Data warehouse automation describes the technique of automating and accelerating the data warehouse growth cycles, while ensuring consistency and quality. D WA is considered to give automation of the full lifecycle of a data-warehousing system, starting from source analysis to data-warehousing design, to data-warehousing configuration, to information-warehousing usage and maintenance. It aims at providing flexibility to data warehouse administrators, through the simplification of business logic. This provides greater objectivity to users, by removing previously-established data warehouse processes. The ability to extract more intelligence from complex data also helps in improving the decision-making process.
The aim of D WA is to allow users to drill down data from a complex data-warehousing system, in order to create more manageable data that can be used in decision-making. D WA does this by combining two previously mentioned techniques – Extract-Transform-Load (ETL) and Automation. ETL involves processing data in a batch mode, while automation deals with transformations of data into other forms, usually used in decision-making. Both techniques lay the foundation for the Data-Wise transformation engine, a core component of D WA.
How does data automation help in data warehousing? Data warehouse activities, especially data mining, entail pulling out insights from large amounts of data. These insights can be extracted using several strategies. Data extraction happens through manual data searches, data-mining or through advanced techniques such as complex algorithms or neural networks. The resulting data will be used to support business decisions. Therefore, data automation provides valuable insights, and hence automation improves decision-making process.
In a data-warehousing system, ETL is carried out on a large scale, which requires substantial man-power. Moreover, it involves complicated analysis and sometimes, even black hat techniques. It results in a lot of data being stored in the data warehouse systems. For such reasons, data automation helps minimize the strain on the people in charge of running the ETL tasks and enables them to focus on more critical tasks.
With data automation, tasks such as the creation of reports and dashboards can be done accurately. The ETL tasks can also be made more dynamic, thanks to recent tools such as the Advanced Business Intelligence (ABI) and the Software Engineering Toolkit (SATK). These technologies are specifically meant for creating highly customized ETL reports. They are also meant to provide users with a rich graphic user interface for accessing data from any source. In addition, data automation also ensures faster retrieval of data, which helps save time and improve the overall efficiency of the business.
Automation also makes it easier for the data to flow smoothly through the organization. Information can be processed quickly thanks to batch processing applications. This helps to cut costs on data entry, data manipulation and data processing. Data can also be automatically updated in data automation systems, which is especially useful for large organizations that process a high volume of data. With data automation, business managers can also take advantage of information technology support services offered by vendors.
With data automation, users can process data faster and more efficiently. However, data automation has some drawbacks, especially when it comes to maintaining data warehouses. Manual data entry is still required, as is the case in data warehousing. Furthermore, data automation is not applicable for all types of businesses; for instance, it is not applicable for retailers because the nature of their business does not necessitate keeping records on customers. Data warehousing is most suited for companies operating in different sectors; data mining is most suited for industries operating in one sector, such as the finance industry.
If data automation proves to be beneficial for your business, you have to be sure to properly plan how it will fit into the business model you have in place. As data automation is quite a complex system, it is always advisable to carefully consider how it will affect your business. Researching thoroughly about the data automation system you intend to use will help you make an informed decision. However, if you feel that it is too complicated for you to handle on your own, seeking help from a data management consultant could be the best alternative.
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