A successful business requires optimized technologies managing big amounts of data which are frequently assisted by OLAP and data mining technologies. And even though OLAP and data mining are often taken as synonyms there’s a significant difference between the terms. OLAP entails multidimensional data structure used for analysis of historical information while data mining implies exploring patterns and methods including the practice of machine learning and database systems. In this article we’re going to examine both techniques of OLAP and data mining more closely.
Online analytical processing is designed for multidimensional analysis of significant business data. OLAP ensures an opportunity to perform calculations on typically small amounts of information. It provides its users with essential insights based on the data accorded by various sources and databases. The main feature of OLAP is its ability to represent data and items relations in a multidimensional structure to be displayed in the form of the cubes. This makes it possible to view data from various perspectives. Users are able to carry out holistic analysis through receiving responses to ad-hoc queries. OLAP Queries are run very fast even given large volumes of data owing to precomputed aggregations. Both OLAP and data mining give an opportunity to handle and analyze big amounts of data from numerous angles such as sales by department, by store, by country, by item, etc. In general, OLAP enables organizations to successfully perform reporting, analysis, budgeting, etc. The information obtained by this BI system commonly provides privileged information contributing to decision making.
- It performs extremely rapid analytical queries execution.
- OLAP enables analysis of current market trends and tendencies.
- Provides plenty of operations to run on the cube.
As we’ve already noted, OLAP represents all data in a multidimensional structured view which ensures multiple perspectives from which the information is available to see.
OLAP cube is built primarily to be managed by any business employee and doesn’t require additional programming or analytical skills. The system is easy to deal with irrespective of the position of the user.
OLAP maintains all business data in a form accessible to be conveniently visualized through OLAP dashboards. Due to the application a user can create pie charts, graphs, maps, scatter plots, spark charts etc. and make adjustments in them if necessary.
OLAP application is deployed in a multi-user client or server architecture which delivers fast query responses regardless of the database complexity.
Thanks to being performed by SQL, OLAP is available to all potential SQL extensions, statements, and functions. A typical SQL statement enables data mining, goal seeking, time series analysis, multidimensional data analysis, trend analysis, cost allocations, etc.
OLAP application possesses a rather high security level: if multiple users need an access right an administrator has to change the settings of accessibility.
OLAP and data mining correlation
Both OLAP and data mining fall within business intelligence processes. OLAP compiles all meaningful historical data, sums it up and analyzes business trends providing users with average rates regarding requisite information. When it comes to data mining, its main goal is to disclose covert tendencies at a detailed data level. Data mining techniques are also up to determine the potential for desired changes based on predictive analysis available.
Data mining provides advanced analytics contributing to the detection of the objects which are commonly bought together. It also can identify the demographics which usually results in best sales. Such information undoubtedly assists in well-thought-out strategies of product branding, placement, and promotion. OLAP technologies ensure similar opportunities: a user is able to conduct in-depth data analysis to figure out current trends concerning demands and use the occasion to act.
As a supplement
OLAP and data mining may reinforce each other. In case OLAP finds out general issues related to sales, for example, data mining tools can facilitate the process of analyzing more detailed information regarding particular clients. When OLAP monitors and tracks the results, data mining will predict future income and its increase based on the given data. As it is seen, operating together the systems can bring up more substantive insights.
Data mining and OLAP may operate separately. OLAP enhances organization’s productivity overall; data mining suits those who need to know their further perspectives. Plenty medium-sized businesses do not require data mining as they’ve just started their development. Moreover, data mining tools are designed for specialists with particular skills while OLAP is rather easily adopted and is often sufficient for those who need only reporting and multidimensional analysis.
The target users of OLAP and data mining are different. If OLAP is designed for average employees, data mining is utilized by business statisticians and strategists possessing professional skills.
Data mining techniques
There are several purposes data mining generally serves. For instance, it assists in revealing fraud cases, and in foreseeing changes in customer churn, thus adding value to the business process. If a user exploits data mining in a native database there’s no need to transfer data between an external server and the database. In this case, there’s a higher chance to avoid data redundancy and at the same time enhance data handling.
Data mining applications commonly perform the following functions:
- Classification. The function provides an opportunity to group items for further understanding how to classify a new item.
- Clustering. The feature looks for frequently used native data classes of grouped items to determine customer segments.
- Regression. It helps foresee and set approximate results which may occur in the future.
- Outlier Detection. The tool identifies aberrations in the system (cyber attacks, fraud cases).
- Feature Extraction. Due to the function, it’s possible to generate derived meaningful features leading to data redundancy reduction.
- Attribute Importance. It defines and graduates the most significant attributes so that to foresee a target attribute.
- Associations. The function examines the market to find out which items are typically bought together.
In order to prevent error-prone data mining process there are important steps to keep in mind:
As it has already been mentioned, it’s useful to conduct data mining in a native database system. In addition to the above, this will ensure data hiding, split-second data caching, close connection to user-defined features, and SQL implementations in the database system.
Efficient data processing is provided by multiple databases which have to be supported by the data warehouse utilized. Thereby, for example, the attribute weight is taken from the original data under the neural network method, then according to the weight specified characteristics are determined by the decision tree method. And at last, the final model is created through clustering.
Relational or complex types of data
As we have already mentioned, OLAP and data mining technologies can operate together but moreover have to be integrated in order to maintain interactive mining of heterogeneous data in complex and relational databases. Data mining technologies may involve clustering, classification, association, characterization, and prediction. Tight integration assists in rapid interactive mining by means of the tools some of which are represented below:
- Statistical analysis in an OLAP multidimensional database.
- Meta-rule guided mining.
- Data visualization through an OLAP dashboard.
- Aggregate queries to examine graph databases.
- Sub-graph histogram representation for classification of images.
Classification accuracy and efficient data mining are ensured by complete structured data. If there are gaps in data a user should undertake additional measures to solve the problem of the incomplete data:
- Independent component analysis and self-organizing maps - ICA and SOM manage data which involve gaps by assessing lacking information through the given data.
- Parametric and non-parametric methods of imputation develop strategies built.
- Multi-task learning develops pattern classification with missing inputs.
Parallel data mining application makes it easier to adopt support vector machines, tune scalable data mining, provide scalable and parallel data mining algorithms performance.
OLAP functions for data mining
Data mining is frequently utilized to explore knowledge data by allowing the algorithm to discover items and relations between them. Nevertheless, OLAP is apparently distributed more widely and is more preferable. In case the warehouse or the data mart in use is fresh and updated OLAP will probably exploit it as a database. Beforehand, the warehouse has to compile all required data from various sources and arrange it in unified formats for OLAP MDX queries. The queries are carried out in a data copy thereby the original warehouse won’t be damaged or altered.
Owing to data mining there are multiple techniques available to take a note of essential aberrations and significant insights requiring closer inspection. To add value to the organization data mining tools have to be understandable, interactive, visual, and operate explicitly on the organization’s data warehouse. Due to multidimensional data structure, OLAP responds to the MDX queries dartingly and delivers holistic analysis through advanced aggregation with operators, forecasting, and calculations. With the help of integration of OLAP database and data mining, the multidimensional analysis provides users with security, scalability, availability, and reliability. The database integrated with OLAP provides simple administration, reliable security, and development tools. The OLAP program also gives an opportunity to compose queries with the help of SQL which implies that Ranet OLAP analytics can run with any database application. Thus the process of creating reports, dashboards, and applications is dramatically facilitated. Additionally, automated cube support ensures effective and easy administration: the administrator doesn’t have to develop specified procedures to retrieve updated information from the source. There’s either no need to transfer the data to OLAP and upload and change the cube. The integrated system will provide the cube updated fully by the database.
Due to precalculated aggregations in the multidimensional OLAP cube, most BI applications run very fast and efficiently. The applications operate through typical data actions such as drill-up, drill-down, slice-and-dice, pivot, etc. These actions perform plenty of necessary operations aimed to represent meaningful information from a specified perspective. The operations are commonly random in the whole aggregations database provided.
To sum up it’s worth mentioning typical business values added by data mining techniques:
- It assists in revealing cybersecurity attacks and cases of fraud which contributes to the security of the business.
- Data mining serves to detect risk on time and take the right action.
- It predicts the needs and demands of the market making one ready to consider the strategies of supply.
- These BI tools help business create tailored and seamless solution sought after by the customers which automatically improves the clients’ feedback.
- Data mining is intended to help solve day-to-day business issues keeping the client aware of the situation.
As compared to data mining tools, OLAP technology by default includes only historical data processing which means that this solution cannot carry out predictive analysis. Thereby forecasting and foreseeing may be conducted merely by data mining. Nevertheless, it also comprises a significant pitfall: data mining tool is generally built once and for all, however, it can’t stay valid permanently. Additionally, the tool is usually developed for professional statisticians dealing with mathematical models and predictive analysis. Data mining includes complex procedures not accessible to average employees. That’s why if the main advantages of the tool can’t be entirely available to the masses, data mining complete efficiency can’t be achieved. Thereby to take advantage of the system it has to be run by expert statisticians.
OLAP and data mining tools can be utilized separately and while being reconciled regarding the user’s requirements. Although when integrated data extraction and mining processes run more fast and efficiently. The main feature of data mining constitutes the process of data discovery and analysis. Through these procedures it can predict further results and disclose significant insights for opportunities hence bringing up the profit. It’s a fact that the amount of data doesn’t matter, what really makes difference is how rapidly and usefully one is able to attain important insights from it. As an American businessperson and politician, Carly Fiorina, says, ”The main goal is to turn data into information, and information into insight.”