What is Business Intelligence?

Business intelligence (BI) refers to technologies, processes, and strategies used by companies to analyze raw data and turn it into meaningful and actionable insights. It involves collecting, storing, and analyzing data from various sources such as databases, enterprise software, and external sources like social media and market trends.

The goal of Business Intelligence is to help organizations make informed decisions by providing insights into their operations, performance, customers, and market trends. This can involve a range of activities, including data mining, data visualization, reporting, and predictive analytics.

BI tools and platforms often include features such as dashboards, data warehouses, ad-hoc reporting, and online analytical processing (OLAP) to facilitate data analysis and decision-making. By leveraging BI, businesses can improve efficiency, identify opportunities for growth, optimize processes, and gain a competitive advantage in their industry.

Be more data driven:

Becoming more data-driven involves embracing a mindset and adopting practices that prioritize data as a cornerstone of decision-making and strategy development. Here's how to foster a more data-driven approach:

1. Cultural Shift - Cultivate a culture that values data and encourages its use in decision-making. This includes promoting data literacy among employees, fostering a collaborative environment for data sharing and discussion, and recognizing and rewarding data-driven initiatives.

2. Data Governance - Establish clear data governance policies and procedures to ensure data quality, integrity, and security. This involves defining data standards, roles, and responsibilities, implementing data management processes, and leveraging technology to enforce compliance.

3. Data Collection and Integration - Invest in systems and processes to collect and integrate data from various sources, both internal and external. This includes implementing data warehouses, data lakes, and integration tools to ensure a unified view of data across the organization.

4. Data Analysis - Develop analytical capabilities to extract insights from data effectively. This involves employing data analysis techniques such as descriptive, diagnostic, predictive, and prescriptive analytics to uncover trends, patterns, and correlations in the data.

5. Data Visualization - Utilize data visualization tools and techniques to communicate insights in a clear and compelling manner. This includes creating interactive dashboards, charts, and reports that enable stakeholders to understand and explore data easily.

6. Continuous Learning and Improvement - Foster a culture of continuous learning and improvement by regularly evaluating and refining data-driven processes and strategies. This involves soliciting feedback, measuring performance against key metrics, and adapting approaches based on insights gained from data analysis.

7. Leadership Support - Secure buy-in and support from leadership to drive the organization's data-driven initiatives. This includes providing resources, aligning priorities, and championing the value of data-driven decision-making throughout the organization.

8. Experimentation and Iteration - Encourage experimentation and iteration to test hypotheses and validate assumptions based on data insights. This involves adopting agile methodologies and embracing a mindset of learning from both successes and failures.

By embracing these practices and fostering a culture that values data-driven decision-making, organizations can leverage the power of data to drive innovation, improve performance, and achieve their strategic objectives.

Tools of Business Intelligence:

Business Intelligence encompasses a wide range of tools and technologies designed to gather, analyze, and present data in meaningful ways to support decision-making and strategic planning. Here are some common types of BI tools:

• Data Visualization Tools - These tools allow users to create interactive and visually appealing charts, graphs, and dashboards to represent data in a digestible format. Examples:

  • Tableau,
  • Power BI,
  • QlikView,
  • Domo.

• Reporting Tools - Reporting tools enable users to generate predefined or ad-hoc reports based on specific criteria. They often allow for customization and scheduling of reports. Examples:

  • SAP Crystal Reports,
  • IBM Cognos,
  • Oracle BI Publisher.

• Online Analytical Processing (OLAP) Tools - OLAP tools facilitate multidimensional analysis of data, allowing users to explore data from different perspectives and drill down into details. Examples:

  • Microsoft SQL Server Analysis Services,
  • Oracle OLAP,
  • IBM Cognos TM1.

• Data Mining Tools - Data mining tools use statistical techniques and machine learning algorithms to discover patterns, correlations, and trends within large datasets. Examples:

  • IBM SPSS Modeler,
  • RapidMiner,
  • Knime.

• ETL (Extract, Transform, Load) Tools - ETL tools are used to extract data from various sources, transform it into a consistent format, and load it into a data warehouse or repository for analysis. Examples:

  • Informatica PowerCenter,
  • Talend,
  • Microsoft SQL Server Integration Services (SSIS).

• Data Warehousing Tools - These tools are used to design, build, and manage data warehouses, which serve as centralized repositories for storing and organizing large volumes of structured and unstructured data. Examples:

  • Snowflake,
  • Amazon Redshift,
  • Google BigQuery.

• Predictive Analytics Tools - Predictive analytics tools use historical data and statistical algorithms to forecast future trends, behaviors, or outcomes. They help organizations anticipate customer behavior, optimize operations, and mitigate risks. Examples:

  • SAS Predictive Analytics,
  • IBM SPSS Statistics,
  • RapidMiner.

• Business Performance Management (BPM) Tools - BPM tools enable organizations to monitor and manage key performance indicators (KPIs), track progress towards goals, and align strategic objectives with operational activities. Examples:

  • Oracle Hyperion,
  • SAP BusinessObjects Planning and Consolidation,
  • Adaptive Insights.

These are just some examples of BI tools, and the landscape is constantly evolving with advancements in technology and the emergence of new trends such as augmented analytics and self-service BI. The choice of BI tools depends on factors such as the organization's specific requirements, budget, technical infrastructure, and skillset of users.

Enhancing data visualization:

Power BI, developed by Microsoft, is a robust business analytics tool that empowers users to extract insights from diverse data sources. Through features like data preparation, modeling, and visualization, it facilitates the creation of interactive reports and dashboards. With its support for natural language queries, seamless sharing, and collaboration capabilities, Power BI fosters informed decision-making.

It's a simple and usefull tool, however it lacks extensions to create your own plots. That's only one of many reasons to start using Mercury.

Mercury is our tool allowing to create and share reports, results or presentations with other users in Jupyter Notebook. Mercury outscores Power BI in the field of possible ways to present your data. Where Power BI has finite reports, Mercury gives you possibility to create your own plots and charts, making it possibly infinite amount of reports. In Mercury user can write them or use pre-made graphs and then share them easily with anyone they may want.

Or even generate your own invoice.

Literature:

• "Business Intelligence Guidebook: From Data Integration to Analytics" by Rick Sherman - This comprehensive guide covers the entire BI lifecycle, from data integration to analytics, providing practical advice and real-world examples.

• "Competing on Analytics: The New Science of Winning" by Thomas H. Davenport and Jeanne G. Harris - This book explores how organizations can leverage analytics, including BI, to gain a competitive advantage in today's data-driven world.

• "Business Intelligence For Dummies" by Swain Scheps - A beginner-friendly introduction to BI concepts, technologies, and implementation strategies.

Conclusions:

Business Intelligence is pivotal for modern businesses, providing actionable insights from data to drive efficiency and competitiveness. By analyzing data from various sources, BI informs strategic decisions, enhances operational processes, and identifies growth opportunities. Successful BI initiatives foster continuous improvement and innovation, underpinned by robust data governance and security measures to ensure integrity and compliance.

Moreover, BI promotes user empowerment and collaboration through accessible data exploration, facilitating informed decision-making and organizational transparency. Overall, BI empowers organizations to navigate complexities, innovate effectively, and achieve sustainable growth in today's dynamic business environment.