Posts

Data discovery is a “new” technique that takes a less formal and more agile approach to analyzing data. Okay, well, it’s not really new — people have been doing this with spreadsheets for decades — but the products that support it have improved greatly and have forced a more formal consideration of these techniques. The data discovery approach produces insights very quickly, but it also encounters challenges when dealing with data transformation. Most data discovery tools are limited in their ability to manipulate data. Additionally, understanding relationships between different data entities can require expertise that some users may not possess. In order to enable agile data discovery, organizations need agile data warehousing. Read more

Even though we’re already blazing full speed ahead into 2016, it’s always important to take a minute to look back at the past year and recognize the high points that made it special. In addition to being named a Boston Business Journal Pacesetter for the second time, making the Inc. 5000 list of fastest-growing companies for the third time, and receiving IBM’s 2015 Business Intelligence Partner of the Year award, we’ve produced several valuable and popular pieces of thought leadership to enrich the analytics community. Here are the top 5 articles 2015 saw us release. We hope you find them useful as you start your journey into the new year. Read more

By now we all know that Hadoop is a central part of many big data projects, but how do we integrate this technology with some of the more traditional approaches to data handling? Is there a way to make sure our Hadoop cluster is interacting with and enriching the rest of our analytics environment? Luckily, there’s a whole suite of utilities that interact with Hadoop to address these questions, and it’s important to know what they are and how to take advantage of them. In this article, we’re going to take a quick look at five commonly used utilities in the Hadoop Ecosystem to help you understand how they can be used to integrate the Hadoop framework with more traditional relational databases and leverage the data for analytical purposes. Read more

The modern data landscape is so much more diverse than it was in the past, and the modern data warehouse needs to increase its flexibility to keep up. In the modern warehouse, it’s not enough to just source all the data; it’s equally important to source all data types as well. Data professionals need to derive insights from various systems of engagement (social media, mobile), systems of record (ERP systems, CRM systems, databases), and the Internet of Things, which means sourcing unstructured, semi-structured and structured data. These needs are driving a rapid evolution away from the familiar enterprise data warehouse (EDW) and toward a new, more flexible solution: the logical data warehouse (LDW). Read more

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

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

Today, our world is filled with data. It has quietly become part of our daily life, and we’ve changed our routines to accommodate it. Beyond the impact it’s had on us, data itself has dramatically changed over the past two decades due to continual advances in internet and storage technology. Almost everything we interact with now is digital: web browsers, smart phones, tablets, social media accounts, and much more. Read more