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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Category Archives: Data Quality


Top 3 Reasons You Need a Chief Data Officer

chief data officer 150x150 Top 3 Reasons You Need a Chief Data Officer In order for any business to realize the value of its data, the data should be activity managed across the entire organization. And that means someone should have clear responsibility for the data.

But who should that be?

According to Capgemini, that person should be the chief data officer (CDO).

Unlike the chief information officer and the chief technology officer who are concerned with the technological infrastructure of an organization, the CDO is focused on the quality, management, governance and the availability of data.

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The Key to Analytics Success: Savvy Analysts, Quality Data and Effective Tools

Competing on Data or Analytics 300x117 The Key to Analytics Success: Savvy Analysts, Quality Data and Effective Tools A recent Harvard Business Review blog suggests that successful analytics efforts are largely based on the quality and the uniqueness of the data that’s used and less dependent upon the people and programs that are used to make sense of the information. While the quality of the data that’s used is absolutely essential to achieving successful outcomes, what ultimately delivers superior results is a careful blending of the right data, tools, and people skills.

As data management continues to move closer to real-time execution, data quality issues remain top of mind for many practitioners. The ability for analysts to be able to make sense of this information quickly and make it applicable in business terms for key decision-makers is critical to helping business leaders act on relevant information quickly. In turn, these capabilities can help an organization to obtain a competitive edge, whether this involves identifying an emerging market opportunity ahead of industry rivals or discovering and reacting to the root causes of customer churn in a particular region.

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Big Data Challenges and Opportunities

big data opportunities and challenges Big Data Challenges and OpportunitiesConsider a simple trip to a child’s birthday party. You send a tweet that you’re headed to the party and you create data. You get in the car, stop to get gas, pay at the pump and you create data. You buy a card at the store, scan your frequent shopper card, pay with cash and you create data.  You take pictures and a short video at the party, post them on Facebook, Flickr and YouTube and you create data. You send a text message while at the party and you create data. Throughout the entire trip, your cell phone creates data as it continually sends out GPS signals and your car creates data as it tracks fuel efficiency. Take the data for this one activity, multiply it by the number of activities you have, multiply that by the number of people who have activities, and you probably have only a small fraction of the data that’s constantly being generated.

According to IBM, we create 2.5 quintillion bytes of data every day. Ninety percent of the data we have has been created in the past two years and the amount of data is expected to increase exponentially. The data we create is expanding rapidly as enterprises capture more data in greater detail, as multimedia becomes more common, as social media conversations explode and as we use the Internet to get things done. This is “big data,” and it’s getting even bigger.

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Filed under: Big Data, Data Quality


The ABCs of Data Quality

data quality 300x225 The ABCs of Data QualityA:  What is it?

Fundamentally, “data quality” refers to the reliability and value of data in terms of the purpose for which it is being used.  For some uses, data can be approximate, inconsistent, and even inaccurate, but still serve its purpose effectively.  For other uses, data must be absolutely pristine—clean, consistent, and perfectly formatted—in order to have value.  Every organization should identify the level(s) of data quality it actually needs, and commit to maintaining that level.

Data quality is a broad concept that breaks out into several key components.  Among the most important:

  1. Correctness:  How well does the data correlate with the reality it represents?
  2. Suitability:  Is the data appropriate for its potential uses?
  3. Consistency:  Are the same facts represented the same way throughout the data aggregate?
  4. Cleanness:  Is the data aggregate free of inaccurate or outdated data, duplicated data, etc.?

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How “Dirty Data” Derails Your Company’s Data Analytics and ROI

Data Analytics and Dirty Data How Dirty Data Derails Your Companys Data Analytics and ROIHow much of what your company knows is useful? Which data is current? In a recent webinar about the dangers of “Dirty Data” Jay Hidalgo of The Annuitas Group says many companies simply don’t know. He estimated that 30 percent of companies have no strategy for “data hygiene” – removing duplicates or obsolete information. He said 34 percent of companies ask the front-end sales team to update customer and prospect files but even that can be flawed among multi-product or different “views” of the same company, client or transaction.

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