business intelligence presentation

Business intelligence (BI) is a set of technological processes for collecting, managing and analyzing organizational data to yield insights that inform business strategies and operations.

Business intelligence analysts transform raw data into meaningful insights that drive strategic decision-making within an organization. BI tools enable business users to access different types of data—historical and current, third-party and in-house, as well as semistructured data and unstructured data such as social media. Users can analyze this information to gain insights into how the business is performing and what it should do next.

According to CIO magazine : “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” 1

Organizations can use the insights gained from BI and data analysis to improve business decisions, identify problems or issues, spot market trends and find new revenue or business opportunities.

IBM Planning Analytics benefits businesses with more accurate reporting, analysis and planning.

Report from Ventana Research: Debunking myths about data and analytics

Business intelligence (BI) is descriptive, enabling better business decisions that are based on a foundation of current business data. Business analytics (BA) is then a  subset  of BI, with business analytics providing the prescriptive , forward-looking analysis. It is the umbrella of BI infrastructure that includes the tools for the identification and storage of the data for decision-making.

BI might tell an organization how many new customers were acquired last month and whether order size was up or down for the month. As opposed to this, business analytics might predict which strategies, based on that data, would most benefit the organization. For example: What happens if we increase advertising spending to give new customers a special offer?

BI platforms traditionally rely on data warehouses for their baseline information. The strength of a data warehouse is that it aggregates data from multiple data sources into one central system to support business data analytics and reporting. BI presents the results to the user in the form of reports, charts and maps, which might be displayed through a dashboard.

Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. For example: “What are the sales for our eastern region versus our western region this year, compared to last year?”

OLAP provides powerful technology for data discovery, facilitating BI, complex analytic calculations and predictive analytics . One of the main benefits of OLAP is the consistency of its calculations that can improve product quality, customer interactions and business process.

Data lakehouses are now also being used for BI. The benefit of a data lakehouse is that it seeks to resolve the core challenges across both data warehouses and data lakes to yield a more ideal data management solution for organizations. A lakehouse represents the next evolution of data management solutions.

The steps taken in BI usually flow in this order:

• Data sources : Identify the data to be reviewed and analyzed, such as from a data warehouse or data lake , cloud , Hadoop , industry statistics, supply chain, CRM , inventory, pricing, sales, marketing or social media.
• Data collection : Gather and clean data from various sources. This data preparation might be manually gathering information in a spreadsheet or an automatic extract, transform and load (ETL) program.
• Analysis : Look for trends or unexpected results in the data. This might use data mining , data discovery or data modeling tools.
• Visualization : Create data visualizations, graphs and dashboards that use business intelligence tools such as Tableau , Cognos Analytics , Microsoft Excel or SAP . Ideally this visualization includes drill-down, drill-through, drill-up features to enable users to investigate various data levels.
• Action plan : Develop actionable insights based on analysis of historical data versus key performance indicators (KPIs) . Actions might include more efficient processes, changes in marketing , fixing supply chain issues or adapting customer experience issues.

Some newer BI products can extract and load raw data directly by using technology such as Hadoop, but data warehouses often remain the data source of choice.

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The term “business intelligence” was first used in 1865 by author Richard Millar Devens, when he cited a banker who collected intelligence on the market before his competitors did. In 1958, an IBM computer scientist named Hans Peter Luhn explored the potential of using technology to gather BI. His research helped establish methods for creating some of IBM’s early analytics platforms.

In the 1960s and 70s, the first data management systems and decision support systems (DSS) began to store and organize the growing volumes of data. “Many historians suggest the modern version of BI evolved from the DSS database,” says the IT education site Dataversity. “An assortment of tools was developed during this time, to access and organize data in simpler ways. OLAP, executive information systems and data warehouses were some of the tools developed to work with DSS.” 2

By the 1990s, BI grew increasingly popular, but the technology was still complex. It usually required IT support—which often led to backlogs and delayed reports. Even without IT, BI analysts and users needed extensive training to be able to successfully query and analyze their data. 3

Business intelligence is as much a way of thinking as it is composed of hardware and software. By adopting a data-driven culture—based on a complete set of approaches, processes, digital technology and data analysis—an organization can find new insights to make better business decisions and gain new advantages. Installing a new BI software package alone does not bring about this culture shift.

Benefits of BI :

• Clearer reporting : BI gives organizations the ability to ask questions in plain language and get answers they can understand. Dashboards can prioritize the most important insights, saving time for both data experts and nontechnical team members. Instead of using best guesses, staff can base decisions on what their business data is telling them—whether it relates to production, supply chain, customers or market trends. The data can help answer an organization’s pressing questions: Why are sales dropping in this region? Where do we have excess inventory? What are customers saying on social media?
• Consolidated data : BI delivers business insights by pulling in and consolidating data from multiple sources—internal and external—for complete analysis. By providing an accurate picture of the business and market, BI provides an organization with the means to design a business strategy.
• Create new efficiencies : Organizations can monitor business operations against benchmarks and fix or make improvements on an ongoing basis—all fueled by data insights. Analytics can discover and help eliminate manufacturing or supply chain bottlenecks. Managers can monitor staff performance to help pinpoint where organizational changes can be made. Supply chain management can be improved by monitoring activity up and down the line and communicating results with partners and suppliers.
• Deeper data insights : BI helps organizations become more data-driven, to continually improve business performance, gain competitive advantage, and locate new customers and new opportunities. They can improve ROI by understanding their business and market, and intelligently allocating resources to meet strategic objectives. New data insights can reveal customer behavior, preferences and market trends. Those insights enable marketers to better target prospects or tailor products to changing market needs.
• Faster decision making : As progress is monitored and analyzed digitally, better informed decisions can be made more quickly for faster adjustments in the marketplace.
• Increase customer satisfaction : When customer service staff have access to customer data and insights, they can provide requested information and resolve issues more quickly.
• Increase employee satisfaction : Self-help access to important business data can optimize workflows so that staff can do their jobs faster, with fewer added or repetitive steps.

Challenges of BI

• Contradictory conclusions : Self-service BI empowers multiple teams to search for the insights they need, but can also lead to divergent conclusions, which can create more friction instead of a unified plan of action. This can be especially true if human bias creeps into the analysis.
• Skills shortfall : The need for data integration might be difficult, given a wide variety of sources, and integration might exceed current capabilities. Expertise in data science, engineering and architecture is required to help ensure that analysis yields insights that reflect reality.
• Up-front costs : The initial costs to develop a powerful, modern BI system might appear large—but the cost savings generated by analysis will offset the investment.

Data is the lifeblood of successful organizations. Beyond the traditional data roles—data engineers, data scientists, analysts and architects—decision-makers across an organization need flexible, self-service access to data-driven insights accelerated by artificial intelligence (AI) . From marketing to HR, finance to supply chain and more, decision-makers can use these insights to improve decision-making and productivity enterprise-wide.

Organizations benefit when they can fully assess operations and processes, understand their customers, gauge the market, and drive improvement. They need the right tools to aggregate business information from anywhere, analyze it, discover patterns and find solutions. To deliver a BI system that can make all of this possible, organizations should:

• Set clear business objectives : Determining the most valuable and actionable information enables an organization to determine the data that needs to be collected or sourced and help select the BI system features needed to deliver that information.
• Comprehensive user training : The culture change to becoming a data-driven organization is most achievable when all users are given clear and compelling lessons on the new tools. Perfunctory training or self-guided hacking might discourage team buy-in or produce inaccurate results.
• Monitor data quality and relevance : Constant data monitoring is needed to help ensure that results are consistent and trustworthy. As market conditions change, new measures might need to be added or different reporting formats developed to add clarity. The input data sets must be sound and unbiased , and managed according to clear governance standards that ensure it is secure, private, accurate and usable. Any AI models that inform decision-making and forecasting must be explainable and transparent . And the BI system should connect to a wide variety of data systems across business functions and be usable by those who are not professional data analysts.
• Ensure data access to decision-makers :  Many businesses are behind. Essential data is not sufficiently captured or analyzed, according to an IDC report 4 that estimates up to 68% of business data goes unleveraged. Companies with a modern data architecture and robust BI adoption enjoy competitive advantage: They are positioned to move even further ahead by adopting real-time decisioning practices and predictive analytics.

Business intelligence adds value across multiple functions in almost any industry. For example: Customer service : With both customer information and product details available through a unified data source, customer service agents are able to quickly answer customer questions or begin to solve customer concerns.

Finance and banking : Financial firms can determine current organizational health and risks, and predict future success by viewing combined customer histories and market conditions. Data can be reviewed branch-by-branch with a single interface to identify opportunities for improvement or further investment.

Healthcare : Patients can quickly get answers to many pressing healthcare questions without asking time-consuming questions of staff or medical personnel. Internal operations, including inventories, are easier to track, minute-by-minute.

Retail : Retailers can boost cost savings by comparing performance and benchmarks across stores, channels and regions. And, with visibility into the claims process, insurers can see where they are missing service targets and use that information to improve outcomes.

Sales and marketing : By unifying data on promotions, pricing, sales, customer actions and market conditions, marketers and sales teams are better able to plan future promotions and campaigns. Detailed targeting or segmentation can help boost sales.

Security and compliance : Centralized data and a unified dashboard can improve accuracy and help determine the root causes of security problems. Compliance with regulations can be simplified with a single system to gather reporting data.

Statistical analytics :  Using  descriptive analytics , organizations can review statistics to spot new trends and uncover why those trends are developing. Supply chain : Worldwide data on a single pane of glass (SPOG) can speed the movement of goods and the identification of supply chain inefficiencies and bottlenecks.

Recent developments in business intelligence are focused on self-service BI applications that enable non-tech-savvy users to use automatic analysis and reporting. The IT team remains responsible for managing corporate data—including accuracy and security—but multiple teams can now have direct access to data and be responsible for their own analysis, rather than having the job wait in a queue for IT to run.

The ongoing advances in modern business intelligence and analytics systems are expected to integrate machine learning algorithms and AI to streamline complicated tasks. With the new emphasis on self-service, these capabilities can also accelerate the enterprise’s ability to analyze data and gain insights at a deeper level. AI-based systems can read from multiple sources automatically while grabbing the most relevant information to lead decision-making.

As an example, consider how IBM Cognos® Analytics  brings together data analysis and visual tools to support map-creation for reports. The system uses AI to automatically identify geographical information. It can then refine visualizations by adding geospatial mapping of the entire globe, an individual neighborhood or anything in between.

Modern BI solutions live on cloud-based platforms to extend the reach of BI worldwide. Consumer insights can be drawn from big data , producing information that ranges from descriptive to predictive. Many BI solutions now include real-time processing, enabling immediate decision-making.

Further advances in enterprise-grade BI systems include natural language queries, which are easier for users who are not SQL experts. Low-code or no-code development capabilities are available in some BI systems so users can create their own tools, apps and reporting interfaces to further speed the answers and time-to-market.

Your AI-powered business analyst and advisor will answer your business questions in seconds, guiding you toward the most impactful decisions.

Automate planning, budgeting, forecasting and analysis processes. Go beyond spreadsheets to create efficiency and remove manual steps. Echoing the benefits, Mick Ferguson, Finance Manager, Hunter Industries, states: "We're delighted with IBM Planning Analytics on Cloud; it’s become the one-stop shop for all of our finance and accounting needs."

Take advantage of this single analytics solution across your entire organization to confidently monitor, explore and share insights from data. Stefanie Nicholson, Head of Operations, Go Health Clubs, underscores this impact: "We're much more confident in our metrics—in fact, there’s now an attitude in the business that 'it doesn’t count if it doesn’t come from Cognos."

Use predictive analytics to help you uncover data patterns, gain accurate insights and improve decision making. Mark Lack, Strategy Analytics and Business Intelligence Manager, Mueller, Inc., confirms this benefit: "Deep analytics. Just add data."

Read why companies that thrive will be those that make fast, data-driven decisions using augmented analytics.

Find out how businesses use artificial intelligence, learn about its benefits and advantages, and more.

Find out about the importance of a responsive supply chain plan and how to achieve it.

Discover how this customer is helping to ensure the quality of care by calculating daily performance metrics, identifying trends and fine-tuning its processes.

Predict outcomes with flexible AI-infused forecasting and analyze what-if scenarios in real-time. IBM Planning Analytics is an integrated business planning solution that turns raw data into actionable insights. Deploy as you need, on premises or on cloud.

All links reside outside ibm.com

1 CIO magazine:  https://www.cio.com/article/272364/business-intelligence-definition-and-solutions.html

2 Dataversity Digital:  https://www.dataversity.net/brief-history-business-intelligence/

3 Better Buys:  https://www.betterbuys.com/bi/history-of-business-intelligence/

4 Report by Seagage and IDC: https://www.seagate.com/files/www-content/our-story/rethink-data/files/Rethink_Data_Report_2020.pdf

What is business intelligence?

Business intelligence (BI) uncovers insights for making strategic decisions. Business intelligence tools analyze historical and current data and present findings in intuitive visual formats.

How business intelligence works

There are four keys steps that business intelligence follows to transform raw data into easy-to-digest insights for everyone in the organization to use. The first three—data collection, analysis, and visualization—set the stage for the final decision-making step. Before using BI, businesses had to do much of their analysis manually, but BI tools automate many of the processes and save companies time and effort.

Step 1: Collect and transform data from multiple sources

Business intelligence tools typically use the extract, transform, and load (ETL) method to aggregate structured and unstructured data from multiple sources. This data is then transformed and remodeled before being stored in a central location, so applications can easily analyze and query it as one comprehensive data set.

Step 2: Uncover trends and inconsistencies

Data mining, or data discovery, typically uses automation to quickly analyze data to find patterns and outliers which provide insight into the current state of business. BI tools often feature several types of data modeling and analytics—including exploratory, descriptive, statistical, and predictive—that further explore data, predict trends, and make recommendations.

Step 3: Use data visualization to present findings

Business intelligence reporting uses data visualizations to make findings easier to understand and share. Reporting methods include interactive data dashboards , charts, graphs, and maps that help users see what’s going on in the business right now.

Step 4: Take action on insights in real time

Viewing current and historical data in context with business activities gives companies the ability to quickly move from insights to action. Business intelligence enables real time adjustments and long-term strategic changes that eliminate inefficiencies, adapt to market shifts, correct supply problems, and solve customer issues.

Why companies benefit from using business intelligence tools

Because business intelligence tools speed up information analysis and performance evaluation, they’re valuable in helping companies reduce inefficiencies, flag potential problems, find new revenue streams, and identify areas of future growth.

Some of the specific benefits that businesses experience when using BI include:

• Increased efficiency of operational processes.
• Insight into customer behavior and shopping patterns.
• Accurate tracking of sales, marketing, and financial performance.
• Clear benchmarks based on historical and current data.
• Instant alerts about data anomalies and customer issues.
• Analyses that can be shared in real-time across departments.

In the past, business intelligence tools were primarily used by data analysts and IT users. Now, self-service BI platforms make business intelligence available to everyone from executives to operations teams.

Here’s how business intelligence improves the way work is done in six key areas:

Customer experience.

Access all your customer information in one place, so you can direct resources to key areas that will positively impact customer engagement and support.

Sales and marketing

Gain visibility into sales and marketing performance, consumer behavior, and buying trends which ensures future marketing initiatives are effective and drive revenue.

Improve operations by automating routine analytics tasks, refining processes, reducing inefficiencies, and increasing productivity.

Use custom dashboards to get a holistic view of the company’s financial health, study historical data, calculate risk, and predict trends.

Inventory control

Automate data analysis and reporting to improve inventory management, accelerate fulfillment, and help anticipate buying trends.

Security and compliance

Centralize data to improve accuracy and transparency, making it easier to uncover errors, security issues, and reduce compliance risks.

When evaluating business intelligence tools , look for a product that’s secure, compliant, globally available, and trusted. It should also have features that make BI insights accessible to your entire organization—such as data visualization, shared dashboards, artificial intelligence, and machine learning.

“It has gotten our key decision makers out of the weeds by providing them with metrics they need to do their job, and not have to wrangle the data to get answers. Flexibility, interactivity … just jump in.”

Manager, Clinical Research Analytics

Real-life business intelligence examples

Business intelligence is applied differently from business to business and across a range of sectors—finance, retail and consumer goods, energy, technology, government, education, healthcare, manufacturing, and professional services. Here’s how business intelligence is being used by different industries to achieve success.

Financial services

A large financial institution in the United Kingdom used business intelligence to connect all its data sources and enable business users as well as IT staff to develop reports and BI solutions, making the business more agile and responsive. Customer service, online banking, and branch staffing are just a few of the teams that now use BI tools to improve efficiency.

A global healthcare solutions provider developed a configuration management system in the cloud to give the company’s IT business operation teams one source of truth for its asset and configuration management inventory. It also integrated the system with a business intelligence tool with advanced visualization capabilities, enabling less technical employees and consumers to quickly get answers to questions.

Manufacturing

A large manufacturing company in the United States deployed a cloud-based business intelligence solution that balanced governance and autonomy in report creation while providing advanced analytics. The cost-effective solution keeps the spirit of self-service alive, while giving IT more control over how and where data is stored, accessed, and maintained.

Getting started with business intelligence

Explore how your organization can use business intelligence tools, such as Microsoft Power BI, to work more efficiently and enable everyone across your company to make data-driven decisions.

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Business Intelligence. Zamaneh Jahed. What is Business Intelligence?. Business Intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing and providing access to data to help enterprise users make better decisions.

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Business Intelligence Zamaneh Jahed

What is Business Intelligence? 1 Business Intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing and providing access to data to help enterprise users make better decisions.

BI has its root in the MIS reporting system of the 1970. • This term was introduced by Gartner Group in the middle 90s. • Until 2005, BI systems had Artificial intelligence capabilities. A Brief History of BI 2

BI Architecture Conformed Dimensions Analytical Layer Presentation Data storage, retrieval Layer Web Portal NightlyReplication Fact Tables Portal Portal Semantic Portal Portal Data source layer Security SASInformation Maps Excel Flash Dashboard HTML Data Transformation Metadata Web Services Metadata Registry 3/

Operational Data Source: Business Intelligence system collects data from various sources including operation database, OLTP, ERP, external database and etc. ETL tools (Extract, Transform, Load): They are used to pull data from source database, transform the data so that it is compatible with the data warehouse and then load it into data warehouse. 5

ETL Process E X T R A C T T R A N S F O R M LO A D Customer E T BI Target Database Accounting E T Inventory E T Orders E T 6

Data Warehouse Data warehouse is a database that stores current and historical data. It consolidates and standardizes information from different operational database so that the information can be used across the enterprise for management analysis and decision making. 7

Data Mart Data Mart is a subset of a data warehouse in which a summarized or highly focused portion of the organization’s data is placed in a separate database for a specific population of users. 8

Cube The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that’s been aggregated to allow queries to return data quickly. • Dimension Each cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data • Fact table A fact table contains the basic information that you wish to summarize. This might be order detail information, payroll records, or anything else that’s amenable to summing and averaging. 10

OLTP and OLAP • OLTP (On-line Transaction Processing) • Major task of traditional relational DBMS. • Day to day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. • Aims at reliable and efficient processing of a large number of transaction and ensuring data consistency. • OLAP (On-line Analytical Processing) • Major task of data warehouse system. • Data analysis and decision making. • Aims at efficient multidimensional processing of large data volumes. 9

OLAP or multidimensional analysis: Means you can analyze ‘measures’ at the intersection of any combination of dimensions: “Show me the Revenue we made from Customer A for Product 1 in January 2007” Revenue:2,000,000. Measure Time Product Customer 11

References • Butuza- A. 2011.Increasing the Business Performances using Business Intelligence • Microsoft SQL Server 2008 Analysis Services Step by Step. • Kenneth C. Laudon and Jane P. Laudon, Management Information systems. • Larissa T. Moss, ShakuAtre, Business Intelligence Roadmap: The complete Project Lifecycle for Decision Support Applications. • Turban, Efraim.Business Intelligence: managerial approach. 12

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Home Blog PowerPoint Tutorials Business Intelligence PowerPoint Templates

Business Intelligence PowerPoint Templates

Business intelligence or BI departments use methodologies that can convert raw data into the type of information that can have implications for business purposes. While professionals working in a Business Intelligence department may require making good use of databases, spreadsheets and platforms that support Big Data, all aggregated information and analysis may boil down to a presentation which may have to be presented before the senior management to address strategic decision making. Below is a list of some fine Business Intelligence PowerPoint Templates that can help you present large and complicated data in a summarized and easy to grasp format.

Business Intelligence Data & Dashboard templates for PowerPoint

Whether you are making a PowerPoint Presentation where you require showing a product lifecycle, a presentation about the demand and supply structure for a product or require presenting various chunks of data in a Dashboard style slide, Data and Dashboard templates are just what you need. You can use the business intelligence data dashboard presentation templates to make Business Intelligence reporting . For more details, see the link given below.

Go to Download Business Intelligence Data And Dashboard Templates For PowerPoint

Map PowerPoint Templates for Presentations

Business Intelligence is about more than raw data conversion into graphs and lengthy documents containing an analysis of business data. Instead, it requires keeping an eye on various strategic assets of the company and how to make them more meaningful.

Interpreting such data for geographically mobile divisions can be quite difficult; this is why using a map template can help you present the most detailed geographical data in easy-to-understand slides. PowerPoint Map Templates (see the link given below) can help you create such presentations by showing maps for individual countries, regions, continents, etc.

Go to Download Map PowerPoint Templates

Sales Chart PowerPoint Template

Transforming data and analysis in the form of charts is one of the essential aspects of a presentation which may contain data from the Business Intelligence division of an organization. This template makes it possible to easily create visually appealing bar charts with an excellent blue background that is unobtrusive and quite easy on the eyes.

Go to Sales Chart PowerPoint Template

PowerPoint Diagram Templates

These set of templates offer different types of sequential and generic diagrams that can be easily edited for making various types of business related presentation slides about organizational charts, production processes, project timelines, statistical data representation and the like.

Go to Download PowerPoint Diagram Templates

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What Is Business Intelligence (BI)? Types, Benefits, and Examples

Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.

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What Is Business Intelligence (BI)?

Business intelligence (BI) is a process driven by technology that analyzes business data in order to provide information that can be actioned so that executives and managers can make better-informed business decisions.

Business intelligence 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 that inform management decisions.

Key Takeaways

• Business intelligence represents the technical infrastructure that collects, stores, and analyzes company data.
• Business intelligence parses data and produces reports and information that help managers to make better decisions.
• Software companies produce BI solutions for companies that wish to make better use of their data.
• BI tools and software come in a wide variety of forms such as spreadsheets, reporting/query software, data visualization software, data mining tools, and online analytical processing (OLAP).
• Self-service BI is an approach to analytics that allows individuals without a technical background to access and explore data.

Understanding Business Intelligence (BI)

The need for BI was derived from the concept that managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information. Creators of financial models recognize this as “garbage in, garbage out.”

BI attempts to solve this problem by analyzing current data that is ideally presented on a dashboard of quick metrics designed to support better decisions.

Most companies can benefit from incorporating BI solutions; managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information.

To be useful, BI must seek 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 in a way that makes broad analysis possible.

In practice, however, companies have data that is unstructured or in diverse formats that do not make for easy collection and analysis. Software firms thus provide business intelligence solutions to optimize the information gleaned from data . These are enterprise-level software applications designed to unify a company’s data and analytics .

Although software solutions continue to evolve and are becoming increasingly sophisticated, data scientists still need to manage the trade-offs between speed and the depth of reporting.

Some of the insights emerging from big data have companies scrambling to capture everything, but data analysts can usually filter out sources to find a selection of data points that can represent the health of a process or business area as a whole. This can reduce the need to capture and reformat everything for analysis, saving analytical time and increasing the reporting speed.

Types of BI Tools and Software

BI tools and software come in a wide variety of forms. Let's take a quick look at some common types of BI solutions.

• Spreadsheets: Spreadsheets like Microsoft Excel and Google Docs are some of the most widely used BI tools.
• Reporting software: Reporting software is used to report, organize, filter, and display data.
• Data visualization software: Data visualization software translates datasets into easy-to-read, visually appealing graphical representations to quickly gain insights.
• Data mining tools: Data mining tools "mine" large amounts of data for patterns using things like artificial intelligence, machine learning, and statistics.
• Online analytical processing (OLAP): OLAP tools allow users to analyze datasets from a wide variety of angles based on different business perspectives.

Benefits of Business Intelligence

There are many reasons why companies adopt BI. Many use it to support functions as diverse as hiring, compliance , production, and marketing. BI is a core business value; it is difficult to find a business area that does not benefit from better information to work with.

Some of the many benefits companies can experience after adopting BI into their business models include faster, more accurate reporting and analysis, improved data quality, better employee satisfaction, reduced costs, increased revenues, and the ability to make better business decisions.

BI was derived to help businesses avoid the problem of "garbage in and garbage out," resulting from inaccurate or insufficient data analysis.

If, for example, you are in charge of production schedules for several beverage factories and sales are showing strong month-over-month growth in a particular region, you can approve extra shifts in near real-time to ensure your factories can meet demand.

Similarly, you can quickly idle down that same production if a cooler-than-normal summer starts impacting sales. This manipulation of production is a limited example of how BI can increase profits and reduce costs when used properly.

Examples of BI

Coca-Cola Bottling had a problem with its daily manual reporting processes: they restricted access to real-time sales and operations data.

However, by replacing the manual process with an automated BI system, the company completely streamlined the process and saved 260 hours a year (or more than six 40-hour work weeks). Now, the company's team can quickly analyze metrics like delivery operations, budget, and profitability with just a few clicks.

What Is Power BI?

Power BI is a business analytics product offered by software giant Microsoft. According to the company, it allows both individuals and businesses to connect to, model, and visualize data using a scalable platform.

What Is Self-Service BI?

Self-service BI is an approach to analytics that allows individuals without a technical background to access and explore data. In other words, it gives people throughout the organization, not just those in the IT department, to have control over the data.

What Are the Disadvantages of Self-Service BI?

Drawbacks to self-service BI include a false sense of security in end-users, high licensing costs, a lack of data granularity, and sometimes too much accessibility.

What Is IBM's BI Product?

One of IBM's main BI products is its Cognos Analytics tool, which the company touts as an all-inclusive, AI-powered BI solution.

Part of the responsibilities of executives and managers is to make their companies more efficient, profitable, and competitive, along with improving the workplace environment for employees. Companies can do this with technology-driven processes, known as business intelligence, allowing them to achieve these goals faster and correctly.

Tableau. " Coca-Cola Bottling Company Empowers the Enterprise with Tableau Mobile Dashboards to Drive Bottom Line ."

Microsoft. " What Is Power BI? "

IBM. " IBM Cognos Analytics with Watson ."

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Business Intelligence Tutorial

• Business Intelligence - Home
• Business Intelligence - Introduction
• Business Intelligence - History
• Business Intelligence - Uses
• Business Intelligence - Resources
• Business Intelligence - Discussion
• Selected Reading
• UPSC IAS Exams Notes
• Developer's Best Practices
• Questions and Answers
• Effective Resume Writing
• HR Interview Questions
• Computer Glossary

Business intelligence is a set of tools, methods, and processes that turn raw data into useful information, helping businesses make better and more cost-effective decisions. These days, many companies are using Business Intelligence to improve their services and stay ahead of the competition.

In this Business Intelligence tutorial, we will learn about key information and important material for learners from all backgrounds to excel in this field.

What is Business Intelligence?

Business Intelligence (BI) is a broad term that includes the entire process of turning raw data into valuable insight for businesses to make smart decisions. It involves collecting and analyzing data from various sources, like internal databases, third-party systems, and even social media, to understand how a business is doing and plan what to do next.

Business Intelligence (BI) isn't just about specific technologies or tools, it's a whole process. It includes everything from the tech we use and business strategies to the people involved. Imagine BI as a black box where raw data goes in, and what comes out is useful information and insights that help make better decisions.

How does Business Intelligence Work?

To simplify how BI works, think of it as a black box where data goes in and information and insights come out. To make this clear, let’s expand the black box idea: on the input side (left), we have data sources, and on the output side (right), we have users who interact with the results.

Now, inside this black box, we have business strategies, infrastructure, technologies, tools, applications, best practices, and more. The initial output provides a quick overview of the business and should spark ideas or questions. From there, you can dig deeper to get more detailed information.

This is an iterative process. After a few rounds of exploring and analyzing, you get insights that help you make better decisions to improve your business. If the first output doesn’t lead to new ideas or questions, it might mean either you already know what’s being shown or there could be an issue with the BI setup.

Stages of Business Intelligence

The stages of business intelligence are −

• Data Collection − We start by gathering data from various sources, like databases, spreadsheets, and online feeds.
• Data Integration − We then bring this data together into a central system, like a data warehouse or data lakehouse, so it’s easier to manage and analyze.
• Data Analysis − With BI tools, we analyze the combined data to uncover trends, patterns, and insights. This can involve complex calculations and making predictions about the future.
• Data Visualization − We present these insights through visual tools like charts, graphs, and dashboards, which help us easily understand and interpret the data.
• Decision-Making − We develop actionable insights based on the analysis and visualizations. This might involve improving processes, changing marketing strategies, addressing supply chain issues, or enhancing customer experiences.

Importance of Business Intelligence

Business Intelligence is an ever-growing phenomenon, and it has some important features. Some of these features are listed below −

• Enhanced Decision-Making − You can use BI to make data-driven decisions, leading to better business outcomes.
• Improved Efficiency − Integrating BI into your processes can streamline operations and reduce inefficiencies.
• Competitive Advantage − We can use BI to gain insights that help us stay ahead of the competition.
• Data-Driven Culture − BI promotes a culture where decisions are based on facts and data, not just intuition.

Challenges to Business Intelligence

Although the usage of business intelligence is required everywhere, it also poses some challenges to existing systems. Some of these challenges are listed as follows −

• Data Quality Issues − You need to ensure that the data is clean and accurate, or BI insights might be misleading.
• Complex Implementation − It requires careful planning and expertise to set up BI systems effectively.
• High Costs − We can expect significant investment in technology and training, which might be a challenge for some businesses.
• User Adoption − Initial training stages and support is needed to make sure everyone in the organization is comfortable using BI tools.

Applications of Business Intelligence

Here are several key applications of BI across various domains −

1. Enhancing Employee Engagement and Performance

Business Intelligence (BI) helps manage remote employees by tracking attendance and work setups, ensuring they have the tools and information needed for effective remote work, which supports smooth operations and employee well-being.

2. Real-time Data Availability

Business Intelligence (BI) enables immediate access to current data, helping companies monitor and respond to changes quickly, which is crucial for effective decision-making and maintaining smooth operations.

3. Data-driven Future Planning

Utilizing trends from tracking, predictive analytics, and artificial intelligence helps businesses make informed decisions about future operations. This reduces the risk of costly mistakes and provides a competitive edge by ensuring decisions are based on solid research.

Companies using Business Intelligence Strategies

The following section provides a list of top companies that use BI strategies in real life −

The automotive company uses BI to wirelessly connect their cars to corporate offices, allowing them to gather and analyze data. This keeps them in touch with customers and helps fix issues like component damage or traffic hazards before they become problems.

By monitoring vehicles in real-time, they can predict maintenance needs and offer personalized recommendations, making for a smoother driving experience.

2. Starbucks

Starbucks, known worldwide for its coffee, uses BI tools to analyze data from its loyalty card program and mobile app to understand what customers enjoy buying.

By tracking individual purchase habits, Starbucks predicts what customers might want next and sends personalized offers through their app and email.

Career Options in Business Intelligence

Business Intelligence experts focus on analyzing data and generating insights to drive business decisions. They can pursue various job roles as −

• Business Analyst
• Data Analyst
• Data Engineer
• SQL Server Business Intelligence
• Business Intelligence Analyst
• BI Consultant

Companies Hiring BI Experts

The following list includes the major sectors of business which hire BI experts, along with the corresponding companies in each sector −

• Tech Companies − Google, Amazon, Microsoft
• Financial Institutions − JPMorgan Chase, Goldman Sachs
• Healthcare Organizations − UnitedHealth Group, CVS Health
• Retail Leaders − almart, Target
• Consulting Firms − Deloitte, Accenture
• Telecom Companies − AT&T, Verizon
• Manufacturing Firms − General Electric, Siemens
• E-commerce − Shopify, eBay

Prerequisite to Learn Business Intelligence

Although this is a beginner's tutorial, we assume that readers have a basic understanding of business environments and a general knowledge of concepts such as data sources, data collection, analysis, and visualization techniques.

FAQs about Business Intelligence

There are some very Frequently Asked Questions (FAQ) about Business Intelligence, this section tries to answer them briefly.

If your sales, marketing, and finance teams are struggling for detailed analytics or missing key decisions because of insufficient data, it’s a sign that you need a BI system.

Business intelligence looks at "What are the current trends and what actions should we take?" while data science examines "Why did this trend happen and what might occur if we take this action?"

Business intelligence is designed to handle highly structured data, while data science systems are built to manage high-speed, dynamic data.

The self-service the user does not require an IT team to access and analyze the data the self-service is a data analytics approach.

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Can Apple’s iPhones Pass the A.I. Test?

On Monday, the company will introduce smartphones with A.I. features that could take the technology mainstream or sow fresh doubts about its viability.

• Share full article

By Tripp Mickle

Tripp Mickle has been writing about Apple since 2016

Shortly before Steve Jobs’s death in 2011, Apple filled a cramped auditorium in Cupertino, Calif., for the unveiling of its fifth iPhone. The device’s biggest selling point was a new software feature called Siri, which helped cause a jump in iPhone sales.

On Monday, Apple is planning to run that play again.

For the first time in more than a decade, the tech giant will unveil a suite of iPhones whose signature feature won’t be an improved camera or an updated design, but new software capabilities. The system, called Apple Intelligence, will sort messages, offer writing suggestions and create a more capable Siri powered by generative artificial intelligence.

The new iPhones represent a big moment for generative A.I., which can answer questions, create images and write software code. As a latecomer to the A.I. party, Apple is in a position to take the technology mainstream — or sow fresh doubt about its viability if it doesn’t live up to expectations.

Early enthusiasm for A.I. has been tempered by questions about its utility. This spring, Microsoft postponed features in an A.I. computer because of security vulnerabilities around the technology’s recording of every second of activity. Humane, a start-up that raised $240 million for a device called the Ai Pin, was panned by tech reviewers because its system was slow to fulfill requests and sometimes fielded them inaccurately.

In the wake of those problems, Wall Street is looking to Apple for reassurance that customers want A.I. The tech giant spent two years watching as Microsoft, Meta, Google and Samsung added A.I. to products. Apple has shown over the years that it can enter a market late and redefine it, as it did with digital music players, smartphones and smartwatches.

Apple is hoping that A.I. can rejuvenate the iPhone. It considers the opportunity in A.I. so important that it canceled one of its big bets — a $10 billion project to develop a self-driving car — and reassigned hundreds of engineers to work on the technology.

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Apple's new iPhone 16 with AI seems ... kind of boring. That could actually be a good thing.

• The iPhone 16 is here, but its features seemed more understated than the hype around the launch.
• Apple wrapped up its Glowtime event on Monday afternoon.
• The Apple event also showcased new AirPods and the Apple Watch Series 10.

Apple just debuted its highly-anticipated iPhone 16 , which will include long-awaited artificial intelligence features — but the delivery just didn't seem as flashy and exciting as its AI competitors' recent updates.

The hype around Apple Intelligence leading up to the Glowtime event was nearly deafening, but CEO Tim Cook and other Apple execs seemed to take a more laid-back approach when showing off the hardware on which it'll operate.

As expected, AI was the centerpiece of Monday afternoon's event. Apple spent much of the presentation emphasizing how the iPhone 16 was "built from the ground up to deliver Apple Intelligence."

For me, the most interesting features coming to Apple Intelligence are the Writing Tools, AI-generated emojis, and the camera's visual intelligence capabilities. Still, I wasn't left in awe by any of the demonstrations — especially considering the showy announcements from big AI players like OpenAI and Google, which featured super intelligent assistants with humanlike conversation skills.

But Apple doesn't have to stun consumers right away with a game-changing product — and maybe that's fine. With 1.5 billion iPhones worldwide, according to Wedbush Securities, the foundation of its empire is solid enough to build to the moon.

Since it's got the trust of those people, who are already in the Apple ecosystem, it maybe means that there's no rush to break boundaries in AI compared to making Apple users' lives easier — and compared to their less-dominant competitors.

All it has to do is what Apple already does — and better than pretty much everyone else: make our daily lives easier. And it's hard to demonstrate that in a 30-second skit showing off Apple Intelligence.

It's maybe also a question of optics. It's hard to show in an exciting way exactly how many times someone might need a ChatGPT-assisted Siri to look up something without having to take their phone out, open the app, and type their question.

That thought was echoed by Deepwater Asset Management managing partner Gene Munster, who attended the event at Apple Park . He said the keynote video doesn't do the new iPhone 16 lineup justice.

"It's a new paradigm, and the paradigm will change how almost all humans will interact with tech," Munster said.

Related stories

This paradigm shift, which Munster compared to the transition from flip phones to touchscreens, will be spurred by people testing these products in Apple stores.

And it looks like Apple knows that.

The tech giant reportedly began briefing its retail employees on all things Apple Intelligence weeks before the iPhone 16 lineup launch. It seems to me that Apple is gearing up to have consumers see its new AI tech for themselves before deciding to upgrade.

Turns out that slow and steady may actually be the best way to avoid being labeled boring.

Watch: How AI will change the iPhone and other Apple products

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• How to Write an Engaging Opening Statement

First impressions are crucial—especially when you’re delivering an opening statement to a court of law, establishing and framing the crux of a case for judges and jurors when a trial begins. Clarity, conciseness, resonance, and other facets of effective writing and public speaking all affect your opening statement’s reception and its impact on judges’ and jurors’ perceptions of parties involved with the case.

However, crafting an engaging and persuasive opening statement involves far more than delivering a compelling speech.

Below, we’ll break down how to write a good opening statement. We’ll discuss the elements they contain, how to avoid argumentative statements, strategies, and more. With this guidance, you’ll be able to craft a compelling opening statement for trials of any size.

What is an Opening Statement?

Opening statements comprise the first significant stage of a trial once it begins, with the lawyers for each party addressing the judge(s) and any jurors. Through the cohesive and thematic presentation of a given case, its facts, its evidence, and what each side intends to prove, lawyers attempt to help judges and jurors understand the issue or dispute, frame it favorably for the party they represent, and position themselves to achieve the desired verdict. 1 Whether in a bench or jury trial , the opening statement sets the stage for the entire proceeding.

What Makes an Opening Statement Good?

Lawyers need to strike a balance in order to craft an effective opening statement. Otherwise, they risk miring juries in the details or failing to communicate and connect with jurors.

Opening statements need to present a compelling story that captures the jurors’ attention and makes them interested in hearing all the details of your client’s case throughout the trial. 

Determining Your Opening Statement’s Theme

As opening statements generally contain so much information, it’s crucial that you distill these elements to their essence and theme. 2 This theme should be the primary takeaway jurors have once you’ve concluded. When developing it, remember that it needs to: 

• Be easily grasped
• Resonate with the jury
• Convey how the evidence you’ll present connects 

This starts with a simple, straightforward description of the case itself. How would you communicate it to someone outside the legal profession in one or two sentences? For example, you may state that you intend to demonstrate one party’s pattern of behavior over multiple incidents.

Your trial theme should also encompass motives and attempt to explain what incident(s) occurred leading to the lawsuit in broad strokes. 2 It should inform juries and provide some context of what they should be looking and listening for as the trial unfolds to pick up on consistent threads tying together your case.

However, you also need to be careful with what your opening statement explicitly asserts.

Is an Opening Statement an Argument?

No, and being argumentative while delivering your opening statement is one of the most common mistakes. It may result in the opposing counsel objecting or the judge stepping in.

But when lawyers need to straddle a fine line between being persuasive and argumentative—and even legal textbooks acknowledge the difference as ambiguous—how do you evaluate your drafted opening statement to ensure it complies? Per Modern Trial Advocacy: Analysis and Practice (Law School 4th Edition) , two questions provide better guidance 3 :

• Does your opening statement involve an attempt to convince the judge or jury of a certain decision or why a certain decision is correct?
• Does your opening statement assert the importance, weight, or meaning of evidence?

If you’re concerned that the theme you’ve built your opening statement around borders on being argumentative, you can re-construct the statement to avoid objections. Lawyers are commonly allowed to state their themes to bookend their statement at the beginning and end, so long as everything between avoids making any arguments. 2

Opening Statement Strategy

Crafting a compelling opening statement requires various strategies and considerations, but let’s examine some of the most fundamental aspects.

Remember—It’s Not Exactly Your First Time Before this Jury

It’s important to distinguish that your opening statement won’t quite establish jurors’ first impressions of lawyers and their clients —only that of the case itself during the trial. So, the art of crafting an engaging and persuasive opening statement (and the context in which it is delivered) begins well before gavels open proceedings.

During “ voir dire ” (or pre-trial jury selection), the following will likely affect how jurors regard lawyers and their clients:

• Body language and nonverbal communication
• The questions asked of them
• The challenges raised and grounds on which to disqualify prospective jurors

Additionally, if the case receives significant media coverage and earns a high profile (even among local communities), there will be narratives disseminated outside the courtroom that may affect jurors no matter how stringent the selection process is. For example, impartial jury selection for the trial and sentencing of the man who bombed the Boston Marathon in 2013 still remains problematic, with a judge ordered to investigate potential juror bias in March 2024. 4

Thus, your opening statement may need to account for and counteract any biases acquired during voir dire, from pre-trial press, or other sources.

Who Delivers Their Statement First?

Like a chess match, the strategy that informs your opening statement will somewhat depend on whether you go first or second. The party bearing the burden of proof—either the plaintiff’s attorney or the prosecutor—always delivers first, followed by the defendant’s attorney.

Write a Good Hook—and Get to It Promptly

Returning to the comparison between opening statements and movie trailers, both need a good hook. For trailers, that might be a tagline or a memorable image. But for opening statements, the hook is the presentation of your theme. Vehicles like memorable quips that stick with jurors or an emotive and resonant depiction of duress can aid your theme’s delivery.

However, if it takes too long to get to a point or foster interest, you may come across as rambling, lacking confidence, or being ill-prepared. If this happens, you might lose the attention and interest of your audience—the jurors.

Construct a Narrative

While your hook should deliver the most impact, it also needs to complement the broader foundation your opening statement lays for your case. And that foundation should be built through storytelling.

Note, however, that storytelling doesn’t mean waxing lyrically before the court.

For example, consider a case determining liability and restitution following an automobile accident. During your opening statement, you can explain that your client’s quality of life has been impacted and that the witnesses you’ll call will testify how.

Perhaps you’ll explain to jurors how you intend to call to the stand your client’s:

• Supervisor , to demonstrate how a physical impairment or disability did not exist prior to the accident but now prevents their ability to work and maintain employment
• Friends and family , who will speak to the active lifestyle your client had prior to the accident, which they can no longer enjoy
• Independent medical experts , who will confirm doctors’ assessments and treatment plans to verify that the impact to your client’s quality of life specifically resulted from the accident rather than caregivers’ mistakes or a separate incident

In each of these witness examples, there are no value judgments or long-winded depictions of hardship. They aren’t needed to convey the story.

How you discuss witnesses or evidence in your opening statement will construct a narrative for the jurors on its own—they now come away with a better understanding of how your client was impacted and to what extent. 

This story will provide a structure to the various arguments of your case, facilitating the jurors’ ability to follow along as you present it.

Stick to the Facts—But Be Discerning with Their Presentation

Since lawyers can’t be argumentative during their opening statements, sticking to facts provides a surefire blueprint for what you should touch on. However, similar to constructing a hook and broader narrative, the presentation of facts matters, too.

For example, the proximity with which you state facts can associate or tie them to others. If you want to demonstrate a link between incidents, present them one after the other in your opening statement; to prevent association, separate them into different parts of your statement and ensure what you say between minimizes the link.

Returning to the example of an automobile accident trial, you could state, “The defense will attempt to link my client’s condition to a prior accident that occurred five years earlier, but we will present medical records and independent experts’ testimony that the injuries sustained from the recent crash cannot be tied to it.”

How to Craft an Opening Statement or Brief for Bench Trials

A “bench trial” refers to a case without a jury, in which the assigned judge solely determines the merits, facts, and decision of the trial. In these instances, a straightforward and concise brief may achieve better outcomes.

If writing a brief in place of an opening statement, be sure it clearly states the 5 :

• Causes of action
• Theories of liability
• Parties involved
• Relevant statutes and applicable case law (as needed)

Do You Have to Deliver an Opening Statement?

As parties can waive their right to an attorney and represent themselves, lawyers and their clients can forgo an opening statement. However, outside of instances like bench trials where briefs may suffice, it’s generally not a good idea to let opposing counsel deliver a speech without presenting your side as well.

Sources: 

• Cornell Law School Legal Information Institute. Opening statement .  https://www.law.cornell.edu/wex/opening_statement  
• University of Houston Law Center. Effective Opening Statements . https://www.law.uh.edu/center4clp/streetlaw/Mock%20Trial/Mock%20Trial%20AY%202017-18/Handouts%20&%20Activities/Opening%20Statements%20Handout%202.pdf
• Temple University Advocacy and Evidence Resources. Opening Statement v. Argument—Where is the Line? https://law.temple.edu/aer/2019/03/23/opening-statement-v-argument-where-is-the-line/  
• AP News. Appeals court orders judge to probe claims of juror bias in Boston Marathon bomber’s case . https://apnews.com/article/boston-marathon-bombing-tsarnaev-juror-bias-4cf3031d41cb0ab1bd1cad2bd3ad107a  
• Mark Mooney. Opening Statements: What Everybody Does Wrong. https://www.advocatemagazine.com/article/2019-july/opening-statements
• Center for Sales Strategy. Seven Seconds to Make a First Impression. https://blog.thecenterforsalesstrategy.com/seven-seconds-to-make-a-first-impression

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Business Intelligence

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