In the process of digital upgrading, business needs and digital needs will be different as the growth stage changes. Therefore, when undertaking digital upgrades, it is important to keep up with the pace of business development and take the right measures at the right time to ensure that the business is better served. In this article, we will share with you the insights of enterprises on the core needs of digitalization at different stages, and how to achieve these needs.
What is the significance of digital transformation?
The investment is huge and time-consuming, and if the decision-makers blindly push forward without deep thinking, the risk of failure is extremely high. Therefore, before embarking on the digital upgrade, we should ask all planners a profound question:What is the value of digitalization? In general, there are two main core points for the goal of enterprise digitalization:
Optimize and standardize business processes through information tools, reduce costs, improve efficiency, and improve service quality. This mainly depends on the capacity of the system, and the focus is on the construction and improvement of the information system.
Conduct business operations and business analysis based on data to identify problems and explore business opportunities. This relies more on the ability of the data, with a focus on the collection and application of the data.
These two goals, one inImprove information system capabilities, two inElevate your data capabilitiesare two very different directions. Therefore, it is wise for decision-makers to first understand the current state of informatization and core business needs of their enterprises, and then decide which direction they should work towards.
Enterprise development and digitalization
From inception to maturity, a business usually goes through four important stages:Initial formation, rapid expansion, stable operation and excellent improvement. At the same time, the digitalization process of enterprises also needs to go through four corresponding stages:
Phase 1: From manual operation to automated system operation
Phase 2: From system isolation to full interconnection
Phase 3: From interconnection to deep data integration
Stage 4: From data analysis to intelligent decision-making assistance
In the first two stages of information system construction, the focus is on building and improving the functions of the information system. In the last two stages of digital capacity building, data is the core for in-depth mining and application. It should be emphasized that if the infrastructure of the information system is not perfect, the data construction is like a castle in the air, which is difficult to achieve.
Phase 1: From manual to systematic
Corresponding business stage: business formation stage.
In the start-up phase of the business, the focus is on validating the effectiveness of the model. Since the model is not yet mature, it relies more on manual operation and adjustment. When the business model is stabilized, the volume increases, and manual operations are no longer efficient and accurate. At this point, it is necessary to introduce system support and move from manual to systematic.
The main objective: to solve the problem of efficiency and accuracy of manual operations.
Implementation Steps:
Define your business goals.
Sort out and transform into systematic processes.
Select or develop the right system.
The data at this stage is primarily used for logging and querying and is of limited help to the business.
Phase 2: From information silos to system interconnection
Corresponding business stage: high-speed development period.
As business expands rapidly, point systems struggle to keep up with demand. The lack of flow of information between different systems hinders overall efficiency. To solve this problem, we are moving on to the second phase of digital upgrading: from information silos to system connectivity.
Core requirements: information exchange, efficient collaboration, data sharing, and compliance.
Implementation methods: Unified basic data, development of ERP system shared by multiple departments, etc.
Implementation Steps:
Sort out all your business models.
Design the overall architecture and subsystem architecture.
Completed system development and linkage.
The data at this stage is used for problem tracing and troubleshooting. The business has basically been online, and the data is available, but it still needs to be improved. The focus is still on using systems to process business processes, and less on operational analytics. Therefore, data is mainly used for traceability and troubleshooting after problems are discovered, which we call "post-knowledge" data.
Phase 3: From system interconnection to data interconnection
Corresponding business stage: stable operation period.
When the market is saturated and the business enters a stable period, it is necessary to find growth points from the existing market. At this time, data becomes the key, and it enters the third stage of digital upgrading: from system interconnection to data interconnection.
Key Appeal: Analyze data in-depth to identify growth opportunities.
Implementation method: Strengthen data analysis capabilities and explore the value of data.
Implementation Steps:
Sort out all your business models.
Design the overall architecture and subsystem architecture.
Completed system development and linkage.
The data characteristics at this stage are:"Insight"to dig deeper into problems and opportunities. The demand for data is strong in various departments, and although the data department is busy, it is a good omen, indicating that everyone has started a refined operation strategy.
Phase 4: From data parsing to decision automation
Corresponding business stage: the business maturity stage.
As the reliance on data grows, the workload of analytics becomes more and more arduous. Although the decision-making after analysis is more scientific, the analysis and decision-making process is long and error-prone under large business volumes. Can the system replace manual in-depth analysis and decision-making? This is the fourth stage of the digital upgrade: from data analysis to decision automation.
At this stage, data is not only an analysis and monitoring tool, but also needs to act on its own, anticipating and automatically resolving issues to help the business take its business to the next level. This includes global visualization, automated policy enforcement, instant response, and data-driven decision-making. Data display is no longer limited to dashboards and reports, but through AI self-learning and in-depth analysis, unique algorithms and strategies are formed, and business systems are empowered in reverse. Technologies such as RPAs, control towers, and digital twins are typical use cases.
Implementation Steps:
Identify business pain points.
Develop a strategy algorithm.
Continuous iterative optimization.
At this stage, the data is not only used for analysis, but also helps the business to anticipate and automatically solve problems, reducing human intervention. Forward-looking.
Conclusion
In the digitalization process of enterprises, we will inevitably go through four stages: from manual to system construction, from system isolation to comprehensive interconnection, from interconnection to deep data integration, and from data analysis to intelligent decision-making assistance. This is not only a journey of technological upgrading, but also a path of business development and business awareness growth.
While each company's specific situation is different, our goal is the same: to move from a traditional manual approach to a full-scale digital transformation. Each stage on this path of transformation is accompanied by challenges and difficulties. No matter what stage your business is in, whether you've gone through or are going through these phases, I invite you to get involved. Because with each step, we see a whole new world, and our business awareness improves.
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