Photo by C Dustin on Unsplash Data warehousing was introduced in 1988 by IBM researchers Barry Devlin and Paul Murphy. Since then the concept has evolved and taken on a life of its own. Increasing challenges and complexities of business have forced data warehousing to become a distinct discipline. Over the years this has led to best…
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.Photo by Kelly Sikkema on Unsplash As 2020 has comes to a close, we wanted to take a moment to reflect on all the changes in technology as well as look to see where things are going. Whether you are looking at start-ups and their IPOs, improvements in technology or you paid attention to Amazon Re:Invent.…
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Photo by Isaac Smith on Unsplash Analyzing data is a skill required but more than just data analysts and data scientists in 2021. Many roles require some form of data analysis whether it be done in Excel, Jupyter Notebooks, or Tableau. However, data analysis is not generally a course taught in college. It might be…
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Photo by Kaleidico on Unsplash Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage. Our team has worked with and for companies across industries. We have seen the good, the bad and the…
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We recently had a client ask us why they might want to build a data warehouse. They had several operational relational database and third parties like Salesforce. We saw clear ways that customers could benefit from a data warehouse. A data warehouse seemed like an appropriate fit for their uses cases they explained. They had…
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Photo by Eric Krull on Unsplash. Parsing and processing documents can provide a lot of value for almost every department in a company. This is one of the many use cases where natural language processing (or NLP) can come in handy. NLP is not just for chatbots and Gmail predicting what you are going to write in your…
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Photo by Margaret Weir on Unsplash One of the biggest roadblocks for data scientists looking to analyze data is getting access to that data. The data itself will need to be processed and placed into a central data system like a data warehouse or data lake. This is done by using what we call an…
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Photo by Shahadat Rahman on Unsplash With the increased need for data analytics and data science that is even more granular and allows for self-service analytics. Getting access to the data you need still is far from easy. If you have worked in any role related to data, or even just adjacent to it, you…
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Photo by ev on Unsplash There are plenty of cliches about data and its likeness to oil or companies being data-driven. Is there truth to all this hype about data strategy, predictive modeling, data visualization, and machine learning? In our experience, these cliches are true. In the past few years, we have already helped several…
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Photo by Science in HD on Unsplash Unlike software engineering, there aren’t a lot of college courses in data engineering. Nor a huge list of boot camps teaching the practice. This leads to best practices often being learned on the job, as well as a cornucopia of technologies being used across teams and companies. So what defines a…
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