Research

I won’t argue the merits of analytics here, though there are many, but I will describe for you one successful approach that forward-thinking analytics leaders have taken to create a thriving analytics program and a healthy analytics culture: Think of building your analytics program the way an entrepreneur would build a startup business.

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Text Mining Versus Text Analytics

Aug 29, 2014

Available to ERS Clients only

We define textual analysis to be the automated analysis of unstructured textual data, containing within it the methodologies of text mining and text analytics. Leading textual analysis use cases include Sentiment Analysis, Natural Language Processing (NLP), Information Extraction, and Document Categorization. Historically, text analytics practitioners have backgrounds in computational linguistics and knowledge management, whereas text mining practitioners come from the fields of data mining and statistics.

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Direct conversations through on-site engagement with different lines of business is the most direct and effective means of understanding how sourcing processes are occurring today, as well as understanding how the sourcing need evolves and is expressed to suppliers. The initial approach should be selected based on the greatest likelihood of success, not necessarily the area of biggest spending.

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Evaluating Hadoop for Enterprise Big Data ETL

By Ajay Chandramouly, Aug 26, 2014

Like many leading IT organizations, my employer, Intel, has embraced the challenge of extracting business value from big data and turning the insights gained into a competitive advantage. Part of this challenge involves the process used to extract big data from multiple sources, then cleanse, format, and load it into a data warehouse for analysis, a process known as ETL (extract, transform, and load). But the conventional wisdom around ETL is shifting.

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Plan for Big Data Like It’s 2000

By Thomas H. Davenport, Aug 21, 2014

What kinds of activities and decisions should a company pursue as it wrestles with its big data strategy? I see two major decisions at first, and then several others that follow from them. I’ll use Monsanto as an example, since it is a company that is clearly moving from being a provider of seeds and herbicides to one that provides data and analytics-based products and services.

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This powerpoint presentation is the companion to the DELTA series phone briefing led by Lee Pierce, AVP of Analytics and BI at Intermountain Healthcare. Lee shares the strategy and approach that Intermountain Healthcare has adopted to integrate their successful traditional analytics practices and their Big Data analytics practices.

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What Works in Data Management: Data Management Success in the Era of Big Data

By IIA Faculty, Aug 15, 2014

Available to ERS Clients and Professional Members

With the exponential growth in data volumes, sources, and systems, businesses are facing a myriad of challenges managing data. Organizations are quickly realizing that what may have worked in the past is no longer suitable; users have become more sophisticated and there is an expectation that data will be readily available in a variety of formats so as to make informed decisions. While the challenge and risk to manage data effectively is high, there is huge opportunity and the returns can be significant.

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Many organizations fall victim to what I’m about to discuss and a fundamental shift in how organizations think about and fund analytics is required to address it. Today, the systems used to facilitate analytics within most organizations are owned by IT, which means that IT owns the budget to purchase and maintain the systems

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Corporate Real Estate Analytics Part II

By John Serri, Aug 13, 2014

Available to ERS Clients only

Some corporations by nature of their core business have a more analytics-based-tradition than others. While the large fiscal and functional footprint of Corporate Real Estate is well known, and the analytics tools and skills are available, adoption has been slow. What are the barriers that are hampering adoption of analytics? How can adoption be accelerated? In this respect, we briefly examine the roles and responsibilities of those involved with managing CRE.

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Eyes Wide Open: Open Source Analytics Software

By David Macdonald, Aug 11, 2014

Available to ERS Clients only

Across a broad spectrum of information technology projects, the primary cost drivers have traditionally fallen into three categories: hardware, software and people. In a similar way, the costs of assembling the technology and expertise necessary for a robust analytics ecosystem inside your enterprise will fall into these same categories. The unique feature of a data and analytics program, however, is the growing cost of supporting end-users who are playing the largest role in how analytical answers are generated for complex business problems. The human capital costs are not simply confined to IT.

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