An unlimited collection of business data that is useful in deciding on an organization is called a data warehouse. It is not a very new concept. Rather, it existed right from the late 1980s. That was the time when this data warehouse concept was developed to get ease in the field of transition data. It was used right from the activity like powering operations. The data warehouse is even seen in the platform of fueling decision support system. The concept of warehousing is on based data.
Having a perfect data structure is an important consideration for an organization. The perfect data sets also help in boosting business intelligence. A huge collection of data comes to the data warehouse from several sources. Some of the sources include marketing, sales, finance, and the purchasing department within the internal organization. Getting the perfect data model over the collection is an important consideration. The experts are working within the organization for data analysis. It is important for a perfect data transformation.
How does data warehouse work?
People with very faint knowledge about the concept of data warehousing would always wish to know about its working concept. They don’t know the data history also. The concept of its working is based on the data structure. It is a technical activity carried on by the experts in the IT industry. Following are the activities in technical level:
- Data warehouse pulls the data sets from several apps and systems
- Then such data is sent for formatting
- The third step is for the import process. This is done to match the same with based data already in the warehouse
- Then it is stored in the processed data. This completes the action of the operational database.
After the above steps, it will be quite easy for users to access the data. You can also view the data analytics after the above procedures are completed. The experts, as well as users, can easily go ahead with the data integration process. It is through the standard database format that one can easily have access to all the processed data. Some other vital questions that come in the mind of every individual are:
- What is the frequency of the data pull?
- How are the data formatted?
- What is the data timeline? How data Does management take place?
- What is the utility of integrated data?
All the answers to these questions will vary as per the need of the organization. The sizes of the organization as well as their nature of working are the considerations. Some of the organization has a strategy for data management. The large firms always need processed and integrated data rather than the raw and unprocessed data.
Advantages of data warehouse
The data warehouse definition is based on its use for assisting the organization in its analytics as well as business intelligence. Most of the business uses the format that is based on transactional systems. The database warehouse is based on various functional areas. A standard database format is essential always. Thus, they have some distinct benefits:
1. Enhances data quality
The data warehouse converts the data from various sources to a consistent format. It creates a normalized data structures out of the unstructured and raw data from unknown sources. Further, the data is made standardized and furnished to each department. The result so extracted from each department is going to be of good quality. The information quantity will be accurate.
2. Time savior
This is another very important benefit which is based on the data warehouse architecture. Organizations, as well as people working in it, do not have much time in hand. They want everything to be done very quickly. Since the data warehouse process the raw data from other sources to standardized structure, users can quickly pick up the data from the transactional systems. There is no need for further verification and rechecking. Thus, it saves a great time.
3. High ROI
Each organization or businessmen wishes to get profit. It is nothing but a good return over investment. Now, by the use of data warehousing, companies can easily save. This, however, becomes an advantage over the Return on investment. Yes, the companies using the data warehouse system get high ROI as compared to those companies that have not invested here.
4. Improves decision making
Taking the right decision at every step is an important consideration. A single wrong decision can make the company suffer loss. Data warehousing maintains organized data. Thus, it creates better insights into the decision making process. The present, as well as past data in the information centers, are consistent with the use of data warehouses. Here, the data is transformed into purposeful information within the transactional systems.
5. Proper forecasting
Forecasting the future is always an important step for every organization. Data professionals are involved in analyzing the business data for forecasting its future endeavor. Here, the appropriate data quality plays a vital role. The experts can also find out the potential KPIs within the business by the forecast of structured data. They can also predict the result of the business’s future.
6. Boosts business intelligence
Data warehouse access the information from various sources to a single platform. Sometimes the individuals also come with complex queries. In such a situation, the decision-maker of the business may not need to depend upon the limited data. Through the data warehousing process, one can easily get the central view of all the data. Thus, business users can apply them to several business processes without any effort. The places where they can boost business intelligence are sales, market segmentation, finances as well as inventory.
7. Competitive advantage
If you are into business and dealing with some products and services it is quite important to note that you will have good competition in the market among all the businessmen. But, if your technology, as well as approach, is better, you are going to have an advantage among your competitors if you have the data warehouse.
8. Smooth flow of information
There seems to be a communication barrier within the organization if the flow of information within the same or different departments is not in a proper sequence. But, with the help of the data warehouse system, the dimensional equivalents are achieved. As a result, there won’t be any communication barriers within the organization.
9. Benefit of scalability
There are two dimensions in which the scalability should be provided. In vertical scalability, the workload can easily take advantage of the storage capacity of the CPU. This is also done in a single-dimensional approach when the capacity of the hardware is saturated. Now, the system can easily handle larger databases as well as more users with the scalability benefit.
10. Security concern
These days’ people are too internet savvy. They are almost doing everything with the help of web services. This helps them to do their work in an easy as well as effective way. But, along with the advantages, the internet world is quite dangerous when security is concerned. Several cyber crimes take place now and then. The data sources can be violated as well. But if you have the benefit of the data warehouse, the data storage will stay secured.
Misconception about data warehouse
Few people mix up the data warehouse with something else. They are now aware of what exactly is a data warehouse. People usually confuse it with a database which it is not. Some of the misconceptions about the data warehouse are as under:
1. The engineering team can build the data warehouse just like their side project
Some of the companies or the head of management make this big mistake. They think that the data warehouse can be managed just like a side project. Since the engineering team has the technical knowledge on it, they can handle it well. But, this is a wrong conception as well as a myth. Usually, the task of data warehousing comes with a great bunch of responsibilities. It manages data analytics also. Thus, this must be handled by the team dedicated to this purpose. It cannot be handled by the one who already has other occupations.
2. The scientist can solve all the data problem
We usually depend upon the scientist when it comes to any complicated issue. The functional area of the data scientist is different. The management system must be associated with the right data granularity as well as data history. The experts dealing with data won’t be able to solve the issues related to the data warehouse. It is important to hire someone who has:
- Prior experience in cleanup and structuring of data for a precise understanding
- For perfect handling of the operational database, the individual needs to know the distribution system, scripting, SQL.
- Perfect transaction processing is another important consideration. The expert must understand the context as well as the purpose of the data.
- They must have proper experience in implementing the data warehousing process. Some of the examples are google Big query, etc.
3. Very little use of data warehouse
Most of the user has an opinion that something is better than nothing. Though they need a data warehouse for several activities starting from transaction processing to data mining yet they make only a partial investment in the building data warehouse. As a result, there is an issue of de-prioritize. The organization gets incomplete and inaccurate data. Sometimes the information system is also correct. This leads to poor decision making within the organization and harms productivity as a whole.
4. Least interest in the data warehouse
It is going to be fascinating when you hear the name of the new technology for the first time. Usually, people get interested in it. But, the people with technological knowledge working within the organization are not passionate about the concept. Finding the people who get interested in it becomes difficult. The organization also requires source data for the business user.
5. Duration of building data warehouse
Some people have a concept that the data warehouse can be built in a very fast and quick time. But, this is again a misconception within the operational data store. It takes a really good time to build the data warehouse correctly. The staging area also takes time. Usually, most of the business organization has lots of debts. The period of data warehouse building becomes long. Usually, it takes around six months to 1 year for building a strong as well operating data warehouse.
6. It is not a data lake
Both data warehouses, as well as data lakes, are made for business analytics purposes. But, there is a difference between both. The task of the data lake is to store all types of data starting from structured to unstructured in a native format. It is stored until the need arrives. But, the data warehouse is observed to store all the folders as well as data in such an organized fashion that it is ready for end-users.
7. Confused with the data mart
The data mart is also a word that comes in connection to data allocation and warehousing. But, some people also get it confused with the data warehouse. But, if you compare the two, the data warehouse has much more capacity and has a large amount of data. The data marts are the subsets of the data warehouse. They are usually designed for specific user purposes. Also, it is confined to a single application.
8. Use of data warehouse information
Usually, large amounts of data are available in the data warehouse. But, utilizing such data is also important. The decision to support technologies through machine learning can easily help in utilizing such data. The executives can now quickly and easily use the warehouse through these technologies. The terms like a relational database, star schema, data cleaning, etc are associated with the process. The below-mentioned domains are the places where the information from the data warehouse can be used:
- Production strategies– Organizations willing to have good profit must tune their production strategies. Through the information gathered from the warehouse, they can tune it with repositioning products and managing the entire portfolios.
- Operational analysis – A company can get a great advantage of customer relationship management through the data warehouse. Data can be connected to multiple heterogeneous sources. But, with the help of proper screening in the warehouse, analyzing business operations becomes easy in real-time.
- Customer analysis- A business house or a corporation with sales of products and services will be incomplete without the customers. The need for buyers is important. The information for the database helps in analyzing the buying preference of customers. Even the complex queries are answered. To cover big data, online analytical processing is done.
Integrated heterogeneous database
Way beyond the unstructured data and transaction data, the integrated heterogeneous database has a higher position. Most of the organization uses the Customer relationship management (CRM) system. Using this will be quite easy once you have a data warehouse. It consists of two main approaches:
An Approach with the query– This type of approach was used before. Most people claim it to be a traditional approach. The utility extracted from the query-driven approach was through making the wrappers and integrator. This was known as a mediator in a database management system.
Working of query-driven approach- There is a step by step process through which the query-driven approach is run. First of all, a query is issued to a client site. Thereafter the translation takes place from the metadata dictionary. The result reaches the end-user. This consists of a standard database format with the best data modeling techniques.
The mapping of the queries takes place after the previous steps. This helps the decision-makers’ to work effectively after it reaches the local query processor. The business analysts get the business data right from the sources of several raw data. Thus, processing those raw data is quite important. The last process of the query-driven approach is the integration of heterogeneous data to the answer set that is recognized globally.
Approach with an update-
The other name for this is the update driven approach. The traders and businessmen can easily use it as an alternative to the traditional approach. Rather, the update driven approach is much more effective and well used by people as compared to a query-driven approach. Here, the information collected from several heterogeneous sites gets integration in advance. Thereafter, they are stored effectively in the warehouse. The info that you get from here is ready for the direct query as well as analysis.
Advantages of update driven approach-
The individual, as well as business, can get several advantages from the update driven approach. One of them is a high performing feature. The data from the source is transferred to the transactional database. After the query optimization, the process of integration takes place. Further, it is followed by summarization and reconstruction. The approach also helps in data extraction.
Data warehouse vs Database
It is very important to know the basic difference between the data warehouse as well as the database. Few people often confuse the two terms. But, they are quite different in various characters:
Processing of data- The major difference between the Database and the data warehouse lies with the processing of data. The database uses the method of online transactional processing ( OLTP) for the same. But, the data warehouse uses online analytical processing (OLAP).
With the help of online transactional processing ( OLTP) database can easily delete, replace, insert, and update a large amount of data easily. This is the technology through which one can easily update the short and small online transactions. On the other hand, the online analytical processing (OLAP) analyzes the data with massive volume. Also, the procedure is quite rapid. Here, it will be quite easy to look at data from a different point of view.
Optimization
Data so extracted need to be optimized as well. The maximum speed, as well as efficiency, is used within the process of optimization.
For each transaction processing, the response time should be quick enough. One of the greatest advantages of database optimization is the fact that it has the capacity of recording the write operation within the system. It will be really hard for a company to have a stable business if the database doesn’t make a record of each purchase.
Data structure- If you observe the structure of the Database as well as the Data warehouse, you can easily find the stunning difference among the two. The objective of the normalizing data is not only to reduce but also to eradicate the data redundancy. Within the normalized data structure, minimum disk space is taken. But, one of the disadvantages is the fact that it is not query efficient. Rather, it can be slow as well as cumbersome. Instead, another name that comes to the mind for the replacement of normalizing data is denormalized data. This consists of not once but repeated data. As a result, the user can have easy access.
Data warehouse and data mining
Data warehouse deals in organizing as well as compiling of data. But, the task of data mining is to extract the useful information right from the platter of databases. The act of data mining solely depends on the data warehouse as it extracts the information from the compiled data which is available with the data warehouse. The data warehouse is a huge process that is developed for better support as well as the working of the management systems. This helps the organization to have a proper operational system.
Data warehousing process
Here the data is stored regularly for data mining. This also includes analytical processing. You can also term it as a quick computer system with an enormous amount of storage capacity. It includes the act of collecting data from various sources as well as organizations that are brought for the business process. But, the process also involves the removal of the errors. The information is further used for business transactions.
Data mining process
Data mining is nothing but an analysis of data. This is one of the business intelligence tools that analyze data for a specific purpose. The response time is quick with the real-time data. Here the subject areas are not vivid within a single place. It usually searches for the hidden patterns within the data sets. The process also involves the prediction of the future behavior of the data that has been extracted. The fact tables are made with the processed data so found. The data sources with a dimensional model have their importance.
Use cases of data warehouse
With the help of data warehousing, one can enjoy high-level reporting as well as analysis. This boosts the empowerment of the business and the traders will get more informed business. The most used case in this situation includes:
- With the help of the last ten years of data, the taste and preference of the customers is predicted
- The activity of making segments among the customers in various groups. This would be based on the type of purchase they make. As a result, it provides much more tailored content
- The data warehouse also helps in the areas of creating a sales forecast as well as demand. As a result, it becomes easier for the businessmen to decide on the areas to focus for the next quarter.
Conclusion
The data warehouse has gained its popularity in recent years. The market research shows demand for this process in both small as well as big organizational setup. Being constructed by integrating data from multiple heterogeneous sources, the system also supports the activity like decision making, analytical reporting, data cleaning, data consolidation as well as integration. It is a process that brings out high performance within the organization.
A data warehouse is often confused with database and data mining. But, all three are quite different. If an organization moves beyond the simple database, it will be easier for it to get more with the help of its analytics. But, it is always good to find out the right warehouse solution. The particular solution must fit the business needs. This will serve the customers attached to the organization and have the proper growth of operations along with its productivity as a whole.
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