Data Driven Business Decisions – The True Future for any Business

Full Details provided by?Todd Mostak (Harvard, MIT,CSAIL)

What is Business Intelligence?

Business intelligence systems combine data gathering, data storage, and knowledge management with data analysis to evaluate and transform complex data into meaningful, actionable information, which can be used to support more effective strategic, tactical, and operational insights and decision-making. Business intelligence environments consist of a variety of technologies, applications, processes, strategies, products, and technical architectures used to enable the collection, analysis, presentation, and dissemination of internal and external business information.

What are Business Intelligence Techniques?

Business intelligence technologies use advanced statistics and predictive analytics to help businesses draw conclusions from data analysis, discover patterns, and forecast future events in business operations. Business intelligence reporting is not a linear practice, rather, it is a continuous, multifaceted cycle of data access, exploration, and information sharing. Common business intelligence functions include:

• Data mining: sorting through large datasets using databases, statistics, and machine learning to identify trends and establish relationships
• Querying: a request for specific data or information from a database
• Data preparation: the process of combining and structuring data in order to prepare it for analysis
• Reporting: sharing operating and financial data analysis with decision-makers so they can draw conclusions and make decisions
• Benchmarking: comparing current business processes and performance metrics to historical data to track performance against industry bests
• Descriptive analytics: the interpretation of historical data to draw comparisons and better understand changes that have occurred in a business
• Statistical analysis: collecting the results from descriptive analytics and applying statistics in order to identify trends
• Data visualization: provides visual representations such as charts and graphs for easy data analysis

What are Business Intelligence Tools (BI Tools)?

Modern business intelligence systems prioritize self-service analysis, empowering businesses to gain insight into their market and improve performance with comprehensive data discovery tools, methods, processes, and platforms. Such business intelligence solutions include:

• Ad hoc analytics: an analysis process designed to answer specific questions on the spot
• Online analytical processing (OLAP): a computing method that enables multi-dimensional analytical queries
• Mobile BI: software that optimizes desktop business intelligence for mobile devices
• Real-time BI: a data analytics approach that delivers real-time information to users by feeding business transactions into a real-time data warehouse
• Operational BI: a data analysis approach that utilizes real-time business analytics to automatically integrate real-time data into operational system for immediate use
• Software-as-a-service BI (SaaS BI): a cloud-hosted, subscription-based delivery model for business intelligence software solutions
• Open source BI (OSBI): business intelligence software solutions that do not require purchasing a software license
• Collaborative BI: the merging of business intelligence software with collaboration tools in order to streamline the sharing process
• Location intelligence (LI): software that is designed to relate geographic contexts to business data
• Data visualization software: facilitates the detection of patterns and correlations by providing visual context

What are Business Intelligence Platforms?

A business intelligence platform enables businesses to utilize existing data architecture and create custom business intelligence applications that make information available for analysts to query and visualize. Modern business intelligence platforms support self-service analytics, making it easy for end users to create their own dashboards and reports.

Simple user interfaces combined with flexible business intelligence backend software enables users to connect to a range of data sources, including NoSQL databases, Hadoop systems, cloud platforms, and conventional data warehouses, to develop a cohesive view of their diverse data.

As artificial intelligence and machine learning continue to grow, and as businesses strive to be more data-driven and collaborative, so too does business intelligence continue to evolve, enabling users to integrate AI insights and harness the power of data visualizations. Popular business intelligence platform providers include Oracle, Microsoft, IBM, and Salesforce.

What are the Benefits of Business Intelligence?

The importance of business intelligence continues to grow as businesses face an ever-increasing flow of raw data and the challenges of gaining insight from enormous volumes of information (big data). With the employment of business intelligence systems, businesses can gain a comprehensive view of their organization’s data and translate it into insights about their business processes, enabling improved and strategic business decisions.

Business intelligence helps organizations analyze data with a historical context, optimize operations, track performance, accelerate and improve decision-making, identify and eliminate business problems and inefficiencies, identify market trends and patterns, drive new revenues and profitability, increase productivity and accelerate growth, analyze customer behavior, compare data with competitors, and ultimately gain a competitive advantage over rival businesses.

Business Intelligence vs Data Science

Both business intelligence and?data science?offer methods for data-interpretation with the goal of supporting improved, tactical decision-making. The main difference lies in the types of questions that they ask. While business intelligence interprets past data and provides new values of currently known information, data science focuses more on predictive analytics. Simply, business intelligence asks, “What happened and what needs to change?” and data science asks, “Why did X happen and what will happen if we do Z?”

Data science may be seen as an evolution of business intelligence in response to the rising volume and complexity of data and data input technologies. While business intelligence is designed to manage highly structured, static data and provide solutions for decision-making in the present, data science systems are designed to manage high-speed, multi-structured data and provide future solutions by continuously refining their algorithms.

Data science reinforces business intelligence, providing algorithmic models into which business intelligence developers can feed their prepared data; business intelligence analysts offer in return their expertise on business intelligence analytics requirements. Together, the two disciplines can collaborate to build a powerful model for forecasting the future.