Big data, as the most active element of technological innovation in the new industrial revolution, is comprehensively reconstructing the fields of global production, circulation, distribution and consumption, which has a profound impact on global competition, national governance, economic development, industrial transformation and social life. In this article, we will learn more about Big Data – Concepts, Importance and Career Prospects
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Big Data – Concepts, Importance and Career Prospects
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What is Big Data?
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Big data, refers to huge amounts of data that is too large to be accessed, managed, processed, and organized in a reasonable time through the human brain or even mainstream software tools to help enterprises make more positive business decisions.
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Big Data Concepts
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Big Data is a collection of data that is large enough to acquire, store, manage and analyze much beyond the capabilities of traditional database software tools, and has four characteristics: mass data scale, rapid data flow, diverse data types and low-value density.
Big data has the characteristics of large scale, complex data category, fast data processing speed, high data authenticity and data potential value. The processing and mining of big data depends on the distributed processing, distributed database, cloud storage and virtualization technology of cloud computing platform.
The development of science and technology and the Internet is driving the advent of the era of big data, with huge amounts of data fragmentation generated every day in all walks of life, from Byte, KB, MB, GB, TB development to PB, EB, ZB, YB and even BB, NB, DB to measure. Data acquisition in the age of big data is no longer a technical issue, but how can we find value in the face of so much data?
Big data can be applied to all walks of life, people collect a large amount of data for analysis and collation, to achieve the efficient use of information.
In general, big data is a large, dynamic, and sustainable amount of data that can be mined with new systems, tools, and models to gain insight and new value.
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Big Data analytics
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Big Data analysis is defined in the Book of Big Data: Instead of analyzing all data by random sample survey, the distribution status of the data is not taken into account, because sampling data needs to consider whether the sample distribution is biased, consistent with the population, and does not need to consider hypothesis testing, which is also a difference between big data analysis and general data analysis.
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The Importance of Big Data
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Big data analytics has three main functions:
First, the processing and analysis of big data is becoming the node of a new generation of information technology convergence applications. Mobile Internet, Internet of Things, social networking, digital home, e-commerce, etc. are the applications of the next generation of information technology, these applications continue to produce big data. Cloud computing provides storage and computing platforms for these large and diverse big data. Through the management, processing, analysis and optimization of data from different sources, the results will be fed back into the above applications, which will create great economic and social value.
Second, big data is a new engine of sustained and rapid growth in the information industry. New technologies, new products, new services, new businesses are emerging for the big data market. In the field of hardware and integrated equipment, big data will have an important impact on the chip and storage industries, and will also create an integrated data storage processing server, memory computing and other markets. In software and services, big data will lead to the development of data processing analytics, data mining technology, and software products.
Third, the use of big data will become a key factor to improve core competitiveness. Decisions across industries are shifting from “business-driven” to “data-driven.”
1, big data is a large number of high-speed, changeable information, it needs a new way of processing to promote stronger decision-making ability, insight and optimal processing. Big data provides unprecedented space and potential for deeper, more comprehensive insights for businesses.
2, with the help of big data and related technologies, we can target customers with different behaviour characteristics, and even from “recommend a product to some suitable customers” to “recommend some suitable products to a customer”, to be more focused on customers, personalized and accurate marketing.
3, the era of big data precision marketing refers to the acquisition of object preferences through big data preferences, behaviour preferences, different objects for different marketing. The core of big data precision marketing can be summed up in several key words: users, needs, recognition, experience.
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Big Data visualization
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Big Data Visualization visualizes the data intuitively, allowing it to speak for itself and let the audience hear the results. To meet and exceed customer expectations, big data visualization tools should have these characteristics:
1· Ability to handle different types of incoming data.
2· The ability to apply different kinds of filters to adjust the results.
3· Ability to interact with datasets during analysis.
4· Ability to connect to other software to receive input data, or to provide input data for other software.
5· Ability to provide users with collaboration options.
Although there are actually countless tools dedicated to big data visualization, and they are both open source and proprietary, some of them stand out because they provide all or many of these features.
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What principles do Big Data visualization designs follow?
Because previous data analysis reports have made it difficult to quickly and clearly understand the information behind large amounts of data, data visualization can clearly and effectively transfer important data generated in the course of an enterprise’s operations in the form of visual charts.
What are the principles that need to be followed in the design and production of data visualization?
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1. Clarify the project objectives
Data visualization should answer important strategic questions, provide real value, and help solve real-world problems.
For example, it can be used to track performance, monitor customer behaviour, and evaluate the effectiveness of processes. At the beginning of a data visualization project, you should identify the time it takes, the purpose of the project and the priorities of the data analysis presentation, and the ultimate data visualization to be useful, so as not to waste time creating unnecessary visual effects.
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2. Get to know your audience
Data visualization is useless in designing without taking into account clear communication with the target audience.
It should be compatible with your audience’s expertise and make it easy and fast to view and process your data, taking into account your audience’s familiarity with the fundamentals of data rendering, whether they might have a background in data visualization, and whether they need to view charts regularly.
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3. Use the correct data chart
Charts are varied, and choosing which type of data is best for visual presentation is an art in itself. The correct chart will not only make the data easier to understand, but will also be displayed in the most accurate way. In order to make the right choice, you must give full consideration to what type of data you need to transfer and to whom.
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4. The most popular data visualization chart type
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1) Line chart
Line charts are applied to compare values over time and are useful for displaying large and small changes, and they can also be used to compare changes to more than one set of data.
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2) Bar chart
Bar charts should be used to compare several categories of quantitative data, which can also be used to track changes over time, but are best used only when these changes are important.
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3) Scatter chart
Scatter charts are applied to the values of two variables that display a set of data. They are ideal for exploring the relationship between two groups.
In this example of data visualization best practices, displaying data visually makes it easier to understand.
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4) Pie chart
Pie charts are applied to show a portion of the whole.
Many business executives see the value of data visualization in practice, enabling decision-makers to solve problems that are difficult to read quickly with data analysis reports and to use data visualization patterns to understand data to make better decisions for businesses.
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Career Prospects in Big Data Analytics
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With the rapid development of the Internet in the information age and the rise of big data, many people began to choose the big data industry. When choosing data analysis, they see more of the prospects for the job and good pay. In fact, they need to have a clear understanding of themselves before they enter data analytics or any industry. Weigh whether you’re really right for the data analytics industry. Here are a few tips for getting started with data analysis, which will help you choose.
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Tips for Getting Started with Big Data Analytics
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1. Solid foundation of expertise
To engage in any industry requires professional knowledge of the industry. To do a good job in data analysis needs to master a variety of knowledge and skills, mainly divided into soft and hard two major strengths, soft power including communication skills, expression skills, design skills. Hard power is the ability to use complex systems and reserve knowledge for the data analysis industry.
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2. Ability to understand the theory of data analysis
Data analysis needs to have a variety of theoretical basis, such as basic knowledge of data analysis: statistics, probability theory, data mining basic theory, etc. , for those engaged in the data analysis industry, for the theoretical thinking understanding is equally important, which determines the distance forward.
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3. Mastery of data analysis tools
Mastery of data analysis tools, i.e. the ability to harness them. As for tools, which tool is better, not to say that Hadoop is better than Oracle, python is better than SPS, different scenes of different backgrounds on the use of the tool is also different. For data analysis, the most basic tools Python, SQL are must know and to be mastered. This determines the pace of follow-up learning.
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4. Sensitive and learned mentality
Industry development is also technology innovation, data analysis is also the same. In the face of difficult to break through, not only need to have a good attitude, actively looking for breakthrough points and improve their own innovation. A good data analyst must be proactive in finding problems, solving them, and carrying the pressure. Life is a long and sustained process, do not have to worry too much about the immediate win and loss, if the direction is right, slow is also fast.
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The importance of big data analytics has led to fierce competition and increased demand for big data professionals. Data science and data analysis are an evolving field with great potential. Data analytics helps analyze the value chain of your business and gain insights
The data age will be increasingly demanding for the data analytics industry. Choose an industry, to meet the development trend of the industry, in the process of continuous learning to improve the comprehensive strength.
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