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
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
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
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 it to deliver the transformative impacts
The majority of data science projects fail to reach production (something like 20 percent of data science projects make production). Data science adoption is crucial to the success of an organization’s investment in data science. Thus, this 80 percent failure rate often is the result, not of the data science capabilities, but of the lack
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
Five easy steps to gain support from your company leaders to invest in developing a Business Intelligence infrastructure Today, the increased demand for big data has resulted in Business Intelligence being in high demand. According to a 2019 article by Beroe Inc., the demand for data-as-a-service and personalized BI capabilities are also drivers of increased
Remote work has become a necessity amid the pandemic and some companies are even keeping their workforces remote indefinitely. In some ways, this affords more flexibility for work location than ever. Before the peak of the pandemic, I decided to travel around the world for a year. My employer generously allowed me to keep working
There are many reasons to launch a new Business Intelligence (BI) system in your firm. They include faster and more accurate reporting, better data decision analysis, data quality improvements, customer satisfaction, employee satisfaction, and increased competitive advantage. These are really good and positive for your company, but it’s not always as easy as it sounds.
The United States Presidential Election occurred on Tuesday, November 2. It concluded with a historic victory by former Vice President Joseph Biden and Senator Kamala Harris. The election of Harris to Vice President-Elect is groundbreaking as the first woman, the first woman of color, the first black woman, and the first Asian elected as Vice
According to industry experts, 85% of big data projects fail to meet their potential. But the problem isn’t with the technology. Rather it lies in the companies’ failure to harness the tech’s full potential. This article highlights the reasons organizations fail in successful big data initiatives.