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April 16, 2024

What does your Data Warehousing say about your Business?

The importance of data in business strategies cannot be disputed. Fortunately, more and more companies are realizing this and are paying more attention to the quality of their data and the optimal organization or storage to manage it properly.

Therefore, good data storage is an essential requirement for any company, and data integration is an essential resource. We help you to understand what these warehouses consist of and why you need one.  

Why do you need a Data Warehouse?

A data warehouse is designed and prepared to improve data analysis and data-driven decisions. It can be hosted on the local server or in the cloud and is capable of receiving information from a variety of sources, such as business management software, relational databases, individual files in various formats, or web platforms that collect data.

They also store metadata, which is used to prevent errors or ensure that data is updated correctly. Thus, a warehouse collects data and prepares it for easy work, as well as promoting data analysis and business decision-making.

There is no point in having data about our business activities, customers, or third parties if we cannot understand and interpret it correctly. Therefore, the data stored in a data warehouse goes through an integration process that normalizes and standardizes it so that it is compatible with each other, regardless of its format or structure.

As they are compatible, they can be easily compared, filtered, and worked with by means of data analysis or visualization tools, as well as consolidated, and possible errors or duplications can be detected. This improves decision-making, reduces operational costs, and ensures data quality 

In addition, another of the main advantages is that the information stored is not lost or modified, remaining accessible and adaptable to new data. Therefore, it is the best way to have a historical record of all the company’s data in an updated way and to make temporary analyses, detect inefficiencies correct them, and identify strengths and opportunities.

If you want to know more about the differences between Data Lake, Data Warehouse, and Data Mesh, you can consult the article we published a few months ago and learn more about what each one consists of.  

How does it work?

After extracting data from its source systems and integrating it into the data warehouse, it undergoes a process of treatment, transformation, and organization. The most common methodologies are ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).

When they have been transformed and organized, users can access them through SQL queries, and visualization tools such as Power BI, CRM, etc. The warehouse will provide an abstraction layer that facilitates structured and consistent access to the data, allowing relevant information to be obtained for better business decision-making.

The usual architecture consists of 3 layers: 

  • Extraction layer: data is extracted from its source systems, usually using SQL scripts or other extraction techniques.
  • Integration layer: data from different sources are integrated into the warehouse, then transformed and modeled using star schemas. It is then uploaded to an OLPA server for analysis and use in decision-making.
  • Presentation layer: data is prepared for user consumption and organized so that it is ready for use and export in business intelligence platforms, reports, and visualization tools.  

Having control over data assets is critical, and data integration is a necessary process to harness the power of data. This process unifies all of a company’s data and involves a process that involves the transformation and consolidation of data assets.

Organizations often have this type of information, but it is stored in different places because each department stores the data it needs in its repository. However, this mistake can lead to failure because of the impossibility of getting a holistic view of the situation and analyzing all aspects at stake in order to draw clear conclusions and make good decisions.

In other words, to ensure that a new product or service does not fail, the ability to analyze key information about previous products or services will be crucial. The integration process ensures that the information is accurate and useful, as well as containing valuable business insights. This is why data integration promotes the generation of new business opportunities, better decision-making, and improved business productivity and performance.  

picture about data warehouse and plain concepts

Factors indicating that you need a data warehouse

As discussed above, the decision to implement a data warehouse is a fundamental one that will affect multiple facets of your organization. Therefore, there are several factors and considerations that will be critical to making an informed and appropriate decision. Some of the most important are: 

  • Data volume and complexity: A key indicator is the volume and complexity of the data you handle. If your current storage solutions are not able to keep up with the accumulation of data, it is a sign that you need an upgrade.
  • Separate or siloed sources: If you have to deal with data from multiple sources and in multiple formats, a data warehouse can provide the consistency you need.
  • Performance issues: Transactional databases are optimized for operations, not analytics, so they can affect the performance of other critical systems. A data warehouse removes the analytical burden from your operational systems, preserving their efficiency.
  • Inadequate data access: If your team spends excessive time tracking data in disparate systems, it leads to inefficient and error-ridden processes. A data warehouse provides a consolidated view that makes data easier to access and manage.
  • Need for advanced analytics: As businesses mature, so do their analytical requirements. Advanced analytics, data mining, and predictive modeling are much easier to perform with a data warehouse. If this is a growing need, now is the time to consider investing.  

Update your Data Warehouse

Technology is constantly changing and determining the right time to upgrade and modernise can be difficult. If done too early or with immature solutions, it can lead to unsatisfactory results. Conversely, if it is done too late, market opportunities and productivity gains will be lost.

Therefore, if we already have a data warehouse, it is crucial to understand where we are to implement the necessary measures at the right time. To do this, you need to consider five warning signs that your data warehouse is no longer keeping up with the demands of today’s business environment: 

Your workloads are changing

Businesses are creating ever-increasing amounts and types of new data with increasing complexity. So if your data warehouse doesn’t natively support this multimodal data, or is unable to do so at scale, it’s a sign that your system isn’t adapting well to the data sets and workloads.

Each new type of data represents a great opportunity to gain useful insights, whether to create personalized customer experiences or to develop new products. Integrated support for multi-modal data has the added benefit of simplifying the enterprise technology stack by alleviating the need for special-purpose databases.

Thus, the business case for a new data warehouse gets stronger with each new type of data to be managed and analyzed.

Your warehouse is no longer profitable

If your data warehouse has become a cost pit, it’s another sign to initiate change. Continuing to invest in an on-premises legacy system when you’ve leaped the cloud makes no sense and causes you to lose control of spend, as well as restrict data sharing, which hurts many parties, such as business insights.

Fortunately, it is possible to build and deploy a modern data warehouse that supports an exponential increase in data and users, while keeping costs within budget. Also important is the hardware used, which can generate higher throughput and therefore lower costs.

picture about use of financial big data

No technical innovation

Full table scans, large-scale SQL optimization, support for semi-structured data, and machine learning within databases represent the state of the art in data warehousing.

Without these technologies, it is much more difficult to create proprietary products and data-driven services. Without technical innovation, there is no business innovation.

So if your business relies on a legacy data warehouse, you are most likely missing opportunities made possible by next-generation architectures, such as high-velocity data ingestion and continuous analytics.

You don’t have the support you need

One of the key benefits of modern data management is the automation of processes and platforms that previously relied on manual resources. Self-managed cloud services, serverless auto-scaling technologies, or storage optimization are some of how companies can reduce the pressure on their IT teams, allowing them to focus on strategy and solutions.

However these advances do not eliminate the need for technical support during implementation or operation, so having a trusted partner will be crucial to achieving the best results.

Deployment options are limited

Legacy data warehouses built for corporate data centers lack deployment flexibility, which extends to a lack of flexibility in your business.

Also, the type of needs of each company will determine whether their warehouses should be exclusively in the cloud or need a portion to be run locally for governance or security reasons.

That’s why data warehouses with a modern hyper-scale architecture that can be deployed in the cloud or on-premises are the best choice. They combine the cloud benefits of resource elasticity and API integration with the on-premises advantages of compliance and control.

This versatility also improves cost management while minimizing the possibility of lock-in.

picture about comparing data warehouse, data lake y data mesh

How do you get the most out of your data warehouse?

Data warehousing has become a fundamental pillar for the proper functioning of companies thanks to its capacity to store quality data combined with the data-driven decision-making process.

It is the best way to generate business intelligence to reduce risk, avoid errors, and achieve optimal results. But it also faces some challenges in terms of modernization.

There is no one-size-fits-all solution for data warehouse modernization, as each data warehouse is unique and each modernization plan is unique. However, there are several design patterns that help bridge the gap between the current and future goals of a data warehouse. Common patterns include architectural frameworks, cloud storage, automation and virtualization, and the adoption of emerging technologies.

At Plain Concepts we help you develop a modernization plan and design framework to achieve a modern data warehouse.

Our aim is to approach the challenge of digital and data strategy from a business perspective that will deliver benefits, using a structured framework in line with your needs.
With this approach, we define the necessary digital and data strategy through a process of immersion, maturity, and consolidation, working on generating short-term benefits that give credibility to this strategy. 

  1. We assess the company’s data maturity level.
  2. We identify the critical data to manage, control, and exploit.
  3. We establish objective use cases, focus on generating medium-term benefits, and design the initiatives to implement them.
  4. We generate interest and commitment in your team through training on the importance and potential of data-driven management.   

If you want to start turning your data into actionable information with the latest data architecture, storage, and processing technologies, contact our experts and start your transformation now!  


Elena Canorea
Elena Canorea
Communications Lead