Your data transformation journey starts with three Letters: ETL
In our recent blog post, we highlighted the importance of starting your data journey with an ETL strategy if you want to become a data-driven organization. Before I explain why, a little background information is required.
Every company is becoming a data company. Indeed, data has become a key strategic driver for every decision made at each enterprise. Regardless of your industry or your role in an organization, using data is key to making smart decisions, deciding how to optimize revenue operations, where to spend money on ads, and much more.
In fact, every department should be using data as their key signal to make strategic business decisions that will impact the long-term viability of their organization. If you aren’t using data, you are just guessing. And a few wrong guesses can be the difference between greater profits and a failed business.
If you think becoming data-centric is daunting - well, yes, it can be. But, you have to start somewhere. And the best place to start on your data journey is an effective ETL strategy. ETL stands for Extract, Transform and Load. An effective ETL strategy will set the foundation for your data journey because it will ensure your data is normalized, unified, clean, and validated.
The first step in the ETL process is to extract the data. This means pulling information from numerous disparate systems, whether it’s Google Ad spend, Adobe analytics, Facebook social spend, Salesforce, Hubspot, or any of the hundreds of data sources which are flowing into your organization on a daily basis.
Once you have your disparate data sets in a single location, you need to transform your data. This is where many organizations find themselves stuck and frustrated. Because now you have to normalize, validate and unify it into a single format. We previously mentioned how a client of ours hired a team of expensive data engineers to accomplish this task. And while it can be quite expensive, it is a critical step, because it’s how you tell an accurate story with your data.
Finally, you need to load the data into a single location, such as Amazon AWS, Snowflake, Redshift, BigQuery, etc. This is where your data will reside, and how you make your data actionable.
Your ETL strategy will set the tone to make your data useful to your business teams. After all, information is only as useful as your ability to use it. Making sure your data is validated, clean, in one format, and in one location will help transform your organization into a data-driven one.
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