Business Intelligence and Tools

- Overview

Business intelligence (BI) encompasses the strategies, methods, and techniques that businesses use to analyze and manage business information. 

Common capabilities of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

BI tools process large amounts of structured (and sometimes unstructured) data to help organizations identify, develop, and otherwise create new strategic business opportunities. They are designed to make this big data easy to interpret. Identifying new opportunities and implementing effective strategies based on insights is believed to provide businesses with competitive market advantages and long-term stability, and to help them make strategic decisions.

Businesses can use BI to support a wide range of business decisions, from operational to strategic. Basic operational decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. 

In all cases, BI is believed to be most effective when it combines data about the market in which a company operates (external data) with data from sources within the company, such as financial and operational data (internal data). External and internal data come together to provide a complete picture, in effect creating a type of “intelligence” that cannot be derived from any single data set. 

Business intelligence tools are widely used, enabling organizations to gain insights into new markets, assess demand for and suitability of products and services across different market segments, and measure the impact of marketing efforts.

BI applications use data collected from a data warehouse (DW) or data mart, and the concepts of BI and DW are combined as “BI/DW” or “BIDW”. A data warehouse contains a copy of analytical data to aid in decision support.

- BI Tools and Applications

Recent developments in the field of artificial intelligence (AI) demonstrate the scale and power of the technology for business and society. However, businesses need to determine how to build and manage these systems responsibly to avoid bias and errors, as the scalability of AI technologies can have costly impacts on business and society. 

As your organization applies machine learning and automation to workflows using disparate datasets, it's important to have the right guardrails in place to ensure data quality, compliance, and transparency within AI systems.

Business intelligence (BI) is a set of strategies and technologies used by enterprises to analyze and transform business information into actionable insights to inform strategic and tactical business decisions. 

BI tools access and analyze data sets and present the results in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the status of their business.

The term business intelligence also generally refers to a set of tools that can quickly and easily understandable insights into the current state of an organization based on available data.

Business intelligence (BI) tools collect, process and analyze large volumes of structured and unstructured data from internal and external systems. 

Data sources may include documents, images, emails, videos, journals, books, social media posts, files, and more. BI tools find this information through queries, which present data in user-friendly formats such as reports, dashboards, charts, and graphs. 

These tools can perform data mining, data visualization, performance management, analytics, reporting, text mining, predictive analytics, and more. As a result, employees can use this information to make better decisions based on forecasts, market trends, and key performance indicators (KPIs).

- The Benefits of BI

Business intelligence (BI) helps business decision-makers obtain the information they need to make informed decisions. But, the benefits of BI extend beyond business decisions and include:

• Data-driven business decisions: The ability to leverage data to drive business decisions is a core benefit of BI. A strong BI strategy delivers accurate data and reporting capabilities to business users faster, helping them make better business decisions in a more timely manner.
• Faster analysis and intuitive dashboards: BI improves reporting efficiency by compressing reports into dashboards that are easy for non-technical users to analyze, saving them time in gathering insights from data.
• Improve organizational efficiency: BI can help provide a holistic view of business operations, allowing leaders to benchmark results and identify areas of opportunity against larger organizational goals.
• Improved customer experience: Ready access to data helps employees responsible for customer satisfaction deliver a better experience.
• Increased employee satisfaction: Allowing business users to access data without contacting analysts or IT reduces friction, improves productivity, and gets results faster.
• Trusted and governed data: Modern BI platforms can combine internal databases and external data sources into a single data warehouse, allowing various departments within an organization to access the same data simultaneously.
• Improve competitive advantage: A sound BI strategy can help businesses monitor changing markets and anticipate customer needs.

[The Flower Shop - Gerald Harvey Jones]

- A New Enterprise AI Technology Stack

Digital transformation requires a new software technology stack and a new approach to developing enterprise AI applications.

To develop an effective enterprise AI or IoT application, data from thousands of enterprise information systems, suppliers, distributors, marketplaces, products used by customers, and sensor networks needs to be aggregated to provide a near real-time view of the extended enterprise.

Today’s data velocity is so high that it requires the ability to ingest and aggregate data from hundreds of millions of endpoints at very high frequencies (sometimes exceeding 1,000Hz cycles). Data needs to be processed at the rate it arrives, and the system needs to be highly secure and resilient to address persistence, event processing, machine learning, and visualization. This requires massively horizontally scalable resilient distributed processing capabilities that only modern cloud platforms and supercomputer systems can provide.

The resulting data persistence requirements are staggering. These data sets can quickly aggregate into hundreds of petabytes or even exabytes. Each data type needs to be stored in an appropriate database that can handle these volumes at high frequencies. Relational databases, key-value stores, graph databases, distributed file systems, and data BLOBs (Binary Large Objects) are all required, and data needs to be organized and linked across these different technologies.

[More to come ...]