Grid transformation: several levels of understanding of data-driven

The digital transformation of the power grid is a common point of excitement for the two networks. Power grid practitioners are particularly encouraged and excited by the potential for change brought about by "data-driven" and believe that this will bring unprecedented mechanism breakthroughs and progress. To quote a person in the power grid, "every pore in my body is trembling."

However, in the view of external observers, the "data-driven" approach of the power grid is very likely to follow the path of "massive data asset operation", or that "data assets" will continue to be operated in a targeted manner using the strategy of "physical assets". This view quickly triggered a lot of responses in the industry.

Why is there such a difference? In an era when data is regarded as a strategic asset, what is the core of "data-driven"?

There is no standard definition for digital transformation itself, and even the digital model is boundless and shapeable. Even the early informationization process was defined by some companies as digitalization 1.0. When it comes to the stage where "cloud, big data, Internet of Things, mobile and intelligence" are the core technical tools, it is called digitalization 2.0. So, how should the "data-driven" proposed at the strategic level of the power grid be understood?

Unique and authentic power data itself has scarce value in a certain period of time, and will become a key resource for external competition and cooperation, and an important link to promote cross-border cooperation. This is only one reason. Second, the digital platform can reduce the cost of power grid business departments to develop personalized and customized services, and provide an extremely important foundation for flexible response to changes in demand. After the technical architecture is switched, it can bring an enterprise-level management platform with stronger interaction with the outside world, and a unified platform to accumulate data.

In my opinion, a more critical aspect of "data-driven" is that after the business process of the enterprise is modeled with data, the business process under the original control model can be reshaped, and at the same time, the management decision-making mechanism can be improved by using big data thinking (data insight ability). Whether it is business modeling or big data thinking, the core is not to use empiricism and subjectivism as the yardstick, but to use data that can track and project facts to improve the process, and as a scientific tool and practical basis for decision-making.

If "business datafication" is simply the online and internet-based transformation of the original management and control processes, then the core of "data-based businessization" is actually a huge shift in management thinking patterns. When everything can be digitized and modeled, data becomes the common language of circulation in the digital world. Not only will the efficiency of circulation be improved due to the extensive connections, the management hierarchy will become flatter, but there will also be a new carrier that can better reflect objective facts to replace the inefficient management mechanism.

Data is collected from the bottom layer of perception to quality management, then to value mining and application, and then modeled or used as a basis for auxiliary decision-making. This is a "bottom-up" process in itself, and also a process that reflects objective needs. An industry insider summarized that process-driven decision-making is "top-down, inside-out", while decision-making in the data-driven era is "upward and downward linkage, internal and external interaction".

For a large and complex power system, the changes and impacts brought about by "data-driven" are huge, which may be why many people in the power grid are encouraged by this. It has long been a consensus that the traditional corporate system lacks flexibility, but how to smoothly transition to an efficient mechanism that can better adapt to market changes? "Data-driven" is a relatively feasible method in both concept and operation. In other words, "data-driven" is actually establishing another set of management mechanisms and decision-making logic that are closer to the facts, and using digital resource allocation to make up for the inefficiency of traditional mechanisms.

Of course, this is not easy and may even be met with a lot of resistance, but it is also the beginning of self-transformation.

Changing from the relatively closed and decentralized IT system to an open, scalable and ecologically friendly cloud platform architecture is only the first step in a long journey. There are still many areas that need to be upgraded in the transformation of the power grid. The "data barriers" of business departments are almost a common problem encountered in the digital transformation of various industries. Some people say that only by letting business departments feel the value brought by data integration and helping business departments solve the pain points in actual work can we truly encourage the cultivation of a data sharing culture.

How can the use of digital tools really help change management thinking and upgrade systems and mechanisms? It still needs to fall on the integration of technology and business, that is, how digital tools can help solve the pain points and difficulties in the business process. This in turn responds to the doubts of external observers. While expanding the pursuit of data assets, the power grid needs to find value closed loops as soon as possible.