No Data Engineers, No Problem: How a Small Data Science Team Can Standardize and Centralize Data with a Data Library

Chris Umphlett IN Blog series, Experfy Insights


Access to and control of data is one of the biggest challenges faced by data analysts and data scientists.  Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult. These teams may not have the budget, skills, or IT support needed to successfully implement a data management application. In this series, I will explain a principled approach to home-grown data management that has a low technical barrier to entry and is platform-agnostic: the Data Library.

Big Data, Cloud & DevOps

Introduction to Data Libraries for Small Data Science Teams

At smaller companies access to and control of data is one of the biggest challenges faced by data analysts and data scientists. The same is true at larger companies when an analytics team is forced to navigate bureaucracy, cybersecurity and over-taxed IT, rather than benefit from a team of data engineers dedicated to collecting and

Big Data, Cloud & DevOps

A Tech-Agnostic, Principled-Approach to Grassroots Data Management

In the introduction to this series, I explained what a data library is and how it can help a small data analytics team that lacks formal business intelligence support create a solid foundation for data management. This article will explain the universal principles that should guide the development of a data library. Let’s Look At

Big Data, Cloud & DevOps

The “Operationalized” Data Library- Using Your Data Library to Create Value Quickly and Efficiently

In previous articles in this series on the usage of a data library I dove into the first two of the four characteristics of a data library. This article will explain how the last two characteristics come together in the “operationalization” of your data library. What is a data library? * A set of principles

Big Data, Cloud & DevOps

Examples of How to Implement Each Principle of a Data Library

In the previous article I explained the technology-agnostic principles behind a good data library. This article gives specific examples of how these principles may be implemented. Let’s dive in to the examples of how to implement Data Library principles Automation There are several components to successful automation. The most obvious one is the ability to

Big Data, Cloud & DevOps

Organizing a Data Library

So far in this series I have explained the concept of a data library and the principles behind it. Now I will explain how it interacts with the various building and water metaphors for data storage. There is no shortage of data metaphors to draw from for your data library Metaphors explaining how data should

Big Data, Cloud & DevOps

Prioritizing Data Sources for Your Data Library

In the previous article, I prescribe prioritizing data sources inclusion in a data library according to business value, difficulty, and privacy concerns. This can be done utilizing a scoring rubric and interviewing the owners and/or key stakeholders of each data source. While these things may not be measurable they can be quantified in a relative

Big Data, Cloud & DevOps

Creating a Repeatable Data Library Process

In this final article in a series on how small analytics teams can build a self-managed data library for effective data management, I will summarize the previous articles and show how to put it all together into a repeatable process. A Data Library is Built on a Set of Principles for Data Management, not a

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