What is the difference between business intelligence and machine learning?
Business Intelligence refers to a set of software capabilities that allows businesses to access, analyze and develop actionable insights from data to guide business decisions. Typically, BI tools present information on user-friendly dashboards and visualizations that graph and chart key metrics to assist with data driven decision making. Machine learning is the science of developing algorithms and deep learning techniques to analyze big data and discover patterns hidden within the data. Machine Learning and Artificial Intelligence allows data scientists and business analysts to automate manual processes to extract data, better understand trends, to forecast, and generate new BI reports.
What are the similarities of Business Intelligence and Machine Learning?
BI is a form of descriptive and diagnostic analytics that analyzes what has happened. ML also assesses what has happened but uses this information to predict future behavior. BI works with structured data, whereas ML can also use unstructured information like emails and photos. Both types of data analysis share a similar purpose, that is to use data to guide informed decision making. ML allows BI systems to extract deeper insights from data patterns that are not readily apparent in datasets.
Key differences between Business Intelligence vs. Machine Learning?
Despite some similarities, BI and ML are two different forms of analysis.
Business Intelligence
Although able to work with near real-time data, BI represents a form of historical analytics that is best described as descriptive and diagnostic analytics. BI analysis typically explains what happened, how it happened, and why it happened. Created by business analysts, BI also includes visualizations, such as dashboards and charts.
Machine Learning and Artificial Intelligence
Machine learning is a subset of artificial intelligence. The key difference between ML and BI is that machine learning is the science of developing algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead. Computer systems use machine learning algorithms to process large amounts of data and identify data patterns. This allows them to predict outcomes more accurately from a given input dataset. For example, data science could be used train a medical application to diagnose cancer from x-ray images by storing millions of scanned images and the corresponding diagnoses.
Summary of differences between Business Intelligence and Machine Learning
|
|
Business Intelligence |
Machine Learning |
| Business objective |
To identify historical trends and establish what happened, how it happened and why it happened |
To create predictions of future outcomes |
| Skills required |
Highly skilled in statistical analysis, data extraction and data visualization using dashboards |
Advanced programming, coding, data science and data mining skills together with advanced statistics, or statistical analysis with no-code ML tools |
| Data sources |
Works with well-organized relational databases and data warehouses |
Works with large structured and unstructured data lakes |
| Complexity |
Less complex, but dependent on analysts' business skills and knowledge |
Relatively complex, requiring intensive resources and time |
| Mathematics |
Uses mathematical techniques |
Relies on algorithms |
When to use Business Intelligence vs. Machine Learning
Here are some examples to further understand the differences and when to use BI and ML. As they represent common problems, it's useful to compare how analysts use these techniques to uncover problems and optimize business processes.
Predict Customer Churn
Customer churn is the number of customers a business loses over a period of time compared to the total number of customers at the beginning of a period. This is a simple BI calculation that presents the results graphically showing historical monthly churn percentages. Machine learning churn calculations are different. Here algorithms can analyze specific factors in your customer database such as purchase history, demographic data and marketing campaigns to predict future churn.
Customer Sentiment Analysis
It's important to gauge customer sentiment, whether it is positive, neutral, or negative. With BI, you can use surveys and ratings to measure what customers think. At the same time, ML helps you go deeper by analyzing sentiment in datasets including emails, call center transcripts, and social media feeds.
How can AWS transform Business Intelligence with Machine Learning?
By augmenting BI with ML, you can bridge gaps between the past, present, and future. And with no-code ML tools like Amazon SageMaker Canvas, you can generate accurate ML predictions without requiring any ML experience or having to write a single line of code so you can drive better data driven business decision making.
In addition, you can visualize predictions generated from SageMaker Canvas with Amazon QuickSight, which provides unified business intelligence (BI) at hyperscale. With QuickSight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, and natural language queries.
To get started with SageMaker Canvas and QuickSight, see the workshop.
