Category: Data

Considerations for Choosing a Data Science Platform

With the proliferation of data science platforms how do you pick one that’s right for you? When choosing a data science platform, the following characteristics should be considered, in no particular order or priority (prioritization may depend on the specific business outcome being addressed or making trade-offs on one characteristic versus another to obtain the

Why Visual Data Science Platforms are Here to Stay

With the continued democratization of data science, the market has responded. Now, there are visual data science platforms for designing and implementing the analytic process flow and advanced analytic solutions. And they’re not going to disappear any time soon. This post takes a look at the background of these platforms and walks you through a

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Data truth – revisited

The differences between the phrases “Single Source of Truth”, “Single Version of the Truth”, and “Single Source of Data”, and use of the phrase “Source of Truth” within the context of data streaming.

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Harnessing your BI Powers

Technology has allowed us to have data at our fingertips and we all know that knowledge is power. Let’s harness our power for good! The definition of Business Intelligence (BI) is to support better business decisions. BI should  empower people to make better choices and gain insights into problems so they can be solved easier, faster, and better or discover new opportunities.

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Data Science: Back to Basics – It’s about being flexible

Learn how the flexible or inflexible characteristics of machine learning or statistical model can impact its performance and predictive capabilities.

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Azure Integration Part III: Azure Storage Offerings

When developing your integration solutions in Azure, there is no doubt that there will be discussions on which storage offerings you will want to use.  Depending on the requirements, there will be an offering in Microsoft Azure that will be available. In Part 3, we will focus on some of these different storage options that are available for any solution integrating with Microsoft Azure.

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Azure Data Lake Store – Service-to-Service Authentication

In this blog post, I explain how to set up service-to-service authentication while working with Azure Data Lake Store and Data Lake Analytics.

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Data Science: Back to Basics – Don’t forget Data Exploration

Data Science is one of the hottest fields today. But as people take on the Data Scientist job title, it appears data exploration has taken a back seat within the data science process. In this blog, let’s break down what data exploration is, and how it is an important step of the data science process.

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A Guide to Using R with Power BI

The integration of Power BI and R has greatly extended Power BI’s capabilities. However, it can be challenging to know the best times to use R. Using R and Power BI within the context of the Power BI service and Power BI desktop does have its limitations, mainly that R output must result in an R graphic object. Therefore, this blog post provides a brief list of guidelines and examples to help determine when to leverage R functionality inside of Power BI.

data architecture

Why Architecture Matters for Data and Analytics – Part II

Following on from our first article on the aforementioned article, we demonstrated how Data Architecture provides an understanding of what data exists, where it is stored and how it flows throughout the organizations and/or systems. We also demonstrated how understanding system mapping, connection and configuration is key to formulating and deploying a data architecture that fits into the company’s objectives.