Data Integration: The cornerstone of any successful BI project

When embarking on a BI project, we often start by considering the indicators we intend to analyze – what we aim to achieve. This is a valid starting point and addresses our objectives. However, as we progress, questions arise about how to gather and connect all the necessary information to realize these indicators. This pivotal stage is essential for establishing a robust and scalable BI system.

Frequently, we encounter offerings of impressive Business Intelligence and Analytics solutions, primarily focused on displaying KPIs. These systems demonstrate the ease of inputting data into Excel and promptly generating on-screen graphs and other elements.

It's undeniably impressive and may initially appear straightforward. Yet, professionals engaged in such projects understand that the presentation of KPIs merely represents the project's final packaging.

In the following section, we'll briefly outline the key components to consider if we aim to execute a sustainable and scalable BI project over time

In this article, we aim to provide a concise overview of the key components related to Data Integration and their significance within the realm of business intelligence.

Let's get started:

1. DATA SOURCES

Before initiating the Data Integration process, it's imperative to gather all the data sources required for calculating the indicators and information that will be displayed on our dashboards. The granularity of these data sources is of utmost importance. Starting with data sources that contain pre-aggregated or pre-calculated information can restrict our ability to present details or specific levels effectively.

2. ETL (EXTRACT, TRANSFORM, AND LOAD)

The ETL component serves as the initial and critical layer in our BI solution. It plays a vital role in extracting data from various sources, applying transformation rules, and ensuring data cleanliness before loading it into the next layer, the Data Warehouse. The complexity of these operations depends on the nature of the data source, with the transformation of data through rules, data cleaning, and harmonization being central to this layer.

3. DATA WAREHOUSE

A comprehensive explanation of the Data Warehouse is a vast topic, so we will provide a simplified overview without delving into its detailed features and functionalities. As the name suggests, the Data Warehouse serves as the structured repository for organized business data. It groups data into smaller sets known as datamarts, typically using multidimensional data structures with hierarchies – commonly referred to as OLAP cubes. These cubes were developed to enhance data query performance for analysts compared to the normalized transactional model (OLTP). This model is widely adopted for data retrieval by dashboard generation tools and for facilitating business decision-making.

In many instances, Data Warehouse systems also include the ETL layer for data extraction, transformation, and loading.

4. MASTER DATA MANAGEMENT (MDM)

In a straightforward sense, MDM encompasses the tools and methodologies dedicated to managing and storing master data. Master data refers to essential entity tables forming the foundation of a data model. For instance, consider the master data for products, which includes product codes, descriptions, and properties.

To illustrate, in a BI system designed to evaluate the commercial effectiveness of the pharmaceutical industry, managing the data related to the sales network's territorial structure is crucial. This territorial structure is known for its constant changes and reorganizations. Without the ability to swiftly update this master data, our analyses would consistently fail to reflect reality. Consequently, our BI system would not fulfill its primary purpose – aiding in decision-making.

As we've observed throughout our discussion of the layers primarily focused on data integration and processing within a BI system, the task of Data Integration is perhaps the most labor-intensive and enduring aspect of any BI project. This holds true both during the initial implementation phase and throughout the system's lifecycle.

It's essential to remember that a BI system is not static; it continually evolves to align with the company's changing business strategies.

In summary, structured data integration is the linchpin for the growth, scalability, and adaptability of our BI system. It enables us to serve as a dynamic decision-making tool, consistently reflecting the relevant and accurate indicators aligned with business objectives.