What is data analytics?

Data analytics is the process of examining data sets to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software. Data analytics is used in many industries to allow companies and organization to make better business decisions.

Data analytics involves the extraction of data using fields within the basic data structure, rather than the format of records. A simple example is Power View, an Excel tool which can filter, sort, slice and highlight data in a spreadsheet and then present it visually in variety of bubble, bar and pie charts.

Data analytics can help in…
  • Validating trends
  • Identifying root causes of issues
  • Comprehensively view performance
  • Illustrating future possibilities
  • Streamlining operations
  • Increasing cost efficiency
  • Forecasting performance
  • Improving budgeting and forecasting
  • Enhancing mission effectiveness

Data analysis tools

Data analysis tools are also known as “data analytics” tools, have the following functionalities enabling organisations to make more-informed business decisions:

  • Dashboards. These provide a real-time overview of key performance indicators (KPIs) in a visual format that is easily sharable. Some data analysis tools allow users to create their own dashboards, so they can get a clearer picture of specific business operations.
  • Data set creation. Business forecasts are only as reliable as the quality and quantity of data behind them. Software for data analysis allow users to scrub, aggregate and split data as needed.
  • Interactive exploration. Static pie charts and line graphs are practically passé by today’s standards. Data analytics tools enable interactive exploration to provide eye-catching ways to visualise trends, such as heat maps and time motion views.
  • Sharing. Collaboration and social sharing functionalities are important because they allow business leaders to work together more efficiently. Data analytics tools enable everyone to see the same data sets concurrently, making it easier for the users to interpret the information in the same way.
  • Ease of use. Data analytics tools do not require an advanced computer science degree to operate. While usability will vary depending on how robust and technical your platform is, the interface is intuitive enough for trained staff to use with minimal support.
Data analysis software can help, for example:
  • Clarify the correlation between new marketing initiatives and improved sales
  • Better predict customers’ needs by analysing past purchases and browsing habits
  • Improve internal workflows and suggest solutions to common bottlenecks

By extracting these kinds of meaningful insights from your data, you’re in a better position to understand what it will take for your business to increase profitability. When you analyse a data set, you’re reflecting on what is (or isn’t) running smoothly at your business. The great advantage of using software for this purpose is it’s often more reliable—and less time-consuming—than manual data coding methods.

This article was co-authored by Kapil Kukreja, HLB Mann Judd Melbourne