Data Quality
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Tackling big data challenges with Hadoop

Tackling big data challenges with Hadoop | Data Quality | Scoop.it
Big data and analytics sounds nice in theory but in practice it can be no mean feat.
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Survey: Data warehouses in no danger from Hadoop -- GCN

Survey: Data warehouses in no danger from Hadoop -- GCN | Data Quality | Scoop.it
A recent survey by Dimensional Research found that data warehousing is still considered a critical business component, in spite of its challenges of scalability and cost. Seventy-two percent of respondents said they expected to increase their investment in data warehousing, and few reported experimenting with big data and Hadoop.

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Carla Gentry CSPO's curator insight, April 4, 2015 4:58 PM

Interest in big data is high, with 91 percent of respondents indicating they’ve considered an investment, but only 11 percent of respondents have a pilot in place, and just 5 percent have fully deployed a big data initiative. One of the big data roadblocks might be Hadoop, with an overwhelming majority of respondents expressing concerns, specifically about access to expertise.

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Data Credibility: A Key Data Quality Dimension in the Big Data Era

Data Credibility: A Key Data Quality Dimension in the Big Data Era | Data Quality | Scoop.it
Recently, there’s been a great deal of discussion within the information management industry about data quality. This discussion has gone beyond simply talking about what data quality means, to cover the potential consequences of poor data quality in operational and ...

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Carla Gentry CSPO's curator insight, July 24, 2014 8:41 AM

“Data credibility is the extent to which the good faith of a provider of data or source of data can be relied upon to ensure that the data really represents is what the data is supposed to represent, and that there is no intent to misrepresent what the data is supposed to represent.”

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License plate datamining may be the next privacy battle for courts

License plate datamining may be the next privacy battle for courts | Data Quality | Scoop.it
In what privacy advocates hailed as a landmark decision, the U.S. Supreme Court ruled Wednesday that police must obtain a warrant to search cell phones belongin

Via Carla Gentry CSPO
Fiona MacDonald's insight:

Data collection and privacy - hot topics that aren't going to go away soon nor should they!

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Carla Gentry CSPO's curator insight, June 26, 2014 2:38 PM

Automatic License Plate Recognition (ALPR) systems are the newrage in law enforcement worldwide. Cameras – affixed to a police car or static locations like light poles, or under bridges – capture thousands of license plates per minute, storing information in databases, recording not only the license plate number, but also the GPS location where each car was "pinged." Tens of millions of federal grant dollars have been doled out to police agencies nationwide for ALPR procurement, setting the groundwork for an expansive, nationwide motor vehicle tracking system.

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We don't have an algorithm problem—We have a data problem | The Big Data Hub

We don't have an algorithm problem—We have a data problem | The Big Data Hub | Data Quality | Scoop.it
RT @MDMGeek: We don't have an algorithm problem—We have a data problem http://t.co/2duy5cSFM2 - by Lorraine Fernandes #MDM #DataQuality #He…
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Healthcare data quality issues

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Why Marketers Should Aim for “Ordinary” Influencers

Why Marketers Should Aim for “Ordinary” Influencers | Data Quality | Scoop.it
For all the attention devoted to “influentials,” consumers with merely average influence may offer a better investment for marketing dollars, according to a study of Twitter data coauthored by network science pioneer Duncan Watts.

Via Carla Gentry CSPO
Fiona MacDonald's insight:

Good article - interesting where the data actually points to rather than what you feel it should point to!

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Carla Gentry CSPO's curator insight, January 28, 2014 3:22 PM

Not surprisingly, the Twitter dataset revealed that the largest cascades tended to be generated by users who were influential in the past and who had a large number of followers. However, most individuals with these attributes were not successful in generating large cascades. The vast majority of URLs did not spread at all and even moderately sized cascades were extremely rare.

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Policing the 'Dark Side' of Internet of Things

Policing the 'Dark Side' of Internet of Things | Data Quality | Scoop.it
The futuristic world of self-driving cars, eyeglasses that e-mail and trash cans that call for pickup is running into the old-fashioned pace of doing busin

Via Carla Gentry CSPO
Fiona MacDonald's insight:

Yes, that Target POS malware is a good example of what is happening already!

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Carla Gentry CSPO's curator insight, January 13, 2014 3:01 PM

The “dark side” of having devices connected to the Internet may involve hackers remotely taking control of appliances inside homes to create a fire or vehicles to kill people, according to Internet Identity, a computer security company based in Tacoma, Washington.

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Simple tips for designing for data quality

Simple tips for designing for data quality | Data Quality | Scoop.it
Is your company struggling with the quality of data across and in your enterprise systems. Most, if not all, data quality problems are caused by human error. Approximately 80% of errors are simple ...
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Great coverage of where and why to check for data quality in your CRM systems.

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Avoiding dodgy marketing dialogues – It’s all about the data - TRILLIUM SOFTWARE INSIGHTS

Avoiding dodgy marketing dialogues – It’s all about the data - TRILLIUM SOFTWARE INSIGHTS | Data Quality | Scoop.it
by Nigel Turner, VP Information Management Strategy, Trillium Software I am not always the most sociable of people. But when walking into town last weekend, a friend of a friend bumped into me.
Fiona MacDonald's insight:

Doesn't matter how fancy your CRM or analytics systems are, if the underlying data they use is bad then so will the results they give you! 

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5 Data Quality Management Challenges | management studies

In just about every field of work, there are quality measures in place to ensure customer satisfaction and product/service effectiveness. Manufacturing.
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Nice coverage of the major data quality hurdles!

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Data Quality "#1 Concern" for CRM Adopters, Survey Finds ...

Data Quality "#1 Concern" for CRM Adopters, Survey Finds ... | Data Quality | Scoop.it
Would it surprise you that as many as 23.8% of respondents to a recent survey are still not using a CRM system, but are tracking sales with Microsoft Excel -- or not tracking any lead and sales pipeline until the revenue is ...
Fiona MacDonald's insight:

Doesn't surprise me that poor data quality causes CRM systems to fail, but it is surprising that more companies aren't taking advantage of lost cost or even free CRM tools!

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Big Data Quality Is Just Data Quality, For Now - Saul Sherry | Big Data Republic

Big Data Quality Is Just Data Quality, For Now - Saul Sherry | Big Data Republic | Data Quality | Scoop.it
“ This infographic from IBM demystifies the management of big data projects. (RT @IBMSmrtrCmptng: The taming of big data!”
Via Sathya Pandalai
Fiona MacDonald's insight:
Data is just data, but is it good data? Good data means it is relevant for the purpose it was gathered for in the first place.
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When the Art Is Watching You

When the Art Is Watching You | Data Quality | Scoop.it
Museums are mining detailed information from visitors, raising questions about the use of Big Data in the arts.
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How good is your customer data (or why are you using that rubbish)?

How good is your customer data (or why are you using that rubbish)? | Data Quality | Scoop.it
In Is Your Data Any Good? Six Questions to Help Score Your Data Resources, Simon Oliver suggests that some data sources simply aren’t worth integrating into your marketing analytics environment.Simon
Fiona MacDonald's insight:

Just because you have data doesn't mean adding it to your big data or database will add value. Sometimes you need to question the data source to see if it will really be beneficial.

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The Moral Dilemma of Big Data

The Moral Dilemma of Big Data | Data Quality | Scoop.it
Over the last few days the news has been breaking over the airwaves, social media and blogsphere about Facebook's social experiment with peoples timeline posts. The results of the study can be found
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Author: Data mining projects depend on excavating meaningful data

Author: Data mining projects depend on excavating meaningful data | Data Quality | Scoop.it
In a QA author David Nettleton explains how to implement data mining projects how to avoid common data analysis mistakes and how to use various data mining techniques

Via Carla Gentry CSPO
Fiona MacDonald's insight:

Preparing the data is an important first step in any data mining process.

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Carla Gentry CSPO's curator insight, June 24, 2014 9:44 AM

Data preparation is a key aspect that determines the success or failure of the later analysis and mining phases. We may find that the required data variables don't exist, and we have to obtain them. We may find that some key variables are available but that the data is erroneous or in an incorrect format. Another problematic step is deployment. We must decide how we will utilize the results in our business processes

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5 Big Data Myths, Busted

5 Big Data Myths, Busted | Data Quality | Scoop.it
Expert Dan Lodin discusses 5 Biggest Big Data Myths of 2013 in an Information Week article. At the top of the list – the myth that running poor quality data through an in memory database will produce a suitable answer.
Fiona MacDonald's insight:

Doesn't matter if you have lots of data or not, if the data is incomplete then it is not fit for purpose.

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4 Women Leading the Way in Business Intelligence

4 Women Leading the Way in Business Intelligence | Data Quality | Scoop.it
To find out what it takes to become a successful female executive within BI, we interviewed four female professionals at the top of the field. Here are their stories.

Via Carla Gentry CSPO
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Carla Gentry CSPO's curator insight, January 19, 2014 4:32 PM

Because of this, Gentry says, women who succeed in BI need to have both strength and desire. “You need a lot of mathematics, a lot of analytics and a lot of business knowledge,” she emphasizes. “But if you stick to the STEM fields, they will always put food on your family’s table.”

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5 Methods for Visualizing Unstructured Data

5 Methods for Visualizing Unstructured Data | Data Quality | Scoop.it
Companies will turn to data visualization methods to make the insights captured from unstructured data accessible to a bigger audience

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Carla Gentry CSPO's curator insight, January 13, 2014 9:29 AM

What can anyone possibly do with so much data? It's not even a question of quantity anymore - it's more a question of feasibility. One can put up a thousand powerful computers in parallel and crunch huge data sets to derive results. But what if the data is also unstructured? What if the problem is not in finding the solution but in finding the correct questions to be asked in the first place? Everybody can obtain a huge data set, and almost anybody can acquire the right set of tools to analyze that data, but very few “somebodies” possess the right mindset to use the data to begin solving business problems.

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Big Data is Like Teenage Sex

Big Data is Like Teenage Sex | Data Quality | Scoop.it
“Yes, that is a catchy headline, but if you've read me for anytime you also know I love a good analogy. This analogy comes from Dan Ariely as shared by Iwona during #cikm2013. Nailed it #cikm2013 #b...”
Fiona MacDonald's insight:
Nice!
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Do you trust your own marketing data? (8 Oct 2013, The Wise Marketer)

Do you trust your own marketing data? (8 Oct 2013, The Wise Marketer) | Data Quality | Scoop.it
The arrival of interactive personalised marketing and digital business via the web, email, mobile and social media has generated a huge demand for collecting, integrating, analysing and using data, according to Nigel Turner, vice president of...
Fiona MacDonald's insight:

Key quote I think "nobody's responsible for ensuring key data is fit for business purpose". Business dumps it on IT and IT figure they don't know if Joe Smoh is valid or not - they didn't call him!

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Ten Reasons Why Your Data May Be Dirty | Data Integration Blog

Ten Reasons Why Your Data May Be Dirty | Data Integration Blog | Data Quality | Scoop.it

 

Data Quality's importance can't be understated. Data Quality can only be taken as a central part of any organization's approach to data coming in and being produced by the organization's knowledge workers. This article gives a good overview of the ways in which an organization can fail to succeed in Data Quality.

Fiona MacDonald's insight:

Yes, data quality is not taken as seriously as it may have once been. Probably because the ownership of the source data is more distributed among people, geographies and applications. Who or what can you hold accountable to poor data quality when the data has so many sources?

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Big data and marketing: an inevitable partnership

Big data and marketing: an inevitable partnership | Data Quality | Scoop.it
Big data has the potential to transform marketing – but making sense of it is a challenge (Big data and marketing: an inevitable partnership http://t.co/6lKPaCTqNu)...
Fiona MacDonald's insight:

The benefits of big data being able to identify what you want before you even walk in the store, etc are great. The privacy issues are the burden though. What if a neferias entities also use this information to target groups of people?

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