DWaaS is an innovative approach to data warehousing that allows your company to access a powerful Data Warehouse without investing in costly infrastructure or hiring specialized IT staff.

Introduction to the service and action plan

The goal of the DWaaS service is to deliver value to the customer as quickly as possible, so each service launch plan is based on iterative phases delivering functionality that has the highest value to the customer first.

01. Initial meeting, determining customer needs:

Meeting with the client to understand needs and expectations of Data Warehouse as a Service (DWaaS). In this meeting, our team also gets to know the specifics of the company and introduces the service and its key objectives.

02. Technical analysis of the data sources needed to launch the service:

The next step is an in-depth analysis of the Data Sources available in the client organization and the Data Volume required. The goal is to determine which of these sources will bring the highest value and should be the focus of the first iteration of the DWaaS service launch. The analysis also assesses how the data will be processed further (Business Intelligence, Artificial Intelligence).

03. Create a plan for launching the service and discuss pricing:

The next step, after gathering information, is to develop a detailed plan for launching the service, including technical steps, planned Reporting Structures and deadlines, as well as providing a subscription price quote.

The plan presented describes in detail the scope of subsequent iterations of the service launch and also outlines the anticipated subscription payment plan.

Preparing the DWaaS environment

In this part, the Phronesis Path team prepares the target environment in accordance with the previously adopted step-by-step breakdown. The transfer of reporting structures is done in an iterative manner, according to a defined plan. Subscription payment begins only after confirmed receipt of the first structure.

01. Service setup, data ingestion, environment preparation, data preparation:

The first step is to configure the DWaaS infrastructure according to the client’s requirements and Phronesis Path’s proprietary methodology. The next step is to ingest the client’s Data Sources to the warehouse, data will be transferred from now on according to established schedules (usually once a day). Historical data is also loaded according to established parameters.

02. Start working on the data and prepare the structures for use:

We then proceed to implement Reporting Structures, according to the client’s business needs. Procedures for improving data quality and incorporating business rules are also developed at this stage. Subsequent structures are handed over to the team on the client’s side (or the partner implementing the data consuming solutions) for testing.

03. Enable access to subsequent reporting structures:

The final stage is the provisioning of access to the reporting structures and training of the client’s personnel on how to access the data. Documentation of the Reporting Structures is also provided, along with the definition of the business rules that were applied in the transformation process.

Enabling the structure is confirmed by an acceptance protocol, the signing of which triggers payment of the subscription.

Added value from the launched service and development

Enabling Reporting Structures allows you to launch processes that generate value for the company. As part of the subscription, the customer can also order development work on existing structures at no additional cost. If the development work requires ingesting more Data Sources, creating new Reporting Structures or significantly increasing the Volume, the customer is informed of the need to extend the subscription.

02. Business Intelligence Reporting:

Reporting Structures are planned under specific Business Intelligence solutions to ensure full compatibility. Most often, the Reporting Structures are created as a Star Model, which is the most readable solution for most tools on the market.

02. Solutions using LLM models:

Data Warehouse allows the implementation of Large Language Models (LLMs) for analyzing text and generating inferences about text data.

03. Advanced Machine Learning solutions based on collected data:

The structures provided allow the use of advanced machine learning algorithms to identify patterns, forecast trends and optimize business processes. Due to specific needs of ML solutions, often the relevant tools use data in a different form than reports, so we create special Reporting Structures for them.

04. Expand customer analytics to include external data:

A cloud-based data warehouse allows for the integration of external data, such as market, social or weather data, to enrich analysis and better understand the business context. Often this data is covered by its own subscription, in other cases it is publicly available. We treat additional external data added to the warehouse as an additional Data Source.