Research

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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Creating A Data Engineering Culture: What it is, why it’s important, and how, and how not, to build

By Jesse Anderson, Jul 31, 2019

Available to Research & Advisory Network Clients Only

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes.

So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

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Operationalizing Customer Analytics in Financial Services

By Robert Morison, Apr 04, 2019

Available to Research & Advisory Network Clients Only

This paper explores the processes and success factors for operationalizing customer analytics by drawing on the experiences of four varied financial services institutions: a large credit union, a full-service bank with a strong focus on retail customers, and two firms focused on small and growing business customers. We’ll profile each and then look across them for commonalities and lessons learned.

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Leveraging Analytics to Combat Digital Fraud in Financial Organizations

By Robert Morison, Ian Holmes, Apr 02, 2019

Available to Research & Advisory Network Clients and Professional Members

Digitization creates major opportunities for financial services – automating operations, expanding channels, delivering engaging customer experiences. There are corresponding challenges – unprecedented data sources and transaction volumes, channel control in electronic commerce, and preventing fraud when the fraudsters are technologically adept. To discuss the opportunities, challenges, and solutions around financial fraud in the digital age, IIA spoke with Ian Holmes, Senior Manager, Security Intelligence Practice at SAS Institute Inc.

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This article describes the potential for AI to augment risk estimation for both individual investors and financial market assets. AI processes vast amounts of a variety of data to identify patterns underpinning processes and metrics. Evolving data resources including digital touch points provide AI with attributes that can enhance risk estimation to ultimately augment elements of modern portfolio theory.

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Everyone seems to agree that analytics, AI and Big Data are changing the world and that cloud computing is accelerating the adoption of these technologies. Combined, they are disrupting traditional business models and creating new market opportunities. If you are an analytics leader, data scientist, ML engineer, software engineer or other related role you see first-hand the power of analytics to unlocked hidden value in existing businesses or to enable completely new businesses. Are you using your first-hand knowledge of analytics to shape your investment strategy?

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Morgan Stanley Delivering High-Quality Customized Advice at Scale

By Jeff McMillan, Aug 01, 2018

Available to Research & Advisory Network Clients Only

Driving growth at Morgan Stanley is about equipping its 16,000 financial advisors to efficiently deliver personal, customized advice to clients in dynamic markets. Morgan Stanley is making tremendous investments in creating real-time decision engines. This Leading Practice Brief is about how Morgan Stanley uses analytics and algorithms in support of a clear business strategy and empowers the entire organization to use analytics to make better business decisions.

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Three More Reasons to Embrace Automated Machine Learning

By Thomas H. Davenport, Jul 19, 2018

Automated machine learning is good for your company’s analytics function. AutoML has the potential to transform not only machine learning, but the practice of analytics in general. This blog discusses the benefits of AutoML in three different categories.

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Don’t Be Hit by the Analytics Backlash

By Thomas H. Davenport, Jun 19, 2018

Analytics leaders and practitioners need to be prepared both to defend analytics and AI where appropriate, ensure that you’re not contributing to issues like how to prevent algorithmic bias, what industries would be least likely to do harm with analytics, and how to reduce the societal damage from AI.

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