The business value of the data middle office
In the customer-centric era, the data middle platform plays an important role in digital transformation, and the data system based on the data middle platform will be located at the core of enterprise applications, bringing huge benefits to enterprises in terms of cost reduction, efficiency increase, and refined operation through data. The business value of the data middle office mainly includes three main points:
Customer-centric, insight-driven business action
The core goal of data middle platform construction is customer-centric continuous large-scale innovation, and the emergence of data middle platform will greatly improve the application ability of data, transform massive data into high-quality data assets, and provide enterprises with deeper customer insights, so as to provide customers with more personalized and intelligent products and services.
Data-based to support large-scale business model innovation
One reason data can't be used by business is that it can't be read and understood. Information technology personnel do not understand the business enough, and the business personnel do not understand the data enough, resulting in the application of data to the business becomes very difficult, the data center needs to consider breaking the barriers between information technology personnel and business personnel, information technology personnel will turn the data into business personnel can read, easy to understand content, business personnel can quickly integrate into the business after seeing the content, so as to better support the innovation of business model.
Revitalize the full amount of data and build a solid barrier to continue to lead
In the face of complex and scattered massive data, the outstanding advantage of the data middle platform is that it can make full use of internal and external data, break the status quo of data silos, and create data assets that continue to increase in value.
The technical value of the data middle platform
Continuously respond to the need for multi-data processing
Enterprises need a unified data middle platform to meet the needs of offline real-time computing, various query requirements (real-time query and ad hoc), and at the same time, when new data engines (faster computing frameworks, faster query responses) emerge in the future, there is no need to reconstruct the current big data system.
Enrich label data and reduce management costs
Data classification mainly includes master data, reference data and index data, but according to the current real data construction situation, it is necessary to define and classify a type of data, such as the label name is "consumption characteristics", and the label value is "**sensitive", "shop around" and "hesitant". The data center allows for quick definition and effective management of such tags.
The value of data can reflect the effectiveness of business systems
And not onlyAccuracy
In the past, the data application scenarios were mainly for report requirements, focusing on the accuracy of data, but in more data scenarios, especially for the application of label data, more and more data needs to be continuously "optimized", and the data itself is not accurate.
Support cross-subject domain access to data
The application data layer ADS (traditional data warehouse ODS DW ADS) built by enterprises in the early stage is more for a certain subject area, such as marketing domain, human resources domain, and risk control domain, and enterprises often need to break various business themes when applying data, and will consider data applications from the main body of business objects, such as people (members, merchants, channels, employees) and things (commodities, warehouses, contracts), and design a complete object-oriented data labeling system from a global perspective.
Data can be quickly reused, not just replicated
The data middle platform can help enterprises aggregate internal and external data, support efficient data services, and ultimately improve enterprise decision-making and business performance. Enterprises expect to transform raw data into data assets through the data middle platform, and quickly build data services, so that enterprises can continuously and fully use data, achieve the goal of data visibility, availability, and operation, use data to drive decision-making and operation, and continuously deepen digital transformation.