What is data transformation? You’re already doing it, even if you don’t know it.
Whatever your business, data is key. Data shows your business story as facts. Revenue generation, costs, return on investment calculations, forecasting, supply chain… It reveals the truth of what makes your company tick.
To reveal that story in the data inevitably requires the imposition of logic. This is described as data transformation.
Clean, model, join, blend, govern and secure – these are all steps in the transformation process. One way or another, they’re happening in your business.
Do you have colleagues sweating over Excel spreadsheets? People wrestling with data visualisations in tools like Tableau, PowerBI or Qlik? Maybe you even have a data team providing data back to the business.
The real question is this: How do we make this process quicker, simpler and, most importantly, automated? There are a couple of steps.
In our last blog post, we talked about Extract and Load. When carried out correctly, the result will be a central store. A store pulling together data from all pertinent sources on a regular, scheduled basis. That might be once a day or it could be more regularly, depending on the data source and connected requirements.
With the data in one place, it’s time to focus our attention on the T in ELT – Transformation.
Build it once, build it well and then automate it. That’s the mantra. Using SQL, put in place the logic that brings the relevant data together. For example, combine CRM, transaction and marketing cost data into a view of your customers. This will give you lifetime value, cost of customer acquisition and all the communications in one place.
Once built, that logic needs to be scheduled and the dependencies handled. It might look something like this:
You could hire some people to build it for you or task existing resources to figure it out. Or you could talk to kleene.ai about how we handle it simply, automatically and cost effectively. Without a single line of code for you to write.
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