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

Cloud and Data Culture at the Ground Level

“Culture still eats strategy for breakfast.” Peter Drucker Cloud and big data projects often fail to deliver business results. Why? Most lists of common causes are likely to include the company culture, and the conversation often stops there because culture can be a hard thing to change. But, there are specific steps you can take

Into the Niche: Data Management and Its Importance in Clinical Trials

Clinical Trials have become quite famous lately. Thanks to Covid-19, the public has become much more aware of the pharmaceutical research and the work that needs to be done before a medication or a vaccine can actually hit the market and bring small or large miracles to the patients and the people who so desperately

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  • Managing the Big Data Project – Lifecycle, Approach, Team Composition, Pitfalls

    Managing the Big Data project Everything ‘expands’ in a Big Data project. There are many more decision points, even before you draw the first entity in your ERD design tool. A typical IT lifecycle, of any type, consists of  Analysis (“You start coding and I’ll go upstairs and see what the business wants”);  Design (“Is

    Utilizing Big Data In The World Of Commercial Real Estate

    if you’re looking to invest in commercial real estate, you could use Big Data to gather information on what are the most current listings, and what areas are drawing the most foot traffic.

    7 Tips To Use Big Data Analytics In Your Business

    To see how you can use big data analytics in a business environment, here are seven tips that shed light on this matter. These tips are practical and can be implemented quite easily, but they significantly impact revenue streams.

    Mainframe Modernization To Cloud

    There is no ‘one-size-fits-all’ strategy when it comes to Mainframe Modernization. Each business case is unique, and they must consider a well-thought and thorough approach to modernizing their legacy Mainframe systems.

    Data Consistency In Microservices Architecture

    Microservices architecture has great features such as high availability, scalability, automation, autonomous teams etc. A number of changes in traditional methods are required to obtain maximum efficiency of the microservice architectural style.

    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

    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

    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

    3 Ways to Determine if Your Organization is Ready for Data Science

    Like many buzzword topics across industries and disciplines, data science faces its similar challenges. It is not unusual for an organization’s leadership to blindly jump into the data science space based on the notion that they “have data”. Data by itself does not mean that they are ready for data science to deliver the transformative