What Is a Data Warehouse?
The study and application of data science is immeasurably important in an era increasingly driven by technology and information. Schools and companies alike are jumping on board to establish data science degree programs and departments due to the field’s growth. A key topic of study is the data warehouse.
A type of management system, a data warehouse is used to report and analyze data derived from one or more disparate sources. It provides businesses with meaningful information about their operations and performance. With aggregated data, businesses can save time, improve data consistency and streamline information. This can give them a competitive advantage—all while generating a high return on investment.
The Basics of Data Warehouses
Also called an enterprise data warehouse (EDW), these repositories amalgamate data from marketing, sales and many other operations in a given business.
Without a data warehouse, businesses are left to analyze data in each distinct operational database. In other words, marketing’s database must analyze its own information while the sales’ database must analyze its own as well, and so on. Issues arise when operational databases must enter a temporarily fixed state in order for their information to be inspected.
A data warehouse provides an external source to analyze all information related to a business. In doing so, operational databases can remain up and running and business activities can be seen as one cohesive unit. What results from creating and leveraging data in a single place is the ability for businesses to grow their operations and expand their customer base, by means of insightful and relevant information.
Data Warehouse Examples
The fundamental premise of data warehouse systems is that they are capable of indexing and assessing vast quantities of historical data. This data could include customers’ addresses and purchasing history. Their support of historic data analysis is what differentiates these systems from operational databases.
A more specific name for an operational database is the operational data store (ODS). These exist as complementary to the data warehouse in that they provide them with a source of data. An ODS facilitates operational reporting in real-time or near real-time. These support day-to-day business activities. Data warehouses assist in long-term business activities.
Put another way, operational databases store individual customer purchase data, whereas data warehouses amalgamate all customer purchases with other types of data. The former can be used to track stock quantities, allowing companies to know when they are running low on an item and when to restock. The latter can be used to better understand aggregate customer behavior and patterns. This allows companies to track customer trends and market movements.
A few of the main reasons for data warehouses are:
- Carrying out data mining
- Conducting market research
- Analyzing customer behavior and patterns
Pros and Cons of Data Warehousing
Unlike operational databases, data warehouses do not run in real-time. Instead, they sift through information that has been gathered over a long period of time. Their historical look at information helps businesses make strategic decisions. With any data-storing system, there are pros and cons. Below are a few of each to be aware of:
- Easily integrated across departments: Data warehouses can compile information from every department in a company. This way business activities can be seen as one cohesive movement, as opposed to fragmented sectors with their own purposes and courses of action. Integration enhances data quality and consistency.
- Adds value to operational business applications: The compilation of historical data provides valuable insights on how a company ought to move forward. With a full scope of operations, companies can forecast with confidence and plan accordingly.
- Fast data retrieval: Retrieving data from a data warehouse is quick and easy. With information from every department compiled in one location, employees do not need to seek out each and every department to receive what they’re looking for.
- Better decision-making: Business executives can make better and more informed decisions with an integrated data management system. Decisions can be made strategically throughout each and every department of a company.
- Data entry: Companies will need to input raw data, which is time-consuming . This may result in extra work.
- Keeping sensitive information secure: With any technological advance, there are cybersecurity concerns. Businesses must ensure their employees and cloud vendors are trustworthy and reliable.
- Potential compatibility issues: Similar to any new system, there may be compatibility issues with existing systems.
- Financial costs and future expenses: The cost/benefit analysis of a product is usually a determining factor in whether a company will sign on. Data warehouses are expensive to build and maintain. Companies may hesitate to invest in the system.
How to Build a Data Warehouse
For any job involving big data, there is a need for educated data engineers and data analysts. People with this experience can successfully build and maintain data warehouses. Here are a few basic steps to understand the process of building a data warehouse:
- Clearly define your business objectives: This enables you to identify which business processes you need correlated. In correlating these processes, you determine key performance indicators.
- Determine how long you should archive data: The more data stored, the more cloud space you may need. This could take up considerable resources. Figuring out how much data you need to be archived for specific business operations will save you money and resources.
- Use effective, sustainable data gathering and assessment practices: Once you’ve defined your objectives and archival period, it is time to gather the data. Using effective and sustainable data gathering and assessment practices can reduce costs and eliminate insufficiencies by extracting and storing information once and for all. Opting for unsustainable data gathering practices such as replicating data may limit a company’s growth and perpetuate a financial burden.
- Create a conceptual data model: The objectives you have chosen now must be transformed into a conceptual data model. These are visualized through fact tables—essentially just the exact measurements, metrics or facts surrounding business processes.
- Be prepared for data transformations: Data needs to be compiled in a consistent, structured manner to be properly stored and interpreted. For business processes operating on different systems, this involves transforming disparate data collections into cohesive data warehouse tables. Moreover, some transformations require more complex tools, meaning businesses must be prepared to move data over to new, more advanced systems.
- Implement your strategy: After all the aforementioned steps are complete, it is time to implement your strategy. This can best be done in phases to ensure the information has been extracted and stored properly.
Keeping Up with Change and Developments
The above steps can help to build a data warehouse, but there is still much more to learn and to be done. Keeping abreast of new technological developments is necessary for data management. Cloud technology, for instance, is one ever-growing development in the field.
With cloud-based technology, companies can quickly and inexpensively assemble a data warehouse. This method of information storing requires no physical hardware, saving companies resources, time and money.
The art of cloud-based computing generates billions of dollars in revenue, proving its worth to businesses and consumers alike. Businesses must be prepared to adapt to new technologies such as the ever-improving cloud. These advancements can help further streamline operations, improve efficiency and reel in earnings.