Research

Three Paths for Aligning Analytics to Business Strategy

By Daniel Magestro, Jack Phillips, Feb 27, 2017

Available to Research & Advisory Network Clients Only

As organizations strive to build their analytics capabilities, an unexpected challenge has plagued many efforts: The activities of analytics teams and the investments made to support them aren’t in sync with what executives expect or desire. On the surface, it might have seemed straightforward for “business analytics” to be in sync with the business’s strategic needs. After all, the decision to invest in the first place was driven by the business’s needs, right?

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Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

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How the Machine Learning of Today is Driving the Artificial Intelligence of Tomorrow

By Andrew Pease, JOSEFIN ROSÉN, Robert Morison, Dec 22, 2016

Available to Research & Advisory Network Clients Only

Machine learning is hot and for good reason. The components — big data, computing power, analytical methods — are in place, and compelling applications are multiplying. To capitalize on the technology, organizations must build experience. They must also proceed pragmatically with one eye on the business and the other on the ethical implications of the algorithms deployed and the decisions automatically made. To explore the opportunities, challenges, and success factors of machine learning today and tomorrow, IIA spoke with Andrew Pease, Principal Business Solutions Manager, Global Technology Practice at SAS Institute and Josefin Rosén, Principal Advisor Analytics, Nordic Government at SAS Institute.

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Improve New Product Development with Predictive Analytics

By Thomas H. Davenport, Dec 13, 2016

Recently on this site, one of us wrote about the new product development analytics used by Netflix. In a nutshell, the company classified the key attributes of past and current products or services and then they modeled the relationship between those attributes and the commercial success of the offerings. This produced a predictive model that provides the company with guidance about how likely a new product or service is to be successful.

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I work with many clients who are trying to effectively adopt advanced analytics – data mining, predictive analytics, data science. One of the biggest problems these clients face is to how to get everyone – business, IT and data science professionals alike – on the same page.

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Bringing Clarity to Data Science Projects With Decision Modeling: A Case Study

By James Taylor, Dec 05, 2016

Available to Research & Advisory Network Clients and Professional Members

This leading practice brief examines an organization that is a global leader in information technology. Like many large companies, it has teams focusing on data science in organizations like marketing, engineering, supply chain, and IT. They also have a centralized business intelligence and analytics capability, shared across internal operations.

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Many times when I speak with analytics managers or business people interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.

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Like many other Americans who went to bed on election night prematurely, I learned about Donald Trump’s stunning victory in the U.S. presidential election on my phone early in the morning. The result was unambiguous but shocking and hard to process, especially at 5 a.m. But also like many other Americans, my shock wasn’t driven by a lack of awareness of Americans’ prevailing anti-establishment mindset and desire for change that tilted the vote (I’ve resided in three of the four key Midwestern states that “flipped”), but by the disconnect between the final result and the longstanding, data-driven expectation we had overly trusted.

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Analytics for Everyone

By Scott Langfeldt, Daniel Magestro, Nov 07, 2016

Available to Research & Advisory Network Clients and Professional Members

Although the focus of analytics initiatives in large organizations often is targeted at enabling data scientists to extract insights from big data sets and complex models, the reality is that users and beneficiaries of analytics capabilities extend across the entire organization. Well, at least that should be the case: Changing the culture to be more data driven is more about changing the mindsets of everyday “consumers” of data and analytics. Putting this broader population of workers in the spotlight, I sat down with Scott Langfeldt at Teradata to discuss the characteristics and needs of these workers, how to support those needs, and the value that better enabling them brings to organizations.

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Stuart William was in one of my former MBA classes at NC State in 2008, and graduated into one of the worst economies ever in May of 2009. Upon graduation, there simply weren’t any jobs available at all! During that period, he networked with as many people as possible, including a fundraising arm at Wake Tech. He met a colleague who worked at Carquest, and after several interviews, took a job there. He started in supply planning, overseeing over $100M of spend in batteries and other categories. He then went into global imports for the central purchasing group in Raleigh. He became a director at that point, working with sales planning, inventory planning, and financial planning, and pulling together the Sales and Operations Planning team, as well as introducing new products and eliminating obsolescence. This was a lot of planning, a lot of analytics, and a lot of work.

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