Research

Inquiry Response: Getting Started With Decision Modeling

By IIA Expert, May 21, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

How does decision modeling factor into organizational design and our analytics efforts?

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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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As a person who has been involved with analytics for a long time, I have historically considered analytics to be a huge differentiator while data was more of a table-stakes enabler. Several trends have come together to make me realize that the equation has been reversed in many cases today.

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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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The “Last Mile”:  Keys to Successfully Embedding Analytics in Core Organizational Processes

By Blake Johnson, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

The critical capability for analytics organizations today is the ability to seamlessly integrate analytics into high-value, large-scale organizational processes. This “last mile” to major business impact depends on two cross-functional “bridging” capabilities: 1) establishing leadership by senior business executives, who are the only people capable of driving change in core organizational processes, and 2) carefully designing analytics so that they can be “embedded” into current organizational processes and IT systems, which is required for them to be executable at scale. Blake described best practices for establishing these capabilities identified in a recently completed two-year study, and the rapid scaling of analytics business impact they make possible.

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Who thought Domino’s Could be a Disruptor?

By Julia Oswald, May 08, 2018

Available to Research & Advisory Network Clients Only

2018 Analytics Symposium – Santa Clara Session Recording

How has Domino’s become such a dominating force in the Quick Serve Industry? Julia Oswald’s presentation shared the key drivers of this success. Domino’s broad strategy is to be a part of the disruption. The company has three key tenants to achieve this strategy:

1) Craft food that they are proud of, 2) Drive growth in the carryout business and 3) Aggressively innovate and invest in digital and in-house analytics. The progression of the Strategy and Insights department has been a crucial element across all three tenants of Domino’s strategy. Julia shared philosophies and some of the details about how the company has and plans to continue to be a disruptor in the category.

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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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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: Using Neural Networks for Item Matching

By IIA Expert, May 07, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We sell our products through retailers, and we’d like to do item matching between our products as we describe them in our brand descriptions and the point-of-sale descriptions used by the retailers, which differ from retailer to retailer.

We achieved 95% accuracy using a support vector machine (SVM) algorithm and were able to increase that to 98%-99% when we tried deep learning—convolutional neural nets. The challenge is that we have huge example sets and wonder how we can achieve scalability to train the set and maintain the accuracy.

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84.51° Builds a Machine Learning Machine for Kroger

By Thomas H. Davenport, Apr 24, 2018

Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models. One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention.

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