Businesses are increasingly turning towards data experts for critical decision-making as the collection and interpretation of data provides organisations with a competitive advantage. As the field of data science continuously evolves and expands based on this increasing demand for more precise and specialised information, so too do the roles within it.
Each area within the field of data science has a specific role to play whether it is data collection, processing, analysis, or visualisation, just to name a few. These different roles lend themselves to specific skills, techniques, and interests, allowing data science professionals to develop specialised skill sets which make them an expert in their field.
Two key positions within data science are data warehousing and data mining. Both roles are vital, however, they operate at different stages of the data collection and interpretation process. Data warehousing is in the initial stages and involves organisational infrastructure building whilst data mining comes once the data pool has already been collected, it is a more analytical role. Both positions support each other as a data warehouse architect will build the database that the data miner needs to extract insights.
It’s important to understand these two ally roles to maximise data-driven business intelligence. Learn about these roles to see which specialism within the data science industry you are best suited towards.
What is data warehousing?
Data warehousing is the process of creating an organised system to record data entries from multiple sources. However, the process of data warehousing does not include the interpretations of the data within the ‘warehouse’.
A database stores a pool of information, whereas a data warehouse will store multiple databases in one location, usually with the ability for each to interact with each other. Data warehousing requires a tailored system that is unique to your business or organisation, it will understand the analytical needs, collect and then store your information accordingly.
Data warehouse architects pave the future of data analytics
A data warehouse architect designs these data warehouses, they manage and execute data storage strategies. Additionally, a data warehouse architect will often program the data warehouse, so those working in this area require hands-on developer knowledge. The role of a data warehouse architect is well suited to methodological, organised individuals who hold a deep understanding of building data systems. Without a reliable and competent data warehouse architect, organisations will struggle to collect and store vital information which can be later used to inform business decisions.
Real-life data warehousing
The successful Australian company, Canva, have attributed using data and data warehousing as a key to their success. They look for talented data warehouse architects to help build the infrastructure that collects and stores their important information. Canva uses collected data to create new products, recognise their areas of strength, and understand how their users interact with their product. Their understanding that high-quality data warehousing will pave the way to successful business intelligence, has led them to become an industry leader. Earning over 20 million users in over 100 languages, Canva’s suite of products is collected for analysis through effective data warehousing.
What is data mining?
Data mining is the analytical stage of data processing, it is the examination and exploration of large data sets to extract meaningful trends, insights and rules. Data mining is also known as ‘knowledge discovery’ as it is the stage where important business intelligence insights are discovered. Successful data mining combines statistics and artificial intelligence with different database management tools to highlight important trends across data sets. The insights found through data mining can be used across different departments and industries. From retail to government, large organisations should not only collect data but recognise and utilise its full potential in order to meet business goals.
Data mining professionals turn data into nuanced understanding
Data mining professionals hold a variety of skill sets, and they must have an analytical and shrewd way of thinking. Data miners must be comfortable with complex data sets but also able to transform these numerical compositions into important business findings. Moreover, data miners should have great communication skills as they are usually responsible for relaying information to business intelligence teams and key stakeholders. Data miners provide the crucial link between collecting data and making important business decisions.
Real-life data mining
Thanks to data mining, the Australian agriculture tech company, AgriWebb, has improved operations and thoroughly understood the key areas of their business that affect important business decisions. They are a record-keeping farm management software that uses technology to track their animals, land and production. This collected data is then translated into simple results which provide cost-effective and practical insights for decision making. Used by thousands of Australian farmers, the data-driven company organises over 10,000,000 animals across 30,000,000 hectares of land. This successful company continues to grow their overseas presence as farmers around the world are seeing how they can better distribute their resources and extract additional insights that come from data mining.
The data science industry holds a range of important roles that drive key decision-making. Pursuing a career as a data warehouse architect or a data mining professional are just two pathways within a developing and flourishing industry.
Gain the skills necessary to succeed by undertaking a Master of Data Science. This 100% online program is designed to prepare students with practical skills and information to lead the data and analytics industry forward. With vital subjects like ‘Working with big data’, students will gain experience with big data tools to manoeuvre complex data sets. Additionally, the course ‘Applied data science’ helps students translate intricate data knowledge into real-world settings.