Business intelligence (BI) represents the tools and systems that play a key role in the strategic planning process within a corporation, allowing a company to gather, store, access and analyze corporate data to aid in decision-making.
Business intelligence tools gather data from various sources into a single repository which is known as a Data warehouse (DW).
The data warehouse architecture is influenced by evolving business practices and goals as the primary aim is to support strategic and tactical decision making.
The volume of data businesses produce are growing as they become more complex and it cannot be stored in spreadsheets or documents, making a data warehouse necessary. It also makes it easier to analyze and with advance reporting and visualization tools, we understand patterns and how the data can be drilled down to gain deeper insights.
It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
- Arthur Conan Doyle, Sherlock Holmes.
To maximize potential, draw the benefits of an analytical tool and deliver results for your business, it is important to open up access to BI tools and promote usage across the organization.
An insurance company with global operations requires analysis based on their products across geographies to refine product pricing and improve performance.
In depth insights about aircraft maintenance & engineering, flight operations, inventory management, aircraft engine checks, yearly maintenance costs, flight delays, loss of baggage, etc.
Understanding sales by different channels, category, region, or comparing sales year on year, top selling products, etc. along with future planning and forecasting of sales.
Business Intelligence tools will not only enable effective aggregation of data for internal portfolio management but will also provide a platform for the automated distribution of information to investors.
Business Intelligence is the world of discovery, interpretation, and communication of meaningful patterns from data.
There exist numerous tools which help business users reduce the complexity of large volumes of data and latency. These data science tools employ the architecture, methodologies and processes which unearth the significance of data.
Data warehouse architectures provide data for reporting and management dashboards, performance management, and online analytic processing.
Discover new facts, trends, patterns, etc. about customers, partners, and the competitive landscape using columnar databases, data appliances, NoSQL databases, and Hadoop.
Along with the volume of data, satisfying big data requirements of business analytics is the leading driver for change in data warehouse architectures today.
Event processing allows businesses to pro-act instead of react to risk as well as opportunities by building real-time capabilities for “data-in-motion” into the architecture.
Federated data warehouse architectures prevent data silos. Databases appear to function as a single entity and data from multiple sources is presented as if stored in one place. This enables the architectural plan to extend across different systems to different departments.
Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software