Blog: Data Insights

Enterprise Data Warehouse and its Role in Driving Business Intelligence (BI)

July 2, 2020 - by Synoptek

Most organizations today recognize the importance of data analytics. In a recent survey by Sisense, forty-nine percent of professionals said analytics were more critical than before COVID-19. With a data warehouse, businesses can standardize data and deliver a single source of insight to everyone across the teams. The data warehouse can then be synchronized with Business Intelligence (BI) tools to derive insights that can help organizations make well-informed data-driven decisions. Traditionally, data warehouses have been on an on-premises setup; however, with the evolution of cloud technology, both enterprise data warehousing and BI have newer avenues to explore.

What is a Data Warehouse?

A data warehouse is a relational database that collects structured, semi-structured, and unstructured data from across the enterprise. Much like a physical warehouse, data warehouse stores data from multiple sources and further organize the data for analytics. Data warehouses give a long-range view of data over time, mainly focusing on data accumulation over transaction volume.

Evolution of a Data Warehouse

Several organizations were stuck with a high volume of detailed but unorganized and unstructured data in the past. While this data held several insights, there was no way for organizations to extract anything useful out of it in the absence of a data warehouse. When introduced in these organizations, a data warehouse collated all the scattered data, organized it, and stored it in a huge relational database through a process of Extract-Transform-Load (ETL). The data warehouses helped organizations take the load off their transactional systems, improve data quality, create a policed structure that a) measures and holds all historically relevant company activity, and b) present the given measurements across a series of structured dimensions.

Enterprise Data Warehouse and its Role in Driving Business Intelligence (BI)
With a data warehouse, extracting or adding data to/from the operational systems became easier. While it wasn’t easier to sift through every fragment of data contained in a transactional database, data warehouse allowed enterprises to work with a relatively cost-effective set up to tap into the raw data or pick and choose what they wanted to see within the database.

Role of Data Warehouse in BI

Data warehouses can perform complex queries, aggregations, and summarization, which transactional databases cannot handle. BI, on the other hand, is the process of analyzing the data collected over a period and deriving insights from it to influence the business decisions positively. In an effective BI process, data scientists develop meaningful hypotheses and try to validate them using the available data.

Additionally, data warehouses can be integrated with BI tools such as Power BI, Tableau, Qlik, and Sisense, and so on. Data warehouses equip data scientists and analysts to leverage BI tools to thoroughly read, analyze, and visually represent the data in multiple dashboards and other formats to track critical metrics. Recently, there has been a wave of technology enthusiasts who advocate BI without a data warehouse technique. However, BI without a data warehouse is more of a silo point solution that does not cater to all kinds of end users’ needs.

For example, a BI tool can have limited and standardized capabilities to read and represent the enterprise data – which does not necessarily help each business function. Whereas, a data warehouse topped with a BI tool equips an organization to manipulate the information in unlimited ways along with a rich visualization capability.

Driving Data-led Insights

The most important thing about BI and data warehouse is that they are both crucial elements of intelligence systems. Both systems have a similar goal: ‘improving business through data-driven insights.’ With data warehousing, users and business leadership have access to data from multiple sources as needed. This way, only a small amount of time is spent on the actual retrieval process. They were drawing better insights into the utilization of their team to process key data elements and drive better performance in a relatively shorter period.

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