Data warehouse native applications

The CRM is no longer seen as the definitive source of trust for enterprises when it comes to collecting customer data. Instead, it has become just another SaaS tool that is unable to handle the complex data architectures that modern enterprises have created.

Warehouses have emerged as the new system of records, revolutionizing the way

we approach data management. The CDW industry has grown from $36B to $80B in the last 5 years. One of the main benefits of a data warehouse is its flexibility, security and ability to grow with your business.

Several apps have realized the true potential of data warehouses and have harnessed their power by building on top of them, enabling business applications to go beyond just business intelligence.

What are the benefits of data warehousing?

In the last 5 years, the number of SaaS tools used by companies has been multiplied by 10.

To face the ever-growing complexity and volume of their data, businesses are increasingly adopting data warehouses as their source of truth, and shifting to modern data stack technologies.

It helps organizations to centralize and manage large amounts of data from various sources, such as transactional databases, log files, and external data sources (ie: third-party tools).

Some specific benefits of data warehousing include:

1. Improved data access: Data warehousing provides a single, centralized repository for data from various sources, making it easier for users to access and analyze the data.
2. Enhanced data quality: Data warehousing helps to ensure the quality and

integrity of the data by enforcing data standards, performing data cleansing, and providing a consistent view of the data.

3. Increased efficiency: Data warehousing enables users to access and analyze data faster, as the data is pre-processed and organized in a structured format. This can help to reduce the time and resources required to perform data analysis.
4. Improved decision-making: Data warehousing provides a comprehensive view of an organization's data, enabling users to identify trends, patterns, and relationships that may not be apparent in individual data sources. This can support better decision-making processes.

What are the most common data warehouse applications?

There are several common applications of data warehousing, including:

1. Business intelligence: Data warehousing is often used to support business intelligence (BI) and reporting activities. The modern BI tools like Looker or Metabase sits directly on top of the warehouse, enabling businesses to get

insights faster and to drive the business with a data driven approach, not intuition.

2. Analytics: Data warehousing enables organizations to perform advanced analytics, such as data mining, scoring, and predictive modeling, on large datasets. This can help organizations to identify trends, patterns and relationships in the data that can be used to optimize business processes and improve decision-making.
3. Customer segmentation: Data warehousing can be used to segment customers based on their characteristics and behavior, enabling organizations to tailor marketing campaigns and other initiatives to specific groups of customers. The 360° view of the customer we’ve all dreamt about is finally here!

What is a data warehouse native app?

Data warehouse native applications are the go-to softwares for maximizing the potential of data warehouses, the place where all your data lives. They are optimized for working with large amounts of data, and custom business entities.

These applications are used for data analysis and reporting and more recently, for orchestrating sales processes and customer engagement.

Different arena of tools understood the potential of this new generation of SaaS, and start leveraging the CDW for specific usecases. Some example includes the BI tools that were originally among the first to do it or more recently tools like Pocus in the

PLG industry.

What are the benefits of data warehouse native apps?

They are software applications that does not have its own data backend, are just an application layer on top of your company data. No need to set new data pipelines to sync data back to your destination app.

Why should you care about this new wave?

Glad you asked.

Well, one of the biggest advantages is that the data doesn’t need to be synced or replicated on the vendor’s application, which is typically the case with traditional SaaS solutions.

We shouldn't have to pay for the same data multiple times. We should be able to get it once and use it for any purpose without paying more.. Or I hope you don’t have 100 tools 😅

Another advantage of these apps is that, because they don't replicate your data, they don't have ownership of it. This means you have complete control over your data, as well as enhanced

1. Observability: The current model of transferring data to a third-party system is problematic because it completely removes visibility and the ability to trace dependencies within the pipeline.
2. Security: You own your data and you keep it in a environment you can control.

3. Cost efficiency: Around 75% cost reduction compare to the sync cost of pushing your data to an external platform (+ most of those platform make you pay record based, so you end up paying multiple time for the same data). With CDW native apps, you don't have to replicate your data.

They also benefit from the data warehouse qualities like greater data accessibility for end users, reliable data integrity that also help bridging the gap between data teams and business folks.

Data engineers ensure data quality, business folks leverage it to drive revenue.

Ultimately, you don't have to be tied to a fixed schema, you can create and own your data models & business entities definition.

Introducing Cargo: The retool for go-to-market teams

As we said earlier, there are several tools leveraging the power of the data

warehouse. Those are idiosyncratic softwares having a standalone approach. Conversely, at Cargo

We are unopinionated by design - Opinionated by usecases.

Cargo let mature organizations build their own internal go-to-market apps on their own, fully customizable and way more effective to tackle their use-cases.

Those mid-market & enterprise companies deal with high volume and ever-growing complexity of data. By sitting on top of their warehouse, we make it easy to build applications that answer their business-specific needs.

The main usecase today for enterprise companies is lifecycle marketing like sending a personalized promotional or product update email to a specific segment, as well as building nurturing automation based on users behaviors.

For mid-market and SaaS companies, the primary use-cases are around building

scoring system, for account grading or churn scoring, or doing lead routing according to their own territories and rules.

Interested to learn more? Join the movement here