A big data project launch has the following 5 phases:
Data planning, data governing, data application, implementation, commercial value.
Strategy purpose: the reason we started the big data program.
Strategy planning: after setting the strategy, the purpose needs to be transferred into strategy planning, through the planning, the staff can know what the purposes of this big data project.
Commercial objective: we need to determine the commercial objective to see if we need to lower the cost or expand the revenue.
Executive plan: after the setting of commercial plan, the executive plan including project principle, interest distribution, and conflict distribution needs to be set.
Organization support: After setting the executive plan, the relevant groups need to set to determine the post and position responsibilities, the different architecture and scale of the organization need to be set.
Product planning: connect different departments and charge persons, setting the organization mechanism, completing overall planning.
Scene planning: as a big data product, the application scene needs to be planned, scene planning is the basis for further steps.
Requirement evaluation: after the completeness of product planning, scene planning, the confirmation of different departments and charge persons need to be made, then the description, evaluation planning and requirements need to be made.
Architecture planning: plan the architecture from the perspective of project launching, architecture planning is an important process for the program.
Cooperation intension: if the project need the third party data support and support from other departments, the evaluation of the project needs to be made.
After the completeness of the phase 1, there is a good foundation for big data project, then other steps after data planning need to made, high quality data have to be ensured.
Source evaluation: in the data organizing phase, the evaluation is needed and checking the source is very important. After the evaluation, the confirmation of source data is needed.
Data collection: data collection is an important work, only after collecting data, the big data relevant work can be done.
Data pre-processing: for better, more effective data, we need to pre-process the data.
Data quality: data quality can ensure the implantation of big data program, in this process, a lot of efforts need to put into form standards.
In the above processes, professional tools are needed.
Data management: data management can influence the cycle of the whole project
Third party data: access third party data by purchasing or other ways.
This is the phase we will launch big data project.
Scene detail: we need to categorize the scenes to form each use case
Function planning: after the Action process, the project is ready to launch, the requirements can be recognized and realized through organized Use Case, data, functions.
Technological selection: with function planning, we need to determine the technologies we use.
Product selection: the technological path needs to be selected after technological selection.
Application analysis model designing: big data project is to use various analysis model to quickly form prediction model. This process involves scientific, business analysis or the third party products like client analysis platform, IoT analysis platform.
PoC: select big data application scene and use PoC to verify, through PoC, we can adjust and improve each Use Case and technological selection, product selection.
After the first phases of the work, the fourth phase is to implement.
Model application: after PoC and commercial verification, only with big data analysis application, the project can be effective.
System development: system development will make the model effective.
Results evaluation: evaluation on implementation results and responsible persons.
Implantation: create the value for big data.
Data security: the project needs to make sure the data is secure.
In this phase, the enterprise can acquire:
Data asset: the enterprise can acquire data, which can advance innovation, industry upgrade, enterprise transforming, etc.
Data service: data service can advance transforming digitally.
Decision support: the big data prediction ability can improve decision support. Acquire the commercial value, realizing application of big data construction.
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