Machine Learning Vs. Business Intelligence: Which One Does Your Business Need?

Machine Learning Vs. Business Intelligence: Which One Does Your Business Need?

What is Business Intelligence used for? And what is the difference with Machine Learning? Here we dive into their meanings, key differences, among other things. Take a look!

Machine Learning Vs. Business Intelligence

Often, people get confused between Business Intelligence (BI) and Machine Learning (ML), especially when they are just getting started in the world of data-based predictions. We believe that there is no simple way to draw out the difference between both.

In an arena of increasing automation, BI is no exception. At the forefront of this automation is the push for artificial intelligence and machine learning—two factors that have permeated the BI market and are currently revolutionizing the way businesses think about their data.

Nevertheless, like with any technological frontier, machine learning and business intelligence happen to be uncertain topics for businesses. You might find yourself asking:

  • What does machine learning look like from a business point of view?
  • Is it designed for the data scientists, the business users, or both?
  • Is it something worth investing in?

At its core, business intelligence helps users make sense of their business data. And machine learning makes this process more efficient. Additionally, it also improves the way BI is shared among various departments and optimizes data-driven decision-making across your organization.

Before getting into more facts about business intelligence and machine learning, let’s understand the objective of each area.

Business Intelligence: What Is It?

BI plays an integral role in the study of Data Analytics. Businesses often employ BI  to collect raw data, which helps them accomplish specific tasks with respect to business strategies. The process typically revolves around collecting, analyzing, interpreting, and acting on data.

The stored data is then manipulated and transformed and classified into structured databases called data warehouses by data engineers leveraging ETL (Extract, Transform, and Load) tools.

Business analysts at the next stage leverage data visualization techniques to explore the data present in structured databases. This type of tool helps them create graphic panels that make information accessible to non-data specialists. The panels also help users analyze and understand past performance. Hence, it is also used to adapt future strategies and to improve KPIs (Key Business Indicators).

In simple words, traditional BI gives you a descriptive vision of the company’s activity, something that’s very visual and based on data. Here aggregated data is used to describe future trends.

Machine Learning: What Is It?

Machine learning is the practice of turning machines clever. This translates to better choices, predicting results, and prescribing things based on your likes. Or in other words, machine learning can be thought of as a machine or system that predicts an output based on the input.

How Does Machine Learning Differ From Business Intelligence?

The mechanism behind ML detects patterns in the stored data. This is a vital difference to which the following three aspects can be added:

  1. Rather than using aggregated data, Machine Learning leverages individual data with specific characteristics for every instance. This way, patterns can be detected using thousands of variables.
  2. Machine Learning isn’t based on descriptive analytics. Instead, it is heavily focused on predictive analytics. Meaning it makes an assessment of what has happened, simplifies general trends, and makes predictions that define future behavior.
  3. In ML, predictive applications replace visualization panels or dashboards. Predictive analysis is one of the greatest potentials of Machine Learning, where an application learns from data and its models. Such models are frequently retrained to learn automatically from new data.

The Main Differences

Business IntelligenceMachine Learning
Functions such as systematic to handle commerce through the required path.Helps the machine learn to memorize from existing data.
Identify commerce opportunities.Data-based learning and choice-making frameworks are made.
Alterations over raw information to valuable information.Put data mining strategies into place to create models for the figure.
It is not dependent on an algorithm but on skills.Heavily relies on algorithms.
Google Analytics leverage Business Intelligence.Amazon recommendations leverage Machine Learning.
BI is a great concept for organizations to utilize data shrewdly.ML capabilities are useful in making frameworks and getting them without unequivocal programming.

An Example: Machine Learning Vs. Business Intelligence

Now, let’s imagine a scenario where a business makes an analysis of the customer behavior in a store. One goal here is to know in advance the greatest detail and the number of customers who will turn up the next month because this is an important KPI for the business.

Machine Learning Vs. Business Intelligence

A BI-based approach will work with previous months or years alongside other variables like the market trends or the number of customers at present compared to other years. Leveraging this data, visual trend sheets can be created to inform the anticipated percentage of customers that are going to churn in the upcoming months.

Based on the insights gained, the business management team can make valuable business decisions, like targeted marketing campaigns for a particular sector of the population.

On the other hand, the ML-based approach uses an entire database of customers, profiles, purchases, and casualties to look for behavior patterns and choose the ones that give signs they are going to churn next month.

  • The data used here include details like historical purchases of customers, their personal information like age, sex, seniority, etc., the data of products like categorizations, prices, SKU, marketing campaigns, and data of promotions, alongside a final field for every customer indicating if they churned.
  • ML makes client-by-client predictions. For instance, a BI system tells you the percentage of customers that are going to churn, while an ML tells you this information about each client. This allows businesses to take customized actions to prevent customer churn.
  • ML helps create real-time applications that can be easily integrated into a booking system to offer information about the likelihood of a customer leaving. Additionally, an automatic system can be built to send emails with personalized offers to the customers at risk.

Making The Right Choice

Business Intelligence is an approach that defines what happened in the past. It enables users to understand data through powerful visualizations and helps make smarter business decisions based on global trends.

BI is an umbrella term for various tools with different functions, audiences, and designs. However, the collective goal of every platform is to offer users information about their data. This can be in the form of visual representations of data models or a text-based data summary.

However, BI tools often fail to succeed in this endeavor because they don’t align well with the way most businesses are structured. Generally, BI tools are made for data analysts and scientists. This choice makes sense as data analysts and scientists are best equipped to understand data and draw valuable insights from the information, thereby putting it to the right use. It also allows them to come up with targeted follow-up questions to get a complete understanding of a data landscape.

Yet, marketers, category managers, and people falling under the business umbrella are involved in the decision-making process. BI tools tend to be complex and cumbersome for these employees to understand.

Machine Learning Vs. Business Intelligence

BI tools typically facilitate a cycle of dependency. All those who are involved in running a business will have to rely on data scientists to use BI tools efficiently. Furthermore, data scientists will end up spending a majority of their time building routine reports, and answering marketing questions, instead of employing their advanced skillsets and degrees for the right purposes.

This is a process that can easily result in a backlog of questions, which can be tiresome for data scientists. Also, people in other roles might be reluctant to reach out to data scientists for every detail, thereby deciding data-driven decision-making isn’t worth all the frustration of being dependent on a third party for every simple doubt.

To put it short, BI tools have the potential to pave the way for incredible gain. However, they can prove to be highly inefficient. And this is where machine learning comes in.

Machine Learning works to close the gap in BI tools by performing important analyses and acclimatizing to different data sets. The type of information that business intelligence should offer include:

  • How your brand is performing
  • The reason why your business is growing or declining
  • Opportunities your business can tap into to get an edge over your competitors and gain market share

These broad questions will help you understand your business’s core performance. Now, machine learning also has the ability to perform the same research as BI tools but generate faster and more accurate results.

Automation is key here. While ML can’t necessarily replace data scientists and analysts, it can free up their time to help them focus on tasks that are more valuable for your business. When data analysts aren’t burdened with routine reports, they can use their time to take their research skills to the next level.

Additionally, machine learning also makes BI tools embrace business-friendly interfaces. After all, when algorithms do all the heavy data lifting, the user won’t require the same technical expertise to find what needs to be found.

What’s more, the world is currently witnessing the implementation of machine learning in BI tools. One such example is augmented analytics, where a mixture of machine learning and natural language generation helps users ask questions about their data and receive insights in plain language.

So, in conclusion, machine learning is an essential piece for truly self-service BI tools. It is important to emphasize that is not a matter of choosing between BI and ML, it is not a BI vs ML. On the contrary, Machine Learning empowers and complements Business Intelligence by augmenting its capabilities and achieving its goals. BI tools with machine learning implementations allow for deeper insight and empower business people to take data analysis to the next level.

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