Data warehouse Technologies in Business Intelligence

In the rapidly evolving domain of Business Intelligence (BI), various technologies play a critical role in transforming raw data into actionable insights. Among these, the data warehouse stands out as a pivotal component. This article explores the intersection of BI and data warehousing, detailing their relationship, implementation, and the benefits they offer to organizations.

The Interaction of Business Intelligence and Data Warehousing

Business Intelligence refers to a collection of processes, applications, and technologies that help organizations transform data into actionable insights for more effective decision-making. At the heart of BI’s infrastructure lies data warehousing—a system designed to consolidate data from multiple sources, ensuring it is organized, standardized, and readily available for analysis.

Historically, organizations managed disparate data silos across different departments, often leading to inefficiencies and fragmented decision-making. Data warehousing addressed this by centralizing data storage, enabling businesses to integrate, process, and analyze data in a more unified and coherent manner. The strategic alignment of BI and data warehousing creates a powerful synergy that enhances operational efficiency, drives accurate reporting, and supports forward-thinking decisions.

Data Warehouse

A data warehouse is a central repository of integrated data, primarily used for reporting and analysis. Also referred to as an Enterprise Data Warehouse (EDW), it is designed to support Business Intelligence activities by consolidating data from various operational systems, transforming it into a common format for analysis.

Before the advent of data warehousing, businesses struggled with fragmented data systems where information was stored in isolated databases across different departments. IT departments had to manually gather, clean, and aggregate this data to perform any meaningful analysis, often a time-consuming and error-prone process. The introduction of data warehouse technology revolutionized this process by providing a unified platform where all data could be centrally stored, managed, and analyzed.

To gain a deeper understanding of how data warehouses function, you can watch this introductory video on data warehouses:

Example: Call Centre Team Performance

Let’s take an example of call centre team performance of an organization. It will help you in understanding the process of data provided from different sources and presented in one dashboard. In this organization, there are 8 teams which attend calls of customers to respond to their inquiries. The performance of these 8 teams is then analyzed over 18 months from January 2023 till July 2024.

The following data under (figure 1) shows the success rate of meeting the target, yellow boxes show the success rate whereas red boxes show the inability to meet the target. All this information taken from the data warehouse is presented in the dashboard so that it will help the organization to evaluate the performance over the period. You can see that a few teams are unable to perform well such as team 2 and team 6 whereas the others are excelling in their performance.

The following data in the dashboard (figure 2) shows the numbers of the call attended by each time. Also, at the top of the below-provided dashboard, the average success rate of an organization’s call Centre performance is given that is 78%.

Data Input and Capturing in BI

A data warehouse is a logical repository where an organization can store its operational and transactional data. It is not a transactional system and does not create data itself, the data stored here has its origins somewhere in the organization. In most of the organizations, data is present in different department or domains for example Point-of-sale system (POS) is transferring transactional sales information, customer data is coming from a Customer Relationship Management System (CRM), and a large variety of operational data is available to store. These all types of data most probably stored in different formats on a range of hardware such as a storage network dedicated for a particular purpose, a database server on the web, or on various types of desktops, data could be present in various departments within the organization.

Data warehouses are different as compare to standard transaction-based data management systems. It combines information about a single subject area, management then uses this information in two ways; for example, management use this information to create a detailed and focused report on a single characteristic of the organization or they may use it to gain insights on that subject. These both are read-only ways because no data is deleted from the data warehouses. However, in standard transaction-based data management systems can delete, add, and update the data they stored.

Manipulation or transformation of the data is the part of data warehouse implementation because data is originated from multiple sources and can be in different formats. Therefore, before storing the data in a data warehouse, it is important to transform it in a single common format.

An Example from Business Challenges

Now, let us understand the working of the data warehouse with the help of an example. Imagine, you are working in a company where you need to combine different phone lists from two Excel spreadsheets. One list contains name as ‘Smith, Jason E.’ and the other has a name written as ‘Jason E. Smith’. If you merge the list without changing the data into a single format, the result will be confusing. You cannot identify where you have stored the name if you will search for someone up and you will be facing the problem of duplicate entries. If you need to update the information of one of them, you will be in trouble as one person is listed twice in both formats. In addition to that, you cannot write a simple summary report of this data because you do not know how many entries there or how many distinctive people are in a list.

In the real world, differences in data can be more than name-formatting on a spreadsheet. The data might be stored in a completely different application using different storage media, data might be corrupted or missing. Data warehousing is a gigantic task, to perform it well you need to put all the information in a single format, check for systematic data errors, and translate data into useful units of information. Sometimes organizations have geographical boundaries which enable them to separate information to be used with other insights, therefore data warehouse technology must not only combine data of all types, it also works within the boundaries of the software and protocols that are used to transform the data into a single format to relate information from one source to other data sources.

Figure 3 illustrates the common architecture of a data warehouse, where three different systems contain similar data and transfer information to data warehouse which represents a single view. Think about a Lemonade company ‘Fresh Lemonade Stands’, have centralized record storage of all the customers who order online or via telephone. They store customers full contact information and some basic knowledge about customer buying habits/trends. They work in a field and their cash register, and Point-to-Sale applications track all the customers and store individual sale transactions at the counter. In accounts, there is a billing system which stores invoices. All three databases are in different parts of the company and kept separate from each other. These databases are on a different platform with different users and usage patterns. These databases are designed to store these different data quickly, which can cause a problem because these systems cannot produce reports on performance such as reports on profit made with sales. However, the CEO of the Lemonade Stand wants to view all the data altogether because he may need to make an accurate decision by knowing the correct picture of the business performance in various areas. For example, he may need to know the cash flow and invoicing to understand the customer’s types and buying pattern to make new marketing strategies or make the amendments in the previous one. Without data warehousing, it is difficult for them because data is available in three different places.

Based on the above you can notice that BI and the data warehouse are closely related. When insight is produced for the CEO on company performance, it is due to the technology which allows all different data to be stored at the same place and in the same format.

Data Processing

Data processing is a way to find data you need, whether you need data to digest or delete. Data processing is always difficult in this progressively data-intensive world. Many organizations want to process data in some way at some time, but what Is data processing? Data processing is the collection and manipulation of the data to produce new and relevant information. Collection, recording, organization, structuring, storage, adaptation, retrieval, consultation, use, alteration, disclosure by transmission, making available, combination or alignment, restriction, erasure or destruction of personal data all are the part of data processing. Any data changes can also be a part of data processing. For example, raw data is not in the condition for reporting, analytics, or business intelligence, therefore it is essential to aggregate, transform, filter and clean it.

Data processing is not a new concept, however, constant use of technology and software makes the processing a difficult task. Using more technologies means you need to handle more data and that makes the processing more complex. Data processing needs a few steps to follow, such as

• Data Collection: Before start processing the data, it is important to collect all of it. Many organizations use data collection methods that depend on automatic harvesting, but there are few methods which rely on interactions with data subjects. It is not important whatever method you are going to use to collect data, the data should be stored in a format and order which is appropriate to the requirement of the business, and that can be easily available for processing. Data can be collected using different methods, for example, surveys, quizzes and questionnaires, online tracking services for data collection, transactional data tracking, online marketing analytics, social media monitoring, collecting subscription and registration data, and in-store traffic monitoring.
• Preparation: Preparation is the primary task in data processing. Once all the data is collected, it is required to prepare data for in-depth analysis. For example, a business wants to collect data for a specific task. They need to select only that data which is necessary for the task and discard all the other which is irrelevant or incomplete. In this way the time required to fully process the data can be reduced, also it can decrease the chances of errors in the data processing.
• Input: After the preparation, the data is then converted into a machine-readable format which is supported by the software which is used to analyze it. It is a time-consuming process because the complete data set requires to be double-checked for error when it is submitted. At this time, any data set which is missing or corrupted can nullify the results.
• Processing: After the submission, the data is analyzed by predefined algorithms which are used to manipulate it into a meaningful format which organization can gather information from.
• Output: It is the process in which the manipulated information further transforms into a format that is suitable for the end-users. These formats can be in the form of graphs, charts, reports, video and audio, or whichever format is suitable for the task. The organization can use these processed data to inform their decisions.
• Storage: It is the final stage of data processing which involves storing data and metadata (data about data) safely for further use. It helps in accessing the data when and where required and ensures the integrity of the stored ones.

Each stage in the data processing is important and compulsory, however, it is a recurring process which means the output and storage steps can lead you to repeat the data collection step and start a new cycle of data processing.

Figure 4 illustrates the data warehouse process that gathers all the information coming from different sources and incorporates the given information into a single database. The business world uses data warehouse in various ways; for instance, it can be used in gathering all the customers’ data and information from different sources like cash counters or cash machines, websites database, email messages and customer feedback. Alternatively, it can incorporate all employee’s data include time entry, personal details and wage information etc.

Conclusion

Data warehousing is vital in Business Intelligence (BI), transforming raw data into actionable insights and enhancing decision-making. Centralizing data from various sources, enables organizations to efficiently organize and analyze information, leading to better strategic planning.

Investing in data warehousing ensures data integrity and accessibility, making it essential for leveraging BI effectively and maintaining a competitive edge in a data-driven world. Tools such as Microsoft SQL Server, Amazon Redshift, and Google BigQuery are excellent options for organizations looking to implement robust data warehousing solutions.