Research

How to Self-Assess the UI/UX Design of Analytics Solutions

By Brian O’Neill, Apr 05, 2018

Available to Research & Advisory Network Clients Only

As internally developed analytics solutions become increasingly sophisticated, analytics teams are faced with many of the design challenges seen in commercial, analytics-driven software. After years of working with a variety of different clients on analytics-driven software products, the display of quantitative data, and dashboards, Brian O’Neill developed a set of axioms you can ask yourself to help you begin evaluating the design of analytics solutions for internal customers.

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Phone Briefing: Learnings from Strata Data Conference - San Jose 2018

By David Alles, Mar 21, 2018

Available to Research & Advisory Network Clients Only

IIA’s David Alles made his semi-annual trek to the Strata Data Conference, and returned with insights into emerging technologies, data science techniques, case studies, and key learnings.

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Inquiry Response: Enterprise Machine Learning Platforms

By IIA Expert, Mar 19, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We are working on a platform to empower data scientists across the enterprise to build and manage their own machine-learning (ML) models. Do you have any ideas for how to approach this project?

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Hearing Aids (Or, Metaphors are Personal)

By Kathryn Hume, Mar 15, 2018

In November, I gave the opening keynote at an event about the future of commerce at the Rotman School of Management in Toronto, where I shared four insights outlined in this blog and also heard a great anecdote from an audience member.

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Records vs. Signals: The Landscape of Digital

By Geoffrey Moore, Mar 13, 2018

Everyone gets that data is the new oil in the digital economy, but not everyone gets that there is a critical difference between data as records­­—data in databases—and data as signals—data from log files, sensors, social media posts, and the like. Let me explain.

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Inquiry Response: Advice on Risk Appetite Scorecards

By Doug Hague, Mar 12, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

As part of our governance efforts, one of our charters is to look at the risk around data and establish a risk appetite scorecard. We have developed a basic metrics-type scorecard around the traditional areas such as data cleanliness, data accuracy, and integration. What else should we be thinking about?

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Within a two- to three-year span, Artificial Intelligence (AI) has gone from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell “Artificial Intelligence” a few years ago are now charging full steam ahead to pursue AI initiatives. A common question that is raised is, “Why is now the time for AI?” After all, there have been bursts of hype around AI multiple times over the last few decades. Is today different? And, if so, why?

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Can Process Robots Deliver Digital Transformation?

By Thomas H. Davenport, Feb 27, 2018

One of the fastest-growing areas of artificial intelligence—at least if that term is defined broadly—is “robotic process automation,” a set of capabilities for the automation of digital tasks. RPA, as it is often called, has some valuable functions, but digital-centric companies may need more intelligence and process simplification to than RPA can currently provide.

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Inquiry Response: How to Think About Cleaning Up Enterprise Data

By IIA Expert, Feb 12, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

My team is tasked with guiding data cleanup as part of our data governance efforts. Is there an efficient way to discover where the problem areas are in the enterprise data sources? What should we consider as we think about solutions?

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Is Real-Time Analytics A Money Pit?

By Bill Franks, Feb 08, 2018

Certainly, it is important to have analytics available in the timeframe needed for making decisions. For many years, it was too difficult and expensive to execute analytics anywhere near real-time and so everything was done using infrequent batch processes. As processing power has increased exponentially and costs have dropped to unprecedented levels, it is feasible to perform a wide array of enterprise analytics on a near real-time basis. However, many organizations today are vastly over-utilizing real-time analytics and are paying a price for it that, unfortunately, isn’t always recognized.

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