Data Science Impact on Business

Data Science is a mutli-disciplinary field which focuses on the application of statistical analysis, machine learning and data visualisation to derive insightful results from data. Business Analytics is more pragmatic and focuses on tools and softwares to improve one’s business by gathering insightful information from internal and external sources. Data Science helps in building models for machine learning and automation tasks whereas business analytics focuses on tasks like feedback analysis and price prediction using software tools.It can be concluded that business analytics highly focuses on the intricacies of the business world. Data science is a broad discipline and covers a lot more than business. In fact, it provides the foundations for business analytics.

Data science focuses on methods like:
=> Collection
=> Extraction
=> Preparation
=> Mining
=> Visualisation
=> Model building

Business Analytics focuses more on methods such as :
=> Descriptive analysis
=> Diagnostics
=> Predictive analysis
=> Prescriptive analysis

Thus, both data science and business analytics play an important role for optimisation of a business or an organisation.

Applications of Data Science in Businesses

➢ Fraud Detection

Fraud Detection is used to analyse suspicious financial transactions and prevent unauthorised activities. The system uses AI based approaches which detect common patterns and exploits and alert the necessary authorities for any suspicious activity Fraud Detection helps in avoiding different frauds like :
=> Identity Theft
=> Cyber Hacking
=> Insurance Scams
=> Credit card frauds
=> Fraudulent transactions

➢ Smart Recruitment

Automated recruitment processes can be adopted to evaluate candidates based on required skills for qualification. Image recognition and Optical character recognition are used to analyse the visual information of the candidate and then based on specific algorithms, potential candidates are selected. We also have candidate pre-screening tools which can evaluate their potential by ranking and grading them based on certain performance based metrics.

➢ Autonomous Driving

Recent technological development has led us to autonomous self-driving cars. Scientists have developed many algorithms which help in controlling the car in a given environment. Based on data collected from different sensors, AI and ML based algorithms are used to operate the car and computer vision works as the visual interpreter to analyse
certain aspects of its surroundings. Intelligent algorithms are used to monitor each time a human interacts with the car and attempts to enhance its own algorithm based on newly obtained data points.

➢ Customer Analysis

Customer Analysis is the amalgamation of qualitative and quantitative research methods with the goal of better comprehension of the customer base. It focuses on the following stage of approach :

=>Identification of the customers based on traits like age, gender, location and other demographics.
=> Observing their requirements and their preferences
=> Grouping them based on similar traits and behaviours
=> Keeping a profile of ideal customers.

We can also fetch data from other sources such as :

=> Search Engine Queries
=> Celebrity and Influence Activity
=> Current purchase data
=> Sentimental Analysis
=> Behavioural Analysis

Advantages of Customer Analysis:

=> Lower Customer Acquisition Costs
=> Improved Customer Retention
=> Effective Customer Service
=> Increased sales and profits

Users are recommended similar and related items based on their preferences via the services. They are also recommended offers which may be useful for them. This approach leads to more pleasant user experience and helps in generating additional
revenue.

➢ Intelligent Manufacturing

The core aspects for manufacturing are cost, quality and delivery. To improve these aspects, we need proper understanding of the manufacturing process and we have developed intelligent systems which can monitor and enhance the
current process. With the help of analytics, we are able to implement:

=> Effective Equipment Maintenance
=> Price Optimisation
=> Inventory monitoring
=> Demand forecasting
=> Risk management

Thus, Effective analysis and processing introduces transparency in the manufacturing workflow and proves to be beneficial.

❖ Main Advantages of Data Science

Business predictability :

On the basis of reliable collected data, companies can predict the upcoming demands and trends in their market. Predictive Analysis can help draw conclusions from this data and improve profitability of the organisations.

Improves Data Security :

We have developed many secured implementations such as :

=>Fraud detection system
=> Impenetrable protocol development using data driven technology
=> Systems which can detect architectural flaws based on recurring patterns of behaviour

These are just the few examples which have definitely helped improve data security

Enhances Decision Making Process :

Businesses put augmented intelligence into action which provides incredible insights and revolutionary models as the basis of the decision making process. This enables the organisations to make faster and more accurate decisions.

Helps analyse complex and unstructured data :

Data science helps understand the business and market better based on the information collected. Depending on different sources of data, we can use visualisation tools to help understand the underlying information obtained from such data

Developing better products:

Our data science consulting services assist businesses in understanding the demands and needs of their customers and also helps in analysing current trends. Based on this information, companies can develop better products which are suitable to the demands and requirements of the customer.