Research

Phone Briefing: AI Bracket

By Bill Franks, Jul 26, 2018

Available to Research & Advisory Network Clients Only

IIA’s CAO Bill Franks gave the audience control of this month’s Client Phone Briefing, allowing them to vote for which AI topics he would discuss. While Bill covered 8 distinct (and sometimes controversial) trends across Artificial Intelligence, Machine Learning, Data Science, and Analytics, there were a few that didn’t make the cut. Find links to Bill’s blog posts and opinions on each subject below.

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Attributes of a Data Program: A Practical Guide

By Gregory Nelson, Jul 25, 2018

Available to Research & Advisory Network Clients Only

The ever-growing volume of data challenges us to keep pace in ensuring that we use it to its full advantage. This research brief addresses the tools, technologies, methods and processes useful in designing a data program that is both relevant and actionable.

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Valuing Analytics: Return on Investment (ROI) and Returned Business Value (RBV)

By Doug Mirsky, Jul 17, 2018

Available to Research & Advisory Network Clients Only

What is the value of analytics? This research brief discusses methods of assigning value to advanced analytics efforts and communicating it to stakeholders.

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Data Science Engineering

By David Alles, Jun 20, 2018

Available to Research & Advisory Network Clients Only

Reflections from Strata Data Conference 2018 and the emergence of data science engineering

The complexity of deploying advanced analytics and artificial intelligence (AI) into production is changing the nature of analytics and data science. As the size of data continues to expand and the pace of innovation accelerates, new roles are emerging. While advances in AI continued to headline the discussion at O’Reilly’s most recent Strata Data Conference (San Jose, March 5 – 8, 2018), the emergence of data science engineering as a new discipline is a clear trend and the primary focus of this brief. The objective of this report is to highlight and summarize the themes and trends supporting the emergence and importance of data science engineering. This report features a mix of unique IIA perspectives coupled with supporting thoughts from Strata presenters. This report also builds on key takeaways from our last 18 months of technology conference coverage

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Organizing Analytics

By Robert Morison, May 16, 2018

Available to Research & Advisory Network Clients Only

This research brief describes and offers guidance on:

  • The fundamental goals of organizational structure
  • Six basic models for organizing analytics
  • Mechanisms for coordinating across organizational boundaries
  • Design variables that enable or constrain organizational shape
  • How analytics organizations commonly evolve
  • How to assess readiness for greater centralization
  • Structural variations driven by technological and business change
  • Questions to ask in planning your next structural move

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Scaling Analytics at Instacart

By Che Horder, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

The Data & Analytics team at Instacart is responsible for delivering meaningful analyses that impact the decisions we make as a company. The team is at the center of an extremely data-driven company culture and there is nothing more important to the team than helping the company learn and make great data-driven decisions. The team found that over time, they accumulated chronic issues or obstacles in the environment that were hampering their ability to scale and deliver on our mission. These obstacles were monopolizing their time and reducing capacity to focus and be productive on Instacart’s core mission. Che discussed 1) what his team’s obstacles were, 2) why it is was critical to address them, and 3) how they have removed them.

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Differentiating Via Data Science

By Eric Colson, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

Companies employ various means of differentiation in order to gain a competitive advantage in the market. Traditional differentiators include network economies, branding, economies of scale, and so on. But the availability of data and compute resources, combined with the emergence of new business models, have enabled data science to become a strategic differentiator. Eric Colson explored what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization. The traditional organization needs to be changed if a company is to differentiate via data science. Data science needs to be a top-level department reporting to the CEO. Further, it needs a completely different workflow. It can’t thrive with top-down requirements or if it is forced to submit to upfront ROI calculations. Data science needs more fluidity, more experimentation, and more iteration.

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Deploying AI in Mobile-First Consumer-Facing Financial Products – A Tale of Two Cycles

By Jike Chong, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

What opportunities does anytime, anywhere access of financial services on mobile devices enable? How can we use AI to capture these opportunities to create better financial services for everyone? Jike dove into AI in the context of two fundamental business cycles in financial services: the intelligent acquisition of customers, and the intelligent sustaining of customer relationships. These two business cycles can be broadly applied to a variety of traditionally off-line service industries. Six areas of opportunities were highlighted where AI technologies are being readily deployed today.

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Data & Analytics – Enabling Nike’s Digital Transformation

By Gus Weber, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

Gus Weber’s talk jumped into the role of analytics in digital transformation and the Nike Consumer Direct Offense – a new company formation that allows Nike to serve the consumer faster and more personally, at scale. Gus leads the enterprise-wide technology strategy for data and analytics to ensure that Nike maximizes the power of data as a competitive advantage. This includes investments in personalization that leverage machine learning to curate assortments and advancing the role that product plays in unlocking experiences. Partnering, experimenting and fast tracking the greatest opportunities are key to success – envision a future where athletes seamlessly engage through digitally connected products to inform product innovation design, global manufacturing and distribution. Data is the new voice of the athlete.

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Inquiry Response: Managing Model Maintenance Tasks

By IIA Expert, Apr 23, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

One of our challenges is that once we’ve deployed a model, it must be monitored and fed data. Unfortunately, the important work of model maintenance isn’t seen as a high priority within the enterprise and isn’t seen as a glamorous job by our analytics talent.

Do you have any advice for helping evolve the culture so that model maintenance work is acknowledged as valuable and can attract and retain high quality talent?

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