Posts

Business intelligence has been around for a long time. From decision support systems in the 1960s through Ralph Kimball’s books on dimensional modeling in the 1980s, the core concepts of the discipline are decades old. As these concepts and the products built around them mature, more advanced techniques and technologies come to light that evolve and redefine what we thought we knew about the business intelligence space and business intelligence’s future. For instance, developments like the cloud, data visualization tools, and predictive analytics are changing the way businesses evaluate and make decisions from their data. Read more

By now, we’ve all heard the V3 definition for Big Data maybe a million times: Volume (Size of Data), Variety (Type of Data) and Velocity (Frequency of Data) with Veracity (Accuracy of Derived Insights) thrown in as an extra sometimes. The issue is that this all-too-common definition has caused some confusion in organizations around who qualifies as having big data or a big data problem. Read more

As we mentioned in a recent article, The Why, What, Who, and How of Successful Hadoop Deployment, there’s a lot you need to consider when implementing Hadoop to manage big data at your organization. Now we’ll build off that perspective and explore the data lake. Like any other new methodology just starting to gain ground in the information management space, there are a lot of assumptions about what data lakes can do and how they tie in with Hadoop-based infrastructures. In this article, we’ll discuss the most essential pieces of knowledge you need to wade into data lakes, dispel some of the rumors around them, and explain how they can fit into your information management ecosystem.

Read more