On September 24th, Ironside hosted a webinar on Exploring Data Warehouse Strategies.   You will hear from our experts on different data warehouse strategies for traditional and emerging solutions.  Whether traditional, hybrid, or cloud-based, this webinar will give you insight on finding the best data warehouse option for your business. Read more

Ironside is happy to announce that we have partnered with Pitney Bowes, one of the top location intelligence and data providers, to transform how our clients see, interact with, and gain insight from the location and business information to which they have access. The Pitney Bowes partnership pairs their sophisticated location intelligence technologies and comprehensive, diverse data sets with Ironside’s unrivaled analytics expertise and technical know-how to bring to market solutions that give geographic context to organizations’ historical data, provide actionable location-based intelligence in real time, and even map future trends using variables relevant to the area displayed. Read more

This article is the second in our Ironside Public Data Powered series, which outlines how we can leverage public data to help us gain insights that drive stronger, faster business decisions. It will take a look at adding public data into a cloud-based data warehouse. 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.

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Public data is everywhere, and if you know where to look, you’d be surprised at the insights it can give you. In fact, when paired with the right tools, this freely available information can enrich and complement your internal data resources to reveal compelling patterns of behavior and trends that you can act on to drive growth at your organization. To showcase what public data can do in the hands of professional analysts, we’re kicking off the Ironside Public Data Powered article series. These publications will periodically take you behind the scenes to show you how our consultants think about and interact with public data using the skills and technologies at their disposal. In this inaugural article, we’ll explore what it takes to start understanding patterns and relationships within a combined public and internal data set through IBM Watson Analytics.

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ELT is a term heard increasingly in today’s analytic environments. Understanding what it means, and how you can make use of it, requires understanding the traditional nature of how data warehouses are loaded and how data movement tools work. That’s why we’ve pulled this article together: to break down the ETL vs. ELT divide and show you where the similarities and differences are. Read more

In 2014, cloud data warehousing services led the information management category in increased adoption rate, jumping from 24% to 34% according to surveys by Information Week . For organizations challenged by data urgency needs that can be difficult to meet with traditional data warehouse infrastructures, cloud services offer an alternative that can provide value at the pace of business, often supplementing existing, on-premise data warehouses. With new technologies and advancements in the cloud data warehousing space, 2015 should prove to be an exciting year for those looking to build out or implement new cloud based DW programs. Whether you are in the midst of a cloud DW initiative, looking to start one soon, or just getting to know the technology – the five trends that we will discuss below are items you will want to keep in mind for the coming year. Read more

Cognos makes extensive use of data warehousing concepts. Most data warehouses are built using dimensional modeling techniques (also known as the “Kimball style”). Data is divided into fact and dimension tables, which are joined together in star schemas. Restructuring data in this fashion takes a great deal of effort, both in planning and implementation. These types of changes are only done because they are necessary for high-quality analytics. Understanding more about how they work and why they are important can help make Cognos a more efficient and effective reporting tool. Read more

Today’s big data challenges for both transactions and analytics are increasing demands on data systems. Traditional data warehouses sometimes struggle as they are often NOT designed to meet the demands of advanced analytics on big data. That’s where solutions like Netezza come in.

IBM PureData for Analytics (formerly Netezza) is a data warehouse appliance that has a purpose-built analytics engine and an integrated database, server, and storage. With simple deployment, out-of-the-box optimization, no tuning, and minimal ongoing maintenance, the IBM Netezza data warehouse appliance has the industry’s fastest time-to-value and lowest total cost of-ownership. Read more