What is Big Data? – A Comprehensive Guide on Big Data

What is Big Data? – A Comprehensive Guide on Big Data. Here Coding compiler sharing a complete beginners guide to big data. If you are new to big data then you must read this article to get familiar with big data analytics. Let’s start learning about big data. Happy learning.

Big Data Introduction – What is Big Data?

What is Big Data – Big Data Definition: To really understand big data, we need to understand some historical background. Around 2001, Gartner proposed the following definitions for big data (currently the most authoritative definition): Big data is Volume, (Variety) data that emerges from Velocity. These three “V” are important features of big data.

In short, big data is a growing and increasingly complex set of data, especially from new data sources. These data sets are so large that traditional data processing software is at a loss. However, this massive amount of data can help us solve many business problems that we have not been able to reach in the past.

Three “V”s In Big Data Definition

There are three V’s in big data, they are:

  1. Volume
  2. Velocity
  3. Variety

1) Volume (Large number)

The amount of data is critical. When dealing with big data, you have to deal with a lot of low-density, unstructured data. These could be data with unknown values, such as a Twitter data source, or a clickstream on a webpage, mobile app, or sensor-enabled device. Some organizations have data volumes of tens of terabytes, and some organizations have data volumes of up to hundreds of petabytes.

2) Velocity (High speed)

Big data reception and operation is fast. In general, higher-speed data is not written to disk but directly flows into memory. Some smart products based on the Internet can run in real-time or near real-time and require real-time assessment and operation.

3) Variety (Diversification)

Diversification means that there are many types of data available. Traditional data is structured data that can be neatly incorporated into a relational database. With the rise of big data, a variety of new unstructured data types are emerging. Unstructured and semi-structured data types such as text, audio, and video require more pre-processing to gain insight and support metadata.

The Value and Authenticity of Big Data

In the past few years, big data definition has added two “V”: value (Value)and authenticity (Veracity).

Data has intrinsic value, but they are not useful until they are tapped. The authenticity and reliability of data are equally important.

Today, big data has become a kind of capital. Let’s think about the world’s leading large-scale technology companies: Most of the value they create comes from the data they have, and they constantly analyze the data to improve operational efficiency and develop new products.

Recent technological breakthroughs have led to an exponential decline in data storage and computing costs. People can now store more data more easily at a lower cost than in the past. With massive data that is increasingly affordable and easy to use, you can make more accurate business decisions.

The value of mining big data is not only by analyzing data, it is an exploration process. In the process, insightful analysts, business users, and managers need to ask the right questions, identify patterns in the data, make informed guesses, and predict user behaviour.

The History of Big Data

Big Data History – Although the concept of big data was recently proposed, the origins of large data sets can be traced back to the 1960-70s. At that time, the data world was in its infancy, and the world’s first data centres and the first relational database appeared at that time.

Around 2005, people began to realize that users of Facebook, YouTube, and other online services generated massive amounts of data. In the same year, Hadoop, an open source framework developed for storing and analyzing large data sets, was released. NoSQL also began to spread slowly during this period.

The advent of open source frameworks such as Hadoop and later Spark is very important for the development of big data, which is what makes big data easier to use and reduces data storage costs. In the years that followed, the amount of big data showed explosive growth. Users are still generating massive amounts of data, but not only humans are generating data.

With the rise of the Internet of Things (IoT), more and more objects and devices are connected to the Internet to collect data about customer usage patterns and product performance. The emergence of machine learning has also further increased the amount of data generated.

Although big data has been around for a while, people’s use of big data has only just begun. Cloud computing further unlocks the potential of big data. By providing true resiliency, cloud computing allows developers to easily launch specific clusters to test a subset of the data.

The Advantages of Big Data and Data Analysis

  • Big data can provide more information, allowing you to get a more comprehensive answer.
  • A more comprehensive answer means that the data is more credible, which in turn helps you to uncover a new problem-solving approach.

Big Data Use Cases

From customer experience to analytics, Big Data can help you with a wide range of business activities. We will only introduce a few of them here as examples.

Product Development

Companies such as Netflix and Procter & Gamble use big data to anticipate customer needs. They classify key attributes of past and current products or services and model the relationships between those attributes and successful commercial products to build predictive models for new products and services. In addition, P&G plans produces and distributes new products based on data and analysis from focus groups, social media, test markets and pre-marketing.

Predictive maintenance of

various structured data (such as device year, construction, and machine model information) and unstructured data (including millions of log entries, sensor data, error messages, and engine temperatures) are often hidden An element for predicting mechanical failure. By analyzing this data, organizations can identify potential problems before an incident occurs, allowing for more cost-effective scheduling of maintenance activities while maximizing component and equipment uptime.

Customer Experience

The core of today’s market competition is to win customers. Compared to the past, companies are now more qualified to clearly understand the customer experience. Big data allows you to collect data through social media, website visits, call logs, and other data sources to improve the user experience and deliver more value. Providing personalized products helps reduce customer churn and proactively resolve issues.

Fraud and Compliance

More than a few hackers who pose a threat to your system’s security, you may encounter a well-equipped expert offensive team. Security prospects and compliance requirements are constantly changing. With big data, you can identify signs of fraud by identifying patterns in your data and aggregate massive amounts of information to speed up the generation of regulatory reports.

Machine Learning

Machine learning is a hot topic today, and data (especially big data) is one of the driving factors behind this phenomenon. We can now use a training machine instead of writing a program, and it is the big data that can be used to train machine learning models that have contributed to this shift.

Operational efficiency

Operational efficiency may not often result in the heavy news, but big data has the most profound impact in this area. With big data, you can analyze and evaluate production, customer feedback, return rates, and other factors to reduce out-of-stocks and anticipate future demand. You can also leverage big data to improve your decisions based on current market needs.

Promote innovation and

big data can help you explore the interdependence between human beings, organizations, entities and processes, and then found a new way to use insight, and ultimately drive innovation. With Big Data, you can use insights to improve financial decisions and planning considerations, understand trends and customer groups that want new products and services, implement dynamic pricing, and realize other countless possibilities.

Big Data Challenges

Big data has infinite potential and brings many challenges.

First of all, big data is huge. Although many new technologies have been developed for data storage, the amount of data is growing at a rate that doubles every two years. Organizations are struggling to cope with the rapid growth of data and are constantly looking for more efficient ways to store data.

Second, storing only data is not enough. The value of data lies in its application, and it depends on data management. We need to do a lot of work to get clean data, which is data that is closely related to the customer and organized in a way that is easy to analyze. Data scientists typically spend 50% to 80% of their time managing and preparing data before actually using it.

Finally, big data technology is updated very quickly. A few years ago, Apache Hadoop was the most popular big data processing technology. In 2014, Apache Spark came out. Nowadays, combining these two frameworks can create the best solution. Keeping pace with the development of big data technology is a persistent challenge.

How Big Data Works

Big data can provide you with new insights that bring new business opportunities and business models. Getting started with big data involves three key actions:

Integrated

Big data brings together data from different sources and applications. Traditional data integration mechanisms such as extraction, transformation, and loading (ETL) are often not up to the task. We need new strategies and techniques to analyze large data sets at the terabyte or even the petabyte.

During integration, you need to import and process data, perform formatting operations, and provide it to business analysts in the appropriate form.

Management

Big data needs to be properly stored. Storage solutions can be deployed in internal and/or cloud environments. You can store the data in any form and set the expected processing requirements and introduce the necessary processing engines for those data sets as needed. Many customers choose a storage solution based on where the data is currently located. Cloud solutions are more and more popular because they can meet customers’ current computing needs and support the use of resources on demand.

Analysis

When you analyze the data and act on the data, your big data investment will pay off. You can: Visualize various datasets for a new understanding; Explore data further for new insights; Share your insights with others; Build data models with machine learning and artificial intelligence; leverage the value of your data.

Big Data Good Practices

To help you embark on your journey to big data, we have summarized some important good practices. These principles help lay the foundation for a successful big data.

Align Big Data with Specific Business Goals

A more comprehensive data set helps you gain new insights. To this end, we must ensure that new skills, organizations, or infrastructure investments are made in a business-driven environment in order to ensure continued access to project inputs and funding.

In order to determine whether the direction is correct, you need to confirm whether big data can support and drive high-priority business and IT tasks? These tasks may include understanding how to screen weblogs to reveal e-commerce behaviours, inferring customer sentiment through social media and customer support interactions, and understanding statistical correlation laws and their relevance to customers, products, manufacturing, and engineering data.

Mitigating Skills Shortages Through Standards and Governance

One of the major obstacles to implementing big data is the shortage of skills. You can mitigate this risk by adding big data technologies, considerations, and decisions to your IT governance initiatives. Standardization helps manage costs and utilize resources. Companies interested in implementing big data solutions and strategies should assess their skill needs early and frequently and proactively identify any potential skill shortages. Training/cross-training existing staff, recruiting new staff, and using consulting firms can all solve this type of problem.

Optimize Knowledge Transfer through Center of Excellence

You can use the Center of Excellence approach to share knowledge, control regulation, and manage project communication. Regardless of whether your big data project is a new investment or an extended investment, all software and hardware costs can be shared across the enterprise. Adopting this approach will help to enhance the power of big data and the maturity of the overall information architecture in a more structured and systematic way.

Maximize Returns by Coordinating Structured and Unstructured Data

Analysis of big data itself is valuable, but you can also connect and integrate low-density big data with the structured data you are currently using to gain more powerful business insight.

Whether you want to capture customers, products, devices, or environmental big data, your goal is to add more relevant data points to the core master data and analysis summary to arrive at more accurate conclusions. For example, identifying all customer grievances is not the same as grievances that only identify good customers. So many people see big data as a necessary extension of their existing BI functions, data warehousing platforms, and information architecture.

Please note that we can build big data analysis processes and models based on humans and machines at the same time. Big data analysis capabilities include statistics, spatial analysis, semantics, interactive exploration, and visualization. With analytics models, you can correlate data from different types and sources and get meaningful insights.

Create an Efficient Exploration Laboratory

Exploring the meaning of the data is by no means a smooth path. Sometimes we don’t even know where to go. These situations are all we expected. Therefore, the management team and the IT department should support this kind of “unintentional” or “lack of clear demand” exploration activities.

At the same time, analysts and data scientists need to work closely with business units to understand key business knowledge gaps and needs. To implement interactive data exploration and statistical algorithm testing, you need to create high-performance work areas. Ensure that the sandbox environment is provided with the required permissions and is properly regulated.

Consistent with Cloud Operating Model

Big data processes and users need access to a variety of resources for iterative testing and production work. Big data solutions involve all data areas, including transactions, master data, reference data, and summary data. You should build an analysis sandbox as needed. Resource management is critical to ensure control over the entire data flow, including processing, integration, in-database aggregation, and all phases before and after analytic modelling. Properly planned private and public cloud provisioning and security strategies are also important to meet these evolving needs.

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