Through a unified digital intelligence base, enterprises can deeply integrate technology, business, and data, implement digital intelligence transformation, improve quality and efficiency, move towards business innovation, and achieve high-quality development. Upgrading the base of enterprise digital intelligence, from scenario-based services to data-driven, to real-time decision-making, is one of the paths for enterprises to achieve business innovation. And with the exponential growth of data, the substantial increase in computing power, and the unstoppable AI technology, the importance of the data-driven link is also increasing day by day.
However, many enterprises still have doubts about this: in this era of data-driven has become an inevitable trend, how can enterprises better accumulate data assets, generate value based on data and intelligence, realize flexible expansion, drive business growth and effectively allocate resources ?
What does a data-driven digital intelligence enterprise look like?
For enterprises, if they have never been exposed to data-driven before, it is easy to confuse it with the concept of "speaking with data". But in fact, speaking with data is only the lowest-level concept of data-driven. The more common operation is to use visual tools such as report graphics to describe the data that needs to be explained in words before.
And data-driven, you can collect massive data through the Internet or other related software, integrate the data into information and then refine it to form an automated decision-making model, and predict the future of the enterprise through the method of "data + intelligence + algorithm + scene". Future development, or risk management and control, etc.
If you can sum it up in one sentence, the former is "people looking for numbers", and in the final analysis it is still up to people to make decisions; while the latter is "numbers looking for people", which realizes a fully automatic process from data collection, to data governance and final decision-making.
So, what are the characteristics of a data-driven enterprise? Through data analysis and control, the core enterprise conducts data analysis of member enterprises to share business information of member enterprises, such as real-time sharing of inventory information of dealers, sharing of sales information, and forecasting of sales of member enterprises based on shared data. According to the data analysis of member companies, intelligent replenishment can be achieved to reduce the bullwhip effect.
Taking traditional procurement as an example, the previous procurement process is usually project establishment-finding a suitable supplier-inquiry price comparison-purchasing. During this period, enterprises usually choose suppliers based on several information such as supply cycle, credit, and historical transactions. Manual screening. However, the market is changing rapidly, and it is obviously impossible for enterprises to rely on the previous information screening to meet the current actual business needs. For example, due to the outbreak of the epidemic, factories that used to purchase frequently closed down, or high-quality suppliers were unable to deliver goods in epidemic areas. These problems will cause great difficulties for manual screening.
And if it is a data-driven enterprise, based on the real-time data changes inside and outside the enterprise, combined with AI and machine learning capabilities, it will combine multiple dimensions into a supplier recommendation factor. After screening the recommendation factors, the procurement manager can quickly Locate those suppliers with high quality, short lead time, low cost and good service. In this way, the efficiency will be greatly improved. Before, a procurement manager could only manage 100 suppliers, but now he can manage 200 or even 300 suppliers.
As mentioned above, replacing the previous human flesh search with automatic matching is one of the data-driven methods. Based on the combination of digitalization and intelligence, it focuses on the scenarios provided by customers to provide digital and intelligent services, and then enhances business value. Post value, and then greatly enhance the commercial value.
It can be seen from this that if an enterprise wants to adapt to this rapidly changing market and break through numerous challenges, it must firmly grasp the "knife" of data drive.
How should enterprises become data-driven?
However, it should be noted that if an enterprise wants to be data-driven, it is not just as simple as introducing a few tools and building a few platforms. There are three issues that need to be prioritized: awareness of transformation, understanding of business, and capabilities.
The first is the awareness level. Enterprises must be aware of the value of future data and have the concept of digital intelligence transformation; second is the understanding of business. Enterprises must ensure that the business field is connected to actual business needs, and then sceneize this demand; and Needless to say, the importance of capabilities needs to be ensured that there are people and capabilities in the enterprise to help the enterprise complete the iteration from 0 to 1, from 1 to 2, and from 2 to 10 in terms of digital and intelligent transformation.
The last is the ability at the tool level. Through tools, technical capabilities and business scenarios are integrated, and various algorithms or models are used to realize data empowerment and solve the needs of actual business scenarios. In recent years, the middle-office architecture centered on the construction of enterprise digital intelligence capabilities has attracted much attention.
Through the middle platform structure, enterprises can open up and integrate the business data of the enterprise's online and offline channels, and perform data cleaning, conversion, processing, and extraction after data access and integration, and finally form a unified data standard, data specification, and standard data management. After the data is effectively managed, based on the data assets, data analysis models and algorithm models are created in combination with actual business scenarios. By using technologies such as big data mining, cloud computing, and AI, enterprises can realize intelligent analysis and user portraits. , industry data map and other data service capabilities and comprehensively grasp the data, and finally truly realize data-empowered organizations and data-driven enterprises.
In addition, another reason for the rise of the middle platform structure is inseparable from the trial and error costs of enterprises. With the development of enterprise business, the increasingly large background system cannot respond to the needs of the front desk in a timely manner, and a layer of middle platform is added between the two to unify the packaging of the back-end subsystem and connect to different terminals on the front desk to support the enterprise Rapid trial and error and innovation.
For traditional enterprises to adapt to the changing external environment, the importance of building an agile middle-office architecture is self-evident. However, in the process of building the middle platform, it is difficult for enterprises to achieve "just right", and they often fall into the situation of lacking rational thinking and blindly following the trend.
The realization of digital intelligence in large enterprises needs to unify digital intelligence planning and top-level design, data governance and key application system construction, and at the same time, through the support of the digital intelligence base platform, improve the overall digital intelligence capabilities of the organization and support the sharing of functional applications , Industrialize business applications, optimize basic capabilities to support business innovation, and realize management changes.