If the Business Intelligence (BI) likened to a house, then the data warehouse is its foundation. Sentence summary: BI data warehouse is the engine behind it.
database:
That data warehouse from the literal sense, in order to integrate operational data into a unified environment to provide decision-type data access. Data warehouse concern is to solve the data consistency, credibility, collective ....... these problems, the increasingly complex business data into business operations for the business analyst is easy to use data in the form of .
The ultimate goal is to make the data warehouse data applications personnel (whether CEO or ordinary analysts) think about how to use the data warehouse data, information and create more value; and not worry about where the data in the data right.
BI (Business Intelligence):
BI to analyze the data and get insights to help a range of methods, techniques and software companies to make decisions. Compared to the data warehouse, BI also includes data mining, data visualization, multidimensional analysis, classification labels and so on.
Take for example a multi-dimensional analysis, data warehouse provides the only dimension of the data, but based on some tools, such as FineBI, Tableau, etc., can support any combination of user dimensions and metrics within a certain range, which rises to that decision support level instead of "advanced data warehousing" level, that is, the use of the data warehouse, but not the function of the data warehouse.
The relational data warehouse and BI
The supermarket is a complete solution to meet customer demand for one-stop shopping, including the procurement of goods, cargo transport, cargo storage, display of goods, commodity pricing, billing and other sectors.
BI is to satisfy customers with information-based decision-support needs of the complete solution, including data acquisition, data processing (ETL), data storage, data warehouse, data presentation, interpretation of results and other sectors.
Database warehouse supermarket like, may be one or more, it could be shipped directly from the manufacturer (heterogeneous data sources);
Data warehouses like display of goods on the shelves, according to a variety of topics, categorized the various merchandising on different shelves (theme library), such as personal care areas (product types), near the shelf life zone (time attributes), sports District (usage scenarios), the checkout counter small shelf (Minato single product), etc., to facilitate customers to quickly make shopping decisions.
Traditional BI project to build a path decisions must rely on its data warehouse to perform data analysis . Such as MicroStrategy, SAP BW, Microsoft Analysis Server, IBM's Cognos, Oracle's OBIEE, these traditional BI tools do not have the ability to standardize data integration, there is a data warehouse is to help them establish a data governance structure, to solve data redundancy, inconsistency, error, unable to easily access and other issues.
On the other hand, BI This reliance on data warehouse fact, there is a major flaw. In general, data warehouses typically takes a high economic cost, time cost from the planning to the floor, but the creation of value in most cases is limited, low ROI. After building a successful data warehouse also supports only a handful of specific types of analysis, enterprise business if adjustments need to be addressed or new types of data, then turn again to face major development work.
The actual BI project
Traditionally the most orthodox application mode, the data warehouse DW + BI BI, the former is responsible for background data processing, data integration, according to the size of storage; the latter is responsible for the user data to show, report management. A lot of people around these stations collectively referred to as BI framework, this type of BI applications are mostly the cost of the most expensive model, mainly banks / telecommunications and other companies take the lead in the implementation of mature applications.
And in recent years, a model developed in general, is based on the BI tools or methods to develop complete applications, or apply a template. Common are performance management, BSC strategy management, financial analysis, channel analysis, industry analysis, etc., regarded as the first two examples of products based BI applications, the latter two is purely a template application.
Based BI product of fact and traditional software project / software products relatively close, business knowledge will solidify in the system, BI more as a tool for data analysis, visualization and reporting tools exist.
Distinction and advantages
Past, the traditional BI model, data warehouse, the ETL (data cleansing), OLAP analysis are different products, different from the person responsible. Suppose a change analysis to relate to the data layer. IT departments will have to improve data and business layers, traditional BI platform needs one or two months to sort out the model.
Modern BI application mode, that is agile BI, OLAP modeling and analysis are integrated into the application in the. Without prior modeling, doing the report, drag the data fields when random will be able to see results-based analysis that is done, and the flexibility to adjust the dimensions of analysis and reports show during the analysis, the demand for change within a day may response.
From the perspective of modern business decision, to re-examine the relationship between BI and Data Warehouse
Turned to service-oriented architecture (SOA) (* proposed by Gartner in today, "service" as the basic elements to form enterprise IT architecture way .SOA main problem to be solved is: quickly build application integration has become to solve enterprise business best contradiction between development needs and IT support capability.) technical background, a costly effort whether large-scale data integration and data integration operations is also necessary? Whether the number of bins can build income is greater than the cost you would pay?
Coupled with rising body mass of enterprise data, business development more rapidly, rapid analysis and decision confirms also put a higher demand, more companies want to be able to reduce technology infrastructure costs, as close to real-time access to operational data sources, responding to user requests in a very short period of time.
Agile enterprises to build strategic analysis of the decision-making framework
Analysis of business prospects for future decision-making structure, depending on the direction of development of business drivers and technology. Today, enterprise data growing exponentially, demand for real-time analysis of intense than at any time in the past, in view of this, how to balance rapid landing and high scalability, the combination of the data warehouse to build enterprise architecture analysis and decision, is still placed in many a huge problem in front of businesses.
I think it is a good solution
- When the number of positions has not been set up or analysis of ideas not yet formed directly in the BI platform to quickly build analytical applications, rapid feedback, rapid iteration, implement quick win
- After analyzing the results obtained confirmed service, then the data analysis logic precipitated and gradually solidified to complex data warehouse or data platform inside embodiments, only this time BI platform responsible for data analysis and visualization lightweight pressure
We believe that the nature of the data analysis for business development, business decisions and services, rather than creating a bunch of useless visualizations. With this agile mentioned above, quickly confirmed, continuous precipitation process, will ensure that the direction of the enterprise architecture analysis and decision can correct a greater extent on, to get the business end of the recognition, drive business development, creating real business value.
to sum up
Talk about the real situation of the industry nearly two years now, I usually participate in a lot of data, and large data analysis of industry conferences, Sharon, found a very obvious change is now on the outlet of large and small Internet company, He says that they use big data more and more, but less and less data warehouse company, and engage in growth hacking more and more, and be able to support their own business to do fast, accurate reporting fewer a.
Personal feeling is, then that help engage dimensional modeling, the old guy warehouse system does not teach good apprentice, the other is halfway diverted elt accounted for half of the data warehouse so now many companies see the bi system in general is a big layer + "source stick" to see the data warehouse project theoretical system less and less.
