What is Business Intelligence?
At Teradata, business intelligence is viewed as a way for businesses to avoid the less-than-helpful results of inaccurate, insufficient data analysis using a procedural and technical infrastructure that collects, stores, and analyzes data produced by a company’s activities. BI is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI parses all the data generated by a business and presents easy-to-digest reports, performance measures, and trends driving management decisions. Business intelligence addresses the needs of casual users, including executives, managers, front-line workers, customers and suppliers. It delivers reports, dashboards and scorecards that are tailored to each user’s role and populated with metrics aligned with strategic objectives and goals. This top-down style is powered by a classic data warehousing structure that consolidates enterprise data and enforces information consistency by transforming shared data into a common data model (schema) and BI semantic layer (metadata).
For comparison, Gartner defines business intelligence, also known as BI, as “… an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.” Similarly, CIO.com points out that “Companies use BI to improve decision making, cut costs and identify new business opportunities. BI is more than just corporate reporting and more than a set of tools to coax data out of enterprise systems. CIOs use BI to identify inefficient business processes that are ripe for re-engineering.”
Business Intelligence Challenges
For decades, business intelligence (BI) professionals have tried to shoehorn diverse types of business users, workloads and data into the same architecture, often with disappointing results. The problem is that BI is a broad domain. Strategically, it’s about using information to make smarter decisions; tactically, it’s about building applications for reporting and analysis. To succeed in the new world of data, BI professionals need to adopt new thinking and approaches. They need to break away from the one-size-fits-all framework of the past. To meet emerging business demands, they must manage multiple domains of intelligence and their associated architectures, each of which is optimized for different classes of users and workloads.
Better decisions require better information, and that’s the goal of business intelligence: analyze current data that presented on a metrics engineered to produce better decisions. BI does this by improving data accuracy, timing, and volume. For maximum return BI must work to increase the accuracy, timeliness, and amount of data. These requirements mean finding more ways to capture information that is not already being recorded, checking the information for errors, and structuring the information so that broad analysis is possible.
In the real world many, maybe even most, companies have data that is unstructured or in multiple formats, making collection and analysis difficult. This drives the need for software developers to deliver business intelligence solutions that optimize the information pulled from the available data. These enterprise-level software applications are designed to unify a company’s data and analytics ecosystem.
Software solutions grow and evolve at a steady rate, but data scientists are faced with the need to juggle the tug-of-war between reporting velocity and depth. The sheer volume that an organization is privy to thanks to the Internet of Things requires data analysts to sort through the findings to pinpoint select data points that represent the strength of an entire business area or a specific process. This reduces the need to spend time capturing and reformatting data for analysis, subsequently saving time and increasing the speed of reporting.
For an organization to compete effectively with analytics, it must provide personnel with BI and analytics applications that excel not just for select, well-understood requirements and for the full range of modern users’ needs—so all users can realize value from data and develop insights with analytics. Designers and developers must look beyond traditional methods of requirements gathering and instead collaborate with users to understand their needs. An organization must devote the same level of intensity to creating exciting and fulfilling internal user experiences with applications as it does to enhancing external customer experiences