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2016/07/28 16:24:42

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The Pros and Cons of Combining Master Data Management with Big Data

Confusion surrounds the topic of how to bring some sense of order to Big Data. Depending on the day, the discussion might come What is Master Data Management? down to data quality, data governance or master data management.

Here's a hint: One of these is much less necessary than the others. You should always understand the quality of your data -- big or otherwise. And it's just basic legal smarts to create governance rules about data lest you fall afoul of regulatory compliance.

But when it comes to master data management and Big Data, you may be better off leaving each to its own. If you're not clear on why, I recommend this post by veteran integration technologist Kumar Gauraw, who takes you through his thought process on why MDM and Hadoop don't match.

"Master data is, usually, relatively small in volume when compared with transactional data and definitely much smaller than the big data when it comes to volume," he writes. "Big data, on the other hand, is about much larger volumes. In fact, big data is about https://www.amazon.com/MASTER-DATA-MANAGEMENT-GOVERNANCE/dp/0071744584 processing a data volume so large that the current RDBMS databases struggle to handle it."

So, Big Data might fill in MDM gaps or MDM might serve as a reference source for customer or product data when running Big Data analytics. But for the most part, Gauraw concludes that they're two separate systems with different goals.

Until recently, that seemed to be the conclusion most people reached about MDM and Big Data. Then last fall, Capgemini's Christophe Leroquais penned a piece demonstrating three possible points of intersection between these two systems:

Big Data can feed MDM, he points out, by enriching the data in the MDM hub.

MDM can feed Big Data by "providing the data model backbone to bind the Big Data facts."

MDM can help navigate within Big Raw Data. This fits in with a relatively new use case for Big Data called exploratory analysis, he writes.

"The user does not always know exactly what he is looking for; he may discover insights by navigating through the raw data," writes Leroquais. "In order to avoid getting lost within the data ocean, it is necessary to navigate while keeping sight of reference points. This is where the exploration must call on the MDM database."

He adds that this last use case presents major challenge: MDM tools would need to evolve.

That certainly helps in the short term, but I think it actually leaves the real question unanswered. What everybody really wants to know is whether there might one day be an MDM tool designed for Big Data sets.

The real barrier here, as Justin Risch points out in a recent blog post, is that MDM is designed to work with structured, relational data, and Big Data sets are often unstructured data. Risch focuses specifically on how MDM might work with NoSQL databases, such as Cassandra.

Big Data Analytics

"Unfortunately, NoSQL and MDM do not go well together conceptually - Master Databases are relational by definition," Risch writes. "So how, then, do we get the two to play nicely without feeling like we're shoehorning concepts together?"

His reason for wanting to merge MDM with NoSQL is simple: Master data is found in all web applications of sufficient size and complexity, he writes. For instance, an order will have at least customer and product data. There's also the coming data deluge from the Internet of Things, which is getting attention from MDM vendors, as Software AG's Charlie Greenberg points out.

The problem is, how do you capture master data without giving up the scalability and flexibility of NoSQL? Risch envisions several options, including using Key-Value schema and a Document database, but finally settles on a graph database as a potential means of merging both systems.

Risch found one example of a NoSQL graph-based solution, which is the Spectrum MDM Hub by Pitney Bowes. Alas, he wasn't able to vet it and the documentation he finds is a year old. But Risch, at least, is hopeful.

"In the end, Graph Databases may be the key to merging the best of both of these systems," he writes. "There may not be a well-known, easily implemented, out-of-the-box-solution for integrating the two concepts at the moment- but the future is bright. I look forward to seeing more from the best in the MDM industry."

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.



Data Storage Solutions For Large Businesses

Large business enterprises have mission critical applications. The data storage solutions for large businesses need to be most extensible, resilient and controllable. These storage solutions offer maximum scalability and industry leading performances. They have fully integrated centralized management tools and provide unmatched data protection and disaster tolerant features.

The large business data storage solutions have broad selection of data protection applications. These solutions include multiple servers, adapters, switches, operating systems and software. They provide highly efficient and complete backup and restore applications. The large business data storage solutions are generally designed to provide cost-effective high performance along with high volume storage and backup. Most of them also provide innovative storage management software. They generally support encryption as well as compression features thereby enhancing security as well as efficient memory usage.

Efficient storage management strategies reduce both management as well as administrative overhead. Large businesses implement storage networking SAN solutions which deliver scalability, performance and broad interoperability which are required for critical applications and data. Another data storage method is using high performance disk array which are tuned for most popular applications. The storage https://technet.microsoft.com/en-us/library/aa964123(v=sql.90).aspx solutions should be capable of expanding as the storage needs change. It should reduce labour intensive retrieval of data. Certain data storage solutions come with security manager software which reduces the risk of costly data breach.

Strong backup data privacy secures the confidential information stored. As the size of business increases, the size of data that needs to be stored also increases. So enough storage memory is the minimum feature any data storage solution should have. The failure rate while retrieving the data should be the minimum as any loss of data can affect the enterprise adversely. The various data storage solutions for large businesses available in the market are extensively tested to avoid any accidental failure. HP provides a wide range of large scale storage solutions which are designed to meet all requirements of large businesses. The viruses and spy ware attack demands more vigilant backup which requires more What is Master Data Management? storage capacity. The growing demand for storing data among the large businesses results in sophisticated storage solutions.

The behind the scene success of any business is dependant on efficient storing and managing its data. Identifying and implementing the most suitable data storage solution is the first as well as the most important step in securing data. As the size of digital data increases daily at a good pace in large business enterprises, the scalability of the storage solution is a critical factor affecting the performance and efficiency. Depending on various factors like size of data, frequency of storage and retrieval, the ideal solution among Fiber storage, NAS storage, SAN storage and SAS storage is selected and implemented. As data is always one of the most valuable components of any business, secure storage of it gains high importance. So with this in mind it is clear to see why any business who values its data and wants to protect it should look towards using the right kind of data storage.

By: grap

Article Directory: http://www.articledashboard.com

XENON is a manufacturer of high-performance servers and data storage for the digital content creation and corporate market.



Who is Sears' Target Market?

Since it first opened in 1886, Sears has been one of the most famous department stores in the United States. The store is known for its overwhelming selection of products, a range evidenced by the size of its specialty catalogues. In fact, the sheer breadth of products at Sears makes it difficult to determine Sears' target market, which can pose challenges for marketing and advertising. Overall, however, adult female homeowners seem to be the primary Sears shoppers.


Sears opened in the late 19th century, when women were still expected to be responsible for all domestic and household duties. The company initially gained a reputation as a store with a predominantly (though not entirely) female clientele. This has continued to the present day, with the result being a circular marketing tactic: ads target female consumers, who frequent the store more often than men, thus making them Sears' best customers, which in turn makes women Sears' target market for ad campaigns.


According to Sears' target market research, a female head of household is responsible for nearly 70% of the overall purchases made at the department chain. This is largely due to the fact that the store http://disa.mil/Enterprise-Services/Applications/Enterprise-Search itself sells practically everything that would be needed to sustain a household--from hardware to furniture to lawn mowers to toiletries to appliances. Therefore, women who respond to Sears' ads, and who assume most of the shopping duties not only for themselves but also for their families, seize the opportunity to purchase a variety of household necessities in one convenient location.

Bargain Shoppers

In addition to focusing on female consumers (generally aged 25 and over), part of Sears' target market is also the bargain or discount Enterprise Search shopper. In the past, Sears marketed its products as "cheap," but as the word developed negative connotations over time, Sears has begun to emphasize the value of their products. That is, the chain targets shoppers on a budget who still want to purchase items of reliable quality. Therefore, many of Sears' ads feature "daily deals" or seasonal discounts to draw in more bargain hunting shoppers.

Ads Targeting Female Shoppers

Because of the heavy female demographic, many of Sears' marketing campaigns--from commercials to website design--feature women at the center of the advertisements. Often, the ad's models are female celebrities, best known for the appearances on reality TV shows whose chief demographic is women between the ages of 18-35. As of August 2011, the Sears featured members of the Kardashian family, who have designed a new discount clothing line to be sold by Sears. Also featured prominently is fellow reality TV star Audrina Partridge, who is the new face for the Bongo line.

As long as women remain the top consumers, they will continue to be Sears' target market within commercials and print ads. They will also factor in to any decisions the company makes in terms of what new products or signature collections to carry. While men and children are also featured in the ads and marketing campaigns, they generally receive less attention, and mostly serve to model the products for the chief female consumer.



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