There is no need to say the importance of data today. All enterprises are aware of the importance of data and hope to use data to drive business development.
However, many enterprise informatization managers still have some misunderstandings about data intelligence and data driving. These misunderstandings will plunge the enterprise's data utilization into an abyss.
Trap 1: The application has not yet been built, so data utilization is not considered
When we talk to some enterprise information management managers that we should consider the use of data as early as possible, and make overall plans for data, we often hear such a sentence.
"I have not done business yet, and it is not time to consider data utilization"
This sentence represents a large part of the enterprise's understanding of data utilization, that is, data utilization starts from existing data, and data is stored in the database after the application is built, so first build the application, and then wait for the database After we have the data, we are considering how to use the data.
It sounds like the logic is completely correct.
But in fact, this is the primary misunderstanding of data utilization in many companies: "build applications first, then consider data utilization".
If you use this kind of thinking to build, after a year, often the company will immediately raise new questions, "The data between multiple application systems is not connected, not aligned, inconsistent, and data is not available."
This misunderstanding is a fundamentally insufficient understanding of the two essences of data utilization:
First, the data exists objectively and does not depend on whether you build the application or not
An enterprise, as long as the business is running, even if it has not built any system, its data is generated in real time, but you have not collected it.
Data is the constituent atom of business in the digital world. Business processes and behaviors will generate all kinds of data at all times, rather than having to build and apply these data. For example, when a courier receives an express order, the sender, recipient, type of goods, place of delivery, place of delivery, type of transportation, distance, etc. data have been generated, and will drive this The direction of express delivery. Whether there is the support of an information system, it just changes whether the means of recording and transmitting these data is a piece of paper or a network. These data exist objectively, and they will not change because of the information system itself.
We need to realize that data is the projection model of business in the digital world. It is the mirror image of business and exists objectively.
As long as there is business, there is corresponding data. The application just collects the data into the storage device through the software.
Second, the planning of data utilization is earlier than the construction of applications and processes
Before we build the house, we have to do the overall design and plan out the various utilization scenarios of a building, so that there will be no house that cannot be entered.
Now, every enterprise realizes that data is the core asset of an enterprise, and applications are tools for collecting and using these assets. In order to make full use of data after collection, each enterprise must complete data utilization planning before application and process planning.
This includes the planning and design of the enterprise's data asset catalog, the planning of the enterprise's data utilization scenarios and the storage of data, and the demand planning of the technical platform for processing and analyzing these data.
At Data First, when the system has not yet been built, the blueprint of the data is planned and the data distribution panorama of each application system is completed, so that the enterprise can avoid the existence of data islands.
So, if you haven't built an application yet, congratulations. This is the best opportunity to plan data utilization blueprints. Start now.
Trap 2: There is no big data, so data utilization is not considered
"We have very little data now, it can only be called small data, so we can't talk about data utilization", this is also a typical misunderstanding of data utilization.
The first time I heard this sentence was in a B2B2C retail company. Indeed, traditional brands that use distributors as their main channel often have not established their own e-commerce system, so the final consumer behavior data is not available. What they have is the data of Sell In, and the data of Sell In is often not large and has not many dimensions, so the use value is limited.
However, what the current enterprise is doing is to establish various contacts with end consumers and customers through individual small programs and small applications, so as to obtain various types of data. Large, not many dimensions, but when all these points are connected together, it forms a rich and diverse user data panorama.
The business person in charge of this company believes that in the digital age, whoever has more data scenarios will have a stronger competitive advantage.
This example fully illustrates that perhaps your business model now determines that you do not have a wealth of data, but you still have to use various application innovations to obtain user and consumer data through multiple channels and in all directions. And what applications are to be built, what data are acquired, and how are these individual point-shaped data obtained to form a mutual connection to combine the value of the data scenario?
This is the need to have a data plan before building the application, outline a data scene map, and then build a small and medium-sized application along this map.
Trap 3: Data utilization is to do data analysis and mining, transaction application systems do not use data technology
The past application systems are divided into OLTP and OLAP, online transaction system and online analysis system. Therefore, it is often seen that the application itself is a transactional software. According to the traditional architecture, it is an OLTP system, so some OLAP technologies are often not used.
However, the current situation has changed dramatically.
In terms of car scheduling system, according to the traditional division, this is a typical trading system, creating orders and assigning drivers. However, if you want to be able to support the dispatch and distribution of tens of thousands of orders per second, it is impossible to use manual allocation. This dispatch system needs to have real-time data analysis capabilities, and the price determination and route planning parts need to be referred to. Historical data analysis results. In this way, this typical trading application is driven by data, and its bottom layer and core are actually batch data analysis and real-time data processing.
All future applications will be like this, that is, OLAP is supporting every decision and behavior of the OLTP system, thus becoming an intelligent application.
Data technology is gradually reconstructing all traditional process applications, making them a data-driven system and thus becoming smarter.
Trap 4. The most important thing is the algorithm, so software engineering companies cannot do data science projects
When it comes to data projects, the first thing many people think of is the algorithm model. It seems that only those who do research, do algorithms, and do artificial intelligence are doing data.
Therefore, there is a kind of view that the information industry is divided into algorithms and software, and only algorithms are artificial intelligence and data.
This is a typical misunderstanding that separates algorithms from software engineering. Just like not long ago, a long-term customer used an inherent impression that "Site Walker is not doing artificial intelligence" and denied an opportunity for us. This is a misunderstanding of artificial intelligence applications.
We use the following picture to reflect the relationship between algorithms and artificial intelligence (data science).
The bottom layer of artificial intelligence is composed of various algorithms. However, the commonly used algorithms used by everyone in the industry are all public, and the real research and production of these algorithms are academic research institutions.
Artificial intelligence is divided into two fields, one is the frontier research field and the other is the application field. For enterprises engaged in industrial production and commercial operations, the latter is needed. The most important thing for the latter is to use software engineering capabilities to apply suitable algorithms to valuable scenarios to empower business.
On top of algorithms, the application of artificial intelligence is more important than sufficient high-quality data sets, and the ability to develop algorithms and data into intelligent software with good user experience.
Therefore, in addition to the ability to tune and call public algorithms and codes, outstanding artificial intelligence companies are more importantly capable of business innovation and software engineering.
Summary and inspiration
By analyzing these four traps for data intelligence one by one, we can draw the following enlightenment:
1. Data planning should take precedence over the construction of business systems. Build a comprehensive and consistent data panorama to avoid data islands between applications.
2. After constructing a data panorama, build one by one along this map to collect Fill these small applications with data to build your own data assets.
3. All application software will be
empowered by data technology and become data-driven intelligent applications. 4. The most important thing for artificial intelligence to be applied to business is scenario innovation and software Engineering capability
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