Why do companies need to build smart BI?

Business Intelligence (BI) is booming like mushrooms after the rain. Business intelligence started from a decision support system, and with the popularization of computers in the early days, it has made considerable progress. Later, IBM put forward the concept of “data warehouse”. At the same time, the expansion of hardware, software updates, and the wide application of databases in enterprises made business intelligence truly emerge.

In recent years, on the basis of data warehouses, online online analysis (OLAP) and data mining technologies have become popular. At present, smart business can help enterprises do more and more, and they are changing from traditional functions to enhanced functions. , From a single business intelligence to embedded business intelligence development. BI, which has been stagnant for many years, has once again attracted a large number of companies’ strategic perspectives. Today I will talk about business intelligence (BI).

First of all, intelligent BI represents the future trend. First of all, it is not a simple report tool or a large visualization screen. It should be a kind of enterprise data analysis thinking.

First, in terms of technology, the latest big data underlying technology must be adopted. The current domestic frontier is based on the Cassandra+Spark architecture, which has better scalability, rapid development of enterprise business, and rapid growth of data, which can be better supported.

Second, data processing capabilities, which is what we call BI tools, must be powerful. Users can perform data processing operations and data fusion on multiple data sources in a simple, convenient, drag-and-drop manner, and easily solve data processing problems before data analysis.

Third, it’s easy to use and quick to get started. Traditional BI is basically the IT business department’s requirements, and the IT department is responsible for producing the corresponding reports. There is no need to waste human resources, and the data timeliness is not strong. A good new BI must be that everyone is a data analyst, and business people who do not understand code can also make reports.

Fourth, there are many analysis modules. Different companies analyze different business scenarios, which can help small partners who have just used BI to provide more advanced business analysis scenarios in the industry.

Why companies need business intelligence BI

In fact, there are plenty of opportunities within companies to save money by optimizing business processes and centralized decision-making. When the business encounters major setbacks, business intelligence BI can bring a glimmer of light and produce a significant return on investment ROI. For example, employees in Albuquerque used business intelligence BI software to identify opportunities to reduce the use of mobile phone calls, overtime and other operating expenses, saving the city $2 million over a three-year period.

Similarly, with the help of business intelligence BI tools, Toyota Motor Corporation realized that it had doubled its freight forwarders’ fees, bringing the total to US$812,000 in 2000. Companies that use business intelligence BI to reveal the flaws in their business processes are in a more advantageous position in successful competition than companies that only use business intelligence BI to monitor what will happen. The application of business intelligence BI in enterprises is mainly manifested in the following three aspects:

  1. Display of visual reports

In BI, the company’s daily business data (finance, supply chain, human resources, operations, marketing, sales, products, etc.) are fully displayed using graphical visualization methods such as bar charts, pie charts, line charts, and two-dimensional tables. Then, you can view various business indicators through various data analysis dimensions, such as filtering, correlation, jump, and drilling.

This level of visual report analysis is a kind of presentation, allowing users to have a clear, direct, and accurate understanding of daily business. At the same time, it liberates business personnel to manually use various functions of Excel to do summary analysis and drawing work, which improves Work efficiency. For example, the finance department will care about this year’s operating income, target completion rate, operating gross profit rate, return on net assets, etc.; the sales department will care about the sales amount, order quantity, sales margin, return rate, etc.; the purchasing department will care about the purchase income Inventory amount, return status, accounts payable, etc.

  1. Data “abnormal” analysis

The data anomaly analysis uses a comparative analysis method. Business personnel present through visual reports, if they find that some data indicators reflect the situation beyond the daily experience judgment. At this time, it is necessary to carry out a purposeful analysis of these “abnormal” data, and explore the possible reasons through analysis methods such as related dimensions and indicators using drilling and correlation.

For example, for a website or product, the average number of user registrations per month under normal circumstances is about 100,000. However, it was discovered that in August of this year, the number of member registrations reached 230,000. This is an “abnormal”, far exceeding empirical judgments and expectations. At this time, we have to analyze and judge whether it is caused by the promotion of the marketing department or a large-scale promotion activity.

Of course, in addition to positive anomalies, there may also be negative “abnormalities”. For example, the number of registrations is only 50,000. At this time, we need to find the cause through analysis and avoid similar situations in the future.

In the end, the business personnel gradually formed a relatively reliable and solid analysis model through one or more dimensional and index chart constructions. At this stage, business personnel no longer passively accept the information reflected in the chart, but use “abnormal” data to locate a business problem behind it. The data and business have a direct correspondence at this level, and you can use it at this time. The logical relationship between the data charts to find solutions to improve the operating efficiency of the enterprise.

  1. Business modeling analysis

Business modeling analysis is usually put forward by business professionals who are proficient in business. Through reasonable modeling to find out possible problems in the business, reflect them on the visual report, and finally return to the business to form a decision-making and continuous optimization. Process.

In simple terms, business modeling can also be understood as a logical thinking model of business analysis, just using data and diagrams to effectively organize them to verify our logical judgments on business analysis. It can be composed of one or more charts, and can also be supported by one or more sets of data charts, which is determined according to the business model of the enterprise.

Yixin Huachen conforms to the trend of the times and continuously reforms, innovates and polishes its products. Among them, Yixin ABI embeds AI into the whole process of BI analysis, and helps companies by empowering BI in four aspects: data preparation, data processing, data analysis, and forecasting and decision-making. Quickly obtain insights from data, realize more accurate forecasting and decision-making, and improve the operational efficiency and competitiveness of enterprises.

As a new generation of intelligent data processing and analysis platform, Yixin ABI implements AI layout in phases and steps. Through big data capabilities such as program automation, ML machine learning and deep learning, it has planned three steps of intelligent inquiry, intelligent reading, and intelligent calculation. Strategies to create a BI system that can think, speak, and make decisions, and help move towards the best practice of ABI.

At present, the Zhiwen module of Yixin ABI makes the system understand human and smarter through speech recognition + NLP + knowledge graph! It can be applied in the following scenarios:

  1. You can make statements with your own lips, and you can produce what you say.

For example, if you say “xx company’s stock price” to Zhiwen, the system will automatically generate a trend chart that reflects the company’s stock price and matches the data. It saves time and trouble, and it is a great gospel for beginners. There is no learning cost. You can get data charts by speaking or inputting text.

  1. There is no need to prepare reports in advance for the meeting, and you can deal with it calmly.

For example, by continuously saying “the completion of this month’s goals”, “the completion of each area”, “the completion of each product line” and so on, Zhiwen can also understand the context, automatically construct data and display corresponding charts.

From the 1.0 reporting era, to the 2.0 era that focuses on visualization, to the intelligent BI 3.0 era that lowers the threshold of data analysis, it has brought technological changes to business analysis. Yixin ABI, a “understanding” and smarter BI platform, you deserve it!

The appearance of business intelligence BI is the presentation of visual analysis reports, but its essence is still business problems and management problems. Business intelligence BI data analysis comes from the business. Through the presentation of data, business problems are discovered, such as good or bad, within or outside of experience, and then return to the business again to re-optimize and improve a process of business operations. The true connotation of data-to-information, information-generated decision-making, and decision-making value in business intelligence BI.

What is BI (Business Intelligence) On Technology

Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information.[1] BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

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