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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Monthly Archives: April 2012


Tips To Get Your Organization Ready For Big Data

big data hype or value trend alert1 150x150 Tips To Get Your Organization Ready For Big DataWith all the hype about big data many organizations are trying to figure out how to fully and immediately capitalize on its arrival.

And although there are plenty of articles that highlight the successes of big data initiatives experienced by large data-producing organizations (Google, Facebook, etc.), how they achieve that level of success currently evades most companies.

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The Data Analytics of the NFL Draft

draft 2007 2010 2009 2008 The Data Analytics of the NFL Draft Today is a big day for all you football fans and data geeks. Are you ready for the NFL draft?

This year’s draft features more analytics than ever before. Plus, the business decisions of NFL owners are center stage in light of what happened in Denver and Indianapolis the past couple months.

Tonight, after nearly not having a 2011 season, we’ll see NFL teams lessen their risks as new NFL collective bargaining rules go into effect. According to an article in Forbes, this year’s draft is less of a risk than years past and “drastically” reduces the amount of moolah “an unproven player” can receive in his first year.

From a business perspective, it’s a good move to make a decision based on analytics and performance over the perceived value of talent. And some teams fare better than others when you look at the historical data.

To determine which teams have scored touchdowns in past drafts and which teams couldn’t even split the uprights, Forbes turned to a source after our own hearts – the analytics of drafted players over the course of four years (2005 to 2009).

The author defined parameters and specific questions before engaging the data and was able to make some solid decisions on which teams have made the best and worst NFL draft picks over time.

Best & Worst Teams in Draft Proficiency

We won’t leave you in suspense for the best and worst teams regarding drafting proficiency (according to research conducted by the author, Patrick Rishe, and a Syracuse sophomore with a bright career ahead – perhaps in analytics – named Tyler Wasserman).

According to Rishe’s feature in Forbes, the best teams at draft proficiency are the Green Bay Packers and the New York Jets and the worst teams at draft proficiency are the Cincinnati Bengals and the St. Louis Rams.

Now, for the data analytics criteria:

  • The researchers looked at a strict time period – 2005 to 2009 – to ensure that the players had sufficient time in the league to adapt to the pro environment;
  • They analyzed seven rounds of draft picks for 32 teams. (That’s all of the teams and all of the rounds.);
  • They looked at four variables to determine draft proficiency: the percentage of games a draft pick played; the percentage of games the player started; the career of a player (still playing in the NFL); and the number of Pro Bowl nods a player earned.

With this criteria, Rishe calculated an index that he explains here to get his top picks for draft proficiency. It’s well worth the read.

Bad Draft Pick = Costly Decision?

According to our featured infographic, the impact of a bad hiring decision in football can result in the loss of millions of dollars. However, a traditional college grad who works from age 25 to age 64 earns about $2.1 million or roughly $51,000 per year. That’s just 1% of an NFL draft flop. But here’s a question for you to ponder: Would a $51,000 flop hurt your business? Analytics can help any HR executive come up with the right answer.

The Year of the Quarterback Draft

Who will be the lucky first named pick – the appropriately named Andrew Luck (Stanford) or Heisman Trophy winner RG3 (Robert Griffin III of Baylor)? The expectation is that Luck will get the draw and take Peyton Manning’s old seat at the head of the Indianpolis Colts and RG3 will go second to the Washington Redskins. But strange things can happen in the NFL draft.

An interesting note about this year’s draft is that it’s only the fourth time since the NFC and AFC combined their selection processes in 1967 that two quarterbacks will go first and second. Even more interesting – in each of the other three times two quarterbacks went first and second in the draft, only one of the two went on to have a successful career in the league. Maybe RG3 and Luck will change this stat.

Defensive Deficiency Draft Indicator

data analytics of nfl draft 300x238 The Data Analytics of the NFL Draft Just for fun and to give the data geeks a little context around the defensive side of the draft, we’ve put together a heat map visualization on the 2011 season defenses. It breaks down the most important team defense KPIs and ranks each team by yards per rush, yards per pass, total yards allowed and total points allowed. It could give you a few insights into NFL draft strategy based on the defensive deficiencies. For instance a team that has high yards per rush allowed may need to draft interior linemen and middle linebackers and teams with high pass yards allowed may need a little help in the pass rusher and corner back positions.

Next Steps:

  • Tweet us your thoughts on this year’s draft and our heat map on the defensive deficiencies.
  • Don’t forget to check out our complimentary “5-Minute Guide to HR Analytics“ to see how you can get solid answers to questions like: “How do we spot and retain the most valuable players?”

Amanda Brandon
Spotfire Blogging Team

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How To Develop A Top-Notch Data Warehousing System

DataExplosion How To Develop A Top Notch Data Warehousing SystemUnlike just a few years ago, each of us generates a “ton” of data every day.

Almost everything we do generates date including surfing the web; buying groceries from the supermarket; and sending text messages. In fact, with the right mobile technology, simply walking into our local malls generates data.

With this explosion of data, developing a top-notch data warehousing system is paramount to the success of any company. Processing data in a well-developed data warehousing system provides the competitive edge that companies are striving for. The question then becomes: What steps should you take to ensure you build a data warehouse that enhances and supports the decision-making process?

Mike Ferguson makes the point that you and your data warehouse development team have to understand certain things including:

  • The corporate business strategy: For your decision makers to make the best use of the data, your data warehouse development team must understand the corporate business strategy. Once they do, they can work with the decision makers to determine which objectives are priorities. They can also figure out how the data can lead to a higher return on investment. They must continue to work with the decision makers to join the data elements to the objectives then determine how to capture the appropriate data and build the necessary dependencies to make the data meaningful.
  • The data requirements: Your development team must work with the corporate data scientists and analysts to define data requirements, data sets and desired data visualizations to ensure that the data warehouse is highly interactive and that it allows users to customize the data sets, charts, and graphs. And they must make that information available in a variety of formats including dashboards, scorecards, and reports.
  • The technical environment: You must learn as much as you possibly can about the proposed technology and use that knowledge to draft the data warehouse technical architecture. Then you should participate in all facets of the technology selection process and work with the team to develop an implementation plan, which should include:
    • A metadata repository – to track information about both the data and the system including processes. Define the business vocabulary, store it within the repository and share it across the organization.
    • An ETL process – to extract data from transaction systems, transform the data into something suitable for the data warehouse and then load the data into the target system; i.e. the data warehouse. Pay close attention to how the data is manipulated, how long the process takes to completely execute, and the accuracy of the data. Be prepared for data that may be incomplete, incomprehensible, and inconsistent and develop processes to handle the issues.
    • Security – what level of security will be required? How will the data warehousing system be maintained to the level required by the organization, internal compliance, and external laws and regulations?
    • Data integration – combine data from several disparate sources into a unified data warehousing system. Then attempt to embed the system within existing corporate software for quicker adoption as the user may feel familiarity with the look and feel, leading to less end-user training.

Next Steps: 

  • To learn more about how analytics can improve your business and increase your bottom line check out these complimentary guides:
    • 5-Minute Guide to Business Analytics,” to find out how user-driven “analytic” or “data discovery” technologies help business and technology users more quickly uncover insights and speed action.
    • 5-Minute Guide to HR Analytics,” to discover the three critical capabilities a modern analytic environment must provide to the entire spectrum of HR staff so they can adequately support the enterprise.
    • 5-Minute Guide to CRM Analytics,” to learn how agile analytics technology can help you deliver critical value to executives and front-line marketers.

Dennis Hardy
Spotfire Blogging Team

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3 Ways HR Analytics Can Boost the Bottom Line

head hunting 300x225 3 Ways HR Analytics Can Boost the Bottom LineHuman resources touches every department across a company, and people are often a company’s most valuable – and expensive – asset.

Why, then, do so many companies make critical HR decisions – like realigning workers to fill shortages in-house versus hiring new workers with the necessary skill sets – without grounding those decisions in HR analytics?

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Infographics Get Pinned on Pinterest

pushpins 300x225 Infographics Get Pinned on PinterestIf you haven’t given much thought (yet) to Pinterest, that’s not surprising.

Although the three-year-old social media site has become wildly popular over the past few months, the huge majority of Pinterest fans are women from twenty to thirty-five – and most of the image boards created on Pinterest are focused on “life-style” topics such as fashion, gardening, home décor, travel, and food.

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Are You Asking the Right Questions with Predictive Analytics?

asking right questions Are You Asking the Right Questions with Predictive Analytics?Predictive analytics can help companies forecast business results with stunning accuracy – for example, how a particular group of customers might react to a targeted offer or what the potential business impact might be as a result of a marketing program.

But do data scientists always ask the right questions to get at the heart of what business leaders are attempting to accomplish? For instance, when is the right time to make an offer to a customer in order to generate the most favorable results?

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From the Experts: 3 x 3 Tips for Implementing an Agile BI Strategy

agile BI 150x150 From the Experts: 3 x 3 Tips for Implementing an Agile BI StrategySometimes when you want answers to important questions you have to ask the people who’ve been there, done that.

And that’s exactly what Michael Schmier did when he wanted the answer to this question: What are your three tips for implementing an agile BI strategy?

Schmier, a product, marketing, and customer experience professional, asked his question on, a business professional social network whose members offer advice on a variety of business and IT subjects.

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Using Analytics to Stoke Innovation

customer analysis 21 Using Analytics to Stoke InnovationForward-thinking companies are gathering, analyzing and acting on information that’s coming to them from multiple inputs, including social, mobile, email, chat, call center, operational, transactional, and market data.

Leading companies are using this blend of insights to identify and act on new product opportunities or improvements for delivering on customer experiences in different channels.

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What Data Scientists Must Learn About Customers

generate customer insights1 What Data Scientists Must Learn About Customers2012 has been coined the year of the customer by experts in a number of industries.

Because of this, companies are striving to become more customer-centric in their approaches to business.

But companies that hope to improve business outcomes by tightening relationships with consumers must gain a deeper understanding about their customers from the multiple channels customers use to interact with them.

While social, web, mobile, and other sources of customer data can help companies develop richer views of their customers, data analysts who work with this data need to develop a greater understanding about what makes customers tick.

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Data Scientists – Is it Really a Matter of Degree(s)?

data scientist education certification  Data Scientists – Is it Really a Matter of Degree(s)? There’s no disputing the fact that big data is creating a big demand for data scientists.

In a report on big data, the McKinsey Global Institute released some pretty stunning numbers: “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

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