How “Smart” Businesses Use Business Intelligence to Harness the Power of Data

Companies increasingly rely on business intelligence (BI) to gain a competitive edge and drive better outcomes in today’s data-driven business landscape. BI empowers businesses to transform raw data into meaningful insights, enabling informed decision-making, optimizing processes, and identifying growth opportunities.

According to Forbes, in 2020, one report found that over half (54%) of enterprises claimed that cloud BI “is either critical or very important” to current and future initiatives. Additionally, that same year, one survey found that COVID-19 prompted nearly half (49%) of businesses—especially smaller businesses with 51 to 200 employees—to rely more heavily on data analytics than ever before. While data analytics is just one part of BI, companies were encouraged to use data wisely to respond to the crisis brought on by the pandemic.

Overall, the global BI market is on track to reach $43.03 billion in revenues by 2028 (up from 22.26 billion in 2020 and 24.05 billion in 2021). This surge, having a CAGR of 8.7%, is driven by rapid digitization, increased online business, and strong demand for data personalization.

Here’s what every “smart” business should know about using business intelligence effectively to harness the power of data.

What Is Business Intelligence & Why Is It Important?

Business intelligence (BI) refers to collecting, analyzing, and interpreting data to gain insights into a company’s operations and performance. BI aims to help businesses make data-driven decisions that lead to improved efficiency, profitability, and growth.

BI uses various technologies and tools to extract insights from large and complex data sets. These technologies include data mining, data warehousing, dashboards, and reporting tools. BI can provide valuable insights into a company’s sales, marketing, finance, and operations performance. Businesses can identify patterns and trends that inform their decision-making by analyzing this data.

BI is important because it allows businesses to make informed decisions based on data rather than intuition or guesswork. By having access to accurate and timely data, companies can identify opportunities for growth and improvement and make decisions that lead to increased profitability and competitiveness. BI can also help businesses identify potential problems before they become significant, enabling them to take proactive measures to mitigate risks.

Overall, BI is a critical component of modern business strategy. Businesses can gain a competitive edge and achieve long-term success by using available data effectively.

Who Benefits Most from BI?

The versatility of BI makes it applicable to virtually any sector that deals with data and seeks to optimize operations, improve decision-making, and enhance performance. Examples of various industries that benefit from BI and how they use BI to gain insights and drive better outcomes include:

Retail

• Analyze sales data, customer behavior, and inventory levels to optimize pricing strategies
• Improve demand forecasting
• Enhance inventory management
• Identify trends, segment customers, and personalize marketing campaigns to increase sales and improve customer satisfaction

Healthcare

• Analyze patient data, clinical outcomes, and operational metrics
• Identify patterns and trends
• Improve resource allocation
• Optimize workflows
• Enhance patient care
• Aid in monitoring population health, identifying disease outbreaks, and predicting patient readmissions

Finance

• Analyze financial data
• Detect fraud
• Manage risk
• Enhance regulatory compliance
• Enable banks and insurance companies to gain insights into customer behavior, improve credit scoring, and optimize investment portfolios
• Assist in real-time monitoring of transactions, detecting anomalies, and identifying potential fraudulent activities

Manufacturing

• Monitor production processes
• Track quality metrics
• Optimize supply chain operations
• Analyze equipment performance, identifying bottlenecks and streamlining production workflows
• Reduce costs
• Improve product quality
• Enhance overall operational efficiencies

E-Commerce

• Analyze website traffic, customer demographics, and purchasing patterns
• Personalize product recommendations
• Optimize pricing strategies
• Enhance overall customer experience
• Monitor inventory levels
• Identify popular products
• Optimize logistics operations

Telecommunications

• Analyze customer usage patterns
• Monitor network performance
• Improve customer service
• Identify network congestion
• Predict equipment failures
• Optimize network capacity
• Identify customer churn risks
• Implement targeted retention strategies

Hospitality & Tourism

• Hotels, resorts, and travel companies analyze booking data, customer preferences, and revenue metrics
• Optimize pricing strategies
• Forecast demand
• Improve customer satisfaction
• Identify popular destinations
• Tailor marketing campaigns
• Personalize guest experiences

Energy & Utilities

• Monitor energy consumption
• Optimize distribution networks
• Manage renewable energy resources
• Analyze production data
• Identify energy inefficiencies
• Optimize resource allocation
• Aid in predicting demand, managing grid reliability, and optimizing energy trading

Transportation & Logistics

• Optimize route planning
• Monitor fleet performance
• Improve supply chain visibility
• Analyze transportation costs
• Optimize warehouse operations
• Improve delivery efficiency
• Assist in monitoring key performance indicators (KPIs)
• Enhance overall logistics management

Education

• Analyze student data
• Monitor academic performance
• Improve educational outcomes
• Identify learning gaps to personalize student interventions and track student progress
• Analyze enrollment trends to optimize course offerings and enhance institutional effectiveness

BI vs. Data Analytics: A Side-By-Side Comparison

BI and data analytics are closely related disciplines, both involving extracting insights from data. However, they differ in terms of their focus, purpose, analysis approaches, and user profiles. BI emphasizes historical and current data analysis for decision support. Contrarily, data analytics encompasses a broader range of analytical techniques and is often used for predictive and prescriptive purposes.

In addition, BI tends to look at the big picture by answering more high-level questions regarding business strategies. Data analytics tends to answer single questions or concentrate on more narrow problems.

In short, data analytics is a necessary part of BI, but BI is not needed for data analytics.

Here’s a side-by-side comparison of BI and data analytics:

Ten Ways to Best Use Business Intelligence to Drive Better Outcomes

Harnessing the full power of BI requires a strategic and holistic approach. From defining clear goals to selecting the right tools, fostering a data-driven culture, and measuring results, these approaches can help businesses leverage BI to its fullest potential, unlocking valuable insights and driving success.

Getting started can be challenging, but after implementing these strategies, businesses can pave the way for smarter decision-making, enhanced operational efficiency, and sustained growth in a rapidly evolving marketplace.

Consider the following steps to use BI effectively and drive better outcomes:

1. Define Clear Goals and Objectives
2. Identify Key Metrics and Data Sources
3. Implement a Robust Data Infrastructure
4. Select the Right BI Tools and Technologies
5. Perform Data Analysis and Visualization
6. Foster Data-Driven Decision-Making Culture
7. Monitor and Iterate
8. Communicate and Share Insights
9. Take Action and Measure Results
10. Continuously Improve and Evolve

Latest BI Trends

The following business intelligence trends have been gaining traction for some time now and continue to shape the BI landscape:

Augmented Analytics

This technology combines machine learning and natural language processing (NLP) to automate data preparation, insights generation, and sharing. It enables business users to interact with data and gain valuable insights without requiring advanced technical skills.

Embedded Analytics

Embedded analytics integrates BI capabilities directly into business applications like CRM systems or project management tools. This trend allows users to access data and insights within their familiar workflows, improving decision-making and increasing efficiency.

Self-Service BI

Self-service BI empowers non-technical users to access and analyze data independently, reducing the reliance on IT departments. Self-service tools provide intuitive interfaces, drag-and-drop functionality, and pre-built templates to facilitate data exploration and visualization.

Cloud-Based BI

Cloud computing has revolutionized the BI landscape by providing scalable and cost-effective solutions. Cloud-based BI platforms offer easy data integration, accessibility from anywhere, and seamless team collaboration.

Advanced Data Visualization

Data visualization techniques have evolved to offer more interactive and intuitive visual representations. Advanced data visualization tools allow users to explore complex data sets, uncover patterns, and present insights in a compelling and easy-to-understand manner.

Real-Time Analytics

Traditional BI focused on historical data analysis, but real-time analytics has gained prominence. Real-time data processing and analysis enable organizations to make informed decisions based on up-to-date information and respond quickly to changing market conditions.

Predictive and Prescriptive Analytics

Predictive analytics uses historical data and statistical modeling techniques to forecast future outcomes, while prescriptive analytics provides recommendations on actions to take. These analytics approaches enable organizations to identify opportunities and optimize their decision-making proactively.

Data Governance and Privacy

Organizations are investing in data governance practices with an increasing emphasis on data privacy and regulatory compliance. Data quality, security, and compliance with regulations like General Data Protection Regulation (GDPR) have become critical in BI implementations.

Natural Language Processing (NLP)

NLP technologies enable users to interact with BI systems using natural language queries. By conversing with the system, users can ask questions, generate reports, and receive insights more conversationally and intuitively.

Mobile BI

Mobile devices have become ubiquitous, and mobile BI allows users to access and analyze data from anywhere. Mobile BI applications provide responsive dashboards, reports, and alerts, enabling users to stay connected and make data-driven decisions anytime, anywhere. By 2024, the mobile BI market is expected to surpass $20 billion, with over 72% of internet traffic generated from smartphones and tablets.

How Can Oxford Help?

Implementing and maximizing the benefits of BI-related tools can be a complex and daunting task for businesses that lack the necessary expertise and resources. Partnering with Oxford can provide the wealth of knowledge, experience, industry best practices, and collaboration needed to unlock the full potential of data, drive better outcomes, and position your business for long-term success.

We pride ourselves on providing The Right Talent. Right Now.  We pre-vet all our candidates before our clients even reach out so that time isn’t wasted once a request comes in. We strive to be timely and accurate so business can go on as usual with minimal disruption.

We promise to be with our clients every step of the way to ensure that every need is met. We truly believe that our clients’ success is Oxford’s success—and we are committed to leading businesses into a brighter future.