Big Data is a popular term used to describe the massive collection of data, whether structured, semi-structured, unstructured, or raw. Data may be defined as an asset on the balance sheet.
According to Gartner, Big Data comprises high volume, velocity, and variety of information assets that demand cost-effective, innovative forms of information processing for enhanced insights and decision-making. Hence, the globally accepted 3 Vs of Big Data are:
However, recent studies have added two more components which describe Big Data:
5 Vs of Big Data
- Volume: The amount of data,
- Velocity: The speed of data in and out, and
- Variety: The range of data types and sources which include: unstructured text documents, picture, video, email, audio, stock ticker data, financial transactions, etc.
- Variability: At times, the data flow is highly inconsistent with periodic peaks which hamper the process of handling and managing data effectively.
- Complexity: As large volumes of data come from multiple sources, data management becomes a challenging task.
In fact, the data sets are so big and complex that it becomes very difficult and challenging to process them using traditional data processing applications. It is estimated that about 2.5 quintillion bytes of data are created every day.
This implies that about 90% of the world’s total data was created in the last two years. It should be noted here that about 80% of the total data is unstructured – there are data collected from sensors used to gather weather information, social media posts, digital photos, and videos, purchase transaction records, to mobile phone’s GPS and many more.
Both government and private sectors have used Big Data to enhance their productivity. Big Data analytics played a key role in Barack Obama’s successful re-election campaign in 2012. Witnessing the role of Big Data in addressing the problems faced by the government, the Obama Government announced the Big Data Research and Development initiative in the year 2012. The United States Federal government owns six of the ten most powerful supercomputers of the world.
In the private sector, Facebook uses Big Data to handle 50 billion photos from its user base. Amazon.com used Linux based technology to handle millions of back end operations every day. eBay.com uses two data warehouses of 7.5 PB and 40 PB as well as a 40 PB Hadoop cluster for search. FICO Falcon Credit Card Fraud Detection system secures 2.1 billion active accounts across the globe.
Walmart handles 1 million+ customer transactions every hour, which are imported into databases estimated to contain more than 2.5 petabytes of data. According to estimates, the volume of data worldwide doubles every 1.2 years.
Role of Big Data in an Enterprise
The evolution of Big Data databases has enabled enterprises to know the importance of data in their growth and success. These databases have helped enterprises to save money, increase revenue, and achieve many other business objectives. The real challenge faced by enterprises is finding that critical piece of information that provides a competitive edge. Hadoop helps in managing and handling massive amounts of data. It also helps in transforming the data into a more usable structure and format, and extract valuable analytics from it.
Big Data in International Development
The scope of Big Data is not limited either to IT companies or any particular sector. As per Research on Effective uses of Information and Communication Technologies for Development (ICT4D), Big Data technologies can be extremely useful and can make important contributions in solving challenges in international development. Advancements in Big Data technologies result in the creation of cost-effective opportunities that help in improving the decision-making process in critical areas of development such as healthcare, employment, law and crime, security, natural calamity, etc.
Big Data Job Opportunities
Big Data offers huge job opportunities in the IT sector, provided one possesses the right qualifications. A study by Mckinsey & Company in 2011, reports that the United States can face an acute shortage of people with deep analytical skills in Big Data. Companies are and will continue to look for skilled people who can tap Big Data’s promise of competitive advantage. There are several Big Data jobs that require skilled professionals.
1. Chief Data Officer
A person is responsible for the overall implementation and execution of Big Data in an organization. He or she holds an important chair in the organization. He or she should be a member of the executive board of an organization, reporting directly to the CEO.
2. Big Data Engineer
Big Data Engineers are in demand and they develop, maintain, test, and evaluate Big Data solutions within an organization. He or she must possess extensive knowledge of different programming or scripting languages like Java, C++, PHP, Ruby, Python, etc. Building data processing systems with Hadoop and Hive is another important skill, he needs to possess.
With all that said, it can be claimed that Big Data is definitely getting mainstream in the tech-savvy world of today and more and more organizations are investing in it to save a lot of time and effort and still gain that success in their businesses.
3. Data Scientist
This is going to be one of the most sought after jobs of the 21st century. As the Big Data and Data Science industries is witnessing a magical growth, the demand for Data Scientist is more than ever. But, this is not an easy task. In order to become a successful Data Scientist, one needs to possess some specialized skills such as natural learning processes, machine learning, conceptual modeling, statistical analysis, predictive modeling, and hypothetical testing, etc.
In order to be a successful Data Scientist, one needs to master the following capabilities too:
- Ability to work in a fast-paced multi-disciplinary environment
- Strong written and verbal communication
- Be able to develop program databases.
- Ability to query databases and perform statistical analyses
- Ability to create examples and demonstrations
- Ability to work autonomously
- Good understanding of design and architecture principals
4. Big Data Analyst
Big Data Analyst assists the Data Scientist in performing the necessary jobs. His job primarily is to work on data in a given system and analyze different data sets. The next job for a Big Data Analyst can be that of a Data Scientist. And, hence he needs to possess similar sets of skills and capabilities. The skills include data mining skills (including data auditing, aggregation, validation, and reconciliation), advanced modeling techniques, testing, creating, and explaining data in clear and concise reports. Testing skills of a Big Data Analyst is very important for the successful analysis of databases. It is required for a Big Data Analyst to be a successful communicator as he needs to communicate complex findings and ideas in a much simpler language.
5. Big Data Visualizer
6. Big Data Manager
Big Data Manager acts as a bridge between the technical team members and the strategic management in an organization. He or she leads and manages the teams of Data Scientists, Big Data Analysts, and Big Data Visualizers. He or she must master the core management skills like communicating effectively and efficiently, building personal relationships with the Big Data team, flexibility in changing the environment, and the ability to understand, interpret and relate the organization’s strategy to the team.
7. Big Data Solutions Architect
This domain aims to address specific Big Data problems and requirements. Big Data Solutions Architect is quite important for an organization as they are trained to describe the structure and behavior of a Big Data solution. He or she must be familiar with Hadoop. Some of the skills required for a Big Data Solutions Architect are the ability to clearly articulate the pros and cons of various technologies, the ability to document used cases, solutions, and recommendations, strong written and verbal communication skills, self- starter, ability to work in teams, etc.
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