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As data became more and more available to companies, data integration became one of the most crucial challenges for organizations. In the past decades, ETL (extract, transform, load) and later ELT (extract, load, transform) emerged as data integration methods that transfer data from a source to a data warehouse.
In our experience at Secoda working with many data teams, we've seen most data teams do not have the tools they need to succeed. For growing organizations, the data function is usually an afterthought. The first data hire is brought on before raising a Series A and is expected to manage the workload that comes afterward with little to no support.
From personal experience, I have always found it interesting to learn how to create an organized catalog of data. However, this interest was transformed into a passion when I began to realize the amount of time and effort it could save me within my job responsibilities. Creating a data catalog can greatly help you with organizing the data they collect, therefore making it easier to find what you need when you need it.
Building a data practice is not only about making technological choices; and you will likely have to start with a first iteration and expect it to evolve as your business grows.
Let’s not mince words. Product led growth (PLG) isn’t something that happens overnight. It has to infuse company culture and involves commitment from every team - not just the go-to-market teams on the front lines.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
The modern data stack is on the rise. Many companies use raw data from their SaaS analytics tools as input for their data warehouse, but this introduces problems downstream. Are there better ways?
Breaking down some of the problems I’ve seen in data collaboration and offering advice on how to make better, faster decisions with collaborative analytics.
The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
