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Last spring, I had the opportunity to attend a local analytics conference with Dr. Claudia Imhoff as the keynote speaker. As she got on stage to begin her presentation, she started out by making a statement along the lines of “For every time the phrase ‘Big Data’ is mentioned today, we will all take a shot during happy hour.” 

(Tip: Don’t try that for this article.)

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LEXINGTON, MA, Jan. 29, 2018 – Ironside, an enterprise data and analytics firm, has been awarded “Most Innovative with Data as a Differentiator” by Pitney Bowes, a global technology company that provides innovative products, solutions and data to power commerce. Read more

The simple truth about data warehouses is that the traditional “big bang” method of building them doesn’t work for most organizations anymore. The diversity of data sources and formats we can access is constantly expanding, and taking them all on at once to form a single central repository results in a massive project that can take years to show value. That doesn’t mean we should throw out the concept of data warehousing altogether, though. In fact, there’s a better way to do it that is built on proven development practices, provides value as you go, and feels like less of a massive undertaking: an iterative data warehouse. Read more

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

Many of you have heard buzzwords such as “data science,” “big data,” or the “Internet of Things” before. You’re able to piece together that these fields relate to each other and deal with analyzing data in some way, but maybe you’re not so sure what these terms really mean. That’s what I’m here to help with.  As a newer member of the data science field, I developed this short data science guide based on my experiences and perspectives in an effort to help those who are just starting out. Read more

Ironside has placed the reins of our information management and data integration practice into the hands of one of our strongest data handling experts: Geoff Speare. Geoff will now be serving in a broader strategic and management capacity as our Practice Lead for Information Management. His deep expertise across both traditional and big data-oriented infrastructures, passion for bimodal analytics, and focus on governance make him the ideal thought leader to drive this part of our organization.

About Geoff

Geoff Speare PortraitA long-time Ironside veteran, Geoff is an information management mastermind who can transform collections of data into solutions and architectures that keep businesses growing. The powerful integrations and data management strategies he creates show our clients that efficiently organized data is an invaluable asset improving all aspects of a business. Geoff works with tools from SSIS to PureData Systems for Analytics in order to provide the right answers for each client.

Geoff’s Specialties: Enterprise Data Architecture and Integration, Data Warehousing, Data Integration, Bimodal Analytics

Focus and Goals

Information Management Practice Lead is an apt title for Geoff, as many of the initiatives and solutions he’ll take on in his new role bring together elements from Ironside’s other practice areas.

“Information management is critical to all of the solutions that Ironside provides,” he says. “I’m really looking forward to helping businesses understand and solve the challenges they face in organizing and providing the right data to the right people. We’ve been doing this work for a long time at Ironside and have a lot of experience we can bring to bear.”

The infrastructure foundations that the Ironside Information Management team will build under Geoff’s leadership will provide the solid platform upon which organizations build their analytics strategies, meaning that their approaches need to be flexible enough to account for the many data handling options emerging in the modern landscape. Geoff is excited to take on all these different methods and make them a reality for our clients.

“Much more data is moving to the cloud now, which creates new challenges in putting it in one place for analysis,” he asserts. “Add to that the increased demands for timeliness and flexibility created by data discovery tools and the need for dynamic solutions is greater than it’s ever been. Businesses are shifting from warehouse-only architectures to approaches that can meet the requirements of bimodal analytics.”

This atmosphere of change opens up a wide array of opportunities to innovate in the information management and data integration space. Geoff sees this as a chance to get more people exposed to the behind-the-scenes processes they previously haven’t thought about and help them effectively control their environments.

“A lot of data movement is invisible to users – except when it doesn’t work,” he states. “Creating solutions that are easy to maintain and to understand is something I’ve done a lot of and believe very strongly in. I’ve seen many times the benefits of keeping all of these perspectives in mind, and look forward to bringing that approach to even more of Ironside’s clients.”

If you’d like to see more on how Geoff views information management, data integration, and business analytics, check out some of the articles he’s written. You can also check out our Information Management team page to see how Geoff and his team can help you take advantage of the information that’s critical to your organization’s success.

Don’t think you have big data?  Chances are you do.  The fact is if you have a website, you have big data.  Web servers capture and store events related to user traffic.  The web logs they generate essentially tell the story of what users did when they visited your site.  This information can provide your organization with extreme business value.

If you think you have big data to analyze from your website, you may want to look into Apache Spark.  It’s easy to get started with and makes short work of analyzing your web logs.  It’s actually pretty fun to work with, too.  If you enjoyed playing with LEGOs as a kid, you may have a childhood flashback with Spark. 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