Data Transformation and Modelling
Look around you, what do you see? That’s right, numbers! When interrogated, these numbers will tell you a story. However, your reports and analyses are only going to be as accurate as the data used to build them. To make your source data fit for purpose, you need to turn these seemingly random and apparently senseless numbers into organised and meaningful datasets by running them through a number of clever processes.
What is Data Transformation and Modelling?
Data transformation and modelling along with other processes such as data evaluation, validation, aggregation, cleansing or manipulation are procedures carried out on your source data to render it usable. Raw data comes in numerous different formats from systems talking different languages and needs to be translated before you can start consolidating data silos into single source of truth, crafting reports or building integrations.
Why should I care about Data Transformation and Modelling?
Data is at the very centre of everything you do, and if it’s not it should be. Without processing your source data, you may end up comparing apples and oranges or working with misaligned performance indicators. Data needs to solve problems, not create them. A house without strong foundations won’t pass the test of time and neither will a business built based on speculations as opposed to concrete evidence.
How can I improve my Data Transformation and Modelling?
We live in a digital age; you’re gathering more data than you’ll ever need even without trying. Data is at the core of everything we do, and you need to embrace it. Don’t hold back from investing in data processing, the insight you’ll gain in result will yield solid return. Consolidate your data silos to get a single source of truth about your customers. Be selective about which data you process, focus on impact. Set goals for your data processing.