Data warehouse refers to the process of compiling and organising data into one common database. It is a place where data can be stored for useful mining. It is like a quick computer system with exceptionally huge data storage capacity. Data from the various organisation’s systems are copied to the warehouse, where it can be fetched and conformed to delete errors.
The purpose of a data warehouse is to store a huge amount of historical data and empowers fast requests over all the data, typically using Online Analytical Processing (OLAP). A database is made to store current transactions and allow quick access to specific transactions for ongoing business processes, commonly known as Online Transaction Processing (OLTP).
Combining data from numerous sources, a data warehouse can ensure data quality, accuracy, and consistency. Data warehouse boosts system execution by separating analytics processing from transactional databases. Data flows into a data warehouse from different databases, then the warehouse works by sorting out those data into a pattern that depicts the format and types of data. Then, query tools examine the data tables using patterns.
Data warehouse process. Source: dremio.com
Data warehouses work to create a single, unified system of truth for an entire organisation. It is built to support decision making and reporting for users across many departments. They are also used as archives, holding historical data which is not maintained in operational systems.
Features of data warehouse
There are four main essential features of a data warehouse described as follows:
- Subject oriented as it provides useful data about a subject instead of a company’s ongoing operations. The subjects stored can be customers, suppliers, marketing, product, promotion, etc., which then can help business and organisation make data-driven decisions.
- Time-variant which is used to provide information for a specific period.
- Integrated – data warehouse is built by joining data from heterogeneous sources, such as databases, level documents, and the like.
- Non-volatile, meaning once the data is entered into the warehouse, it cannot be changed. This function helps the data remain as it is.
Why building a data warehouse?
Data warehouse might be complex to build and manage, but it is essential to streamline businesses. A data warehouse is not only useful as a single point of access for all data, but also good as an assurance of data quality. A history of the data they store is top-notch because of its non-volatility. Data warehouses can also provide separation between day-to-day operational systems and analytical systems for security systems, as well as a standard set of semantics around data. For example, leaders can have consistency in naming conventions, codes for different types of products, languages, and currencies which enable easy tracking.
Storing comprehensive data unstructured relationships means that data warehouses provide an answer to a whole variety of complex questions, such as follows:
- How many revenues has each product brought in per month over the past ten years?
- What is the average transaction size at one of banking’s ATMs, broken out by time of day, total customer assets, or total money taken per month?
- What is the percentage of employee turnover for the past year in stores that have been open for at least three years?
- How many hours do employees work per week?
- How many benefits do a company need to allocate for current employees? Etc.
Data mining refers to a computer-supported process of analysing huge sets of data that have either been compiled by computer systems or have been downloaded into a computer. In the process of data mining, the computer is used to analyse the data and extract useful information from it. It looks for hidden patterns within the data set and tries to predict future behaviour. Data mining is primarily used to discover and indicate relationships among the data sets.
Data mining can also be a powerful tool to detect malware, intrusion and analyse audit results to detect a malicious pattern. It is a great source for businesses to identify demand for products and/or service in both local and global market.
Data mining process. Source: wideskills.com
Generally, there are six classes of processes in data mining, namely anomaly detection, association rule learning, clustering, classification, regression, and summarisation. As seen from the image above, data should be prepared and processed first before it can yield expected results. The mining processes begin with an expected pattern/hypothesis that is tested. The results of the tested pattern are then observed to find a pattern within a process. Professional data mining can pre-process to analyse the multivariate data sets before the mining is conducted. It should be noted that the data processed must be large enough to contain these patterns while remaining concise to be mined, thus businesses can use resources from their data mart or data warehouses.
Characteristics of data mining system
- Large quantities of data – the volume of data so great it has to be analysed by automated techniques, such as satellite information, credit card transactions, etc.
- Noisy and incomplete data – imprecise data is the characteristics of all data collection.
- Complex and structure, such that conventional statistical analysis is not possible.
- Heterogeneous data stored in legacy systems.
Why should you consider using a data mining process?
Data mining uses large data quantities to generate outcomes. It turns raw data into useful information that helps businesses predict their clients and/or customers. For example, by using software to look for patterns in a large database, data mining professionals can learn more about their customers to develop more effective targeted marketing strategies, increasing sales and decreasing costs of productions. Data mining also helps businesses detect fraud, help model the financial market, and analyse trends within certain groups.
Data Warehouse vs. Data Mining: Key differences
|Data Warehouse||Data Mining|
|a database system designed for analytics.||a process of determining data patterns, turning raw data into informative ones.|
|a process of combining all relevant data, creating a large set of data.||used to extract useful data from a large set of data.|
|entirely carried out by engineers.||can be operated by business entrepreneurs with the help of engineers.|
|allows easier reporting, but complicated to implement and maintain.||uses patterns recognition techniques to identify patterns.|
|useful for operational business systems like CRM.||helps create suggestive patterns of important factors like buying habits.|