"Panorama" of Best Practice in Enterprise Digitalization Construction

The COVID-19 epidemic has accelerated the global step into the era of data capitalization. Enterprises in China are facing new opportunities of digital transformation. For entrepreneurs, it is particularly important to grasp the trend of global industrial restructuring under the epidemic situation and reshape the core competitiveness of enterprises.

Moreover, the enterprise managers who directly grasp the lifeblood of the enterprise agree with the digital transformation, hoping to enhance the service ability of the enterprise and enhance the customer experience through the digital transformation.

However, the road to digital transformation of enterprises is not achieved overnight. From the IT point of view, enterprises need the support of IT technology, so that business and technology can truly interact and master the ability to build and use technology. On the other hand, data has become the most important asset of enterprises. Building a digital platform will effectively use data to continuously create value for enterprises.

How to make a choice for enterprises in transition? Recently, Zhang Xu, Vice President of Kangaroo Cloud Strategy, combined with his years of practical experience, summed up the best practice "panorama" of enterprise digitalization construction, and analyzed the specific implementation path of enterprises in the process of digitalization.

Panoramic display of enterprise digital construction

Since enterprises began to pay attention to digitalization, we will find that many enterprises have turned from traditional BI, or started to build technologies or products from various data. However, the digital construction of enterprises is a very huge system, involving top-level design, consulting planning, technical facilities, data operation and so on. Even many enterprises have realized the value of data and invested a lot of manpower and material resources, but the effect is very small.

From this point of view, if an enterprise wants to be a data center, it should let the consultation go first, do the top-level design and consultation planning of data, and then do the data platform, data assets, data services and data value. These are the main channels for digital construction of enterprises, and ultimately it must realize its commercial value. Data operation and organizational guarantee, including data asset management, data governance, data security and standards, etc. , should make way for the main channel.

Of course, digitalization needs infrastructure. Whether it is consulting or data value, every step needs relatively good tools to support it. On the one hand, it can improve efficiency, on the other hand, it can also fix products. Therefore, when planning the digital construction path, enterprises should distinguish between the main content and the auxiliary content, so as to get twice the result with half the effort.

Panorama of enterprise digital construction

1, digital consultation

The first step of digital consultation should be to make a research report on the construction of digital value of enterprises, so as to have an objective and comprehensive understanding of enterprises.

Specifically, when doing business research, you can understand business processes and business scenarios, and understand the data needs of each position. What needs to be done next is information research, which can understand the current informatization construction of enterprises and service providers, and informatization is the premise of digitalization, and the construction of digitalization will in turn promote the promotion of informatization, so it is necessary to do a good job in information research. Finally, digital research can help us understand the data structure, content and achievements of enterprises.

Now, in fact, many enterprises have started the digital precipitation, but now they need to manage together. Therefore, it is necessary to evaluate the digitalization as a whole, and find out the problems and weaknesses of digitalization at present, such as whether the capital investment of enterprises is sufficient, the organizational guarantee, and the overall framework.

After figuring out the current situation, the next step is to plan the application of enterprise data, which is also the value of answering digitalization.

This is written in the White Paper on Data Asset Management Practice (Version 4.0). No one can tell the value of numbers clearly at present, and even the value seen at present may be just the tip of the iceberg.

Although the future may be as mentioned above, what we can do now is to sort out the scenarios of data application and the possibility of data application in enterprises, which is convenient for enterprise leaders to make decisions.

At this level, the first thing to do is to sort out the business structure of the enterprise, conduct a comprehensive survey of the enterprise, and objectively describe the enterprise structure as the premise and foundation of data application planning. Then, plan the data application scenario, understand the enterprise and data application, which posts and scenarios can be served, and what problems can be solved for each post.

With data application planning, data application scenarios can be decomposed into indicators and label granularity, standard indicators and report systems can be constructed according to enterprise business scenarios, and management and maintenance mechanisms can be established to ensure the authority and internal consistency of data.

After the construction of these dimensions is completed, the planning priority, implementation priority and action route of enterprise data application are finally determined. Only in this way can we judge what the enterprise can do first and then do. The reason for doing this is to promote enterprises to make a plan for at least two to three years, reflecting the value of digital construction.

2. Data platformization

The second part is data platformization. The first thing to do is the platform selection criteria and strategies, which are mainly divided into five points:

First, product selection, including database, data warehouse, open source big data construction and big data development platform, are all digital at present. In addition to the internet industry, the most advanced is the banking industry. At present, many banks have chosen several warehouses and big data from a few years ago, and both of them coexist in the enterprise, but there are certain problems and it is impossible to calculate them in real time. Therefore, enterprises need to choose products that suit their commercial value.

Second, the choice of cloud access scheme. Enterprises should choose public cloud, private cloud or hybrid cloud, which needs attention. Third, how to integrate with the original system. For traditional enterprises, the original data warehouse belongs to the original assets, but we can't overthrow all the original ones just because of the new facilities, we must learn to integrate them.

Fourthly, the problem of data interoperability, that is, how to integrate and interoperate data, is also very important.

Fifth, the choice of upgrade scheme. It is a problem for an enterprise to build a bunch of new things, but it can't be planned later. Therefore, enterprises should make a complete technical selection report on the existing technical solutions to solve the above five problems.

After selecting the platform, the next thing to do is to count and aggregate the data resources.

For enterprises, the first thing is to sort out all structured data such as information systems, and understand how much they can really use, and then how many platforms they need. Although the initial investment will be large and the construction period will be long, the overall benefit is high.

Finally, there is another way, which is to put a lot of manpower and material resources into the intermediate platform and put all the systems in it. But now it is basically selected according to business areas, such as marketing, logistics, supply chain and so on. It can not only meet the current demand, but also take into account some future developments. It is also relatively convenient when expansion is needed, and the comprehensive input and output will be high.

3. Data assetization

In recent years, data assets have been widely recognized. In the future, data assets will be incorporated into the financial system and become intangible assets of enterprises. From this perspective, how do you view data assets?

First, global data can be aggregated; Second, we can build an extensible data warehouse model, which is relatively flexible; Third, perfect data development standards and norms can be established; Finally, lay a solid foundation for data assets and support the construction of data applications.

Now what we value most is the last step, which can be demonstrated by the model. In practical observation, we found that a robust data asset vendor can reduce the application development cost by 50%, improve the development efficiency by 50%, and improve the success rate of 100% complex data programs.

As far as the value of data assets is concerned, many enterprises think it is very important, but in fact there is only one solution with the lowest cost. In fact, we should build a data asset layer, so that its value can be reflected when there are 20, 50 or even 100 data applications.

In addition, there are three points to emphasize: first, let the boss know the data assets and let the boss feel intuitively; Second, we must learn to manage and cooperate with the online and offline data assets; Third, there is an achievable process to support data applications well. Only by doing these three things can data assets truly become the core asset management category of future enterprises.

4. Data services

The process of data service produces a large number of scattered data on the business side, which are distributed in various libraries or files, without forming enterprise data assets or data services, but directly data applications.

But now when we emphasize data assets and data services, we will organize a large number of data assets into available data application services, import many invisible things, such as data API, tag engine, visual analysis big screen, etc., and put the Deon relationship of a large number of engines into the data service layer. We do data application, which is an efficient use of the service layer and can make this layer support data application more healthily.

To sum up, data service is the process of realizing data assets. Data center should not only carry data assets, but also undertake a lot of development and processing work in data assets, refine data assets into data for business use, and provide these data directly to business personnel or indirectly to application systems in the form of data services.

5. The value of data

The value of data is a process of realization. Due to the complete construction of data platformization, data assetization and data service, data applications can be built and tried in enterprises with low cost and high efficiency, and the number of data applications is greatly increased, and finally delivered to all levels, positions and business scenarios of enterprises, thus realizing data value in many aspects such as improving revenue, reducing costs, controlling business risks, improving business efficiency and innovating business models.

Take Li Ning, a shoe and clothing company, as an example. In every store, there is a position called buyer, which determines the use of purchasing funds in the store. Suppose a buyer has 6,543,800 yuan, how to allocate the proportion of shoes and clothing to be purchased and how to choose the shoes to be purchased. This role needs to be judged according to the market acumen, and its position in the store is very important. If this role is not done well, there will be unsalable sales, and a large number of stocks need to be discounted or sold out in advance.

In view of Li Ning's situation, Kangaroo Cloud made the configuration of intelligent group goods at that time. By analyzing the crowd around the store and the sales volume, print it out for buyers to see, and find that the accuracy rate exceeds 80% of the buyer's sensory judgment. The final result shows that the sales volume or profit has been significantly improved, which is actually the value of the data.

6. Full digital operation and guarantee.

Finally, special emphasis should be placed on the operation and guarantee of data. We found that every link of the main process of data supply chain needs to be guaranteed. Today, I will mainly talk about data organization. In many projects, I encountered a problem, that is, kangaroo cloud itself is relatively technical, but later found that we should pay more attention to organization and management.

For example, when Ali organized the data center, all the data personnel, data analysts and data development related to TO B were transferred to the data center, and the business department was only responsible for the demand, and then there would be someone to connect with the business department. After completion, the results of data application are fed back to the business department. The value of the business is judged by the business department, and the data production department is only responsible for execution.

Therefore, we suggest that enterprises must have a division of labor in digital organization, technical departments should build a robust platform, and business departments should be responsible for business value. If the data applications of enterprises are independent of each other and the business departments are fragmented, there will be many obstacles, which are not conducive to exerting the maximum value of data applications.

Therefore, in the process of digitalization, enterprises must ensure the digital operation and guarantee throughout, so as to ensure the success of digital transformation of enterprises to the greatest extent!