The intelligence is created on top of collected and transformed data and a data warehouse is its key component. While BI is considered more technical, descriptive, and retrospective, Data Science is the prospective approach utilizing advanced techniques to extract meaningful insights further utilizing BI.

The product Clever Business Intelligence (CBI) and Data Science (DS) is designed to add value to your BI ecosystems by offering both business and technology domains knowledge and experience.

With utmost respect to your strategies and approaches, we are ready to assist with data architecture, integration, reporting, business sandbox or prototype standardization and many other BI and DS related tasks.

Why cooperate?

The digital transformation process is in full swing all around, but considering that some data preparation and utilization tasks are quite demanding we are prepared to assist with this challenge.

Business Intelligence (BI)

Strategies and technology for data analysis and management of business information. In its most common form it employs data architecture, data warehouse, processes, and reporting to achieve its goals.

Data Science (DS)

Interdisciplinary study of how to use data to extract meaningful and often also forward-looking insights. It employs statistics, mathematics, computer science, and subject matter expertise from various business domains. Advanced Analytics is also a subset of DS.

Digital transformation

Making the most of digital technologies and practices to create and improve efficiency, value, or innovation.Both BI and DS components enable or improve the quality of digital transformation projects. Such projects usually address market and business changes where effective data management and automation play an indispensable role.

Product scope

Apart from end-to-end delivery – from collection to data reporting – our product is also designed to assist with problems related to BI and DS applications.

Examples of such applications include: new source system DWH integration, solution architecture proposal or review, quality assurance for third party delivery, business and data dictionary delivery, functional and technical specification formulation, standardization and automation of proprietary data flows or calculation engines, test management, prototyping, both regular and ad-hoc reporting, etc.