Business Intelligence (BI) and Analytics Layer of a Modern Data Platform

The modern data platform or also refereed as modern data stack is composed of five critical foundation layers.

1.?????? Data Storage and Processing

2.?????? Data Ingestion

3.?????? Data Transformation and Modeling

4.?????? Business Intelligence (BI) and Analytics

5.?????? Data Observability

Data Transformation & Modeling layer is the fourth layer of a modern data platform. In the modern data platform, there is a need to get meaningful insights from the collected data.

?Business Intelligence (BI) and Analytics

Definition:

?Business intelligence (BI) & analytics refers to the collective infrastructure, tools, applications, and other resources that generate data and insights, which in turn inform how businesses make decisions, uncover revenue opportunities, and evaluate performance.

?1. What is Business Intelligence (BI)?

BI combines technology, processes, and strategies to transform raw data into meaningful insights,1 helping individuals and organizations make informed decisions and drive business success.

BI is not to be confused with business analytics. Business intelligence takes a descriptive approach to give clear insight into how a business is performing. Business analytics, on the other hand, is a predictive effort to describe what an organization might do to achieve greater outcomes.

BI is a useful compass for organizations, as it can guide people through enormous amounts of data and help them achieve their business goals. It empowers business leaders, executives, and analysts to gather, analyze, and interpret data from sources across the enterprise for a comprehensive view of operations, customers, market trends, and more.

?a) The Importance of BI: Improving Return on Investment

?Business intelligence is a powerful tool with which organizations can improve their return on investment (ROI). Applying appropriate BI concepts increases ROI in three essential ways:1

?b) Revealing Operational Inefficiencies and Opportunities for Enhancement

BI gives a comprehensive view of an organization’s business operations, allowing leaders to flag inefficiencies and possible areas for improvement. After thorough data analysis and review of performance metrics, professionals within each company can go on to streamline processes, optimize resource allocation, reduce operational costs, and more. When used effectively, business intelligence can help people eliminate workflow bottlenecks, enhance productivity, and maximize efficiency.

?c) Uncovering Valuable Customer Insights

The customer is king! The importance of understanding customer preferences and behavior in today's competitive market can't be overstated. BI tools analyze information such as customer data, purchase history, and interactions to reveal valuable insights.

A customer's purchasing history, for example, holds a wealth of knowledge. By studying past transactions, business analysts can gain a better understanding of what products or services resonate with their customers the most. This knowledge enables businesses to optimize their product offerings, fine-tune marketing strategies, and even develop personalized recommendations for each customer, leading to increased customer satisfaction and loyalty.

Similarly, information gathered from customer interactions with a company is crucial. It allows companies to assess the quality of customer service, identify pain points, and uncover opportunities for improvement. Understanding how customers engage with a company's products or services helps businesses to better meet customer needs, ensuring a positive and seamless customer experience.

?d) Identifying Needs for New Products or Services

BI insights can spark new ideas. When analyzing market trends and consumer demand, business analysts and their colleagues often identify gaps in the market and opportunities for new products or services. Using BI, they can confidently develop offerings that align with customer needs, ensuring a greater chance of success. The ability to innovate and offer products or services that cater to specific market demands is a valuable means toward boosting revenue and contributing significantly to ROI.

?2. Relationship Between Artificial Intelligence and Business Intelligence

Artificial intelligence (AI) enhances BI best practices on multiple fronts. AI is a broad category encompassing technologies that mimic human intelligence. It includes machine learning–a common AI application used in BI2–natural language processing, predictive analytics, and more.

Through machine learning capabilities, AI continuously refines data analysis to make BI outputs more accurate and efficient over time. Natural language processing offers user-friendly interactions with BI platforms, making data insights accessible to a wider audience within an enterprise. Predictive analytics enable BI systems to forecast trends, risks, and opportunities to improve strategic decision-making.

The marriage of AI and BI enables business leaders to make informed decisions, optimize operations, and stay ahead in an increasingly competitive landscape.

?3. Benefits of Business Intelligence

The benefits of business intelligence are far-reaching; BI revolutionizes how organizations operate in the modern business landscape.4

Savvy use of many business intelligence tools empowers business leaders to make faster, more accurate decisions, ensuring improved productivity and streamlined operations. Business intelligence enables them to enhance customer service through a deeper understanding of customer behavior and preferences, leading to higher satisfaction and loyalty. With fast and accurate reporting, businesses gain a competitive advantage by staying agile and responsive to market changes. Moreover, BI eliminates guesswork, providing actionable intelligence that uncovers new opportunities and market trends.

Overall, the adoption of BI sets the stage for growth, innovation, and success in an increasingly data-centric world.

?4. What do business intelligence analysts do?

Business intelligence analysts play a pivotal role in the data analysis process, driving everyday efforts that uncover valuable insights for organizations.3 By collaborating closely with executives and business users, analysts identify the specific data needed to address crucial questions and challenges.

They meticulously gather and consolidate data from various sources and use their expertise to create impactful data visualizations, comprehensive reports, and interactive dashboards. This enables other members of the organization to derive meaning and actionable insights from data sets. Business intelligence analysts empower their organizations to make informed decisions, discover opportunities, and navigate the complexities of today's data-driven landscape.

?5. Data Analysis Techniques

Data analysis can take a variety of forms. Practices such as the following enable organizations to identify patterns, take advantage of opportunities, and solve challenges.

a) Descriptive Analysis

Descriptive analysis involves summarizing and presenting data in a meaningful way to provide a clear picture of past events or trends, enabling easy understanding of key patterns and insights.

b) Exploratory Analysis

This technique uncovers patterns or relationships in data through visualizations and initial investigation. The data is often used to generate hypotheses to be used in further analysis.

c) Diagnostic Analysis

Diagnostic analysis focuses on understanding the root causes behind specific outcomes or events by analyzing historical data. This helps organizations pinpoint reasons for successes or failures.

d) Predictive Analysis

By analyzing historical data and applying statistical algorithms, predictive analysis forecasts future trends and outcomes, assisting in proactive decision-making.

e) Prescriptive Analysis

This technique goes beyond predictive analysis, recommending specific actions or strategies to optimize future outcomes and drive business success.

f) Quantitative and Qualitative Analysis

Quantitative analysis involves using numerical data to identify patterns and uncover trends, while qualitative analysis involves interpreting non-numeric data, such as text or images, for deeper insights.

g) Statistical Analysis

Statistical analysis employs various statistical approaches to analyze data and draw meaningful conclusions. This technique helps organizations make data-driven decisions based on empirical evidence.

?6. Data Analytics Solutions

Below, we outline some popular BI solutions among top data teams:

·?????? Looker – A BI platform that is optimized for big data and allows members of your team to easily collaborate on building reports and dashboards.

·?????? Sigma Computing – A BI platform that delivers cloud-scale analytics with the simplicity of a spreadsheet and familiar data visualizations.

·?????? Tableau – Often referred to as a leader in the BI industry, it has an easy-to-use interface.

·?????? Mode – A collaborative data science platform that incorporates SQL, R, Python, and visual analytics in one single UI.

·?????? Power BI – A Microsoft-based tool that easily integrates with Excel and provides self-service analytics for everyone on your team.