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Any journey requires a few things before getting started. Wandering through the forest can be a very pleasant experience, but if you don’t plan ahead and bring your compass and map, what happens if you get lost? (I know, you probably brought your smart phone, which has GPS. But then you find there is no signal, way out here in the forest…). Before starting an adventure like this, you need to prepare and make sure you are ready for any obstacles or unknowns that could occur.

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When defining or assessing a Data & Analytics Strategy, Ironside leverages a proven framework of understanding the current state and comparing it to a desirable future state with a focus on six key areas, or pillars.

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I was recently discussing with a colleague what differentiates our various clients. While each of them is undergoing improvement in some form, it is clear that some Offices of Finance perform significantly better than others. The basic difference often lies in their operations. Some run smoothly, with a quiet calm, timely submitted reports, and no finger-pointing; others are a chaotic mess, with deadlines always overdue, high turnover, and dirty mugs scattered around, half full of cold, bad coffee.

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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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Data democratization is the ability of an organization to provide information to end users in an easy and effective way. The goal is to provide self-service of information to end users with minimal IT support. There are many things that can go wrong when rolling out data democratization projects. The purpose of this article is to identify potential issues and provide guidance on how to avoid them in the democratization process.

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2019 is the year that data science, machine learning and artificial intelligence for business will become ubiquitous. Most organizations large and small, across all industries, have recognized the benefits and competitive advantage that these capabilities bring to bear. If you have not already begun the journey, chances are this will be the year you begin to develop this competency. Whether you’re about to take your first step, you’re a team of one looking to scale, or even a more mature organization that is always seeking self-improvement, consider the following traits to maximize your chances of success with data science.

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When asked “What’s your data strategy?” do you reply “We’re getting Hadoop…” or “We just hired a data scientist…” or “If we only had a data lake, all our problems would be solved…”? Plotting a good data strategy requires more than buying a tool, hiring a resource, or adding a component to your architecture. You need something to describe:

  • the goals you are trying to achieve,
  • the stakeholders you are trying to serve, and
  • the internal capabilities required to satisfy those stakeholders and achieve those goals

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At least weekly, I am granted the opportunity to meet and work alongside experienced professionals who serve in a corporate business intelligence (BI) leadership function. When they describe their role upon introduction, there is a common thread to the scope of influence and control which usually intersects one or more of these domains: Read more

We’re often asked how “our methodology” helps drive better user adoption. The key to user adoption is satisfying users’ needs, within the context of their environment. This sounds obvious, but it’s surprisingly easy to miss the mark. And all too often, projects are doomed from the beginning…with the requirements. Read more

This is part five in our five part series on the essential capabilities of the competitive data-driven enterprise.

Over the last 20 years of doing business we have seen a number of different analytical data storage and query concepts fall in and out of favor. Throughout this time, a wave of digital transformation in business has dramatically increased the volume of collected data. Machine learning and other probabilistic methods benefit greatly from the law of large numbers so if by now it wasn’t already clear, all that talk about “big data” has really been about the analytics that it enables. As a result, today’s knowledge workers are predisposed to data hoarding, preferring to save everything including the data for which there are no known use cases, since its future value to the organization may still yet be discovered. Read more

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