Research

Competing on Analytics by Industry

By David Alles, Feb 17, 2020

Available to Research & Advisory Network Clients Only

Organizations today face an increasingly challenging business environment. Across industries, new companies and nimble competitors are taking advantage of analytics and leveraging the full potential of the internet, disrupting traditional business models and markets.

Cloud computing and open source have caused fundamental changes in analytics infrastructure, enabling the introduction of new technologies such as artificial intelligence and machine learning. The most nimble, innovative companies have quickly taken advantage of these new technologies — and the analytics they enable — to gain a competitive advantage.

Traditional companies are struggling to deal with this complexity and effectively compete on analytics. Though many top executives realize that high-quality data, analytics and AI are critical to the future success of their companies, up to 70 percent of analytics initiatives and projects fail to meet their objectives.

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A Decade of IIA

By Thomas H. Davenport, Jack Phillips, Jan 08, 2020

Available to Research & Advisory Network Clients Only

We co-founded the International Institute for Analytics in 2010. Since it’s now 2020, our sophisticated math skills tell us that IIA has been around for about a decade—although our first full year of operation was in 2011. We thought it might be interesting to reflect on the state of the field that IIA addresses and how it has changed over time.

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2020 ANALYTICS PREDICTIONS AND PRIORITIES

By Thomas H. Davenport, Bill Franks, Drew Smith, Robert Morison, Dec 19, 2019

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

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Inquiry Response: The Importance of Demand-Oriented Analytics

By IIA Expert, Dec 02, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

Our advanced analytics team has been tasked with looking at the state of our data program with an eye toward arriving at a new future-state framework. Currently, our biggest challenge is that our internal customers’ processes are not set up to consume what we provide. Do you have any advice?

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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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Inquiry Response: Progression Tips for Growing into AI

By IIA Expert, Doug Hague, Oct 28, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We’re moving toward intelligent automation, trying to add cognitive capabilities to our RPA toolset. We’re looking for guidance to ensure that we’re doing the right things over the next few years to grow into AI.

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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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We’ve had technical people focused on the ingestion and management of data for decades. But, only recently has data engineering become a critical, widespread role. Why is that? This post will outline a somewhat contrarian view as to why data engineering has become a critical function and how we might expect the role to evolve over time.

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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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