Introduction: there is no need to talk about 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 information managers still have some misunderstandings about data intelligence and data-driven, which will lead to the abyss of enterprise data utilization.
Trap 1. The application has not been built yet, so data utilization is not considered
When we talk to some enterprise information managers that we should consider the use of data as soon as possible and make an overall plan for the data, we often hear such a sentence.
“I haven’t started my business yet, and I haven’t considered the time of data utilization”
This sentence represents the cognition of a large part of enterprises on data utilization, that is, data utilization starts with the data first, and the data is stored in the database after the application construction, so build the application first, and then consider how to use the data after the data is available in the database.
Sounds like the logic is completely correct.
But in fact, this is the primary misunderstanding of data utilization in many enterprises: “build applications first, and then consider data utilization”.
If you use this idea to build, after a year, often the enterprise will immediately put forward new problems, “the data between multiple application systems are not connected, misaligned, inconsistent, and the data can not be used”.
This misunderstanding is that we do not fully understand the two essence of data utilization:
First, the data is objective and does not depend on whether you build the application or not
For an enterprise, as long as the business is running, even if it does not build any system, its data is generated in real time, but you don’t collect it.
Data is the constituent atom of business in the digital world. Business processes and behaviors will produce all kinds of data at any time, rather than having to build and apply these data. For example, when a courier receives an express order, the sender, recipient, goods category, place of shipment, place of shipment, type of transport, distance and other data have been generated, which will drive the direction of the express. Whether there is the support of information system only changes whether the means of recording and transmitting these data is a piece of paper or a network. These data exist objectively and will not change because of the information system itself.
In essence, we should recognize 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 software.
Second, the planning of data utilization should be earlier than the construction of applications and processes
Before building a house, we should make an overall design and plan out various utilization scenarios of a building. Only in this way can there be no house that can not be entered.
Now, every enterprise realizes that data is the core asset of the enterprise, and application is the tool to collect and use these assets. In order to make full use of data after data collection, each enterprise must complete the data utilization planning before the application and process planning.
This includes the planning and design of the enterprise’s data asset directory, the planning and storage of the enterprise’s data utilization scenarios, and the demand planning of the technical platform for processing and analyzing these data.
Data first, when the system has not been built, has done a good job in the blueprint planning of data and completed the panorama of data distribution of each application system, so that enterprises can avoid the existence of data islands.
So, if you haven’t built an application yet, congratulations. This is the best opportunity to use the blueprint for planning data. Let’s start quickly.
Trap 2: there is no big data, so data utilization is not considered
“We now have very little data, which can only be called small data, so we can’t talk about data utilization”, which is also a typical misunderstanding of data utilization.
The first time I heard this sentence was in B2C retail enterprises. Indeed, the traditional brands with dealers as the main channel often do not establish their own e-commerce system, so the final consumer behavior data can not be obtained. What they have is sell in data, and sell in data is often small and has few dimensions, so its utilization value is limited.
However, what this enterprise is doing now is to establish various contacts with end consumers and customers through small programs and applications, so as to obtain all kinds of data. A single view is small data, with small amount and few dimensions. However, when all these points are connected together, it forms a rich and diverse panorama of user data.
The business leader of this enterprise believes that in the digital age, whoever has more data scenarios will have a stronger competitive advantage.
This example fully shows that your business model may determine that you do not have rich data, but you still need to obtain user and consumer data through various application innovations in multiple channels and in an all-round way. What applications are to be built, what data are to be obtained, and how are these single point data connected to each other to combine the value of the data scene?
This is the need for data planning before building applications, outlining a data scene map, so as to build small and medium-sized applications along this map.
Trap 3. Data utilization is data analysis and mining, and the transaction application system does not use data technology
In the past, application systems were divided into OLTP and OLAP, online transaction system and online analysis system. Therefore, as soon as you see that the application itself is a transactional software, according to the traditional architecture, that is, the OLTP system, so you often don’t use some OLAP technology.
However, the current situation has changed greatly.
According to the traditional division, this is a typical transaction system, which creates orders and assigns drivers. However, if you want to support the scheduling and allocation of tens of thousands of orders per second, it is impossible to use manual allocation. This scheduling system needs to have the ability of real-time data analysis, and the price determination and route planning need to refer to the relevant historical data analysis results. In this way, this typical transaction application is data-driven. Its bottom and core are actually batch data analysis and real-time data processing.
All future applications will be like this, that is, OLAP supports every decision and behavior of OLTP system, so as to become an intelligent application.
Data technology is gradually reconstructing all traditional process applications to make them become data-driven systems, so as to become more intelligent.
Trap 4. The most important thing is the algorithm, so software engineering companies can’t 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, algorithms and artificial intelligence do data.
Therefore, there is a 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 cooperative customer denied us an opportunity with an inherent impression that “stuerwalker is not an artificial intelligence”, which is a misunderstanding about the application of artificial intelligence.
We use the following diagram to illustrate 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 open, and the academic research institutions really study and produce these algorithms.
Artificial intelligence is divided into two fields, one is the frontier research field, and the other is the application field. As an enterprise engaged in industrial production and commercial operation, what is needed is the latter. The most important thing of the latter is to use the software engineering ability to apply the appropriate algorithm to valuable scenarios, so as to enable the business.
Above the algorithm, the application of artificial intelligence is more important is the engineering ability of sufficient high-quality data sets to develop algorithms and data into intelligent software with good user experience.
Therefore, an excellent AI enterprise not only has the ability to tune and call open algorithms and code, but also has the ability of business innovation and software engineering.
Summary and Enlightenment
By analyzing the four traps for data intelligence one by one, we can draw the following enlightenment:
1、 Data planning should give priority to the construction of business system, build a smooth and consistent data panorama, and avoid data islands between applications
2、 After building the data panorama, build small applications to collect and fill these data along the map, so as to build their own data assets
3、 All application software will be enabled by data technology and become data-driven intelligent applications
4、 The most important application of artificial intelligence in business is scene innovation ability and software engineering ability
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