#4 DATA SCIENCE: Bussiness Intelligence, Machine Learning and Artificial Intelligence.

Hello Steemians, welcome to another post on this journey of understanding some important words and skills in the Data Science world. Today I will be taking you all through the understanding of Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI).

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We will start with Business Intelligence and how it fits into the picture we have been talking about.
Business Intelligence is the process of analyzing and reporting historical business data. After reports and dashboards have been prepared, they can be used to make informed, strategic, and tactical business decisions by end users, such as the general manager. Concisely put, business intelligence aims to explain past events using business data. Business intelligence fits comfortably within data science because it is the preliminary step of predictive analytics. It makes sense when you think about it. First, you must analyze past data and extract useful insights. Using these inferences will allow you to create appropriate models that could predict the future of your business accurately.

Let's delve into the controversial, yet rapidly expanding field of Artificial Intelligence, AI, and its subfield, Machine Learning, ML.
The ability of machines to predict outcomes without being explicitly programmed to do so is regarded as Machine Learning. Expanding on this, ML is about creating and implementing algorithms that let machines receive data and use this to:

• Make predictions,
• Analyze patterns,
• Give recommendations on their own.

Machine Learning can not be implemented without data. hence it should stay within data analytics completely. This could be considered as a bold statement to make as it is debatable whether this is correct. Some argue that data analytics and ML are two unrelated scientific fields. For the sake of argument, we will let the machine learning and analytics overlap and we will exemplify some areas that could be considered parts of both disciplines. Moreover, ML should expand slightly into Business Intelligence because of the increasing tendency towards applying machine learning tools to the context of Business Intelligence.

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You remember we mentioned Artificial Intelligence right? By definition, it is about simulating human knowledge and decision-making with computers. It is quite a general term that can have a rather philosophical interpretation. We, as humans have, only managed to reach AI through machine learning the discipline we just talked about. Let's go ahead and provide a few examples.
The demand for accurate, real-time dashboards opens space for more machine learning applications. Unlike static BI reports and dashboards, ML software can monitor business performance in real-time, allowing managers and other decision-makers to immediately improve a business's operations. For example, a company's HR department may generate monthly or quarterly reports on employee behavior, but if the company used an ML tool to create such reports every week, it could potentially detect an employee's disengagement much sooner. This would help the HR team investigate the reason for their disengagement and try to adjust the department's working process.

In another example, ML software can display sales patterns or customer engagement changes. in addition, ML can also pull data from third-party companies, such as Facebook or Shopify, detect new patterns from that information, and suggest real-time recommendations and insights. In other words, ML can significantly improve your company's operations if appropriately used in BI.

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Conclusion
There are two typical Business activities that involve ML; Client Retention and Acquisition, it helps develop models that predict a client's next purchase, for example. Since we could say data analytics and data science are also applied in client retention and acquisition. Secondly, let's look at Fraud Prevention as another ML-driven activity. We can feed a machine learning algorithm with prior fraudulent activity data and it would find patterns that the human brain cannot see. Having a model that can detect such transactions or operations in real-time has helped the Financial system prevent a huge amount of fraudulent activity. These examples are probably not the first to come to mind when discussing AI and ML. Speech and Imagine Recognition are usually among the most popular examples. They have already been implemented in products like Siri, Cortana, Google's assistant, and more impressively, self-driving cars.

Thank you for reading.