Semantic Data
Four Critical Principles of Data Governance Success
I've said it many times: data governance is one of the most important topics in IT. How effectively you manage the quality, consistency, usability, security and availability of your organization's data will play a large part in how successful your business ultimately is. Data is the lifeblood of any business, and if the data isn't healthy ... well, you know the rest.
Reasons to keep data in your own data center | Servers and Stora
Ummm, I pretty sure that is not what he was referring to.The bandwidth available to you at your house is only one piece of the equation.The real speed at which you can access and transfer data is also... Read Whole Comment + Ummm, I pretty sure that is not what he was referring to.
Of course they can. It's Chris Anderson's book, The Long Tail that describes how you make a lot more money selling lots of small things rather than a few big ones. My MP3 download was just anther tiny but accepted data transaction that, in the business-to-business and business-to-consumer worlds, you can expect a lot more of. You may not see this behind the apps on your iPhone, but it is already happening.
Data as Commerce
Look out IBM, Oracle and SAP — you’re about to lose a bit of your dominance in the master data management (MDM) market to Informatica. On January 28, 2010, Informatica announced that it acquired Siperian for $130 million (representing the largest acquisition Informatica has made to date). Siperian is a multi-domain operational MDM vendor that Forrester named as a leader in our last Forrester Wave for Customer Hubs in Q3 of 2008 (see graphic). Source: August 2008 “The Forrester Wave™: Customer Hubs, Q3 2008”
Introducing The MDM Market’s Newest 800lb Gorilla: Informatica A
In IT we’re very quick to point to our operational systems as creators and owners of data. But maybe the solution is that IT establishes a functional team that’s responsible for data packaging and distribution, just like the movie industry. Traditionally data formats and standards have fallen into the realm of the architecture team. Unfortunately this is typically a paper-only activity without teeth.
Standardizing Data Migration
Build a Powerful Business Case for Data Quality with Metrics
Money and resources wasted; sales missed; extra costs incurred. Recent research by industry analyst firm Gartner shows that the shocking price that companies are paying because of poor quality data adds up to a staggering $8.2 million annually. That number is the average loss estimated by the 140 companies Gartner surveyed in August 2009. Twenty-two percent of respondents thought it was closer to $20 million and 4 percent even put the figure as high as $100 million. It’s a sobering thought. But do business managers really appreciate the scale of the problem?
Data Quality for Operational BI
Operational business intelligence shares many characteristics with traditional BI, but it also differs in many ways, the most dramatic of which is the timeliness of the data acquisition and integration process. Traditional BI can often rely on overnight or intraday batch processing for collecting and processing the data. To meet operational BI needs, the update cycles repeatedly require more frequent processing of the data and do not allow for a batch processing cycle. This has several implications with respect to ensuring data quality, two of which are governance/data stewardship and source data quality. Governance and Data Stewardship Best practices for a BI project dictate effective governance structures as well as a robust data stewardship program.
The Data Management Lifecycle
Many organizations find that they cannot rely on the information that serves as the foundation of their business. Unreliable data – whether about your customers, your products or your suppliers – hinders understanding and affects your bottom line. It seems simple: better data leads to better decisions, which ultimately leads to better business, but many executives need to take data quality and data governance more seriously. We must manage our data in the same fashion that we manage any process – with a defined, predictable methodology. This requires a data management lifecycle methodology that helps us understand how to manage, monitor and maintain our data to benefit our business.
Davos 2010 - IdeasLab with MIT - Tim Berners-Lee
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Data, Data Everywhere
The February 25, 2010 Economist revolves on a 14-page special report entitled Data, Data Everywhere , must reading for those looking for the big-picture future of BI and analytics. Alas, I can touch on only a smattering of the covered topics there. Tech bellwether Cisco estimates that by 2013, 667 exabytes (1 exabyte=1000 petabytes) of data will flow over the internet annually.
Now the amount has increased tenfold. During 2009, American drone aircraft flying over Iraq and Afghanistan sent back around 24 years’ worth of video footage. New models being deployed this year will produce ten times as many data streams as their predecessors, and those in 2011 will produce 30 times as many. Everywhere you look, the quantity of information in the world is soaring. According to one estimate, mankind created 150 exabytes (billion gigabytes) of data in 2005.
Technology: The data deluge | The Economist
One of the principles underlying a true database is that there is no duplication of data: viz, there is no redundancy. Thus, the conceptual data model is pure; and yet for other equally valid reasons of ruggedness and reliability, there is a strong case for designed and inbuilt controlled redundancy. Another valid reason is operational efficiency. The Merits of Controlled Redundancy
The Merits of Controlled Redundancy
April 8, 2010 – The 2009 Oracle Applications Users Group ResearchLine Survey this week revealed 87 percent of respondents blame their performance issues on data growth. The study was sponsored by Informatica and produced by a Unisphere Research survey of more than 225 members of the OAUG. Some enterprise applications and databases increase in size by as much as 50 percent per year.
Call to Action: Deal With Data Growth
