Explain the underlying construction of data: data collection, data integration, and data governance!

If you want to say what is the "top stream" of the IT circle, everyone must think of the data middle platform at the first time, after all, over the years, half of the IT circle has been vigorously touting the middle platform, and the other half is singing about the middle platform everywhere.

However, whether it is touting or singing about the middle office, few people mention the necessary data bottom construction before going to the middle office.

However, a complete data infrastructure can provide a solid foundation for enterprises to ensure the successful implementation and operation of the data middle platform. In other words, without the underlying construction of data, there is no data middle platform at all. Therefore, today I will talk to you about what is the underlying construction of data.

To put it simply, the underlying data construction includes three parts: data collection, data integration, and data governance.

1. What is data collection?

A large amount of data will be generated in the daily operation of enterprises, and these data sources are wide, such as enterprise sales data, user behavior data, social media exposure data, and so on. Data collection is to collect and store data from these different sources and channels to facilitate subsequent analysis by enterprises.

For example, if a company wants to analyze the sales of a product, it needs to collect relevant data such as order information, customer purchase records, and product inventory from various sales channels. This data can come from spreadsheets, databases, sensors, websites, or other systems. Through data collection, enterprises collect these scattered data into one place (generally the business system) to form a data collection for subsequent analysis and utilization.

There are many ways to collect data, such as manual collection, automatic collection, crawler collection, etc.:

• Manual collection can be said to be the originator of the way enterprises obtain data, specifically refers to the manual entry of data through manual means, which used to use pen and paper, and now uses Excel and video. While this approach is relatively simple, it is time-consuming, inefficient, and error-prone. Therefore, enterprises will consider replacing manual labor with automated tools, which is called automatic collection.
• Automatic collection refers to the collection of data through some automated tools, which can significantly improve the collection efficiency and reduce the error rate, but enterprises need corresponding technical support and tool investment.
• Crawler harvesting refers to writing programs to automatically access websites and scrape the required data from them. This approach is flexible and has a wide range of applications, but companies need to be aware of the restrictions of laws and regulations to ensure legal and compliant data collection practices.

In summary, data acquisition is the process of collecting data from different sources and storing it centrally for subsequent processing and analysis. It is the first step to obtain data and the first step of the underlying construction of the enterprise.

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2. What is data integration?

After the enterprise completes the collection of raw data of each business system through data collection, the next step is to centralize the management of these scattered data, which is data integration.

Data integration brings together data from different sources to form a unified view of the data, like piecing together fragments of a jigsaw puzzle to complete a complete picture.

However, unlike the puzzle, data integration also involves solving the differences and incompatibilities between different data sources, including data format conversion, field mapping, data cleansing, and data duplication processing.

Therefore, through the data integration platform, enterprises can eliminate data silos, ensure data consistency and accuracy, and improve data availability and trustworthiness. The result is a more comprehensive understanding of their own business and more accurate and informed decisions.

Imagine that your business has multiple departments that store data in different places, and each system may use different data formats, naming conventions, and storage methods, such as a sales system that stores the details of sales orders, and a CRM system that stores customer profiles and interaction history.

In the past, if you wanted to see what products customers had purchased and what service requests they had for the products, you would need to download the data from the two systems, integrate the data through a series of data processing operations, and then search for specific customer information. The steps are cumbersome, and once the data changes, you need to repeat the operation, which cannot be more troublesome.

Now, with dataset achievement, you can bring these scattered data together to form a unified data set, and you can view the complete information of your customers in one place, including their purchase history, contact information, service requests, etc., without any data integration operations, which is extremely convenient.

3. What is data governance?

In the previous section, we understood the process of data collection and the importance of data integration, however, it is not enough to collect and integrate data to ensure the high quality and effective use of data, which requires the introduction of the concept of data governance.

Data governance can be simply defined as a set of rules and measures taken by an enterprise in terms of data management, it acts like a data steward of the enterprise, managing the quality, security, and compliance of data, making data a valuable asset for the organization. It's like the state makes a series of laws and regulations to regulate our behavior to ensure that society is safe and stable.

In the underlying construction of data, enterprises formulate data management specifications, establish data quality control measures and supervision mechanisms through data governance to ensure the accuracy, consistency and integrity of data. Data governance also involves defining data standards, data security policies, and compliance measures to ensure that data is properly managed and protected throughout the data middle office lifecycle.

Specifically, data governance includes the following four areas:

1. Data quality: Data governance is committed to ensuring the quality of data. This includes the accuracy, completeness, consistency, and timeliness of the data. By establishing data quality standards and establishing data validation and cleaning processes, organizations can identify and correct data quality issues and ensure that data is trustworthy.
2. Data security: Data governance ensures the security of data against unauthorized access, data breaches, and malicious attacks. This includes developing access control policies, encrypting data transmissions, and establishing security audit and monitoring mechanisms to protect the confidentiality and integrity of data.
3. Data compliance: Data governance ensures that organizations comply with applicable regulations, laws, and industry standards. This may involve aspects such as data privacy regulations, data protection regulations, and industry norms. By establishing compliance policies, data usage policies, and compliance audits, organizations can ensure data compliance and reduce legal and compliance risks.
4. Data management: Data governance involves the management and planning of data, including data classification, data identification, and data standardization. This helps to establish a consistent data vocabulary, data model, and data classification system, and improves data manageability and comprehensibility.

IV. Conclusion

In short, the underlying data construction includes three main parts: data collection, data integration, and data governance. Data collection is the acquisition of data from different sources to provide the basis for subsequent analysis. Data integration brings disparate data together into a unified view of the data, ensuring data consistency and accuracy. Data governance, on the other hand, is to ensure the high quality and effective use of data, acting as a data steward of an enterprise, setting rules and measures to manage the quality, security, and compliance of data.

By gaining a deep understanding of the three aspects of data underlay, we realized that this is an indispensable cornerstone for the success of the data middle office. Only on this solid foundation can enterprises carry out the construction and development of the data middle platform more smoothly. Let's work together to build a solid bridge for the efficient management and full use of enterprise data, and promote continuous innovation and development across the entire IT field.