Product managers often encounter this kind of problem:
I just launched a function, and I asked the R&D comrades to help me pull out the data for analysis, but I was cruelly told that it needs to be scheduled.
I stomped my feet anxiously, but I could only understand.
Data R&D personnel have endless data to check and tables to write every day. The data required by the business department is not available for a long time, and even if it is obtained, errors often occur.
Big data is gradually permeating our daily life and every corner, and all walks of life are thriving because of the massive explosion of data. In various fields such as science and technology, finance, and transportation, it has become an essential element of every processing unit.
Enterprise data is very critical. There is a lot of information in a large amount of data, some of which are obviously used by people at the first level, but sometimes even if they get the data, they will be at a loss. This article summarizes the steps on how to build an efficient data platform, hoping to help you.
What is a data center
The first is an IT system, which is a platform for unified management of data, including data generated by business operations, user behavior data, and external third-party data, for unified, standardized, complete, and accurate collection and storage. , processing, management, and a platform that provides data services for the front-end business.
To put it simply, it can be understood as a shelf, and data is the goods on the shelf, which are placed on the shelf in categories.
As an enterprise grows, the number and types of documents will increase. These important knowledge "wealths" need to be managed well. Therefore, the ultimate goal of building a data center is to achieve "rapid market response, refined operations, open sources of income and reduce expenditure" through efficient digital operations. So, it's time to revamp your data platform.
The value of the platform in the data center
To open up data islands, a general enterprise will have multiple business lines, and the data of different business lines will be stored in different databases.
Reduce data development costs, strong scalability and low maintenance costs in the data center.
After unified and standardized management of data, the accuracy and timeliness of data will be greatly improved.
Once the data assets are accumulated, the value and potential that can be exerted are unlimited. Such as user portraits, labeling systems, personalized recommendations, trend predictions, etc.
Steps to build a data center
The success of the data center is based on informatization. Without a sound informatization foundation, enterprises cannot fully understand their business, and it is even more difficult to obtain useful information from it. So how should a general enterprise start to build a data middle platform? Usually we say that the data center is responsible for the "acquisition, storage, management, and use" of data, that is, the collection, storage, management, and application of data.
Data collection: It is divided into real-time collection and offline collection, and the data from each source is synchronized to the data warehouse. Common data sources include business databases, third-party API data, and externally collected unstructured data.
Data storage: Data synchronously imported will be stored in HDFS. Computing engine tasks such as Hive, Flink, and Spark read the data in HDFS for calculation and then write the calculation result to HDFS.
Data management: It is divided into metadata management and data model management. Metadata can be understood as the atomic fields of each data table. The data model is the addition, deletion and modification of the data model through the established data model management system during the data modeling process. Check management, and at the same time follow the requirements of data standardization and data uniformity to ensure data quality.
Data application: Common data applications include BI report platform, user portrait, digital marketing (including recommendation, search), etc.
How should a product manager work?
First of all, clarify the current data status of the company. What are the existing problems? For the data governance team, the key to success is not technology, but the familiarity and grasp of business processes and corporate culture, and to determine the goal of this data center construction based on current problems.
Secondly, what data scope is involved in the current business scope of the research company? Generally, the source system is used as the entry point, starting from commonly used systems such as CRM system, ERP system, order system, etc., and the division of the overall structure is completed by combing the data of each system.
Furthermore, to find the source of data rules, you can also find the person in charge of the system or the development docking personnel. At the same time, it is necessary to clarify the relevant business parties of the data service in this process, so as to synchronize the consistency of data rules and business logic.
Finally, plan the data logic, which is the core content of the data system, and complete the relationship design between each data subject and data table through the data model.
The development trend of data center
Trend 1: Cloud Native
Technology and business jointly drive the data center to "cloud native". The important components in the data center will follow the storage-computing separation architecture. Cloud native technology has a natural object system, containerized orchestration, CI/CD, and cross-cloud multi-domain data governance. Features such as these can meet the needs of enterprise customers for data security and compliance data cooperation technology, and promote the data middle platform to become cloud-native.
Trend 2: Integration of digital intelligence
Data-intelligence integration is a unified foundation for building data governance and AI development, allowing data and artificial intelligence to interact. The digital management center can adopt a custom development model, that is, how to build the management center, and what kind of functional features the layout can be completely decided by the user. JNPF agile development framework, a digital management platform that can realize zero-based learning, quickly and conveniently produce cool and dazzling large screens, and realize rapid and low-cost development of personalized digital management.
1. High-performance development platform: The platform has a powerful code generator and a stable underlying development framework, which can easily realize low-code secondary software development;
2. Flexible, agile and easy to use: the platform provides source code, and enterprises can freely configure according to different business scenarios, personalized development, easy to maintain and update;
3. Low-cost R&D platform: The traditional development model requires many people to collaborate on R&D, but now only two or three or even one person can complete the development work;
4. High-efficiency development work: the platform has a visual development process, built-in multiple sets of high-quality UI templates, wizard-style development components, and rich chart designs;
5. Powerful function development: one-stop platform, lightweight integrated development of Windows+Web+App+small programs and other PC-side + mobile-side intelligent management systems;
6. Highly adaptable interface functions: Rich and complete API interfaces and powerful port engines can realize the interconnection of all things, and realize the joint management and control of mobile phones and PCs;
Open source address: https://www.yinmaisoft.com/?from=csdn
Trend 3: Pan-China-Taiwanization
The era of 5G and AI intelligence is coming one after another, and innovative technologies continue to develop. With multi-device access and multi-system data fusion and interconnection, new data islands are formed, posing new challenges to the intelligent use of enterprises. More and more, the conceptual system of the data center is gradually improving. Taking the JNPF low-code rapid development platform as an example, it includes all the features of the collection platform, communication center and data center, and supports data analysis, processing, transactions, etc. The collection and communication capabilities other than abstract business services are more suitable for enterprise business scenarios than data centers, and provide more in-depth and refined basic capabilities for enterprise digital construction.
The data center is a very large system, and each part can be talked about separately, but today’s article is just an introduction. I want to know what the data center is and what problems it solves. I have an overall preliminary understanding , about data modeling, data index system construction, data warehouse construction, BI visualization, etc., if you are interested, I can write about it in detail later.