Analysing data comes about in many forms.
Questions, such as "What and why did this happen?" through to "How do we make this happen again?" are generally the drivers behind an organisation investing in Data Analytics.
The team at DATAMetrics have expertise with massive datasets, powerful tools, data-science languages, and most industry verticals. These combine to provide a tremendous advantage in attaining correct assumptions from an organisations information, and can provide actionable insights for any given challenge - to understand not only what has happened, but why. These insights pave the way to building deep understanding of your data assets in a way that reveals the living entity it is. With this understanding, an organisation has the ability to foresee future problems, navigate a accurate path through uncharted territories, and influence correct levers to ensure you get the results your organisation demands.
DATAMetrics Analytics Methodology
At DATAMetrics, we believe in a holistic approach to data analysis. There is no one-size-fits-all, however, data analysis typically involves activities such as gathering, cleaning, and organising the data. It is the science of analysing raw data to draw informed conclusions based on the data.
The two primary methods for data analysis are quantitative data analysis and qualitative data analysis.
Quantitative data analysis involves working with numerical variables – percentages, calculations, measurements, and statistics. An example of quantitative data analysis would be forecasting future revenue based on annual growth and other measurable factors.
Qualitative data analysis typical involves working with data that is non-numerical. An example of qualitative data analysis would be analysing sentiment following a marketing campaign, for example, on a social media platform. This information can be used to improve future marketing campaigns.
Depending on the needs of our users, we will determine which of the above methods is most appropriate and develop a roadmap of what information we would like the data to explain. We will determine the following:
- The intended audience – is it executive management or a team leader needing the information for day to day operations?
- The business process or function – are we doing a sales report? An inventory report?
- The questions we want to answer with the data – what was the overall net profit for our southern region stores last month? Do we have enough stock for the next quarter?
- The level of detail required, or the “grain” – do we need to show information based on the store, or the salesperson who works at the store?
- The facts or measures – do we want to show percentages? Sales totals?
Once we have transformed the raw data into meaningful insights, we will analyse the data and apply several different modelling techniques dependent on the type of data and what we wish to determine. An example would be regression analysis where can estimate the relationship between a set of variables to see if there is a correlation between a dependent variable and any number of independent variables. Another example is factor analysis which is a technique used to reduce many variables to a smaller number of factors.
After sufficient data analysis the findings are presented to the end-user. Any areas that need attention are illustrated, as well as any suggestions on improvements or a direction that should be focussed on to maximise success.