How to transform your business into becoming Data-Driven
Data Analytics is the fastest growing market currently in the world and businesses now access more data than they ever had.
With the growth of this industry, you may have heard of phrases like:
"Data is an organization's most valuable asset", "Should data be recognized as an asset in the balance sheet?" and even data is more valuable than oil as a resource.
The problem now remains for many organizations is how to leverage the data into insights and knowledge.
Many companies in NZ are still relying on manual spreadsheets, anecdotal information, and gut-feeling when it comes to making key commercial decisions and the question still remains how to transform an organization into becoming Data-Driven.
While this may all sound hard and daunting it actually is a really simple process and more of a maturity thing.
The below image helps to breakdown the process in 5 simple iterative steps.
The most common form of data is called 'Raw Data'. This data is unstructured and usually in CSV format.
Normalizing data is the process of giving raw data structure and a model. The data is then harvested in a database.
Once the data is structured we can now pull the data into Power BI and visualize it. Visualizing data allows users to perform some sort of basic analysis of the data. Eg. Trend analysis, product split, etc
After the data is structured and there is a range of different visualizations we are now able to make some predictions on future outcomes. E.g. Looking at the data sales trends usually pick up during November, December. We are forecasting a lift in sales for the same period this year.
We can also run different statistical techniques to drive predictive analytics.
The process of using data to make business decisions is called being Data-Driven. Eg. Now that we know sales usually picks-up during November, December. It may be a good time to launch a new design during November and run a campaign offering discounts. This should help us increase sales YoY%.
Running this process over and over a few times by involving different data-sets helps organizations move up the analytics maturity curve. The managers feel more used to the data the organization transforms into becoming data-driven