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Data Quality

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Data, Data, Data: Communicating Successfully in the Deluge

September 1, 2015 by Daniel Matthews

Now that big data is in the mix, there’s potential for a ton of noise. The business best able to cull what’s vital from the deluge of data, turn it into information and communicate it loud and clear will ride the landslide of data to success.[read more]

Automate the Boring But Essential Parts of Your Data Warehouse

August 12, 2015 by Keith Peterson

To deliver on your company’s future demands for data and insights, you will need to maintain your existing data warehouse – and add the great new capabilities available with big data management and in-memory analytics. The real opportunity is in making those technologies work together smoothly with minimum effort and risk.[read more]

Want More Actionable Information from Your BI? Support Your IT Team’s Need for Data Warehouse Automation

August 6, 2015 by Keith Peterson

If your organization is relatively new to BI but has successfully built some new reports with one of the great visualization tools on the market, you will soon find yourselves in need of a better data organization environment.[read more]

Using Procurement Analytics to Simplify Your Supplier Reconciliation

July 20, 2015 by Keith Peterson

Ask finance managers to name a necessary evil of their responsibilities and many will cite reconciling goods received against invoices not received (“GR-NI”). The GR-NI issue is time-consuming to manage but not exactly mission critical to finding new business.[read more]

Challenges of Working with Big Data: Beyond the 3Vs

July 16, 2015 by Venky Ganti
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Among many challenges in working with big data, the 3V’s (Volume, Velocity, and Variety) have gotten a lot of attention. Googling yields many results worth reading. Almost all of these focus on technological challenges in managing and processing big data. In this post, I would like to highlight a different set issues that make working with big data challenging, even if the underlying infrastructure is admirably able to handle all three V’s.[read more]

Get the Most Out of Your Oracle Application

July 6, 2015 by Xander Schofield

With so many features and benefits it’s possible to get the most out of your oracle application. Oracle is used in virtually every large application due to its ability to integrate technology and pre-integrated, comprehensive business applications and the fact that fixing query performance can also be done fast.[read more]

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5 Tips for Protecting Your Data Assets More Effectively

June 26, 2015 by Keith Tully

Actions taken after data has been compromised in any way can have a significant bearing on how matters unfold and how much damage is done as a result. Companies and particularly their IT experts need to be ready at all times to react to and deal with such issues as they arise. There is no way to guarantee that all data breaches can be protected against but a robust contingency plan can go a long way towards ensuring the ramifications are minimized.[read more]

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The Importance of Cleaning Up Your Dark Data

June 24, 2015 by Rick Delgado

Few trends have had as big of an impact on businesses as big data. Companies of all shapes and sizes have taken to big data with eagerness as they realize how much it can benefit their organizations. There’s little disputing big data’s impressive advantages, from opening up new avenues of innovation to increasing business productivity.[read more]

Taking Control of Your CRM Data

June 23, 2015 by Martin Doyle

CRMs are supposed to be used to achieve better efficiency. By investing time in the CRM, sales teams should be able to identify leads, retain existing customers and successfully recruit new clients to the fold. Sadly, many CRMs fail to perform well. No system could feasibly solve every problem in your business, but if the CRM is creaking under the weight of dirty data, it could actually be hindering progress.[read more]

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Redefining Loyalty Programs with Big Data and Hadoop

June 11, 2015 by Dave Mendle

A loyalty program should not be about points, rewards, or status. While these perks may attract consumers, they don’t foster loyalty. The focus of these programs should be to collect useful data that can be used to build relationships that benefit both the consumer and the brand.[read more]

How Small Businesses Can Use Big Data

June 10, 2015 by Keith Tully
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As a small business owner, you may think that Big Data could not be any sort of help to you. After all, that market is for big businesses with lots and lots of resources. In reality that isn’t actually true. As a small business you can make use of Big Data in your own unique way.[read more]

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Why the Chief Data Officer is the Hottest Job of the 21st Century

May 20, 2015 by Jeff Brown
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Cheif data officer.

In 2012, Harvard Business Review boldly named the Data Scientist “The Sexiest Job of the 21st Century.” In 2013, reporters at CNBC gave the same nod to data analysts, saying it was one of the most sought-after positions. Even U.S. Secretary of Commerce Penny Pritzker came out praising the Chief Data Officer (CDO).[read more]

Do Your Customers Hide Their Data?

April 29, 2015 by Martin Doyle

Hidden data.

Personal data has an image problem. People are increasingly wary of handing over their details to websites – particularly when they have no intention to engage long term. Many marketers set out to obtain email addresses, yet do very little to ensure the email addresses they get are valid. This is the beginning of a vicious circle for the business they’re working for.[read more]

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Learn from Carnegie Mellon’s School of Data Management Hard Knocks

April 23, 2015 by Jeff Brown

Big data error.

The school mistakenly sent acceptance emails to hundreds of hopefuls who applied to Carnegie Mellon’s prestigious Master of Science in Computer Science program. A few hours later, an email with a much different tone arrived in their inboxes. A school statement read: “This error was the result of serious mistakes in our process for generating acceptance letters.”[read more]

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A Better Way to Model Data

April 21, 2015 by Mark Hargraves

Data modeling.

Over 8 years ago, the Spider Schema Data Model was created to provide an easier way to model OLTP data into a supported OLAP data model with the advantages of the OLTP data model. Over the last 8 years this data model has been proven out and is: faster at data processing, uses less storage space, is more flexible, and provides full support for not only OLAP, but OLTP, and Big Data.[read more]