Onsite Data and Artificial Intelligence Jobs in Nigeria (Page 89)

What is Big Data?

Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modelling and other advanced analytics applications.

Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support big data analytics.

Also, big data is often characterized by the three V's:

• the large volume of data in many environments;

• the wide variety of data types frequently stored in big data systems; and

• the velocity at which much of the data is generated, collected and processed.

Big data has become relevant in almost every field and industry. Currently, companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that don't because they're able to make faster and more informed business decisions.

What is AI? 

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.

This AI system works by ingesting large amounts of labelled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states.

AI programming focuses on cognitive skills that include the following:

• Learning. This aspect of AI programming focuses on acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.

• Reasoning. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.

• Self-correction. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.

• Creativity. This aspect of AI uses neural networks, rules-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans.

Relationship between Big Data and AI

Big data and artificial intelligence have a synergistic relationship. AI requires a massive scale of data to learn and improve decision-making processes and big data analytics leverages AI for better data analysis. 

With big data AI-powered analytics, you can empower your users with the intuitive tools and robust technologies they need to extract high-value insights from data. 

By bringing together big data and AI analytics technology, companies can improve business performance and efficiency by:

• Anticipating and capitalizing on emerging industry and market trends.

• Analyzing consumer behaviour and automating customer segmentation

• Personalizing and optimizing the performance of digital marketing campaigns

• Using intelligent decision support systems fueled by big data, AI, and predictive analytics

Generally, AI makes big data analytics simpler by automating and enhancing data preparation, data visualization, predictive modelling, and other complex analytical tasks that would otherwise be labour-intensive and time-consuming

Job Roles in Big Data and AI 

1. Data Scientist: A data scientist is responsible for analysing and interpreting complex data to find patterns and insights that can help organizations make better decisions. They need to be skilled in programming, data analysis, and machine learning.

2. Data Analyst: A data analyst is responsible for collecting, cleaning, and analyzing data to identify trends and insights. They need to be skilled in statistics, data visualization, and programming.

3. Machine Learning Engineer: A machine learning engineer designs and implements machine learning algorithms and models to automate tasks or create predictive models. They need to have strong programming skills and knowledge of statistics and mathematics.

4. Business Intelligence Analyst: A business intelligence analyst uses data to help organizations make informed decisions. They analyze data to identify trends and patterns, and then create reports and visualisations to communicate insights to business leaders.

5. Data Engineer: A big data engineer designs and implements large-scale data processing systems using technologies such as Hadoop and Spark. They need to have strong programming skills and knowledge of distributed computing.

6. Data Architect: A data architect designs and maintains the architecture of data systems. They need to have a deep understanding of data modeling and database design.

7. Data Mining Engineer: A data mining engineer develops and implements algorithms to discover patterns and insights in large datasets. They need to be skilled in programming and machine learning.