The Data Product Era Begins in Financial Services

By Thomas H. Davenport, Sep 18, 2014

It’s evident that financial services are going to be very interesting users of big data over the next few years. Of course, there will be important regulatory and consumer privacy issues to navigate. It will also be important to figure out just how to make money from these data products.

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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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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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We’re excited to share our latest market research report, “Keeping Customers: Successful Loyalty Through Analytics.” This project, commissioned by SAS, sought to better understand the goals companies have in mind when they invest in customer loyalty programs, and the challenges they often face. We asked hundreds of executives at large businesses to describe their strengths and weaknesses, and used the information we collected to identify the key building blocks utilized by all high-performing customer loyalty programs.

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Building an Analytics Team for Your Organization Part I

By Emilie Harrington, Sep 08, 2014

Analytics can no longer be considered an optional capability for businesses that strive to be competitive in today’s environment. In working with organizations across a number of industries, one of the critical components of any successful program or initiative is driven by finding the right people to lead and participate in the program.

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Big Data Underachievers

By Thomas H. Davenport, Sep 04, 2014

The big data underachievers are companies that have had a lot of data for a long time, but haven’t done much with it. They had big data before big data was big, but for various reasons they simply didn’t use it to improve their business.

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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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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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