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

Press release issued by Forbes Corporate Communications; Ironside comments on how to capitalize on data

NEW YORK, May 31, 2017 — Corporate leaders consider data and analytics capability a top investment priority. Yet despite this, faith in the data businesses rely upon is low. According to the Forbes Insights and KPMG “2016 Global CEO Outlook,” 84% of CEOs are concerned about the quality of the data they’re basing their decisions on. Read more

In the world of data and analytics, technical debt is what happens when organizations make conscious decisions to solve short-term problems, even when they know there could be long-term and potentially negative implications around their actions. It exists in every organization, including yours. Not all technical debt is bad, as long as it is strategically planned and tactically paid off. Read more

Yesterday, Ironside’s partner Pitney Bowes announced that they are forming a new data practice built to accelerate businesses’ digital transformation initiatives. This practice will reach across the whole company to accomplish the goal of helping organizations “utilize data and analytics to deliver a superior customer experience, support product and service innovation, and optimize business processes” according to the announcement. We see this as a major benefit for our clients. Read more

IT and business leaders share a common goal – to leverage the data available to them in order to make more informed business decisions. The first step to achieving that goal is to create a data & analytics roadmap, a task many companies find daunting. Where do you begin?

 

“Most organizations are ineffective in communicating data & analytics-related concepts across departments, resulting in suboptimal management and utilization of information.”

– Doug Laney, Gartner Blog Network

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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

Creating a data and analytics roadmap is an important first step in helping your organization utilize data to achieve growth, understanding, and action. But often we hear from clients that their roadmap isn’t leading them to their desired destination. While many are successful in laying out the technical requirements, identifying the path to business outcomes, and building the right foundation, they still find themselves unable to make sufficient progress towards their goals. Read more

On June 23rd, Ironside hosted a webinar with Pitney Bowes on how to gain valuable insight when you seamlessly integrate Location Intelligence and business analytics.   Crystal Meyers, BI Engagement Manager with Ironside and Heidi Geronimo, Principal Product Manager for LI with Pitney Bowes presented on how Location Intelligence, regardless of size, is a strategic priority for insurers, especially in the Property & Casualty area.

Insurers need precise efficient ways to aggregate, visualize, and assess P&C risk.  We will review what can be done to diminish these risks and show a live demo of a risk aggregation solution which was designed specifically for P&C Insurers.

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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

The immense amount of data being collected today, in any industry, expands the reality of advanced analytics and data science. In concept, it creates an explosion of opportunities and expands what can be accomplished. In reality, we are often limited in scope by our data processing systems which may not be able to handle the complexity and quantity of data available to us. The introduction of Spark has offered a solution to this issue with a cluster computing platform that outperforms Hadoop. Its Resilient Distributed Dataset (RDD) allows for parallel processing on a distributed collection of objects enhancing the speed of data processing. For this reason, Spark has received a lot of interest and promotion in the world of big data and advanced analytics, and for good reason. Read more