Turning data into actionable insights

Business Intelligence, also called business analytics, provides tools and techniques to turn large, disparate datasets into useful information. The term business intelligence encompasses several key techniques:

• Data mining: Uncovering hidden patterns, relationships, and trends within large datasets to gain actionable knowledge.

• Data analysis: Examining, cleaning, transforming, and modeling data to derive meaningful insights and inform decision-making.

• Performance benchmarking: Comparing an organization's key performance indicators against industry standards or competitors to identify strengths and weaknesses.

• Descriptive analytics: Summarizing past events and performance using data aggregation and mining to understand historical context.

Leveraging BI in business operations

These tools use various data points from the organization to uncover insights and assist in making evidence-based decisions. When executed properly, it helps decision makers run a data-driven enterprise. This way it ensures everyone uses the same data and insights.

By leveraging BI, businesses can:

• Identify trends: Spot emerging patterns and anticipate market shifts.

• Optimize operations: Streamline processes, reduce costs, and improve efficiency.

• Enhance customer experiences: Understand customer behavior and preferences to deliver personalized experiences.

• Mitigate risks: Identify potential problems and take proactive measures to address them.

• Drive innovation: Uncover new opportunities and drive strategic initiatives.

Business intelligence tools can help significantly improve performance. However, it is crucial that decision have access to the same insights across the organization.

Traditional Business Intelligence and its challenges

Data discrepancies and data silos are some of the most common challenges that impede successful data-driven decision making. They make it hard to see the overall perspective and forces teams to analyze data in different ways. These obstacles often result in a time-consuming and fragmented BI process.

To overcome these challenges, modern BI solutions have emerged, offering a more agile and user-centric approach.

How Business Intelligence works: A tale of two approaches

In the past, collecting data for business intelligence took a long time. This involved going through data from various departments and depending on manual work.

Modern BI tools have simplified the process. Robust data visualizations and cloud-based tools that allow multiple decision makers to access the same information simultaneously. Leveraging machine learning to big data has enabled decision-making based on even more powerful predictive analytics.

Let's review two approaches to making business decisions based on data analytics. We'll look at the traditional linear journey and an agile process utilizing the latest advances in generative AI.

The traditional BI process: A linear journey

In the past, business intelligence followed a rigid, linear path that often hindered agility and responsiveness. Here's a breakdown of the typical workflow:

• Set requirements: Analyzing the business problem to determine the BI solution.

• Curate data: Collecting, cleaning, and transforming raw data for analyzing.

• Build and test: Developing and testing the BI application or reporting tool by IT teams.

• Run reports or analyze: Using the BI tool to generate reports and conduct initial analysis.

• Dive deep with SMEs or data scientists: Bringing in specialists for complex analyses, often requiring more time and resources.

• Acquire more data: Collecting additional data to gain deeper insights, restarting the data wrangling cycle.

This straightforward approach sometimes caused delays and obstacles, especially when trying to understand complex data problems. Each stage relied on the completion of the one before it, and any obstacles could greatly delay the whole process.

Additionally, manual data collection could lead to errors.

If you need more information, you may need to consult experts or data scientists. This could cause delays and require additional resources.

To gain further insights, we may need to collect more data, which will lead back to the data collecting phase. This created a recurring issue that hindered flexibility and quick responses. Errors could also arise from inconsistent data or manual processes.

The modern BI process: Agile and GenAI-powered

The rigid, linear BI process has been reimagined with a focus on agility, collaboration, and self-service capabilities. User-friendly tools with advanced analytics and AI make data accessible to everyone, regardless of their skill level. Using a GenAI Analytics Lifecycle enables a more dynamic and user-centric approach.

Conversational BI 

Natural language queries allow you to receive instant answers that fit your needs.

Automated data preparation

AI-powered tools handle data cleaning, transformation, and enrichment.

Accelerated development 

Low-code/no-code tools and AI-driven recommendations expedite the creation of applications and dashboards.

Natural language explanations

AI generates clear interpretations of insights, accessible to all users.

Interactive exploration

Data exploration through follow-up questions and related insights.

AI-powered expertise 

Access to intelligent recommendations and domain knowledge.

Continuous learning and scalability

The AI engine constantly learns and adapts, ensuring insights remain accurate as data volumes grow.

This simplified BI process enables faster decision-making and frees analysts to focus on higher-value tasks.

BI solutions use cases

Financial services – monetizing data

The financial services sector has an abundance of data and a complex regulatory landscape. Business analytics must therefore provide financial institutions with the insights they need to make informed decisions and drive growth. BI tools also need to ensure compliance across the organization.

Some of the common use cases for financial and insurance services companies include:

• Commercial and business banking: Improving customer service through targeted insights.

• Customer relationship management: Using analytics to enrich customer experience and engagement, build loyalty, and drive revenue growth.

• Data commercialization: Strengthening partner relationships by offering useful insights through secure portals and based on anonymized data.

• Asset management: offering advisors strong analytics and real-time information about markets and portfolios.

• Risk management: Leveraging scalable and sophisticated analytics to effectively manage the demanding and evolving requirements associated with regulatory reporting.

Transforming retail with data-driven insights 

The retail landscape is undergoing rapid change, with evolving consumer preferences, omnichannel shopping experiences, and the increasing importance of personalization. Modern AI-powered BI solutions offer a wide array of capabilities specifically designed for retail:

• Inventory optimization: Analyze sales data, supplier performance, and demand forecasts to optimize inventory levels, reduce stockouts, and minimize carrying costs.

• Customer analytics: Understand customer behavior, preferences, and purchasing patterns to deliver personalized shopping experiences, targeted promotions, and effective loyalty programs.

• Pricing and promotions: Optimize pricing strategies based on real-time market data, competitor analysis, and customer demand, maximizing profitability and sales.

• Supply chain management: Ensure a seamless flow of goods from procurement to distribution.

• Omnichannel analytics: Integrate data from various channels (online, in-store, mobile) to gain a holistic view of customer interactions and optimize the omnichannel experience.

Empowering healthcare with tailored solutions

AI-powered business analytics tools offer tailored solutions for both healthcare providers and payers.

Providers can:

• Improve hospital performance and operations: Uncover potential gaps and shortcomings in operational efficiency, leading to improved quality of care and patient satisfaction.

• Enhance patient care: Leverage integrated and secure patient data to create more personalized care plans and reduce wait times.

• Optimize resource allocation: Analyze patient flow, staff workload, and resource availability to make informed decisions and improve overall efficiency.

Payers are able to:

• Empower healthcare professionals by providing timely and accurate information to help them make informed decisions and enhance patient outcomes.

• Improve supply chain management by giving buyers better information on costs and vendor performance. This will help streamline procurement and cut costs.

• Support patient-centered care by giving Medical Home teams access to centralized patient data. This will help coordinate care, improve quality, and achieve better outcomes.

MicroStrategy ONE: Empowering data-driven decisions

MicroStrategy ONE helps with specific challenges and opportunities in different industries, as shown by real-life examples.

The key features include: 

AI built-in at every stage

MicroStrategy ONE leverages large language models (LLMs) and generative AI to boost employee productivity and organizational efficiency through four new capabilities:

HyperIntelligence

Integrates data analytics into workflows effortlessly. HyperIntelligence follows users throughout their day to provide nuggets of intelligence wherever they work.

Embedded analytics

Embedded analytics infuses AI-powered analytics into any software, app, or portal. It allows users to instantly access data-driven insights from their everyday tools.

Cloud-powered analytics

Most BI tools are cloud-based, but not all are completely cloud-agnostic. MicroStrategy ONE allows companies to deploy the most cutting-edge AI tools using any major cloud provider.

These features help organizations use data effectively, promoting a culture of data-driven decision-making within the organization. MicroStrategy ONE aims to make data accessible and enjoyable for all users, encouraging informed decision-making through widespread use.

Ready to transform your business with data-driven insights? Try MicroStrategy ONE now to see how you can use your data to drive innovation, growth, and success.