Data Warehousing and Business Intelligence Simplified 101
Do you wish to understand what Data Warehousing and Business Intelligence are and why they’re necessary? Do you wish to know which are the best Data Warehousing and Business Intelligence Tools? If yes, then you’ve come to the right place.
Table of Contents
Data can be considered to be one of the most valuable assets for any business if used correctly. As the volume of data with any business increases, its ability to perform quality analysis and make better data-driven decisions also increases. But these data-driven decisions would only lead to growth if the data that was used to perform the analysis was accurate.
Even accurate data can lead to inaccurate or partial insights if the data is not used properly. To ensure accurate analysis, businesses should ensure that their staff is well trained and Data Warehousing and Business Intelligence Tools are properly set up and used.
This article will provide you with an in-depth understanding of what Data Warehousing and Business Intelligence are, why they’re necessary, and which are the best Data Warehousing and Business Intelligence Tools available in the market.
Table of Contents
- What is Data Warehouses?
- The Need for Data Warehouses
- Top Data Warehousing Solutions
- What is Business Intelligence?
- The Need for Business Intelligence
- Top Business Intelligence Tools
- Conclusion
What is Data Warehouses?
A Data Warehouse in the simplest terms can be defined as a storage platform for historical business data allowing organizations to perform analysis on it and make data-driven decisions.
This business data could be coming in from various sources such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) Systems, Supply Chain Management (SCM) Systems along with many other OLTP (Online Transactional Processing) Systems.
The benefits of using a Data Warehouse are as follows:
- Better Quality Data: The data from all the data sources go through various transformations that ensure that the data stored in the Data Warehouse is of the best quality possible. This means that various inconsistencies that might be present in the Operational Data have been addressed to ensure that only consistent and good-quality data is present in the Data Warehouse.
- Faster Decision Making: Since the data in the Data Warehouse is already consistent and of high quality, it can be considered to be in a form suitable for analysis. Hence, the business team can perform the required analysis in less time without worrying about inaccurate results.
The Need for Data Warehouses
To understand the need for Data Warehouses, you first need to understand what Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) systems are.
Online Transactional Processing (OLTP) System
An Online Transactional Processing (OLTP) system can be defined as a system that is required by any business for its day-to-day operations. Hence, these systems should be able to handle a large number of operations happening on the database in real time. Some examples of OLTP systems include financial transactions systems, retail systems, etc.
As can be observed from the examples, most database operations that take place in an OLTP system are INSERT, UPDATE or DELETE. An OLTP system ideally requires a low number of READ operations.
Characteristics of an OLTP system are as follows:
- Low Response Time.
- Small Transactions.
- Large Number of Users.
- High Availability and Concurrency.
- Large Data Volumes.
Online Analytical Processing (OLAP) System
An Online Analytical Processing (OLAP) system can be defined as a system that allows users to conduct Multi-Dimensional Analysis on large volumes of data at high speed. Since an OLAP system is only used for analysis, most operations that take place in it are READ operations.
Here, it can be observed that the Operational Data of any organization might be stored in the databases of various OLTP systems and an OLAP system requires all data to be present at a single centralized location from where it can fetch data as per the requirements of the analysis.
Also, the data present in the OLTP system might not be in a form suitable for analysis, and hence, some transformations will have to be performed on it before any analysis can be conducted.
So for any useful analysis to be conducted, the data from all the OLTP Databases has to go through an ETL (Extract, Transform, Load) process and then be stored in a central database. This central database is referred to as the Data Warehouse.
A Data Warehouse will always contain historical data of any organization that is used primarily for analysis to help the organization make data-driven decisions. Once data has been moved to a Data Warehouse, it cannot be deleted or updated but only be analyzed.
Using the Operational Database for both Operational and Decisional Support can lead to low performance and possible downtime in extreme cases due to the overloading of the database. Using a separate Data Warehouse for analysis would hence, reduce the load on the Operational Database.
Since the data to be analyzed is in a separate Data Warehouse now, day-to-day operations on the Operational Database and analysis by the Analytics Team can occur independently of each other.
Using a separate Data Warehouse would allow businesses to fulfill their Operational, Data Warehousing, and Business Intelligence requirements.
An example of explaining the difference between OLTP and OLAP systems using Data Warehousing and Business Intelligence can be seen below:
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SIGN UP HERE FOR A 14-DAY FREE TRIAL!Top Data Warehousing Solutions
Some of the best Data Warehousing Solutions available are as follows:
1) Amazon Redshift
Amazon Redshift is a part of the Amazon Web Services (AWS) platform. It is one of the most popular and the most preferred Data Warehouses out of all the options available in the market. Various brands such as McDonald’s, Intuit, etc use Amazon Redshift as their Data Warehouse.
Amazon Redshift allows developers to query large volumes of Structured and Semi-Structured Data. Amazon Redshift uses AWS infrastructure to provide a quality user experience.
AWS also allows you to connect your Amazon Redshift Clusters to the AWS BI Service called Amazon QuickSight for your Data Warehousing and Business Intelligence requirements.
Amazon Redshift Pricing
Amazon Redshift offers a two-month free trial following which the user has to start paying. Refer to this for details on Amazon Redshift pricing.
2) Snowflake
Snowflake is a Data Warehousing and Business Intelligence solution that allows your business to become more data-driven. One of the biggest advantages of using Snowflake over other Data Warehouses is that Snowflake has a per-second pricing model which means that you only pay for what you use.
Snowflake also allows you to scale Storage and Compute Nodes independently which means that you can deploy each node separately instead of deploying and paying for the Integrated Bundle.
Snowflake Pricing
Snowflake offers a 30-day free trial following which the user has to start paying. Refer to this for details on Snowflake pricing.
3) Google BigQuery
Google BigQuery is Google’s Data Warehousing solution and a part of Google Cloud Platform (GCP). Google BigQuery uses SQL queries and was built to analyze data having billions of records.
It is a serverless technology that uses columnar storage for faster data scanning along with a Tree Architecture model that makes querying and aggregation significantly faster.
GCP also allows you to integrate Google BigQuery with its BI tool called Google Data Studio for your Data Warehousing and Business Intelligence requirements.
Google BigQuery Pricing
Google BigQuery offers a 90-day free trial following which the user has to start paying. Refer to this for details on Google BigQuery pricing.
What is Business Intelligence?
Business Intelligence (BI) can be defined as a set of processes and technologies that can be used to convert the raw data of any organization into actionable insights that drive the organization’s tactical and strategic business decisions in order to generate maximum profit.
Data Warehousing and Business Intelligence solutions are usually required together since a quality analysis cannot be performed if the data integrated from all data sources are not present in a centralized database.
The various advantages of Business Intelligence are:
- Faster Reporting: Use of a Data Warehousing and Business Intelligence Tool would lead to faster analysis of data for insight generation which would have otherwise taken a long period of time if done manually.
- Increased Operational Efficiency: Use of Data Warehousing and Business Intelligence would allow businesses to understand what their customers want and hence what services their team should be working on providing. Having a clear goal in mind would increase operational efficiency drastically.
- Increased Customer Satisfaction: Since the services being provided by the business would be based on an accurate analysis of what the customers want, it would lead to high customer satisfaction since all or most of their Data Warehousing and Business Intelligence requirements would be fulfilled.
- Increased Profitability: Since there is an increase in operational efficiency and customer satisfaction, Data Warehousing and Business Intelligence solution would obviously lead to an increase in profit due to a decrease in the extra amount spent because of operational inefficiency and an increase in revenue due to customer satisfaction.
The Need for Business Intelligence
Business Intelligence is necessary for any business because of the following reasons:
- Data Warehousing and Business Intelligence can enhance the quality of decision-making.
- Data Warehousing and Business Intelligence solution is used to create and keep a track of KPIs (Key Performance Indicators) which can be used as a measure of how well a business is achieving its desired objectives.
- Data Warehousing and Business Intelligence solutions can be used to identify various market trends and business problems.
Top Business Intelligence Tools
Some of the best Business Intelligence Tools available in the market are as follows:
1) Looker
Looker is a Business Intelligence Tool which is part of the Google Cloud. One of the most significant advantages of using Looker over other BI tools is that it allows you to perform any analysis you wish to on live data using its own SQL-like querying language called LookML.
Looker also has its own tool for data integration called DataVirtuality Pipes, which allows you to integrate data from multiple sources into a central Data Warehouse directly leveraged by Looker for your Data Warehousing and Business Intelligence needs.
Looker Pricing
Looker does not offer a transparent pricing model. The pricing is based on the user’s business and data needs and can be finalized upon discussion with the sales team at Looker. You can request a demo or quote on the Looker website.
2) Tableau
Tableau is considered to be one of the most powerful and fastest-growing Business Intelligence Tools. It has various customizations and features such as data blending, real-time analysis, etc., that allow raw data to be converted into a form that anyone can understand.
It also offers a large number of products as a part of its suite. These products are Tableau Desktop, Tableau Public, Tableau Online, Tableau Server, and Tableau Reader. Tableau also allows you to connect to a large number of Data Warehouses and hence, can be considered a perfect addition to your Data Warehousing and Business Intelligence suite.
Tableau Pricing
Tableau offers a 14-day free trial following which the user has to choose one of its three tiers as follows:
- For Individuals: Includes a single User License for Tableau Desktop and Tableau Prep Builder along with a Creator License for Tableau Server and Tableau Online.
- For Teams and Organizations: Tableau allows large teams and organizations to use Tableau only on Tableau Server. If the server can be hosted on the On-premise hardware or Public Cloud offerings of the organization, the pricing per user is as follows:
If the organization wishes to use a fully-hosted online solution for Tableau Server, the pricing per user is as follows:
- Embedded Analytics: Offers automatic creation of visualization using powerful analytical tools controlled by a set of robust APIs.
3) Microsoft Power BI
Microsoft Power BI can be seen as a collection of Business Intelligence, Reporting, and Visualization Tools. One of the biggest advantages of using Microsoft Power BI over other tools is that it offers easy integration with all Microsoft products and services making it one of the most widely used Data Warehousing and Business Intelligence solution.
Microsoft Power BI Pricing
Microsoft Power BI offers a Free version with limited functionality, a Pro version, and a Premium version. A comparison of the pricing for both paid options is as follows:
4) SAP BusinessObjects
SAP BusinessObjects Business Intelligence is a central suite of Analytics and Reporting Tools. It allows users to create reports, visualize data and share them with whoever they want to. It supports integrations with many Data warehouses which allow organizations to load their data. It is an all-in-one Data Warehousing and Business Intelligence tool.
SAP BusinessObjects Pricing
SAP BusinessObjects does not offer a transparent pricing model. The cost can be finalized after a discussion with the SAP team. You can request a demo or quote on the official website.
5) Google Data Studio
Google Data Studio is a free Data Visualization Tool by Google. Google Data Studio allows you to create visual and informative reports that can be shared with anyone. It can also be easily integrated with Google BigQuery for your Data Warehousing and Business Intelligence needs.
Google Data Studio Pricing
Google Data Studio is currently available free of charge for all its users.
Conclusion
This article provided you with an in-depth understanding of what Data Warehousing and Business Intelligence are and why they’re necessary. It also provided you with a list of the best Data Warehousing and Business Intelligence Tools available.
The most important task before any analysis can be performed on the data is ensuring that it is loaded correctly into the Data Warehouse in a form suitable for analysis. Businesses can either choose to make their own ETL solutions in-house or use existing platforms like Hevo. Hevo provides a No-code Data Pipeline allowing you to integrate data from all your sources for your Data Warehousing and Business Intelligence requirements.
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