01 preface
Enterprise data plays a decisive role in business decisions. With the development and maturity of AI and big data technology, the data consciousness of enterprises has gradually awakened, and they have taken the action step of data enabling business. Among them, the online BI platform has become the choice of many enterprises. Why do enterprises choose BI platform as the "advisor" of business analysis? What is the difference and relationship between digital analysis and traditional data analysis? It will be described in more detail below.
02 rich usage scenarios
What exactly does the intelligent analysis platform do? It is a data collaborative analysis platform with powerful big data computing performance, advanced intelligent analysis and visualization capabilities. The scope of analysis covers the whole process from data source, data acquisition, data integration, data analysis to data application.
According to Dongxinda observation, using EXCEL to do report analysis and making visual charts by hand is still the current data analysis state of many enterprises. On the whole, this kind of method can meet the needs of summary, but the data response speed is slow, the integration process takes a lot of energy of business staff, and tends to data collection, so it is difficult to play a decision-making role in the business. When you realize the problem, the whole business optimization cycle lengthens from a few hours to a few days or even weeks, which is extremely inefficient.
03 how does the BI platform grasp data?
Take a food enterprise with a store as an example, the number of store members, the number of products sold every week / month / day, the consumption level of members, the number of returns, the number of lost customers and other data need statistics, in order to find store management problems through the data, and then improve business decision-making, and its manufacturing business also needs data analysis.
Intelligently grabbing data makes everything seem efficient. Taking manufacturing enterprises as an example, BI platform can connect 100 kinds of data sources, grab the data needed by warehouse, energy, sales, procurement, delivery and logistics system with one click, and build a database.
Get rid of repetitive and useless data content and do data cleaning. These are the contents of data collection and data integration. In the past, the content that needed to be collected and sorted out manually was handed over to BI to do, which seemed to save a lot of time and effort. It makes it easier for business people to do data, and it can also focus more energy on thinking about the relevance of data and business, and drilling for data value.
04 window for enterprise insight into the market
But data capture is only an action, and data analysis and decision-making highlight the wisdom of BI.
When the enterprise develops to a certain scale, it needs more in-depth data analysis, faster data response speed and stronger data interaction ability. Dongxinda sees that many enterprises do not have data skilled talents, and although enterprises often rely on ERP and other information tools to establish basic databases, their data analysis capabilities still remain at the level of manually collecting data from ERP, CRM and other tools to make EXCEL reports, which can easily lead to incomplete analysis dimensions, data statistical errors and then affect judgment.
In addition, this kind of data collection method has lag, which often leads to a lot of poor market sensitivity of enterprises. For example, at the weekly regular meeting, the boss asks the reason for the abnormality of a certain data, but the executive does not notice this. Should the boss wait until the analysis of the data is released and then listen to the report next week? This approach is obviously lagging behind.
In the transformation from traditional BI to intelligent BI, improving the response speed to abnormal data is one of the outstanding features. It has become an important window for enterprises to gain insight into the market, help to monitor all data in real time, and make it easier to find problems.
In the visual pages of many BI platforms on the market, business growth, decline, market share, personnel changes, product sales fluctuations, purchase prices, returns and other data are clear at a glance, and abnormal data will be reminded immediately. If the data sensitivity of the enterprise is strong enough, it can smell the changes of environment and business opportunities from these abnormal data, and then adjust the business decision.
05 interactive visual exploration
BI can quickly form all kinds of data change charts and produce graphic and textual data analysis reports, which are applied to meetings PPT and monthly annual business reports, that is, "data applications", which no longer require clerks to knock on the report forms all afternoon and do PPT for a day to complete.
In addition to being efficient and fast, the data collected by BI is also global. Traditional data aggregation is from the personal point of view of business personnel, but many factors in different areas will also affect business results. BI can effectively solve this defect and help management grasp the overall situation in multiple dimensions and pay attention to all aspects of the business that drive the results. This is equivalent to breaking the traditional data analysis methods, making changes from the root, so that the integration of data processing and analysis.
The value of BI lies not only in telling you what the data is like, but also "why" and "how to improve it". It is the product of a new type of data management logic.
With the evolution of enterprise demand, BI has gradually changed from an "analysis tool that is easier to make reports" to an "enterprise decision brain" that goes deeper into business scenarios. It can subdivide and analyze the purchasing, production, sales, distribution and inventory scenarios of manufacturing enterprises, reduce data granularity, adjust business strategies, and control the overall situation.
When you find that your competitors are much more responsive to the market, it's time to put data management on the agenda!
