Big Data – Simply Defined
Big Data is nothing but large and complex data sets, which can be both structured and unstructured. The concept of Big Data also encompasses the infrastructures, technologies and tools created to manage this large amount of information. However, the data array with Big prefix is so huge that it is impossible to “shovel” it with structuring and analytics. This is the reason why Big Data is also understood as a set of multiple technologies to search, process, and use large unstructured data.
How Big Data Works?
After learning what is Big Data, it is time to know how it works. Well, the analysis of large amounts of structured and unstructured information is performed in 3 stages:
Stage 1: Data Cleaning
Find out and fix the errors in the primary set of information. For example, Typos (manual input errors), incorrect values by measuring instruments etc.
Stage 2: Feature Engineering
Variables to build analytical models, like education, length of service, gender and the age of a potential buyer.
Stage 3: Building and Training Analytical Model
Model selection to predict the target variable. This is how the hypotheses about the dependence of target variables on predictors are tested. For instance, how many days it will take to borrow with secondary education and work experience.
The 5V Rule: Critical V’s of Big Data
In order to designate an array of information with “big” prefix, it works in the following terms:
First of all, the data is measured by its physical size and space it occupies on a digital storage medium. The “big” includes arrays over 150 GB each day. Moreover, you can use the Data Catalog to understand your data well.
After that, the information is regularly updated and real-time processing requires intelligent Big Data platform and technology.
Information in arrays can have heterogeneous formats which can be structured partially, completely and accumulated. For instance, social media networks use Big Data in the form of text, video, audio, transactions, pictures, and others. The collection and combination of data from various resources is known as Big Data Integration.
Since some data streams can have peaks and seasonality, periodicity. Managing a large amount of unstructured information is difficult and requires powerful processing techniques.
Various types of information can have different complexity for perception and processing issues, which makes it complex for intelligent systems to work with. So, the information must be managed in such a way that it delivers value eventually.
Additional V’s of Big Data
Big data moves beyond Volume, Variety, Value, Variability, and Velocity alone. Other characteristics and properties of big data are as follows:
Visualization means collecting and analyzing the huge amount of data using Real-time analytics to make it understandable and easy to read. Without this, it is impossible to maximize and leverage the raw data.
Veracity stands for provenance or reliability of the data source, its context, and how significant it is to the analysis based on it.
Validity means how clean, accurate, and correct the data is to use. The benefit of Big Data analytics is only as good as its underlying data, so good Big Data Governance practices should be adopted to ensure consistent data quality, common definitions, and metadata.
Volatility or how long the data should be kept because before Big Data, there was a tendency to store data indefinitely because of its small volume it hardly involved expenses.
Big data comes up with so many new security concerns since there have been many big data breaches.
Benefits of Big Data
When exploited with Business Intelligence (BI) and advanced analytics tools, the benefits of Big Data are undeniable. First, because it answers multiple questions from organizations, it contributes insight and benchmarks. Second, because it quickly explains the business hurdles that previously required much more time and resources. Simply speaking, the good use of Big Data translates into various Big Data Testing benefits for the company.
Information is essential as it is the basics of correct decision-making. The correct management of Big Data allows us to make smart and fast decisions that help to benefit our business. It helps to analyze opportunity fluently even before putting any product or service on the online marketplace.
Today it is possible to analyze and predict the behavior of a user on the network and get to know what customers think about a specific brand or a product. Moreover, today we can find out the real needs of customers, the products or services they really want on-board. Analyzing these facts enable businesses to develop targeted and highly personalized marketing campaigns.
Big Data provides various monitoring options by which one can learn more about one’s audience, trends, tests, and much more. This means a higher level of personalization and customization in the product can be given by adopting and managing Big Data well.
The correct handling of Big Data can simply boost the speed at which a product or service rises because we have a multitude of data with the information that the market gives us. So, the deadlines for the development of a product or service are shortened with time, as well as the costs associated with the process derived from its development.
Managing a large volume of data represents that Big Data can be a problem of infrastructure. That is why it is convenient to consider working with them in an environment that does not set limits like the cloud. Later, this comes handy with cost savings in hardware. Moreover, it is an improvement in the accessibility and fluidity of the information for the company’s own employees, which increases effectiveness and speed.
Do you know?
The best brands like Netflix, Apple, Amazon, Barcelona Metro, Zara, and others are already using Big Data to achieve incredible results.
Big Data Challenges
The risks associated with big data are not just related to the confidentiality of information. However, it gradually uses the opportunities they offer, both users and organizations can be shocked by the result of their far from optimal processing. Some of the biggest challenges of Big Data include:
- When quantity doesn’t convert into quality.
- Collecting a large amount of data is not enough, it entails costs.
- Labour resources, knowledge, and skills.
- Managing organizational and management structure is difficult.
- Analytical processing of Big Data and identification of patterns is complex.
Big Data Best Practices
- Be clear about your business objectives
- Authorize files access with predefined security policy
- Implement Testing in Big Data
- Implementing Big Data is a business decision
- Safeguard sensitive data by data encryption while at rest
- Use agile solutions
Big Data Use Cases
Since Big Data can help you to detect the range of business activities by analyzing customer experience to proper analytics. You can learn more about this with the following use cases.
Predictive maintenance is significant in different application areas, like the manufacturing industry, information, and technology, heavy-machinery industry, and others. In order to estimate the future performance of a subsystem to make RUL (Remaining Useful Life) estimation.
User Experience (UX) administers with users interaction with a particular product, for business purposes, a website, applications, or the experience they have received from that interaction. A good user experience lets your customers to instantly and efficiently find information.
3. Data Science
Big Data allows delivering business insights, and automates the processes with AI, and enrich customer insights and cost optimization using Data Science and Deep Learning Solutions. Moreover, it uses Predictive Analytics, Natural language Processing, and Computer Vision.
Now though you know what is Big Data and why it is important for your business. And, it’s time to implement Big Data to manage and analyze your business data well. To do so, check out our Big Data Solutions and Services transform your business data into value, thereby obtaining competing advantages.
Implement Big Data to manage and analyze your business data well.
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