The True OLAP

OLAP or simply OnlineAnalytical Processing is the process that allows users to conduct multidimensional interaction analysis operations on real-time business data. This includes performing operations such as drilling, aggregating, pivoting, and slicing multidimensional data.

It is necessary to create topic specific data cubes in advance to support above operations, thereby users can inspect data in tables or graphs and conduct real time pivoting and drilling. Having said that, let’s consider whether OLAP is enough for catering analysis and forecasting in real world.

Figure 1: Information Systems can be divided into transnational (OLTP) and analytical (OLAP). In general OLTP provide source data to data warehouse, whereas OLAP helps to analyze it.

A mature company may have somewhat large data accumulated about its operations. These data can be used to make certain guesses about the business they are engage in. For example, a vehicle importer may guess what kind of people are tending to buy what kind of vehicles. These guesses are just the basis for forecast. Then the company could utilize accumulated data to evaluate above guesses. When a guess evaluated to be true they can be used in forecast and when it is false they will be re-guessed.

Above process could be referred as evaluation process, whose purpose is to justify conclusions with evidence find in historical data. In business analysis process, a query like the first n customers who has purchased a vehicle from vehicle types contributed for half of the sales volume of the company in the year the x is ubiquitous and required some form of a computation or querying, with intermediate steps.

The requirement of building the data cube in advance, and a limited set of actions available to perform against data cube are limiting the analysis process in a situation like above. The data model needs to have capabilities of reconstructing the cube or temporarily build cubes to cater diverse analysis demands. Furthermore, most of the OLAP products are more famous for their rich user interface; only a few has powerful online analytical capabilities.

Figure 2: In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

So what kind of an online analytical tool could fulfill the evaluation process? Theoretically, steps for evaluation can be considered as computation regarding data. This computation can be defined by user and can decide next computation actions to be taken based of the intermediate results without having a defined model beforehand. Additionally this computation should support performing actions on huge amount of data instead of simple numeric computations. At this point of view, SQL is somewhat fulfilling this requirement, but considering its own computational capability, still it has its own limits on solving problems similar to above mentioned problem. This leaves us in a position to think about the limitations of SQL and builds a way through it to make a new generation of computational system for evaluation process, namely, the real OLAP.

I’m still completely a novice in Business Intelligence. J

What Makes Business Intelligence Better?

I’m completely a Novice in Business Intelligence J

Business Intelligence is the process of giving businesses (technically to people who steers the business) insights about what they have been doing in their operations. This is done by processing data gathered during business transactions. This data is mostly in unstructured manner. This unstructured data available in internal and external data sources are transformed into reportable forms such as graphs as tables. The end result of business intelligence process is a set of reports and/or a dashboard that allows non technical business operators to perform ah-hoc queries on their data.

When we hear the term Business Intelligence, more precisely Intelligence, we tend to have an impression of performing huge amount of analytical tasks and serve a set of predictions. Bazinga! This is not the case in business intelligence at all. Yes, Corporations need numbers, facts about how they are performing in the process of achieving KPIs, but most importantly the data should make sense about operations to non technical users. This is where a business intelligence tool gets success, because not every user is going to be a node js geek who is waiting for npm to finish up his work.

Ideally the tool should support to build data visualizations in the matter of minutes, or maybe seconds without requiring technical report development knowledge. For example having functionalities like drag-drop might be quite interesting. In terms of reporting, the tool should require a minimal number of steps/clicks to build a report.

Ability to introduce new fields based on calculations using other existing fields/columns, filtering data based on predefined or auto modeled parameters, exploring data from different angles, and suggesting data types, schema and hierarchies automatically are also important in self service aspects of the tool.

Business intelligence also needs to perform predictive analytics. The tool should be capable of analyzing historical and current data to make predictions about future events. It should have functions to more clearly communicate a huge amount of complex data.

Furthermore it is important that tool could handle data sets in different sizes. The volume of data being consumed and number of queries being run simultaneously greatly affects the performance of the tool.

Ultimately business intelligence tool should support to interrogate data and draw conclusions that help to perform job better. Having a trending software stack doesn’t make the product a better one. A sustainable set of technologies that facilitates the product to get job done is what allows it to stand ahead in the game.