Data Warehouse Reporting With Advance Analytics For Business Reports

The term “Data Warehouse’’ refers to a blend of technologies used to collect and analyze data from various heterogeneous sources. It is the core of the business intelligence system which aids in the process of recording and analysis of historical data reporting. In actuality, this is the electronic storage of a certain organization’s large quantity of data which further performs the function of answering queries and for the purpose of analytical reporting. 

Thus, Data Warehouse reporting implies the process of collecting and arranging data from a varied number of sources. Also, helping in order to form meaningful concepts.

There are four constituent components of data warehouses. They are as follows :

1.  Load Manager – This component is also known as the front component. It is concerned with the collection of raw level data from various sources and the subsequent loading of it in the warehouse.
2. Warehouse Manager – This is the component performing the functions of managing the data for business reporting. It functions like, collecting the data in a way to ensure its consistency, provide indexes to this data, creating analytical reports, and others.
3. Query Manager – You can perform all the backend query functions by this component.
4. End-User Tools – You can categorize these tools into the following kinds:-

•  Data Reporting
• Tools for Query
• Tools for developing applications
• EIS Tools and
•  OLAP and other tools for data mining.

What are different Data Warehouses Kinds:

There are various kinds of databases that can be differentiated on the basis of the database characteristics they possess for data warehouse reporting. These can be classified into the following categories:-

1. Enterprise Data Warehouse – The Enterprise Data Warehouse, abbreviated as EDW is a centralized warehousing system. It performs complex data analysis functions across the enterprise, for instance, the Prism Warehouse. It can also provide support in decision-making while performing predictive analysis. You can also program this system to re-group data according to its subject and subsequently provides access to them accordingly.
2. Operational Data Store – Also abbreviated as the ODS, the operational data store is the collection of optional data. You can utilize this when the data warehouse or the OLTP systems do not provide any support during the reporting functions of the organization. This also aids in warehouse management in the sense that you can refresh the data warehouse for a long period of time. For this purpose, the ODS is preferred for activities like storing employee records.
3. Data Mart – A data mart is a branch of a Data Warehouse. You can use it for a particular kind of business, for instance, sales, finance, and others.

Microsoft BI Data Warehouse: Key concepts

There following are certain key concepts when it comes to data warehouse reporting:-

• Warehouse automation software– This refers to a kind of software that uses concepts like metadata, data warehousing techniques, pattern detection methods, and other processes to automatically generate data warehouse designs.
• Data warehouse Architectures – The concept of data warehouse architecture implies a complex informative structure. It contains both historical and informative data. There are basically three ways of constructing data architecture namely – single tier, double tier, and three-tier.
• Warehouse Database schema – The term ‘Schema’ refers to a logical format of describing an entire database. Similar to a database, a data warehouse also requires maintaining a schema of its own. There are broadly three kinds of schemas of a warehouse. They are – Star, Snowflake, and Fact Constellation.

The ETL Process: From the perspective of Power BI

ETL is the abbreviated version of Extract, Transform and Load. This is the process by which you can extract data from various sources. You can later transform this into a segregated version and later loaded into the data warehouse. In order to maintain the reputation of an agile system, ETL data warehousing requires inputs and feedback from various investors, stakeholders, and top executives of an organization. 

The efficient working of an organization depends on the smooth functioning of this process. This requires change along with the changes in the business patterns of the organization. As this is a recurring activity of the data warehouse it requires to function in a well-documented manner.

As stated earlier, the ETL procedure consists of three steps, namely-

1. Data Extraction: a matter of gathering or extracting data from various external sources.
2. Secondly Data Transformation: converts the extracted data into the format of the warehouse.
3. Lastly, Data Load: sorting, summarizing, or consolidating the data, checking for its integrity and consistency, and finally loading that data into the data warehouse.

Power BI is one of the most popular business intelligence tools available in the contemporary market for data warehouse reporting. It helps organizations aggregate data, create visually attractive reports, and share them across the various teams in their company. 

In this context, the Microsoft BI Data Flows enables the user to store data in the form of an individual entity or a set of entities. These dataflows allow the various teams across an organization to not only import data from the various external sources but also formulate them into a comprehensible format through the establishment of a strong ETL system. By far, the Power BI ETL process is one of the highly reputed computing processes in the business arena. 

What does Microsoft BI use – OLTP or OLAP?

Microsoft BI uses the OLAP or the Online Analytical Processing. This is software that enables the user to process information and data from multiple databases all at the same time. This simplifies the work of analysts and aids them in viewing the same data from multiple business angles. 

Through the process of OLAP, you can calculate the data and sort it before sharing it. This further simplifies and expedites the process of analysis of the data. You can categorize this data into several cubes which makes the data analysis process faster and better. With regards to influence on business patterns and analysis, it can be said that OLAP is a business platform for all organizational spheres where planning, budgeting, and analysis of data are made easier.

EPCGroup has been a market leader when it comes to advance data analytics reporting. We have been a Gold partner of Microsoft for Power BI Consultation

Power BI : Working of OLAP Cubes

The OLAP software has its basis in the core concept of the OLAP cube. There are various cube definitions. To simply state, you can define this cube processing as a structure of data created in order to analyze data faster, in a more comprehensible manner.

The cube constitutes certain numeric facts that you can categorize by dimensions. The OLAP cube is also known as a hypercube. The parts of the cube consist of multi-dimensional data. It is usually derived from unknown external sources. When the cube data structure is put to use, you can analyze and logically group data from various sources. Data cubes play a vital report in data warehouse reporting through Power BI.

Database versus Data Warehouse: The better choice

The term ‘Database’ refers to a collection of data that seems to be a representation of one or more elements of the real world. On the other hand, the data warehouse is an informative application. You can use it to store data received from various sources. 

The following are the key differences between Database vs Data Warehouse –

1. To begin with, these two terms differ in their meaning themselves, for, database refers to a set of relative data that may be representative of some real-world elements while a data warehouse refers to an application that stores data from various sources.
2. While a relational database is designed to record and attribute data, data warehouses are utilized to analyze recorded data.
3. A database designs the recording of data in the application-based format while data warehouses store data in the subject-based format.
4.  While a database uses an Online Transactional Process(OLTP), a data warehouse uses an Online Analytical Processing or OLAP.
5. The tables created in the database are complex and difficult to understand as they are normalized. However, the tables provided in data warehouses are simpler for they are not normalized.
6. The techniques used for modeling Databases are termed ER modeling techniques. Additionally, the ones used for designing data warehouses are termed data modeling techniques.

Quality of Data and its consistency:

The concept of ‘Data Quality’ implies a simple set of practices that you establish in the warehouse management schema. Furthermore, this concept has six different dimensions that you can categorize as follows:-

1. Integrity
2. Accuracy
3. Completeness
4. Duplication
5. Currency and
6. Consistency

Among these six dimensions, Data Consistency is related to both integrity and currency of data. You can apply this concept in a situation where you can store and maintain data in two different places. In a situation where you designate two systems to perform the same work, whereby one performs the original pattern of work and the other performs a replica of the former, organizations ought to run consistency checks within the systems from time to time. 

The check of data consistency is made by comparing the same data in two systems or by comparing a set of data with the data of the overall system. This is because a high system’s data that usually runs smoothly on a cache-free basis may also function upon the risk of running inconsistently.

Conclusion :

Thus, we can unanimously agree upon that data warehouse reporting is an integral part of an organization’s functioning for it provides the necessary assistance in the field of analyzing data from external sources which is an integral method of expanding in the business arena.

It’s indispensable to grasp the meaning of your data in order to be successful in the contemporary landscape. Data Architecture Consulting Services can help you examine your data.