The realities of polyglot persistence in mainstream enterprises
Ritesh Ramesh describes how NoSQL and Hadoop get used in retail environments.
Solving a familiar e-commerce search problem with a NoSQL document store
Mark Unak and Sanjay Agarwal explain how document stores can help deliver precise e-commerce catalog search results.
Security at the level of key-value pairs in a NoSQL database
Adam Fuchs of Sqrrl describes the benefits of data-centric security analytics.
Scaling online ad innovations with the help of a NoSQL wide-column database
Vaibhav Puranik and Ken Weiner of GumGum discuss the challenges and benefits of open source databases for in-image advertising.
Filling in the gaps in NoSQL document stores and data lakes
Matthias Brantner describes the role database virtualization and a business-user query interface can play in heterogeneous environments.
Creating a body language of online learning with graph databases
Sean York of Pearson discusses how graph technology becomes a medium for enriching online environments.
Database futures: How Apache Spark fits in to a larger unified data architecture
Mike Franklin of the University of California, Berkeley, discusses the goals behind Spark and a more unified cloud-data ecosystem.
Intelligent, context-aware data and analytics technologies widen the decision-making aperture
These four innovations can help companies achieve an optimal mind-machine balance when it business decision making.
Demystifying machine learning part 4: Image and video applications
Some firms are using machine learning to process large amounts of unstructured data, but it’s not widespread—yet.
How NoSQL key-value and wide-column stores make in-image advertising possible
Online ad innovators must process hundreds of terabytes a day at the lowest possible cost. How do they do it?
In database evolution, two directions of development are better than one
NoSQL database technology is maturing, but the newest Apache analytics stacks have triggered another wave of database innovation.
The data lake – No longer a pipe dream for today’s enterprises
How data lakes can help reduce costs, increase efficiency, and boost innovation in the enterprise.
Demystifying machine learning: Part 3 – Exploring deep learning
What exactly is “deep learning” and what accounts for its rapid rise in popularity and media coverage?
Using document stores in business model transformation
Healthcare providers are finding they need data collection and analysis capabilities that are different from those that relational databases deliver.
The capabilities and limitations of video analytics
Video analytics promise to help retailers better understand customers. Here are three issues to keep in mind.
Will data lake advocates repeat the mistakes of data warehousing?
A look at some of the challenges enterprises can face in implementing a shift to data lakes.
The future of collaboration: Large-scale visualization
Why large-scale visualization may be the key to success for improving business decision making with data analytics.
The end of data standardization
We can no longer deny the drive to diversify data management technology that began in the mid-90s. The aspiration to achieve one single and simple database management system has died. I grew up with the advent of commercial relational databases in the late 80s and early 90s. At the time, the promise was clear: you could store everything in a relational database that was carefully modeled and expandable. And in doing so, you acquired the ability to access, govern and securely manage every bit of data in a single technology environment. Most companies decided on a relational database standard and ported some or all of their applications towards that single database backend. All the principles of good architecture – including cost and skill optimization played out – until they didn’t. All seemed swimming until one of my clients – a major European railway operator – wanted to geo code every bit of equipment and every centimeter of their railway network. As hard as we tried, we couldn’t meet the client’s demands well with a relational database. The advent of spatial data management systems came to the rescue. Questions like ‘What is the total book value of all assets deployed within …
The 5 Dimensions of the So-Called Data Scientist
What is “data science”? Is it really a new emerging discipline as some claim it to be; or is it the emperor in new clothes – data mining, statistics, business intelligence or analytics re-branded? Moreover, is it possible that one person can fulfil the role of a data scientist? Rather than answering this question directly, let’s review some of the skills required for someone to be a “data scientist.” First and foremost, a “data scientist” is a business or domain expert: Someone who has to have the ability to articulate how information, insights, and analytics can help business leadership answer key questions – and even determine which questions need answering – and make appropriate decisions. The data scientist will need a thorough understanding of the business across the value chain (from marketing, sales, distribution, operations, pricing, products, finance, risk, etc.) to do this well. Second, a “data scientist” is a statistics expert: Someone who has to have the ability to determine the most appropriate statistical techniques for addressing different classes of problems, apply the relevant techniques, and translate the results and generate insights in such a way that the businesses can understand the value. This will be predicated on a …
Mining Customer Insights with Speech-to-Text Technology
From touch and gesture interfaces to advanced facial recognition, our computers are communicating with us on an increasingly human level. One technology that is showing particular promise is a computer’s ability to recognize human speech or Speech-to-Text (STT). Applications such as Apple’s Siri, Google Now, and Nuance’s Dragon have brought voice-activated commands to the masses while enterprise companies are employing the technology to discover new insights from previously untapped audio and video data sources. One of the greatest benefits of STT is the ability to bridge the gap between unstructured audio/video data and advanced analytics such as machine learning, natural language processing (NLP), and graph analysis. A company’s ability to understand their most vocal customers, whether within their call centers or on video sharing sites, can lead to a better view of customers and their experiences. Call center logs can reveal interesting patterns and trends in the quality of customer agent call handling and (when combined with other data) call center operational costs. These insights could then be used to retrain customer service agents, identify and stop a poorly conceived marketing campaign, or quickly understand the root cause for a spike in call center volume. For example, PwC’s Emerging Tech …
The potential of context aware computing
Companies now have a powerful tool to notify, enhance, extend, and rationalize situations to augment our human decision-making capability. It not only helps us to make better, more informed decisions, but it truly becomes an extension of us.