Research

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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Inquiry Response: Enterprise Machine Learning Platforms

By Jennifer Prendki, 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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Inquiry Response: Optimizing Website Experience with Analytics

By IIA Expert, Mar 09, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We have a robust testing program for our website experience where we test various permutations in two-week sprints, each measured in isolation. We’d like to start analyzing how our predictions based on the results of these tests perform against reality. Any thoughts?

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Operationalizing Analytics for Intelligent Fraud Detection and Case Management

By Michael Ames, Robert Morison, Jan 31, 2018

Available to Research & Advisory Network Clients Only

Fraud is widespread and continues to grow, especially online. It’s a major problem in a variety of industries and government agencies far beyond the familiar areas of financial and retail fraud, where credit card information is compromised and fraudsters use it for online purchases. The problem worsens as criminals get more organized and technologically sophisticated and operate at greater scale. As fraudsters innovate and scale up, fraud prevention and investigation become more challenging, and advanced analytics become a bigger part of the solution.

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Any new tool or technology has the potential to be put to use for good purposes or, unfortunately, for harmful purposes. Artificial intelligence is no different. As we see the rapid progress occurring in the AI space, lots of attention has been paid to all of the good uses of AI. However, it is inevitable that those with nefarious intent are also studying AI successes with an eye toward how to twist them into tools to pursue their less than honorable goals.

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