All companies are aware of the importance of data and hope to use data to drive business development. However, many enterprises still have some misunderstandings about data intelligence and data usage. These misunderstandings limit the potential of data utilization in the enterprise. Here we are going to look at 4 potential common challenges/traps that teams face when utilizing data.
When we talk to some managers that we should consider the use of data as soon 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 can then consider how to use the data. It sounds like the logic is correct.
But in fact, this is the primary misunderstanding of data utilization in many companies: “build applications first, then consider data utilization”.
If this kind of thinking persists, after a year, often the company will immediately ask new questions, “The data between multiple application systems are not connected, not aligned, inconsistent, and data is not available.”
“We have very little data now, it can only be called small data, so we can’t talk about 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 behaviour data is not available.
However, the current enterprise is actually establishing various contacts with end consumers and customers through individual small programs and small applications, that can obtain various types of data. Small and without many dimensions are recorded by individual enterprise systems, but when all these data are connected together, it can form a rich and diverse user data lake.
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 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 a 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 data-driven, 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 behaviour 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.
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 using data.
Therefore, there is now 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. This is a misunderstanding of artificial intelligence applications.
The bottom layer of artificial intelligence is composed of various algorithms. However, the common 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.
By analyzing these four traps for data intelligence one by one, we can draw the following insights:
- Data planning should take precedence over the construction of business systems to build a comprehensive and consistent data panorama to avoid data islands between applications
- After constructing the data schema, follow this map to build small applications to collect and fill the data, thereby building your own data assets
- All application software will be empowered by data technology and become data-driven intelligent applications
- Last but not least the most important aspects of AI application in business are scenario innovation and software engineering capabilities