Big data -- a term used with increasing frequency -- refers to the rafts of data that businesses collect on a day-to-day basis, both advertently and inadvertently. The number of avenues that data can be gathered from is always growing and becoming easier to access. By 2025, more than 150 trillion gigabytes of data will need analysis. But it's not the volume that matters, it's how businesses process and use the data that's important.
Structured data -- sorted as a model in a database or spreadsheet (data warehouse), which is easy to search (e.g. a sales order record with purchase dates, item lists, purchase details, and total cost).
Unstructured data -- raw data that is difficult to search, and not a pre-defined data model (e.g. text messages, emails, phone recordings).
Semi-structured data -- a combination of both structured and unstructured data (e.g. a photograph on a smartphone, capturing the unstructured binary data of light reflection information and the structured information such as time of capture and image size.).
As a data scientist, you're responsible for preparing, storing and processing an array of data collected from sources such as:
Personal and business software
Wireless sensor networks
Preparing big data
Preparing big data and its relevant models or algorithms is an important first step for data scientists. It involves liaising with key stakeholders in your business to find out exactly what they want from your analysis. This helps guide and inform how you execute the entire process, identifying what analytical tools are the best fit for your business' goals.
This process is also your responsibility at the end of a project, as you will use data visualization tools to present findings. These tools enable data to be presented in more accessible and engaging forms like graphs, charts, and infographics.
Storing big data
As a data scientist, your storage solutions not only need to handle large amounts of data, but must also have the flexibility to expand to accommodate the constant stream of new information. You need to ensure that storage provides the necessary high level of input/output operations per second (IOPS).
Whether opting for a hyperscale computing environment used by large corporations or the more traditional clustered network attached storage (NAS), your job is to help the storage to handle large data sets quickly.
Processing big data
Data scientists also need to be able to process the data. With the need to divide bigger data streams into smaller and easier to decipher information -- finding patterns and outliers that give your business key information. This can help identify cyber security threats and fraudulent behavior, finding irregular user actions among data patterns and halting threats before they happen. One data processing solution is via open-source software such as Hadoop, which is used by corporations including Yahoo, eBay, Amazon, Facebook and Twitter.
Advancing your data science skills
Being a good data scientist means continually working your coding and business skills, such as stakeholder management and decision-making, your mathematical and statistical skills and your ability to communicate key data insights to your audiences effectively. A Master of Data Science can help you learn and realize that investing in a career and being good at what you do, like any career, is dependent on your ability to invest your time and energy to continually improve your skills. In data science, this can be everything from coding, business skills, and mathematical and statistical abilities. Like any career, your skillset is always a work in progress.
About This Content This content is provided in collaboration with James Cook University. It may have been influenced by the sponsor and does not necessarily reflect the views of the ECT News Network editorial staff.