In this age of information, data engineering – a branch of data science that focuses on the practical applications of data collection and analysis – is the cornerstone of every successful company. By ensuring that all available data and databases are clean, reliable and performative, we make it easy for data and/or market experts to analyse large and varied data sets (i.e. Big Data) in order to understand their customers, discover current and emerging trends, and more.
Simply put, by sorting, managing and making the most out of the data available, organisations have access to actionable insights, which in turn can improve the research, planning, and development of new products and services more precisely.
Advancements in computing technology has also led to the development of Machine Learning – a system that gives computers the ability to learn without being explicitly programmed. By using user-tracking algorithms and/or feeding it large quantities of data, computers can learn to identify patterns by themselves. These patterns can then be applied in real life to automate repetitive tasks, translate text, target advertisements to specific demographics, and more.
Whether you’re looking to introduce Big Data into your organisation, or you’re looking to leverage Machine Learning to streamline your day-to-day operations, our team is ready to assist you in this endeavour. Feel free to contact us to find out more about the boundless possibilities of data engineering today.
How We Can Help
- Determine if data engineering is beneficial to your organisation.
- Conduct workload discovery & evaluation, assess existing data environment, etc. to determine best way forward.
- Examine your business processes to determine areas that will benefit from digitisation, automation, and/or machine learning.
- Build / customise data engineering solutions to suit your needs and requirements.
- Identify and recommend appropriate technologies and platforms.
- Develop a reliable and scalable data environment; integrate with existing environments if necessary.
- Define tasks and desired output (based on predefined business processes); assess quality of data to ensure that results are reliable.
- Identify key metrics and constraints that will help determine the success of the data engineering exercise.
- Plan detailed development, deployment and/or migration strategies for a seamless transition.
- Integrate self-learning algorithms into relevant systems and ensure that there are sufficient guidelines and examples for the systems to learn from.
- Ensure that a support team is on standby to troubleshoot problems that arise during and after development / integration.
- Perform post-development / post-integration tests and system checks to ensure that all components are running smoothly.
- Monitor and manage your solution after deployment.
Streamline Your Business Through Data Engineering
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