The common characteristics of big data can be summarized in five words: big, large, valuable, fast, and reliable.
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Volume
Data collection, calculation, and storage are extremely large, and the data volume is huge.
Currently, the data volume of all printed materials produced by humans is 200PB (1PB=1024TB), and the data volume of all words spoken by all humans in history is about 5EB (1EB=1024PB).
Currently, the capacity of a typical personal computer hard drive is at the TB level, and the data volume of some large enterprises is close to the EB level.
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Variety
There are multiple categories and sources. The categories include structured, semi-structured, and unstructured data, and common sources include weblogs, audio, video, pictures, etc.
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Value
The value density of big data is relatively low. For example, with the widespread application of the Internet of Things, information perception is ubiquitous, and there is a large amount of information.
Still, the value density must be higher, with much irrelevant information.
Therefore, it is necessary to make predictable analyses of future trends and patterns and use machine learning, artificial intelligence, etc., to conduct in-depth and complex analyses.
How to extract the value of data more quickly through powerful machine algorithms is a complex problem that needs to be solved in the era of big data.
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Velocity
Fast data grows fast, is processed fast, and is acquired quickly. This is the most significant feature distinguishing big data from traditional data mining.
According to IDC’s “Digital Universe” report, by 2020, global data usage will reach 35.2ZB. In the face of such a vast amount of data, the efficiency of data processing is the lifeblood of an enterprise.
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Veracity
The accuracy and reliability of data, that is, the quality of data.
Four Key Technologies Of Big Data
Big data technology is a new generation of technology and architecture in the IT field. It is a technology that quickly obtains valuable information from various data types.
Big data is essentially data, and its key technologies are still nothing more than these four significant items:
Collection and preprocessing; Storage and management; Analysis and mining; Presentation and application
(big data retrieval, big data visualization, big data security, etc.).
1. Big Data Collection And Preprocessing Technology?
The importance of big data technology is not in mastering massive amounts of data, but in processing these data intelligently and extracting meaningful information from them.
The prerequisite is having a large amount of data.
Collection is the most essential part of big data value mining.
Generally, sensors, communication networks, intelligent recognition systems, and software and hardware resource access systems are used to realize intelligent recognition, positioning, tracking, access, transmission, signal conversion, etc., of various types of massive data.
To quickly analyze and process, big data preprocessing technology needs to extract, clean, and convert different types of data to convert these complex data into practical, single, or easy-to-process data types.
Even big data service companies find it challenging to give a definite answer to the question of “which data will become assets in the future?”
However, what is certain is that whoever has enough data will have the potential to control the future, and data collection now is the accumulation of future liquid assets.
2. Big Data Storage And Management Technology?
There are many ways to classify data, including structured, semi-structured, and unstructured metadata, master data, and business data;
it can also be divided into GIS, video, text, voice, and business transaction data.
Traditional relational databases are no longer adequate for storing diverse data. In addition to relational databases, there are two other types of storage.
One is a distributed storage system represented by HDFS that can be directly applied to unstructured file storage, and the other is a NoSQL database that can store semi-structured and unstructured data.
Big data storage and management is to use these storage technologies to store, manage, and call the collected data.
In general, big data storage layers can comprise relational databases, NoSQL databases, and distributed storage systems.
Business applications choose different storage modes according to actual situations.
The storage layer may be encapsulated into a set of unified access data services (Data as a Service, DaaS) to improve the business’s storage and reading convenience.
DaaS can completely decouple business applications and storage infrastructure.
Users do not need to care about the underlying storage details; they only need to care about data access.
3. Big Data Analysis And Mining Technology?
Big data analysis and mining is the process of collecting meaningful information and knowledge from massive amounts of incomplete, noisy, imprecise, and random real-world application data.
There are many technical methods involved in big data analysis and mining:
According to the mining task, it can be divided into classification or prediction model discovery, association rule discovery, dependency or dependency model discovery, anomaly and trend discovery, etc.;
According to the mining method, it can be divided into machine learning, statistical methods, neural networks, etc.
Among them, machine learning can be divided into inductive learning, genetic algorithms, etc.;
Statistical methods can be further divided into regression analysis, cluster analysis, exploratory analysis, etc.;
Neural networks can be further divided into feedforward networks, feedback networks, etc.
The analysis and mining algorithms and models required for different analysis or prediction needs are entirely different.
The various technical methods mentioned above are just a way of thinking about solving problems.
When facing real application scenarios, these algorithms and models must be adjusted according to needs.
4. Big Data Display And Application Technology?
There are far more extensive data users than programmers and professional engineers.
How to present the analysis results of big data technology to ordinary users or company decision-makers depends on the visualization technology of data presentation, which is one of the most effective means of explaining big data.
Data visualization presents data results to users in a simple, vivid visualization, graphical, and intelligent form for analysis.
Typical big data visualization technologies include tag cloud, historical flow, spatial information flow, etc.
Benefits Of Big Data, Challenges Of Big Data, And Methods To Resolve The Problems With Big Data
Benefits Of Solving Big Data Problems
Utilizing big data in a company’s business activities can bring various benefits.
Let’s take a look at the benefits of solving the problems of big data.
- Resolve Customer Dissatisfaction
Use internet tools, social media, and other channels to solicit comments and feedback on your products and services.
Customer dissatisfaction ranges from actual, visible opinions to latent dissatisfaction, making it easier to find answers using big data.
Suppose the problems with big data can be resolved.
In that case, the advantage will be that honest customer feedback can be collected and reflected in business activities, allowing you to understand needs and consider future measures.
- Improve Work Efficiency And Productivity.
Utilizing big data in business can lead to innovations that eliminate waste when providing products and services.
Utilizing data about your company’s business operations can help you review your operations, reducing the time it takes to complete tasks.
The benefit of big data is that it can improve work efficiency and productivity within the company, helping solve the problems arising from big data.
- Leads To Cost Reduction.
Big data can also be used to make future predictions, and if used effectively, it can lead to cost reductions across the company.
For example, if you can accumulate and utilize real-time data, you will no longer need to hold inventory in-house, which will help you avoid wasting money.
One benefit of solving problems with big data is that it can lead to cost savings for continuing business activities.
What are the problems with big data?
Learn how companies can solve their big data problems.
Furthermore, when companies start using data, they face challenges before it can be used in the field, such as learning how to select and use information.
Issues regarding the reliability of the data, customer personal information, and privacy may arise when collecting information.
While big data can be utilized in business strategies for corporate activities, it also poses some problems.
The Problems With Big Data?
Big data can be used for business strategies, but there are concerns that it can be difficult and costly to manage.
Let’s examine the issues that arise when using big data in a company’s business activities.
The Quality And Reliability Of The Data Need To Be Higher.
Depending on the source of big data, accurate data may not be extracted and cannot be applied.
For example, it contains incomplete data, unnecessary data garbage, duplicate data, typos, and other data that has no value as data.
Not all big data can be used as it is, so the quality and accuracy of the data must be checked before it can be used.
Difficult To Select Data
Companies often need to know what big data they have collected can be used for their business.
For example, even if information is obtained from social media or communication systems, how to use it in business may need clarification.
The large amount of big data makes it challenging to select and use.
Beware Of Infringement Of Personal Information And Privacy
Big data includes information that deals with personal information relating to customers.
Although this information can be useful for businesses, it must be handled with caution because it might cause worry among customers.
A company must maintain consideration and safety, such as processing data without violating privacy, or else it can lead to problems.
Costly To Manage And Maintain
There are concerns about managing big data, including systems that can store large amounts of information and are costly to operate and maintain.
Also, when problems are found with the systems or tools, it can take time and effort to make repairs or adjustments.
While big data can be effectively utilized, the problem is that it requires ongoing costs to manage and maintain it using internal and external services.
How To Solve The Big Data Problem
By identifying problems and taking measures accordingly, big data can be utilized in a company’s business strategy.
Let’s look at how to solve the problems of big data.
Hire People With Strong AI And IT Skills.
Consider hiring new personnel with specialized knowledge and skills to utilize big data.
Introducing a system that can collect and analyse big data and securing the necessary personnel will enable operations to proceed smoothly.
If you can hire personnel with excellent AI and IT skills, it could be a way to solve the problems associated with big data, so try to put it into full-scale operation.
Protecting Privacy From The Customer’s Perspective
Make sure to put together an easy-to-understand privacy policy so that customers who use your company’s products and services can feel at ease.
We will also continue to operate from the customer’s perspective by allowing them to adjust settings related to their personal information on their online sites and apps.
When collecting and analysing information, establish systems that allow you to fulfill your company responsibilities, such as using data processing techniques to protect privacy.
Educate Employees On Data Management.
Establish an internal system to educate employees who handle big data, including how to handle customer personal information and data management.
To avoid losing the company’s credibility, we will continue to educate on data management and raise awareness of security issues safely.
Implement a management system to utilize big data and resolve problems appropriately.
Solving problems with big data benefits customers by alleviating dissatisfaction, increasing work productivity, and reducing costs.
When utilizing big data, educating employees and establishing internal systems to protect privacy while attracting new talent is essential.
Conclusion
Big data applications are widely used in areas such as business intelligence, government decision-making, and public services.
Big data has contributed to daily life scenarios such as epidemic prevention and control, anti-telecom fraud, intelligent transportation, and environmental monitoring.
The era of big data has brought new challenges to our ability to control data and provided space and potential for more comprehensive and wise insights.
Many new technologies have emerged in big data, which have become powerful weapons for big data collection, storage, processing, and presentation.
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