Digital transformation is the only way for enterprises in the cold winter, but statistics show that 70% of the digital transformation of enterprises ultimately ends in failure, what are the essential factors that enterprises need to have if they want to succeed in digital transformation, combined with the projects of digital transformation strategy landing in the past two years, the three core carriages are summarized as: strategy, organization and technology.
1. Strategy: Digital strategy
change Transformation is generally a bone scraping for enterprises, and will go through a relatively long period of pain, so if you want to succeed in transformation, you first need to have top-down strategic support from the enterprise, that is to say, you need to recognize the value of digital transformation at the CEO level, and take data strategy as one of the core strategic plans of the enterprise in the future stage, operations and other teams work together to execute, there are many obstacles, and the final project result can be imagined. By grasping digital transformation (at least showing the supportive attitude of identification), the executive team can become famous, holding the CEO's sword, and overcoming difficulties along the way.
2. Organization: Organizational foundation
In the field of enterprise management, there is a well-known organizational Yang triangle model, which can be summed up as the success of an enterprise is closely related to organizational ability. A series of the most basic guarantees such as systems, organizations, and mechanisms.
Organizational Structure:Enterprises should have a reasonable organizational structure to support functional departments that have completed the digital transformation from strategy to implementation, such as strategic analysis department, data research and development department, IT software research and development team, etc.
cio/ctoDigital transformation is a first-class project, and transformation inevitably involves the transformation of existing organizations or processes, and bottom-up transformation is almost impossible to succeed, so it requires strategic empowerment, otherwise the teacher will inevitably fail.
2.Diplomat
If you want to make a deep understanding of business processes and pain points in the transformation process, the data team is not working behind closed doors, but has a deep understanding of business processes and pain points, so you need to have a deep data foundation in the role of "diplomats" to continue to deepen the business process, and can tell what changes business data can bring, what data exists, and what work needs to be done. This role is best taken primarily by the data product manager, as he can coordinate the different resources of the organization to get things done. At the same time, the data analyst, or the person in charge of the data warehouse, can also participate as the input and output of information.
3.Think tank
Data analysts mainly use data analysis technologies and means to provide optimal decision-making, while algorithm engineers rely on AI technology to provide more intelligent capabilities, such as AI-based intelligent scheduling processes, personalized marketing or product recommendation services. The data product manager is mainly involved and provides input on some of the business knowledge.
4.Founder
Data is the basis of digital transformation, and without data or dirty data, the transformation process will inevitably be bumpy or face failure. It is mainly the responsibility of data development engineers to aggregate, clean, process, and process data to form data assets that can be highly reusable, and to continuously govern data, ensure data quality, and reduce storage and computing costs. In this process, the data product manager mainly inputs data requirements or data product requirements, and provides the business information input required for the construction of data assets.
5.Architect
In the process of digital transformation, in the process of data collection and analysis and application, data products or tools are used to improve the efficiency of data application circulation, and in this process, relevant data product managers are required to plan and design corresponding data products, and front-end engineers and back-end engineers develop and monetize.
Process mechanism: The project needs to have a standardized project management process, otherwise it will inevitably encounter delays, quality problems, cost problems, etc., and generally some mature companies will have a standard PMO process, such as a regular meeting mechanism, which can be used to supervise the progress of the work and monitor the implementation processPeriodic review mechanism, etc., to review operational processes, optimize operational standards, etc.;In addition, the corresponding incentive and assessment indicators (by setting hard constraint indicators such as incentive measures and KPIs at all levels and all stakeholders, to improve the enthusiasm and work of all parties, such as KPIs and OKR management systems.
3. Technology: data and IT technology
In addition to the strategic thinking and organizational transformation, in order to achieve the actual effect of reducing costs and increasing efficiency, digital transformation must have corresponding digital tools or digital application development technologies to assist in the implementation of the strategy. The main techniques for digital transformation design typically include:
Data Acquisition and Acquisition Technology:Data is the foundation of data and digital intelligence, and for online products and tools, data is related to data burying, while in some other industries such as intelligent manufacturing, data collection is sensor data collection and synchronization technology.
Big data storage and computing technology: The 4V characteristics of big data (low value density, large amount of data, etc.) determine the need for a large number of server resources for data storage and related task computing, so it is necessary to have the technical capabilities for the construction and operation and maintenance of big data clusters.
Data Analysis and Mining Techniques:Data analysis generally refers to the business analysis or business analysis of data analysts, such as SQL and Python.
Data Science & AI Capabilities:The high-level stage of digital transformation is the application of digital intelligence, which mainly uses machine learning and various algorithm models to generate business application value, including the design and development of statistical algorithms and model technologies.
Data Security Technology:Once data is widely used, it means that data leaks under the sun, and data, as a core strategic asset, requires a series of data security technologies such as data desensitization and authentication management.
IT product R&D technology:In the past docking projects, customers did not have front-end and back-end technical capabilities, and all data-based applications relied on the output results of the data team to the application, and the business had no way to use tools for self-service applications.