By Ty Henkaline, Mar 02, 2017
The single most valuable practice any analytics team can engage in is rapid prototyping. Analytics teams already do a lot of making, and some do a lot of designing. What almost none do is a lot of prototyping. Prototyping enables a team to turn a potential analytics opportunity into a minimally viable solution – in just a fraction of the time and with just a fraction of the effort.
By Jack Phillips, David Alles, Jan 12, 2017
Available to Research & Advisory Network Clients and Professional Members
As much as any industry today, retail sits at the intersection of both technological and societal change. Web, mobile, cloud, and data technologies are colliding with social media and changing consumer habits to make today’s retailers and consumers unrecognizable to an observer even 10 years ago. Emerging technologies leveraging the Internet of Things (IoT) will most certainly even further alter the retail experience. To keep pace with disruption, the most forward-thinking retailers are putting data and analytics to work to improve all facets of their business.
By Bill Franks, Jan 12, 2017
In recent times, I have read a number of articles lamenting the frequent lack of value resulting from large scale analytics and data science initiatives. While I have seen substantial value driven from many efforts, I have also seen examples where the results were very poor. My belief is that oftentimes the problems can be boiled down to one basic mistake. Namely, thinking that generating predictions, forecasts, or simulations is enough. It is not. Predictions Are The Starting Point… Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find…
By David Macdonald, Robert Morison, Jan 11, 2017
In financial services and other highly regulated industries, regulatory compliance depends more than ever on a company’s analytical capabilities. With increasing regulatory requirements and scrutiny, reliable analytics are essential to the timely accuracy in reserves calculations, stress tests, due diligence, and regulatory compliance. The analytics tools and technologies employed, the flexibility of the technology platform, and the comprehensiveness of the overall analytics program all can accelerate, or compromise, the business processes for regulatory compliance.
By Daniel Magestro, Jan 05, 2017
In the last few years I’ve observed an increase in interest and attempts to implement an “agile” methodology in business analytics projects. This interest reflects the rapid growth of agile in IT development, where TechBeacon reports that two-thirds of surveyed IT professionals are either leaning towards agile or have fully adopted agile in their companies. Less than 20% of those companies had adopted agile methods just five years ago.
By Andrew Pease, JOSEFIN ROSÉN, Robert Morison, Dec 22, 2016
Available to Research & Advisory Network Clients Only
Machine learning is hot and for good reason. The components — big data, computing power, analytical methods — are in place, and compelling applications are multiplying. To capitalize on the technology, organizations must build experience. They must also proceed pragmatically with one eye on the business and the other on the ethical implications of the algorithms deployed and the decisions automatically made. To explore the opportunities, challenges, and success factors of machine learning today and tomorrow, IIA spoke with Andrew Pease, Principal Business Solutions Manager, Global Technology Practice at SAS Institute and Josefin Rosén, Principal Advisor Analytics, Nordic Government at SAS Institute.