What is data science? What is big data? What does a data scientist do?

Data science uses scientific methods, processes, algorithms and systems to extract knowledge, patterns and insights from raw data. It is primarily used to make predictions and decisions, including creative ways to generate business value

Big data is a term that describes the large volume of complex data – both structured and unstructured – that businesses collect on a day-to-day basis. These enormous volumes of data can be analysed for insights that address business problems and aid strategic business decisions.

A data scientist is someone who makes value out of raw data by turning it into meaningful information that organisations can use to improve their businesses. Data scientists know how to get results from data quickly by gathering, analysing and interpreting large volumes of structured and unstructured information from a range of sources. Using algorithmic, data mining, artificial intelligence, machine learning and statistical tools, they make this data accessible and meaningful by creating actionable plans for organisations.

Key responsibilities

Responsibilities will vary, but examples include:

  • Extracting very large volumes of structured and unstructured data
  • Employing sophisticated analytical methods, machine learning and statistical methods to prepare data for use in modeling
  • Processing, cleansing, and verifying the integrity of data used for analysis
  • Performing exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities
  • Assessing the effectiveness of data sources & data-gathering techniques and improving data collection
  • Discovering new algorithms to solve problems and build programs to automate repetitive work
  • Communicating predictions and findings to management and IT departments effectively
  • Recommending cost-effective changes to existing procedures and strategies
  • Doing ad-hoc analysis and presenting results clearly

Why are they important?

Big data has no value without the ability to interpret it. For this reason, experienced data scientists who have the expertise and knowledge to use technology to translate the data into actionable insights is highly valued

Examples of the value provided by data scientists include the ability to use data to identify when and where an organisation’s products sell best, defining demand through understanding target markets at a granular level and using market insight create the best customer experience possible

Competencies needed for this role

Data scientists must be technically excellent and be highly analytical and results-orientated. They must also have exceptional communication and presentation skills in order to explain findings to non-technical counterparts.

Career opportunities presented by this role

Data scientists are in high demand across a number of sectors in today’s data and tech heavy economy. Salaries and job growth clearly reflect this.


High level competencies required by data scientists include:

  • Data, Digital and Technology

    A. Identifies strategic options to add value, using data and technology.

    B. Analyses and evaluates data using appropriate technologies and tools.

    C. Applies technologies to visualise data clearly and effectively.

    D. Applies scepticism and ethical judgement to the use of data and data technology.


  • Strategy and Innovation

    A. Applies business acumen and commercial awareness to deliver business objectives.

    B. Recommends a range of suitable strategic options from which to develop sustainable plans and objectives.

    C. Evaluates, justifies and implements suitable strategic options.

    D. Adopts and applies innovative methods to implement strategy and manages change.