IM - Data Integration

With the emergence of digitization and IoT, it becomes crucial for some companies to enable faster decision making through faster data insights. Gone are the days when everyone can wait for hours or days to look at insights and performance dashboards. In this insight, we have proven that providing these real-time insights means a change in system architecture for many business intelligence teams. In our expert opinion, we concluded that a best-practice real-time architecture consists of two main building blocks: a receiving end-point to receive the data streams and a real-time analytics layer to transform the data. We conclude that getting started with real-time analytics isn't complex: all leading vendors as well as open-source providers offer a serious of proven solutions and as solutions are offered through the cloud, they start with no investments needed (pay-as-you-go basis).

A CDO is tasked with having the big picture vision on data and information within and around the organization. This entails both governance, security and privacy of data but also includes identifying new business opportunities related to present (or not yet captured) data.

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Bill Inmon is one of the true pioneers of data warehousing and his "Building the Data Warehouse” is an absolute must read for every self-respecting data warehouse professional.

This is the process of converting normalized tables again into a de-normalized form. Here, a table may contain redundant information. This is a common technique within data warehousing where star schemas are used to optimize performance. Denormalizing a database consumes more space and slows OLTP performance but improves query performance in a BI environment.

Change data capture (CDC) is the process of capturing changes made at the data source and applying them throughout the enterprise. CDC minimizes the resources required for ETL (extract, transform, load) processes because it only deals with data changes. The goal of CDC is to ensure data synchronicity. 

Data quality pertains to aspects such as availability, completeness, accuracy, consistency, relevance and timeliness of data. High data quality is essential to business intelligence’s role as a means of decisional support.Poor data quality examples: missing fields, old or inaccurate information, data conflicts, inaccessible data in legacy systems.