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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 governance is a common need across organizations, and can be a very challenging subject to tackle. Understanding data governance’s components, what good governance looks like, and the drivers behind adopting it is essential to creating a successful governance effort. 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

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.

As you’ve seen in some of our previous articles about the financial services industry, there’s a lot that goes on behind the scenes to enable financial services firms to gain new customers and provide accurate investment advice.  What matters most, though, is that the technology powering all the fund transactions, client correspondence, market analytics, and sales strategies remains reliable and responsive on a daily basis. 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

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

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

As consultants, we have found that the majority of struggling IBM Cognos implementations we encounter are due to either poor framework model design, or more often, a flawed database architecture. The case of the latter can present itself in a number of ways, but in the worst cases, we’ve discovered reporting applications built upon highly normalized OLTP systems that are ineffective and detrimental to both analytical and operational performance of an organization’s information systems. Another common case is an implementation of Cognos upon an existing data warehouse where users are provided with unfettered ad-hoc access to the data source for the first time, exposing previously unforeseen or unknown data quality issues. Read more