about what you do with it. The constant work of collecting data from any of the media we have already seen and analyzing it to be able to answer the questions that are formulated in the objectives such as reducing time, reducing costs, making intelligent decisions, product development, and optimization of the different offers that are offered. At the moment when everything reaches a common point, and data analysis converges, issues come to light such as:
- It is detecting fraudulent behavior before it impacts the business.
- Opportunities to create coupons on sites where they become the customer’s buying habits according to the data collected.
- We determine the causes of failures, structural errors, problems, and defects in real-time.
- Recalculate entire risk portfolios in minutes.
How does it work?
Before companies can put data to work, they must first analyze where the information comes from and its influence across sources, owners, locations, systems, and users. There are five steps to follow, all of which work together to master the data center that brings together traditional, structured and unstructured, and semi-structured data.
- Analyze the data: With high technology such as grid computing or in-memory analytics, companies prefer to choose their Big Data for analysis. However, another approach is to choose earlier which data is relevant to their objectives.
- Identify sources of Big Data: Streaming data comes from the Internet of Things, social media data, publicly available data comes from massive amounts of data sources, and other data can come from customers, cloud data, suppliers, and data lakes.
- Establish a big data strategy: A Big Data strategy is designed to monitor and improve how you acquire, store, manage, use and share your organization’s data.
- Make data-driven decisions: by reliably managing data, reliable and competitive decisions are generated; for this reason, organizations should take advantage of the great value of using Big Data to operate based on data and not assumptions. Data-driven organizations perform better in their environments; their results are predictable, productive, and profitable.
Big Data has been of great importance in different sectors of the economy, such as the oil and gas sector and exploration operations; these improvements in seismic devices have generated a large amount of data that help understand and make action plans. In addition, they have efficiently specialized in engineering, performance optimization of electrical submersible pumps, and production allocation techniques.
Undoubtedly, several significant challenges are to be developed, such as lack of business support and lack of experience with data analysis. Still, it is increasingly common to find industries that rely on and succeed with Big Data.