Research

CAO Perspectives: Ideal Analytics Organization

By Doug Hague, Nov 13, 2019

Available to Research & Advisory Network Clients Only

To set the stage, the analytics organizational structure I’m presenting below pertains to an analytics organization between 60 and 120 people; this is the size that seems to be a sweet spot for an effective and efficient team (large enough to have specialized skill sets, but small enough to effectively demonstrate the benefits of the team). Moreover, I’m presenting such an organizational design in consideration of an analytics effort at an established, traditional corporation, not a digital native. Digital natives will break down differently with more need for data science and data management. With 60 to 120 people, I prefer a centralized organization with P&L Analytics/Ad Hoc Analysis dotted-lined to their business partners.

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Detroit 2019 Analytics Symposium Video: Carole Piovesan

By Carole Piovesan, Nov 05, 2019

Available to Research & Advisory Network Clients Only

While early adopters of AI technologies are projected to share a global profit pool valued at $1 trillion, more than 95% of companies have not yet embraced AI technology to reinvent how they do business. A key reason for slow adoption is a shifting and uncertain regulatory landscape. Global, national and jurisdictional regulatory rules about data and AI are unclear and companies are rightly concerned about reputational and legal consequences associated with a possible misstep such as a privacy or security breach.

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Detroit 2019 Analytics Symposium Video: Matthew Johnson-Roberson

By Matthew Johnson-Roberson, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Having just come out of stealth mode in July, University of Michigan Professor, former Ford control algorithm innovator and now startup co-founder and CEO of Refraction, Matthew Johnson-Roberson has jumped into the autonomous vehicle race. But he has a particular angle that has yet to be addressed – delivery robots that can operate in rough weather, such as Michigan winters. Matthew will talk about Rev-1, his new delivery robot, and the decisions that went into defining a market opportunity that leveraged the analytics potential of what could be developed.

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Detroit 2019 Analytics Symposium Video: Tom Davenport

By Thomas H. Davenport, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Many companies are dipping their toes into artificial intelligence, but only a few are attempting to put AI at the core of their strategies and business models. As with analytics, taking a leading position on AI is likely to be rewarded with competitive success. Tom will describe what companies that aspire to be “AI First” do, and how other companies can learn from their pioneering approaches.

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Detroit 2019 Analytics Symposium Video: Abraham Thomas

By Abraham Thomas, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Since the dawn of Wall Street, investors have sought an edge: a source of advantage they could leverage to beat the market. It’s the only way to win in the zero sum game of institutional investing. Today that edge comes from information found within the exabytes of data we all create every single day of our lives; something made possible by the technology boom of the 90s along with Moore’s Law of improvements in processing power and data storage. The prevailing belief is that the predictive signals buried in the “data economy” have the power to move markets. This is the age of alternative data, and investors are racing to get their hands on it. With an estimated market size of $7 billion by 2020, alternative data is transforming capital markets. Abraham will walk you through how investors are finding, leveraging, and profiting from alternative data today.

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Detroit 2019 Analytics Symposium Video: Nick Curcuru

By Nick Curcuru, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Artificial intelligence has become the hottest commodity in recent years, and business, academia, and government have embraced it to propel complex use cases. As AI becomes more woven into the fabric of organizations (and its criticality increases), enterprise infrastructure becomes essential. AI is only as strong as its weakest link. The ability to build out use cases, deploy into production, scale, and secure all relies on the supporting solutions and infrastructure. There are many different decisions to make when choosing the right solutions and infrastructure: On-premises or off? GPUs or CPUs? Which storage system and framework to use? The list goes on. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—you need to exercise to build and deploy a successful AI.

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Detroit 2019 Analytics Symposium Video: Alistair Croll

By Alistair Croll, Nov 05, 2019

Available to Research & Advisory Network Clients Only

The lifespan of a company on the S&P 500 and Fortune 500 has plummeted from nearly 70 years to around 15. And attempts to innovate fail more than 95% of the time. But the best companies survive by balancing a portfolio of innovation approaches. Based on 10 years’ research and interviews with corporate innovation leaders around the world, this talk offers a model for managing and measuring new initiatives that is concrete enough to put to work immediately.

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Inquiry Response: Data Catalog Vendor Considerations

By IIA Expert, Sep 23, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We’re considering partnering with a data catalog vendor. What should we be thinking about?

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The Fuzzy Line Between Good and Evil Data Science

By Bill Franks, Sep 12, 2019

Available to Research & Advisory Network Clients Only

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.

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