1. The background of digital and intelligent transformation of enterprises
In the era of scene and service-driven business, digital technology has penetrated into all aspects of our life and work, and all walks of life in the process of transformation and upgrading are setting off a wave of digitalization. The "14th Five-Year Plan" proposes that by 2025, the added value of China's digital economy core industries will account for 10% of GDP. It can be seen that the digital economy is becoming an important engine of my country's future economy.
As the digital economy gradually enters a stage of comprehensive expansion, industry digitization will also transition to a period of accelerated transformation. Under the synergistic effect of cloud, AI, 5G, low-carbon and other cross-domain technologies, more and more industries are worthy of "redoing", and digitalization and greenization have become indispensable underlying genes in various scenarios.
2. The Essence of Digital Transformation
Ge Xiaobo, CIO of Qingchuang Technology, summed up the essence of digital intelligence transformation from the perspective of technological development: In the cloud-native era, the degree of development of cloud-native in various industries varies, and not all business applications are suitable for cloud-native forms. If excessive pursuit of cloud On the contrary, nativeization will increase the pressure on enterprise operation and maintenance, and the cost of operation and maintenance will increase sharply.
Ge Xiaobo, CTO of Qingchuang Technology
Therefore, the data intelligence transformation of enterprise operation and maintenance must have data thinking, so that when faced with the situation of sensitive and stable states, cloud on cloud and off cloud, and synchronous operation inside and outside the container, it can be more active and dynamic to weave, analyze and process data. The historical data needs to get the answer, and the clue can be foreseen from the future data.
From the perspective of digital transformation combined with independent and controllable requirements, the essence of cloud-native is to help enterprises respond to business changes more quickly and accelerate business application iterations, so as to make business operations more efficient, reduce operating costs, and create more revenue. Effect.
3. Difficulties often encountered in digital intelligence transformation
Summarized from Qingchuang's past 7 years of experience, we believe that the core of operation and maintenance work is data, rather than blindly seeking algorithm optimization. Only when the data is normalized, standardized, and accurate can it provide effective value for the upper-level consumption scenarios. Otherwise, no matter how sophisticated the algorithm is, the data foundation will be unstable, and the results will not be able to provide help for enterprise operation and maintenance or even operations.
Speaking of reality, in essence, the object of operation and maintenance is software and applications. Whether it is centralized, distributed or cloud-native, in fact, the architecture and technology are changing on the surface, but the core of software and applications is not changing. Many enterprises have found that in the cloud native era, operation and maintenance has become more difficult to manage. The reason is not that the technology is not good, but because the initial operation and maintenance system planning is not done well, and the rules are not clear.
Based on our years of practical experience, we found that enterprises often encounter the following problems:
1. The alarm is not accurate
Due to too many alarms and lack of precision, it is difficult to clarify the working status of the current business system, and it is impossible to discover system failures earlier than end users.
2. The problem is not found in time
Existing anomaly detection methods can only detect problems within a few minutes or more than ten minutes before a fault occurs, and cannot quickly detect clues after business adjustments are completed.
3. The root cause is hard to find
After the emergency response, there is no suitable means to reproduce the fault through the timeline and find the real root cause of the problem, so the same problem may recur.
4. Difficult to reuse troubleshooting experience
Lack of knowledge base precipitation ability, unable to effectively save and reuse experts' troubleshooting experience.
It is not difficult to see from the above questions: it is a basic operation to pay attention to the data itself and do a good job in data governance. The key work should be to form the final result of data governance into an operation and maintenance capability system with the operation and maintenance object as the core, which can fully correlate logs, indicators, alarms, events, call chains and other data, and realize panoramic observability (as shown in the figure below).
Enterprises generally want to achieve such an effect in data governance: After proper data governance, scattered data can be fully linked together to form data with unified specifications and standards. That is, it can be associated with the objects of operation and maintenance management (software, applications, etc.), and the objects of operation and maintenance management can also be associated with each other. In this way, when a problem occurs, it is possible to clearly and quickly know where the problem occurs, why it occurs, and how to deal with it quickly, so that such failures can be foreseen and prevented in advance in future operation and maintenance work.
4. The path of digital transformation
Establish a set of full life cycle tools covering data collection, processing, storage analysis, data service and use, etc. According to the corresponding standards, norms and principles, monitor, improve and manage the data quality, and realize the security classification and authority control of the data, and at the same time make the data accurately conform to the life cycle stage, exert the due timeliness value, and accurately communicate to the public Deliver and export data value.
When it comes to standards, norms and principles, the effective implementation of the data governance standard system becomes the key. In fact, the data governance system is not only aimed at the data itself. While defining enterprise operation and maintenance data standards, management standards, and data quality standards in accordance with national and industry standards, it is also important for the enterprise's organizational structure, platform and tool adoption, process and Mechanism settings, etc. should also follow certain standards.
This can effectively solve some practical problems in enterprise operation and maintenance, such as the following two examples:
1. Take the platform tool specification as an example.
After the governance system is officially implemented, when the subordinate department wants to purchase a new alarm tool, the first thing to check is whether the data generated by the tool meets the data standards and whether it can be connected to the data platform for use. If not, it is required to adjust the data format or not to purchase. At the management level, it will be ensured that various departments cannot purchase tools just because they are easy to use, so as to avoid problems such as a surge in the number of tools and data silos.
2. Take the data lifecycle specification as an example.
In fact, the information density of a lot of data is very low but the storage cost is extremely high. Suppose there is a delay of more than ten minutes in the transaction data collected by a certain monitoring tool A, then this part of the data is actually out of timeliness, but this part of the data is still being collected. If it is stored, it has no value for operation and maintenance, which is a great waste of cost.
If the data governance system is well established and controlled through relevant data quality and life cycle standards, this part of data will be eliminated, and tool A will be required to be optimized to improve the timeliness of data collection and output to improve operation and maintenance overall efficiency. "
5. Typical implementation scenarios of digital intelligent operation and maintenance transformation
1. Holographic monitoring
The holographic monitoring here, in essence, is to build an integrated monitoring platform after comprehensive management of operation and maintenance data, manage applications and basic components, and then use Qingchuang’s self-developed low-code tool combination in the operation and maintenance Various operation and maintenance data are displayed on the stage for different operation and maintenance roles and teams, providing flexible and different analysis perspectives.
For example, to query the status of a single application from an overview perspective, you can check the topology, alarms, logs, and other data below it in detail; from the perspective of professional management, you can view the combination of transaction code, return code, and transaction code if you want to segment applications , manage across the board, and more.
This scene has been implemented in a large state-owned bank
This kind of holographic monitoring can detect problems before business through trend and risk monitoring, and can provide trend analysis and prediction capabilities of business system health through dynamic thresholds, index deviation analysis, business health portraits, etc., and discover hidden dangers in system operation. Provide early warning and notification capabilities before failures occur, reserve time for accident disposal, and comprehensively improve the stability of enterprise operations.
The first sharing about digital intelligence transformation is here first, and we will further share how to carry out digital intelligence transformation through examples. Interested friends can pay attention~
Interact: What factors do you think are the most important in the transformation of digital intelligence? Welcome to discuss and exchange in the comment area~
Qingchuang Technology, a benchmark supplier in the AIOps field continuously recommended by Gartner. The company is committed to assisting enterprise customers to improve insight into operation and maintenance data, optimize operation and maintenance efficiency, and fully reflect the influence of technology operation and maintenance on business operations.
The common choice of industry leading customers
Learn more about operation and maintenance dry goods and technology sharing
You can follow with one click in the upper right corner
We have been deeply involved in the field of intelligent operation and maintenance for nearly ten years
AIOps Benchmarking Supplier Recommended by Gartner for Consecutive Years
See you next time~