Data, Data Science and Business Intelligence: A Comprehensive Guide
Data is everywhere. It is the raw material that can be collected, processed, and analyzed to generate insights, knowledge, and value. But what exactly is data? How is it different from information? And how can data science and business intelligence help organizations leverage data for better decision-making?
In this blog post, we will answer these questions and more. We will explore the concepts of data, data science, and business intelligence, and how they are related. We will also look at some of the tools that Microsoft offers to help you work with data effectively.
What is data?
Data is defined as any facts, figures, or observations that can be recorded, stored, and transmitted. Data can be qualitative (such as words, images, or sounds) or quantitative (such as numbers, measurements, or statistics). Data can be structured (such as tables, databases, or spreadsheets) or unstructured (such as text, audio, or video). Data can be generated by humans (such as surveys, interviews, or social media posts) or by machines (such as sensors, or GPS devices).
What is information?
Information can be defined as data that has been processed, organized, or interpreted to make it meaningful and useful. Information can be used to answer questions, solve problems, or support decisions. Information can be presented in various forms, such as reports, charts, graphs, maps, or dashboards. Information can be derived from one or more sources of data, using methods such as aggregation, filtering, sorting, grouping, or calculation.
What are the similarities and differences between data and information?
Data and information are closely related concepts, but they are not the same. Data is the raw material that can be transformed into information. Information is the product that can be derived from data. Data is often abundant and messy, while information is often scarce and refined. Data is often objective and factual, while information is often subjective and contextual. Data is often collected for a specific purpose, while information is often used for a general purpose.
What is data science?
Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain knowledge to extract insights from data. Data science involves collecting, cleaning, exploring, analyzing, modeling, and communicating data using various techniques and tools. Data science can be applied to various domains and problems, such as business, health care, education, social sciences, engineering, natural sciences, arts, and humanities.
Data science involves various steps, such as:
- Data collection: gathering data from various sources and formats.
- Data cleaning: removing errors, inconsistencies, and irrelevant data.
- Data exploration: summarizing, visualizing, and understanding the data.
- Data analysis: applying statistical techniques, machine learning models, and other methods to discover patterns, trends, and relationships in the data.
- Data communication: presenting and communicating the results and insights of the analysis in a clear and compelling way.
Data science can be applied to various domains and problems. Examples include:
- Business: optimizing operations, increasing revenue, reducing costs, enhancing customer satisfaction and loyalty.
- Healthcare: improving diagnosis, treatment, prevention, and management of diseases and conditions.
- Education: enhancing learning outcomes, personalizing curricula, assessing performance and feedback.
- Social good: addressing issues such as poverty, hunger, climate change, human rights, and other areas.
What is data analytics?
Data analytics is a subset of data science that focuses on analyzing data to discover patterns, trends, correlations, or anomalies. Data analytics can be descriptive (such as summarizing what happened), diagnostic (such as explaining why it happened), predictive (such as forecasting what will happen), or prescriptive (such as recommending what should happen). Data analytics can use various methods and techniques, such as statistics, machine learning, artificial intelligence, data mining, or visualization.
Data analytics can be classified into four types:
- Descriptive analytics: describing what has happened or what is happening in the data using summary
statistics and visualizations.
- Diagnostic analytics: explaining why something has happened or why something is happening in the
data using correlation analysis, root cause analysis, and other methods.
- Predictive analytics: predicting what will happen or what might happen in the future using machine
learning models, forecasting techniques, and other methods.
- Prescriptive analytics: recommending what should be done or what could be done to achieve a desired
outcome using optimization techniques, simulation models, and other methods.
What are the similarities and differences between data science and data analytics?
Data science and data analytics are overlapping fields that both deal with data and insights. Data science is broader and deeper than data analytics. Data science covers the entire data lifecycle from collection to communication. Data analytics covers only the analysis part of the data lifecycle. Data science requires more technical skills and domain knowledge than data analytics. Data analytics requires more business skills and communication skills than data science.
What is business intelligence?
Business intelligence (BI) is the process of using data to improve business performance and outcomes. BI involves collecting, integrating, analyzing,
and presenting data from various sources within and outside an organization. BI can help organizations gain competitive advantage, optimize operations, enhance customer satisfaction, increase revenue, reduce costs, and mitigate risks. BI involves various steps, such as:
- Data integration: combining data from different sources and formats into a single repository or platform.
- Data warehousing: storing and organizing the integrated data in a structured and consistent way.
- Data modeling: defining the relationships and hierarchies among the data elements.
- Data querying: retrieving and manipulating the data using SQL or other languages.
- Data reporting: generating standardized reports that show the status and performance of the organization using predefined metrics and indicators.
- Data visualization: creating interactive charts, graphs, maps, and other visual elements that show the patterns, trends, and insights in the data.
- Data dashboarding: designing and displaying a collection of visualizations that provide an overview of the key aspects of the organization.
Benefits of Implementing Business Intelligence
There are a variety of benefits of implementing BI in an organization. These include:
- Improved decision-making: BI provides accurate, timely, and relevant information that can support strategic, tactical, and operational decisions.
- Enhanced efficiency: BI automates and streamlines the process of collecting,
Processing and presenting data, saving time and resources for the organization.
- Increased competitiveness: BI enables the organization to identify and exploit opportunities, monitor and improve performance, and anticipate and respond to challenges in the market.
Some of the key components of a BI system are:
- Data sources: the original places where the data is generated or collected, such as databases, files, web services, etc.
- ETL tools: the software that performs the extraction, transformation, and loading of the data from the data sources to the data warehouse.
- Data warehouse: the central repository that stores and organizes the integrated and cleaned data for analysis and reporting.
- Data marts: the subsets of the data warehouse that are tailored for specific business units or functions.
- OLAP tools: the software that performs the online analytical processing of the data, such as slicing, dicing, drilling, pivoting, etc.
- Reporting tools: the software that generates and distributes the reports that show the status and performance of the organization using predefined metrics and indicators.
- Visualization tools: the software that creates and displays the interactive visual elements that show the patterns, trends, and insights in the data.
- Dashboard tools: the software that designs and displays the collection of visualizations that provide an overview of the key aspects of the organization.
One of the most important BI tools is data visualization. These tools enhance BI by:
- Making the data more accessible and understandable for different audiences and purposes.
- Highlighting the important and relevant information concisely.
- Revealing the hidden and unexpected insights in the data that might otherwise be overlooked or ignored.
- Engaging and persuading the users with compelling stories and narratives based on the data.
Business Intelligence software
There are a variety of popular BI software solutions available. Some of these tools are:
- Google: Google offers a suite of cloud-based BI tools that include Google Data Studio (a data visualization and reporting tool), Google BigQuery (a data warehouse and analytics platform), Google Cloud AI Platform (a machine learning and artificial intelligence platform), and Google Sheets (a spreadsheet application).
- Salesforce: Salesforce offers a cloud-based BI platform called Salesforce Einstein Analytics that integrates with its customer relationship management (CRM) system. Salesforce Einstein Analytics provides users with interactive dashboards, reports, and charts that can be customized and embedded into other applications.
- IBM: IBM offers a cloud-based BI platform called IBM Cognos Analytics that provides users with self-service analytics capabilities. IBM Cognos Analytics allows users to access, explore, and visualize data from various sources using natural language processing (NLP) and artificial intelligence (AI).
- Microsoft: Microsoft offers a desktop-based BI tool called Microsoft Excel that is widely used for data analysis and visualization. Microsoft also offers a cloud-based BI service called Microsoft Power BI that connects to various data sources and provides users with interactive dashboards, reports, and charts that can be shared and published online.
- AWS: AWS offers a cloud-based BI service called Amazon QuickSight that enables users to create and publish interactive dashboards, reports, and charts using machine learning and AI. Amazon QuickSight can connect to various data sources such as Amazon S3, Amazon Redshift, Amazon Athena, or Amazon RDS.
Microsoft and Business Intelligence
Microsoft offers two main tools for working with data: Excel and Power BI.
Excel is a spreadsheet application that allows users to perform calculations, manipulate data, create charts and graphs, and more. Excel is one of the most widely used and versatile tools for data analysis and reporting. Excel can connect to various data sources, such as databases, files, web services, etc., and can also import data from other Microsoft products, such as Power BI. Excel can also use various features and functions to enhance its capabilities, such as pivot tables, formulas, macros, and VBAs.
Power BI is a business analytics service that allows users to create interactive dashboards and reports that show the patterns, trends, and insights in their data. Power BI can connect to various data sources, such as databases, files, web services, etc., and can also export data to other Microsoft products, such as Excel. Power BI can also use various features and functions to enhance its capabilities, such as Power Query, Power Pivot, DAX, and M language.
Conclusion
In this blog post, we have learned about the concepts of data, data science, and business intelligence and how they are related. We have also looked at some of the tools that Microsoft offers to help you work with data effectively. We hope you have found this post informative and useful for your own projects and goals with data. Thank you for reading!