5 Rules for Better Sales Analytics
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Interactive Analytics and OLAP – Part III
In the part II of interactive analytics and OLAP, we left a question: Can the narrowed OLAP be used to complete the computation process as follows (marketing and sales data analysis)? In the part II of interactive analytics and OLAP, we left a question: Can the narrowed OLAP be used to complete the computation process as follows (marketing and sales data analysis)?The first in customers whose purchases from the company account for half of the sales volume of the company of the current year;The stocks which go up to the limit for three consecutive days within one month;Commodities in the supermarket which are sold out at 5 P.M for three times within one month;Commodities whose sales volumes in this month have decreased by more than 20% over those of the preceding month; Of course NOT!Currently OLAP system has two key disadvantages:The multi-dimensional cube is prepared in advance by the application system and user does not have the capability to temporarily design or reconstruct the cube, so once there is new analysis demand, it is necessary to re-create the analytics cube.The analysis actions could be implemented by cube are rather monotonous. The defined actions are quite few, such as the drilling, aggregating, slicing, and pivoting. The complicated analysis behavior requiring multi-steps is hard to implement.Although the current OLAP tools are splendid regarding its look and feel, few on-line analysis capabilities powerful enough are provided actually.Then, what kind of OLAP do we need? What kind of OLAP tools we need? It is very simple, and we need a kind of on-line analytical system that can support evaluation process, which SQL data computing or excel computation can handle.Technically speaking, steps for evaluation process can be regarded as computation regarding data (query can be understood to be filter computation). This kind of computation can be freely defined by user and user can occasionally decide the next computation action according to the existing intermediate result, without having to model beforehand. Additionally, as data source is generally database system, it is necessary to require this kind of computation to be able to very well support mass structured data (tools like esProc) instead of simple numeric computation. And evaluation process is what business need especially in marketing and sales data analysis.Then, can SQL (or MDX) play this role? SQL is indeed invented for this aim and it owns complete computation capability and it adopts a writing style similar to natural language.But, as SQL computation system is too basic, it is very difficult and over-elaborate to achieve complex computation by a SQL data computing, such as problems listed in the preceding paragraphs. It is even not so easy for programmers who have received professional training, so ordinary users can only use SQL to implement some of the simplest queries and aggregate computation (based on the filter and summarization of a single table). This result leads to the fact that the application of SQL has already deviated far away from its original intention of invention, almost becoming the expertise for programmers.We should follow the working thought of SQL to carefully study the specific disadvantage of SQL and find the way to overcome it in an effort to develop a new generation of computation system, thereby implementing the evaluation process, namely, the real OLAP, instant data analytics.Related Articles:Interactive Analytics and OLAP - Part IIInteractive Analytics and OLAP - Part I
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