OLAP is a type of BI software that emerged and gradually developed 20 years ago. OLAP can be used to handle the complex computation flexibly and rapidly according to the requirements of analyzers and present the result to the decision-makers in an intuitive and understandable style. The decision-makers can thus grasp the enterprise operating status accurately, understand the object requirements, and set the right scheme.
The original intention of OLAP is the arbitrary interactive computation on data. To serve the purpose, OLAP tools should get rid of modeling, support the direct analysis on the history data, and provide the decision information through the arbitrary interactive computing timely. The traditional OLAP is stuck in the mistaken ideas of centralizing on modeling and focusing on presentation. According to findings from Google, since 2004, attentions on OLAP drop by 85 percent. In fact, most users just take OLAP tools as expensive presentation tools.
The true OLAP should be featured by:
Free multi-step computation. The OLAP tools must be able to arbitrarily decompose the complex problem into several simple steps. Seek the solution to the complex problem by solving lots of simple problems. The step-by-step computation is the key to solve the complex problems. For example:"Compare the insurance products sales fluctuations in the half year" can be decomposed into these simple steps: Filter out the data of insurance products sold in the half year; Group the data by month; Summarize the sales in each year; Perform the year-over-year monthly comparison. Traditional OLAP is confined by the model and unable to solve the complex problem, not to mention the arbitrary operations.
Support the friendly interaction. It will lower the requirements on technical background to ensure that even the normal business personnel can grasp it easily. The analysis result in any step must be always clear and visible. According to the intermediate result, users can manipulate the data straightforwardly and intuitively on menu. OLAP is intended to serve the purpose of business decision for the business specialist users. For the business experts with limited IT experience, user-friendly interaction is just what they need.
Support the structural data. In the business system, a huge amount of structural data is scattered here and there, for example, in database, or Excel and text file. The ideal OLAP should provide the direct support for the massive computation on the structural data, such as grouping, summarizing, associating, and filtering. Although SQL can support the structural data well, SQL also requires a relatively higher technical background on users. To implement a bit more complex computation, users will have to write a great deal of scripts hard to write and maintain. The ideal product is the one that is comparable to SQL in computational capabilities with relatively lower requirements on technical background, so that the business specialists can use it easily.
Optimize the business computing. Unlike the scientific computing, the business computing often requires the thinking from business perspective. For example "which mobile phones among the top 20 best-sellers last year still remain the top 20 best-sellers this year". In the business computing, users shall seek the interactive set of the 2 objects intuitively, instead of writing a large section of SQL/VB/JAVA scripts. How to represent the intersection set between objects easily using the business terms. The similar examples also include the associative relations to represent the multi-levels, for example, "for the sales managers responsible for the insurance products among the top 3 sales, how much contribution their clients made to it". In addition, the complex problem of commercial computing is always related to orders: ranking, link relative ratio, year-over-year comparison, etc., for example, "stocks whose sales volume keep rising in consecutive 10 days". All these problems need representing in a concise and easy-to-understand way.
The typical usage of traditional OLAP tool is always to build module first and then analyze data as if it was a convention. In facts, traditional OLAP suffers from the seriously insufficient computational capability. Therefore, it can only obtain the limited computational capability at the cost of modeling. Modeling is the means to make up for the product drawback by forcing customers to pay for it. Modeling is not a must. It is better to spend more time and efforts on data analysis than to just model. 20 years ago, the computer performance is low, and modeling can alleviate the pressure on computation. However, the present hardware performance has risen for hundreds times, and the role of modeling becomes less and less important.
Let's use an example to illustrate the drawbacks of modeling. For example, an insurance company has implemented a new insurance policy in the recent 3 months. To analyze the effect of new policy, corresponding actions must be brought up.
Firstly, it is impossible to design a complete analysis model beforehand. For example, the new policy may mainly affect the sales achievement, team relation，or even the sales force management. The sales achievement may probably go through a host of possibilities of sudden rise and drop. The sudden rise of sales achievement may result from either the large insurance product orders placed with short lead time, or the increase in the volume of insurance product orders. There are many branches and fluctuations. Every time, users have to judge momentarily on the unpredictable tendency of branches, which means it is impossible for them to design a clear path to analyze.
Secondly, the model restricts the freedom of analyzers. Modeling means analyzers can only take actions in the stipulated scope. For example, without such data in the model, it is impossible to import the data about fellow insurance companies. For example, without the slicing, rotating, drilling, and other traditional OLAP functions, it is impossible to compute the occupations of VIP clients accounting for 60% of the total profits. The original intention of OLAP is the arbitrary interactive computation. The typical procedure is to firstly make an assumption on the obscure goal, secondly verify or falsify the assumption, correct the assumption continually, and then ultimately reach the right decision. The traditional OLAP does not support the arbitrary interactive computation on data; its limited function hinders the freedom of analyzers, and therefore cannot make the truly valuable decision.
At last, models cannot hold the changing requirement. The business opportunities are evanescent. Facing the ever changing demands, OLAP must provide the right analysis decision in the shortest time. For example, the continual decreasing of insurance product orders volumes draws the attention from executives. It is pressing to determine whether there is a relation between the insurance product order decreasing and new policy. When the traditional OLAP encounters such new requirements, users can only request the technical experts and business specialists to design the new models, and then keep on adapting the models to the practical business, and reworking on the modeling when it turns out to be incapable in the analysis stage. During this period, a great deal of time, money, human resources, and physical resource is spent. When all analyses have been done, the business opportunities have slipped away.
As can be seen from above, the model is not a necessity; on the contrary, modeling makes OLAP lost customers and market. That's why it is definitely necessary to get rid of the mistaken ideas of OLAP.
The esProc/esCalc family product of Raqsoft is just such OLAP software that requires no modeling, capable to compute arbitrarily and friendly in interaction. Of which, esProc is good at multi-step computation and solving the complex problems; esCalc relies heavily on the friendly interaction and ideal for the business personnel. They have the below advantages:
No modeling - esProc/esCalc does not require modeling because its professional computational capability enables its users to implement the immediate analysis on business data to provide the decision support promptly, and ultimately seize the evanescent business opportunities.
Arbitrary computation - esProc/esCalc can be used to perform the arbitrary OLAP computation, enabling analyzers to process the data arbitrarily at their own will and provide the enterprise with the truly valuable data for decision making. It boasts the friendly interaction, arbitrary multi-step computation mechanism, and optimization on business computation, such as the complete set computation, ordered computation, and object reference.
Technical standard is relatively low. esCalc/esProc has lowered the technical requirements on the analyzer and designed especially for the business specialists. Business specialist can complete the whole set of OLAP work independently, such as installation and deploy, determining analysis goal, decomposing task, data retrieval, verifying the conclusion, presenting the conclusion, and print & export.
The computational capability of esCalc/esProc is worth looking forward to in the hope that it will help traditional OLAP to get rid of the mistaken ideas, and work together to develop the next generation of OLAP specifications, and get recognized by the industries and be popular in the market again.
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