Data Warehousing for Business Intelligence: Full Guide

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Data Warehousing for Business Intelligence: Full Guide

Some would say data’s value is like that of petroleum or water, but in actuality, data is much more precious than water or petroleum because these are depleting or depreciating assets; on the contrary, data will be an appreciating resource continuously adding to its value in the near future. So, if the tech pundits and think tanks from the IT domain say that Big Data is going to get bigger and better with time, they surely have a reason to put up such a bold statement.

Those premonitions or rumors that you have heard that data warehousing is going to be dead must kiss the dust as such is not what is going to happen even 30 to 40 years down the line. But just statements don’t make people believe in the larger scheme of things, so you need to read further to know whether DW-a-a-S or Data Warehousing-As-a-Service will see some bright days ahead or it will just settle for no good at all.

Future of Data Warehousing for Business Intelligence

Data warehousing will turn redundant when the old BI or Business Intelligence techniques cannot easily create valuable queries for a large pool of data, but the pressing question is that is it even possible. Numerous use cases when referred to do not give up a concrete viable solution using the old BI technique. To further demonstrate this claim, we need to look at one of the cases that happened with an employee who joined a Tier 1 Investment Bank in London as a Data Warehouse Architect. His job was to process the Query on a multi-terabyte Oracle Warehouse system querying micro-batch data loading and the end-user performance. But doing this using the old BI systems made life tough. Let’s look at the challenges he faced while using the old BI techniques.

Key Challenges Querying Large Chunks of Data Using Old Business Intelligence Dashboard

Maximizing Query Performance

Data miners or analysts need a solution that can minimize the latency and maximize the query per second. With the old BI systems, the end-user query performance does not perform maximum outcomes. As a result, the analytical query demands have been turning high and unaccounted for.

Maximization of Throughput

ETL or Extraction of Data must be done in a faster and much more rigorous manner. Such a demand would maximize and utilize the complete potential of the machine. All these things would require high maintenance of the CPU and faster technology solution that can instantly capture queries and deliver efficient query optimization plans. It is quite unlike for an old BI system to perform up to the true potential of the requirement. Hence, the need for a much more scalable and agile data analytics system arises that can instantly resolve this problem.

Maximum Utilization of Machine

When you have to analyze and process a large chunk of data, it should begin with analyzing and processing 100% of the CPU capacity. The old BI systems do fail to utilize 100% of the machine performance. But the new systems are equipped to utilize the machine at its full potential. Therefore, you tend to get the full volume or true potential of the machine.

ETL Process

The old BI systems completely overrun the true potential of the machine. When the machines are forced to perform beyond their processing levels, they either give botched or inefficient results or at times, they completely heat up and fail to deliver any results at all. At such times, the need for a fully functional data warehousing architecture is required that can cope with the existing tech infrastructure and deliver the results that are expected of it.

How to Overcome These Challenges?

When experts and IT think tanks raise questions on the existence and sustainability of DWaaS (Data Warehousing as a Service) or Data Warehousing in the near future, here are a few key arguments to support that it will go strong without any signs of giving up anytime soon.

As the Business Intelligence is transcending with an advanced time loop for managing key data analytics, the need for agile and advanced warehousing solutions has been felt more than ever. DWaaS or maintaining a Data warehousing architecture is so essential and it will remain that way 10 to 20 years down the line because;

Agility is The Future Trend

Agility will be the new language that most enterprises would love to speak in the upcoming feature and DW-a-a-S will empower businesses by helping them take a collaborative approach to problem-solving. With advanced DWaaS solutions, enterprises need not have to maintain separate departments, teams and setups for data mining and analysis. When the new data warehousing architecture will help adopt a new model that helps in cross functioning of different teams to support the continuous evolution and improvement, enterprises can better deal with data extraction in a much more fascinated and smart manner.

The Dawn of Cloud Systems

The needs of the enterprises will change from MYOS or Maintain-Your-Own-Server to cloud-based movement or shift. Cloud-based DW-a-a-S improve the sources from where the data can be gathered and analyzed for future business intelligence. There will be fewer chances of data duplicity when massive data movement is involved using the DWaaS. These trends completely paint a rosy picture of DWaaS as the game-changer in the near future when enterprises and businesses are in need of the right BI dashboard that can perform multiple business operations.

Do We Still Need Data Warehouse?

One question many people in the industry ask is where we still need a data warehouse. Is it relevant in today’s world? 

The short answer is yes. Though data warehouses are becoming older models, and some enterprises are replacing them with data lakes, the data warehouse is still a part of the business intelligence infrastructure. There are many reasons for this:

Data warehouses can integrate data from multiple sources. A data lake can store them in one place but not integrate structured, unstructured, and semi-structured data the way a data warehouse does. 

A data warehouse is easier to use as it provides granularity at the base levels because of the star schema. In addition, it is easy to scale and can be rolled up as we reach higher levels. Also, the data warehouses are designed for querying and reporting. 

A data warehouse suits all kinds of enterprises. SMEs and large enterprises can build a data warehouse for their requirements. Some enterprises have multiple smaller DWs, each for every department. These are combined together under the enterprise data warehouse system, which is a centralized database containing information about the entire business. 

Data warehouses not only support business intelligence tools but also work seamlessly with machine learning and artificial intelligence applications. Moreover, a data lake cannot replace a dimensional data warehouse as there won’t be conversions between data from different sources to bring them together into a single table. 

How Data Warehouse is Useful in Business Intelligence

Data warehouses and business intelligence go hand in hand in business enterprises. The data warehouse is a part of the BI infrastructure set up to use customer data to derive analytics and make smarter decisions. Business intelligence is an umbrella term that entails various applications, processes, tools, and methodologies to increase business performance. The BI architecture in an organization empowers employees and management to streamline their day-to-day activities at work through automation, augmentation, and analytics. 

Data warehouse and business intelligence are interdependent. The information stored in a data warehouse is of no use if it is not used to derive insights. The BI tools and methodologies aren’t going to a business if there is no data to analyze. The competence of the BI architecture depends much on how the data is stored after it is extracted from internal and external sources. This data is stored in the data warehouse(s) and analyzed using BI tools like Power BI and Tableau. Data warehouses help align the data flow to suit the business processes and make data easily accessible to employees. 

Analyzing data in the data warehouses gives businesses accurate and reliable insights for faster and better decision-making. The data warehouse and BI tools are necessary to adopt the data-driven model in the enterprise and gain an edge on competitors. 


These justifications make DW-a-a-S the most viable choice in the future when enterprises need data visualization, business intelligence and other ETL and data warehousing services. Things would get complicated when the business requires reliable data for faster decision making, presence of cloud-based DW-a-a-S will help enterprises grow and thrive in the ever-changing IT realm. For case studies and more information on Data warehousing for business intelligence contact us.

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