Enterprise How to tap the true value of 80% unstructured data?
Modern enterprise applications in the digital economy will generate a variety of data, including operating system logs, network and security device logs, business access and transaction data, etc. These data are very large in volume and in different formats.

Big data analysis concept

Big data analysis refers to the analysis of large-scale data. Big data can be summarized as 5 Vs, with large data volume (Volume), fast speed (Velocity), multiple types (Variety), value (Value), and authenticity (Veracity).

Big data is the most popular IT industry vocabulary nowadays. The subsequent use of data warehouse, data security, data analysis, data mining and so on around the commercial value of big data has gradually become the focus of profit sought after by industry professionals. With the advent of the era of big data, big data analysis has also emerged.

Challenges facing big data analysis

IDC data shows that at present, structured data in enterprises accounts for 20% of the total data volume, and 80% is unstructured data. How to mine the true value of 80% of the data, including the hidden risks and commercial value, has become a big The main goal of data analysis.

1. Data island

There is a lot of information in logs, network traffic and business process data, but they are isolated from each other, and these logs need to be effectively correlated.

2. A wide variety of log formats

Different systems and devices have different log formats. Especially when dealing with unstructured and semi-structured data, how to quickly normalize various types of logs is the primary problem for big data analysis.

3. Department independence

Security, business, and operation and maintenance are independent of each other, and their respective concerns and perspectives are different. It is difficult to meet the needs of multiple operation and maintenance responsibilities of business operation and maintenance departments in many enterprises on the same data analysis system.

4. Retrospective analysis and evidence collection

Security incidents occur frequently, and I want to learn from the incidents, how to effectively conduct retrospective analysis and obtain evidence.

5. Fast iteration

Once the analysis system is developed, it is difficult to make adjustments to data sources, analysis dimensions, report presentation, etc., to adapt to new requirements.

6. Regulatory compliance

The cyber security law and the security system all put forward relevant requirements for log retention and security situation awareness.

“Faced with massive amounts of data, whoever can better process and analyze the data can truly seize the opportunity in the era of big data.” This is almost the consensus of everyone in the industry. The analysis of massive data has become a very important and urgent need of enterprises and governments.

Holographic data perspective solution

The holographic data perspective system launched by Pangeo Technology is an analysis platform for multi-source heterogeneous massive data. It can collect, organize, archive and store machine data in any format, and provide data analysis, search, and Reporting and visualization capabilities.

For logs from various security devices, network traffic, personnel behavior data, host application logs, and business system data, through intelligent analysis methods such as correlation analysis, user behavior analysis, and machine learning, it can provide business security threat monitoring, applications, and network Security attack monitoring, operation and maintenance security monitoring and analysis functions.

Main functions of holographic data perspective

1. Security threat analysis

Analyze and present the current attack situation of the system. Including: malicious accounts, IP addresses, attacked systems, etc. controlled by hacked products;

2. Business threat perception

Perceives, analyzes and presents behaviors that cause account and data leakage risks such as database collisions and crawlers, as well as business fraudulent behaviors such as swiping orders and spikes;

3. Application security audit

Audit abnormal account operations and access behaviors;

4. Operation and maintenance monitoring support

Provide support for operation and maintenance work such as application operation resource monitoring, application access analysis, and operation analysis;

5. Customizable security visualization

Large screen display can be provided for real-time monitoring of the monitoring center.

Core advantages of holographic data perspective

1. Full-view monitoring

2. Unstructured index

3. Search engine query

4. Drag and drop dashboard

5. Excellent support platform

6. Built-in a variety of mathematical statistics and machine learning models

7. Enterprise-level architecture

Conclusion

Big data is everywhere, and data analysis has become crucial in this era of big data. This is also the key to realizing the commercial value of big data. Only good data analysis can control the lifeblood of industry development in advance. The initiative of the market thus creates greater value.
['Sajjad Hussain']
['Technology', 'Data Science', 'Data Visualization', 'Big Data', 'Data']