When it comes to company culture and organizational structure, Datashift has always been a bit “the odd one out”. We don’t follow the classical format most companies have. We don’t have a layered organization. No one is interested in the big job titles (mind you, we’re still very ambitious!). And we’re all about reaching success as a team!
A business glossary is the foundation that creates a common understanding of your organization’s vocabulary and data. While the concept isn’t anything complex, the implementation can be quite daunting. In this article we provide the key lessons we’ve learned while implementing a business glossary use case at our clients.
Two weeks ago, we posted a blog article about the three lessons we’ve learned in the past six years of Datashift. We received such great responses to our list of “don’ts,” I decided to share with you a list of three “do’s.” So, back by popular demand, here are another three important lessons six years of Datashift have taught us.
Datashift’s story started on January 15th, 2015. From the get-go, it was clear we wanted to do great things with Datashift. But building a company from the ground up is never a straight line. There are bumps in the road, highs, and lows, but most importantly: lessons learned. As we celebrate six years of Datashift, I'd like to take a look back and share exactly some of those lessons.
The truth is, predicting churn is easy. The hardest part is making it actionable. With this approach you’ll retain only your valuable customers that are about to churn, with a personalized retention action at the right time.
Do most data blogs make you feel like everything you should do is expensive, time consuming and requires complex skills? Well, this blog is different. Here are 5 practical recommendations (in no particular order) to leverage your existing business data with less effort than you think.
One of the advantages of running databases on Azure SQL Database is the ability to dynamically manage them to adapt to changing workload demands. Autoscaling databases is the most cost effective way to increase performance of data operations. In this guide we'll show you how to auto scale your database using Azure Data Factory.
No one likes misunderstandings. We've all been in a "the Italian man who went to Malta" situation before. Annoyingly enough, this situation happens in a lot of businesses too. Many discussions between departments have the same root challenge: different people speaking different 'languages'. In this article, we’ll set the stage to take on this challenge.
A thriving Collibra instance is populated with a wide spectrum of data such as business terms, policies, code values, metadata of schemas, tables & columns, …. To keep Collibra relevant within an organization, this data needs to be accurate and up-to-date. Collibra provides tools like Collibra Catalog and the Collibra API to automatically import data with a set frequency.
