Difference Between Business Intelligence And Machine Learning
BI (Business Intelligence) has become an important area of study in Data Analytics. And to accomplish that task of finding success with respect to business strategies; taking time to collect, analyze, interpret, and act on data should be the only goal. Business Intelligence actually differs from traditional and Modern approaches Modern BI makes business users create their own content without depending someone on IT whereas traditional BI leans heavily on IT professionals.
Machine learning, a definition is as simple that it’s a machine or a system that gives perfect output based on the input. In recent years, this has become a common buzzword. Before machine learning, computers had to be programmed (directions were to be given). After the invention of machine learning, computers can think for themselves. Organizations noticed new discoveries and solving issues by using this machine learning technique.
The famous writer quoted machine learning as
“Software with machine learning doesn’t do the same thing the day you install it as it does the tenth or hundredth day you run it.”
Head to Head Comparison between Business Intelligence and Machine Learning (Infographics)
Below are the Top 5 comparisons between Business intelligence vs Machine Learning
Key Differences Between Business Intelligence and Machine Learning
Machine Learning (ML)
The work routine of ML is quite simple
- We feed data and train the system with the help of algorithms, and models
- Once the system gets familiar with the data, it generates the target predicted outcome with respect to the known set of data
Now we shall try to have an understanding as to how ML is categorized and respective functionalities of its learning:
| Characteristics | SUPERVISED LEARNING | UNSUPERVISED LEARNING | REINFORCEMENT LEARNING |
| Data | Labeled data | Unlabeled data | Iterative |
| Prediction | Based on prior knowledge | Without prior knowledge of data | Based on interactions from previous experiences |
| Significance | Predictive model | Descriptive model | Performance based on experience |
- Supervised LEARNING: Predicts output for new data, based on previous knowledge of data sets. Here the scientist feeds data and expected the outcome to the machine.
- Unsupervised LEARNING: This case generally occurs when one does not know what to expect out of the data. With input data, it tries to detect patterns, cluster the algorithms and summarize the data points for the scientist to derive the outcome through meaningful insights.
- Reinforcement LEARNING: Here the machine focuses on interactions within the environment and predicts the outcome though incorporating the interactions.
ML identifies human patterns which are tough to trace in huge masses of data. For any organization, ML brings opportunity to the following aspects:
- User get value results faster for their BI projects
- Making products more suggestive
- To lower the implementation complexities
Business Intelligence (BI)
This term generally refers to the technologies, applications, and practices to provide strategic decisions to the business.
The functionality of the BI is quite simple too. It needs data to work on.
However, the data present over here is not simple. We are talking about Big-Data. This Big-Data needs to be visualized in order to provide efficient business opportunities.
Below is a simple representation as to how Business Intelligence (BI) operates:
BI is often used for 2 purposes:
- Purpose 1. Run the business
- Purpose 2. Change the business
Here we will try to understand how BI is applied to both the purposes and their characteristics constituting towards the same:
| Characteristics | Purpose 1 | Purpose 2 |
| Data | Structured data sources | Mixture of structured & unstructured data sources |
| Support | Better data quality is required | Can function with less qualified data |
| Focus | Directed towards data standards & management | Directed towards data mining & opportunity seeking |
| Speed | Less important | Relies on speed & agility |
Business Intelligence vs Machine Learning Comparison Table
Comparing machine learning with business intelligence is a bit tough task because machine learning is set to unlock the power of business intelligence.
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Business Intelligence (BI) focuses on analyzing the data on its own (ML doesn’t have this skill). With this unique skill set, it predicts the outcome of a business strategy which is more reliable for the syndicate to be influenced by rather than their guts and feelings.
BI is a wonderful concept for organizations to make use of information in a smart way. Here, the results of strategies are based on the data and not on one individual’s instincts
On the other hand, Machine Learning (ML) functions as per the terminology. Its functionality is more like making the systems understand without any explicit programming.
In simple dialectal, the machine focuses to learn by itself via accessing the data present to them and transforming that data to information
The below table helps you understand what significance Business Intelligence and Machine Learning pose to each other:
| Features | Business Intelligence | Machine Learning |
| Body of work | Functions like methodical to process business in the desired path | Enables the machine to learn from existing data |
| Crux of technology | Identifies business opportunities | Data based learning and decision making systems are developed |
| Operation of data | Converts raw data to useful information | Deploys data mining techniques to develop models for forecast |
| Usage of algorithm | Non-dependent on an algorithm and relies on skill | Relies hugely on algorithms |
| Use cases | Google Analytics | Amazon recommendations |
Conclusion
I believe, the above produced information does make one understand the significance of both Business Intelligence and Machine Learning.
The significance of the Business Intelligence and Machine Learning offer is directly proportional to the dependency of data (structured/unstructured). This is the only uphill task that needs to be sorted out (not easy) as it relies on the availability of efficient data and quality algorithms.
Hence it is the job of the organization to make use of structured & unstructured data and strive towards designing fresh algorithms that are more effective and capable to work on for these tools to offer the desired outcome.
Not to forget, these data lakes not only assist the organizations but also offer a great deal of value to the end-user.
Rome was not built in a day, and so is the evolution of effective data handling; it shall take time.
However, it is vital for the people who head businesses to concentrate more on this field as addressing these challenges is the only way to go forward.
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