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Dylan Jones 0
Poor data quality? Check for model gaps

Every business, regardless of size and sector, is subjected to continual change. Business models are constantly evolving and adapting as consumers react to new technologies, laws and trends. Take a look at Amazon as an example. It has adapted its business model in an agile fashion by offering streaming of

Phil Simon 0
Using data brokers: tread lightly

These days, many organizations define themselves in terms of their impact on the environment. Naysayers may dismiss companies like Starbucks, UPS and others as engaging in PR stunts. Still, the fact remains: there's potential competitive advantage to be gained by being carbon neutral – or at least by ostensibly caring about

Jim Harris 0
The antimatters of MDM (part 5)

In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a factor

David Loshin 0
Exploiting connectivity: graph analytics

One of the benefits of the disruptive nature of emerging big data platform technologies is that the combination of scalable performance and lowered costs for high-speed memory opens the door for addressing business problems in ways that used to be too computationally-intensive to roll out on a broad scale. One good example

Dylan Jones 3
3 tips for turning around a sinking data migration

Data migrations can be challenging initiatives at the best of times, but when they start to go wrong they can be devilishly difficult to turn around and keep afloat. In this article I share three simple ideas that may help you gain control of the project and bring that go-live date

Jim Harris 0
Data quality and Paleolithic Rhythm

Early in the terrific book What Technology Wants by Kevin Kelly, he discusses the concept of Paleolithic Rhythm, which describes the short bursts of intense effort followed by long periods of rest employed by the hunter-gatherer tribes of early humans during the Paleolithic Era. Paleolithic Rhythm is also an apt analogy for how many

David Loshin 0
Master data synchronization and eventual consistency

Periodic synchronization of your master data environment presumes batching up new entries to be processed all at once. Full synchronization means that any new entity brought into one of the enterprise systems will immediately be added to the master index. There are benefits and drawbacks to both of these approaches,

Dylan Jones 0
Want smarter leaders? Invest in data quality

When extolling the virtues of data quality, particularly to a leadership community, it pays to focus not just on the corporate gains but also the personal benefits that better quality data can offer. Improving data quality can often be a thankless task. You make changes to a resource that many

Phil Simon 3
Staying employable in an era of big data

Data matters more than ever. Progressive organizations such as Netflix, the University of Texas System and others are using contemporary data visualization tools to find the signal in the noise that is big data. Dataphobes won't be able to hide for much longer. These facts were very much on my mind as

Jim Harris 0
Data quality in medias res

The planning and execution of enterprise information initiatives is definitely not easy. Building the business case involves identifying, documenting, verifying and refining a set of requirements that are representative of the various perspectives of the business and technical stakeholders all throughout the organization. Many such initiatives begin with the very

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