Traditionally, the data we had was structured and small in size, which could be significantly handled with a simple visualization tool like Power BI, excel dashboard, etc. but the nowadays huge amount of data is generated from different sources in different formats like CSV or text files, multimedia forms, financial sheets, sensors.
This massive data is unstructured or semi-structured most of the time which is impossible to handle by tools like Power BI. Here, we need more advanced analytical tools for complex data for processing, examining and getting insights from it.
Let’s dig deeper into the core analysis of structured and unstructured data, consider different cases here to understand how data play different roles for different applications;
1. You have an AI-based self-driven car, you give instructions and car runs itself and reaches to the destination, try to think about how it gets route, how to manage traffic and all. A self-driven car has an intelligence system associated with it that collects data from sensors, cameras and lasers satellite to design a map. Based on this data, it decides when to speed up or down, where to turn with the help of algorithms in machine learning.
2. Suppose you have customer’s past browsing history, purchase history, age, income, address, etc. as the data and you need to investigate the specific requirement for the customers. With the help of available data, you could train models and recommend the product to your customers with more precision. It will add more value to your business.
In the above-mentioned examples, we observed the specific function of advanced and more complex analytical techniques which we are going to understand deeply here with their application in different domains.
The business analytics runs over three of its major aspects, business intelligence, data science, and machine learning engineer. As the name suggests, all three work with different strategies and perspectives under the same business issue, here we will discuss core fields of analytics and their role in various business applications (Read our blog on 8 most popular business analysis techniques).
In an isolated view, Data Science (DS) is the discipline in which the data is analyzed deeply and predicts the business growth opportunities, Machine Learning (ML) is another discipline in which machine learning algorithms are employed to solve any business issue, whereas Business Intelligence is the process where the raw can be converted into some logical information for better business growth.
Data Science and its Application
Machine Learning and its Application
Business Intelligence and its Application
The massive size of data is explored (Big Data), extracted and processed to insight needful information for any business issue. It is a multi-step process which includes various algorithms, scientific and numerical analysis and implementation of business plan and strategies.
It assists to uncover hidden patterns from vast raw data as the data is collected from different sources. a piece of needful information.
The data science term has arisen from the combined nature of data analysis, mathematics, and statistics. The structured and unstructured dataset has been used for the extraction and interrogation.
It facilitates a business problem into the opportunity. it enables individuals to take preferable and quicker actions independently. With the help of data science, a complex data problem could be simplified in the easiest way to aid business values.
Data Science is a looking forward approach, an exploratory way to focus on available data and predicting the outcomes for the future with better decision-making, this helps any business to stay ahead.
In the recommendation system: data science is widely used in recommending a product or an item to the user. For example, on Netflix, movies are recommended to customers based on ratings and past data. On amazon, different products are recommended to customers based on their search history.
In email filtration: An algorithm is constructed in such a way that filters email based on some attribute or specification. For example, spam email gets filtered in gmails, a sort of algorithm is processed which checks the coming message or email is social emails, spam or junk emails.
In the gaming system: Sony, Apple, Samsung, etc are the brands that use data science to upgrade their gaming software in mobiles or computer systems.
In speech and image recognition: Data science is specially used in recognition of speech and images. For example, Alexa, google voice, etc are used for speech recognition and computer vision is used for image recognition.
Apart from this data science is also used in risk management, fraud detection, traffic light signals, internet search, etc.
Machine Learning is the technological field of study where computers have the ability to perform independently without being directed by the programmer.
These computers are the models or sets of algorithms that are designed by the programmer to perform a particular task without using any kind of instructions from outsourced to produce significant results. They are relying on patterns and inference.
In short, machine learning gives the computer software “The potential to learn independently and perform accurately”.
The machine learns in the same way as a human learns from his past experience so as the machine does. Machines are trained in such a way that if they get a similar situation as they got in the past they make accurate predictions.
In the process of machine learning, fine quality of data is fed to computers or ML algorithms to incorporate with statistical tools which are already coded by programmer in order to get predicted results. After that, these results are employed to get business insights from users. In this way, the machine predicts outcomes by getting data as input datasets.
In Healthcare Services: ML is broadly used in health care to on a large scale, such as to find the health status of patients, stock of medicines, allotments of free slots to patient, s, present health condition by reading previous checkup report, etc. (Read IoT applications in Healthcare)
In Transportation Management: ML is in trend in transportation, like, to divert the route of traffic, traffic signals, exact timing of availability of vehicles, in google maps for the accurate path, etc.
In Different Domains: The government uses ML for public safety and utilization of different government items like ATM or electric apparatus. Forensic experts used this in image detection for the identification of theft or culprit. ML also used by the government for reducing prize and increasing efficiency in different sectors.
In Marketing and Sales Services: ML is used in marketing and sales for a recommendation, forecast, telecast, organizing campaigns for marketing, increasing sales, etc.
Other than these major applications, machine learning also used in financial services, oil and gas refinery, automobile industry, spam detection, internet searching, etc.
Business intelligence (BI) is the process that comprises technologies, processes, and applications to analyze data and presenting useful information for business and corporate users. It is mainly used for taking preferable business decisions and conclusions.
Business Intelligence consists of methodologies, tools, and tactics for collecting, integrating, analyzing and presenting results. These results are presented in the form of charts, graphs, dashboards, reports and maps, flow charts and summaries in order to show a detailed analysis of data to users.
Business intelligence accelerates and improves business-decision, optimization of internal business processes, enhancing the efficiency of operations. It also helps in getting business trends in marketing and sales, financial services, and productivity of items.
In sales: BI has a great role in sales marketing as a major part of any business depends on sales marketing. BI is considered for promoting sales of any product or software in the market by analyzing previous data. It further improves the sales performance of a particular business. It recognizes marketing trends for an issue to execute strategies.
In reporting: After analyzing data, BI uses its tools and strategies to initiate various types of reports in different sections such as sales, staffing, financial, recruiting, customer response reports and another process inside a business domain. These reports consist of purpose or motivation, analysis or exploring, assignment done and outcomes of a business problem
In visualization: Similar to the reporting application, BI is used for visualization. In the report, descriptive analysis is presented but in visualization, data is presented in the form of graphs, charts, maps, etc. It is used as a data visualization in sales, retail and logistics industries, educational institutes and IT sectors.
In performance: BI is used in optimizing the performance of a business by comparing it to previous performance. It also provides the performance of customers, executives or employees, products and other sections of a business combine together to satisfy the performance of a business. BI reports consist of some targets and approaches towards targets to get judged by the performance of the system.
BI also used as collaboration, benchmarking, management in knowledge, various SME, etc.
Data science, machine learning and business intelligence are such fields of study where data is utilized for analysis and gives insights for business growth. All three domain uses different approaches and tools for exploring the dataset and interrogating business issues.
There are multiple usual applications of Business Intelligence, Machine Learning and Data Scientist where they are used on a large scale. Some applications are common where BI, ML, and DS work together and sometimes independently. For more such blogs on analytics do read Analytics Steps!
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE