Five Clusters of Opportunity with Big Data

By Robert Morison, Sep 23, 2014

In what areas of your business can big data have the greatest impact? In many organizations, that’s a very difficult question to answer. Why? Big data offers so many new opportunities and so many different kinds of opportunities, from using real-time sensor data to mining the unstructured conversation on social media.

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Do Your Metrics Matter?

By Kimberly Nevala, Sep 16, 2014

Your organization has well-defined metrics. Executives track them diligently. Managers include them in status reports. Key Performance Indicators (KPI) are prominently featured in annual reports and PR. So isn’t the company, by definition, data-driven?

The answer, unfortunately, is: not necessarily.

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Inquiry Response: The ROI of Data Governance

By IIA Faculty, Sep 11, 2014

Available to ERS Clients only

We are in the process of proposing an organizational structure (more formal than we’ve had thus far) for data governance. One of the requests from our exec team was to provide more detail regarding Return on Investment (financial and other) for a DG program. Does IIA have any resources, methods, for building a justification and ROI for DG? Or how would you suggest I tap into IIA resources for this?

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Prepare to be Amazed by Immersive Intelligence

By Bill Franks, Sep 11, 2014

The range of immersive visual worlds that can be created is limited only by the types of data that exist and how that data can be utilized. In other words, it is virtually limitless.

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Health Analytics in a Person Centered World Part II: The Person-Centered Health Analytics Toolkit

By Dwight N. McNeill, Sep 05, 2014

Available to ERS Clients only

Person Centered Health Analytics (pchA) sets a new course for analytics. Health outcomes are no longer confined to the two P’s (providers and payers). Outcomes are centered squarely on the forgotten P, people. It is not about business intelligence for improving revenues and reducing costs; it’s focused squarely on helping people to live a long and healthy life. It is not about worshiping the possibilities of information technology; it’s about getting information technologies to engage people as drivers of their own health.

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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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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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