Updated: Jan 25
Use Bennyhoff Products and Services ( BPS ) BPS’s data warehouse tools offer businesses a sustainable way to report and analyze data to get valuable insights that can assist them in responding to market needs. This helps businesses remain competitive in an ever-changing market.This article describes a data warehouse, its architecture, components, and benefits.
What is a Data Warehouse?
A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.
The data is obtained from various sources like, Microsoft Dynamics, POS (Point-of-Sales) systems, and other centralized business systems. The data is standardized and cleaned before it gets to the warehouse.
Since data warehouses store large amounts of data, it offers businesses access to historical data that can be used in business intelligence reporting,data visualization and data mining. The intelligence from data warehouses then helps businesses improve decision-making.
How Does Construction of a Data Warehouse Work?
ETL - Import Data From Source Systems; Extract Transform Load:
This process involves extracting data from various sources, transforming it, and loading it into a data warehouse system. The data is usually extracted from the source system, and the transformation is done in a staging area. Loading data into a data warehouse is usually done during nightly builds.
ODS - Operational Data Store - Where We Accumulate Data From Multiple Sources:
Operational Data Stores are used as interim areas for data warehouses and are designed to integrate data from various sources for real-time analysis and operational reporting. Data is checked to remove redundancies and ensure it complies with organizational rules.
DW - Data Warehouse Either Azure Synapse SQL Server Analysis Services:
Data Warehouses tools like Azure SQL are essential for data analysis, allowing users to mine data to get valuable insights. The data warehouse software is used with ETL tools to enable analytics and reporting.
End Client - Power BI, Excel, Reporting Services:
Business intelligence is a data warehouse’s primary derivative; in this step, all the data you need to assess are in the database. You need to visualize it using tables, grids, and charts in Power BI, Reporting Services and Excel to make informed decisions.
Data Warehouse Architecture
A data warehouse’s architecture has three tiers which are:
Bottom tier: Comprises a relational database system that uses the ETL (Extract Transform Load) process to collect, cleanse, and transform data from multiple sources.
Middle tier: Comprises an Online Analytical Processing (OLAP) that enables rapid query speeds. Three models can be used — HOLAP, MOLAP, and ROLAP — depending on the existing database system.
Top tier: Consists of a reporting tool or front-end user interface that helps businesses conduct ad hoc analysis of their data.
At Bennhyoff Products and Services, we query data from source systems into an ODS, then load this into a data warehouse.
The Key Components of a Data Warehouse (Star Schema)
A schema is a database’s structure that defines how information and objects are grouped and connected. There are various ways organizations can organize information in a data warehouse, such as star, galaxy, and snowflake schema.
The star schema is the most popular method. The diagram is usually a star with points radiating outward from the core. The star schema is used to create dimensional data marts and data warehouses.
Benefits of a Data Warehouse
1. Delivers Enhanced Business Intelligence
Implementing a data warehouse in your business means you’ll gain insights via enhanced information access. This frees executives and managers from making decisions based on their gut feelings or limited information. The decisions that affect your company’s operations and strategy will be based on credible facts and will be substantiated with actual company data and evidence.
2. Enhances Data Quality And Consistency
Setting up a data warehouse for business usually involves converting data from multiple data files and source systems and transforming it into a standard format. Information from the various units is standardized, and the inconsistencies are removed.
Business units like operations, finance, marketing, and sales will start using the same information repository as a source for their different reports and queries. Therefore, all the units will produce consistent results, thus increasing overall confidence in company data.
3. Generates A High Return On Investment
Implementing business intelligence systems like data warehouses help businesses generate more revenue and save on cost. According to IDC (International Data Corporation), analytics projects significantly impact a company’s financial status. Implementing business analytic systems generates a median 5-year ROI of 112% with an average payback of one and a half years.
4. Provides Competitive Advantage
Since a data warehouse helps a business get better insights and enhances decision-making, companies can identify more opportunities in data faster than they would if the data was stored in multiple places.
5. Improves The Decision-Making Process
Data warehouses support large-scale business intelligence functions like data mining, machine learning, and artificial intelligence — tools business leaders and data professionals can utilize to get evidence to make better decisions in all business areas, from financial management to inventory management.
Start Engaging BPS For Your Data Warehouse Today
Partnering with a company that understands your needs is essential when considering implementing modern data solutions. At BPS, we collaborate with our clients to develop the best data analysis solutions to meet your needs. Here’s how BPS can help your business implement a data warehouse system:
1rst Step: Determining the items that are not in scope:
The success of a data warehouse implementation project heavily depends on data quality; at BPS, we first identify the problem that guides us on the needed solution. While at it, we identify the data that needs to be availed and what transformations need to be done.
2nd Step: Knowing the Project Budget:
BPS helps you adopt data solutions fast at an affordable rate; the rates for our services are $150 per hour with a minimum 15-hour purchase.
3rd Step: BPS confirms the goals and objectives of the project:
At BPS, we use the SMART approach (Specific >> Measurable >> Achievable >> Realistic and Time Frame) to shorten the project duration, set realistic goals, and articulate them clearly so our clients can understand the expected outcome.
4th Step: Data Modeling:
This is a significant stage in the project where we use a data modeling tool to build the warehouse’s schema that defines how objects and data are connected.
5th Step: ETL:
Now that we’ve identified your company’s data sources and elements, we use APIs to extract the data from the sources and then use an ETL tool to load this data into the warehouse database.
6th Step: Work and ODS Database:
We implement an ODS database as a buffer between your data warehouse and OLTP. This minimizes the risk of your data warehouse failing during night builds.
7th Step: Population:
We test the EPL tool needed using an ODS, and once we’re confident everything is working perfectly, we use the ETL tool to populate the Data warehouse based on the schema we employ.
8th Step: SQL SSAS and Azure Synapse Population Move data from ODS to DW:
Integrating data into a data warehouse offers a lot of value; populating the data in your existing SaaS tools and other bases is also essential. BPS uses Azure Synapse to automate loading data from multiple sources to your data warehouse.
Final Step: Consumption of Data With Power BI
The main reason for implementing a data warehouse is to get business intelligence. At BPS, we implement Power BI to help you visualize the data from the warehouse so that you can make informed decisions. Hire BPS today, and we’ll work with you to help you determine the best way for your company to achieve its modern analytics vision.
Here is an Example Of An Excel-based Project Plan