Jelani Harper

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.

Underpinning Security Analytics with Machine Intelligence

Certain use cases are ideal for the exorbitant data amounts frequently required to capitalize on machine learning investments. Others are rendered increasingly difficult—if not impossible—by the lack of exampled data needed to train algorithms on specific business objectives. Security analytics, particularly in the context of cyber security, has never suffered from the latter consideration and …

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What AI’s Really Doing to the Enterprise: The Call for Delegated Data Governance

There’s no disputing the impact of the growing usage of Artificial Intelligence throughout the enterprise. Organizations are becoming more analytically inclined, automation is rampant, and business users are empowered to accomplish more at a greater scale than they previously could. Nonetheless, there’s another side to the pervasive deployment of cognitive computing technologies throughout the data …

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The Pivotal Binary Relationship Between Machine Learning and Master Data Management

The symbiosis between machine learning and Master Data Management may not be readily apparent. After all, the former delivers targeted analytics for cognitive computing, whereas the latter consolidates aspects of data governance, data quality, and data modeling. Upon initial consideration, they appear to be from two distinct regions of the data management landscape. However, many …

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The True Meaning of Artificial Intelligence: Reasoning and Learning

As a concept, intelligence has myriad connotations, denotations, and contexts. When applied to the notion of Artificial Intelligence, however, the most prominent of these considerations is the capability for machines or ‘smart’ systems to a) learn and to b) reason. Although these tenets may sound similar to one another, they’re actually distinct and form the …

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Bringing the AIoT to Life: Pairing Machine Learning with Streaming Data

The coupling of Artificial Intelligence with the Internet of Things represents a natural progression for applications in both these spaces. Machine learning and AI deliver a number of benefits for the IoT which, because of the rapidity of the data it generates, assists AI in multiple ways. For the IoT, tangible advantages of this combination …

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Business Intelligence Reporting Today: The Might of Natural Language Generation

Long gone are the days when organizations were dependent on IT teams for Business Intelligence reporting. Back then, IT teams typically had to adjust data models to facilitate specific questions upfront, were exceedingly slow to change models for any additional questions, and made users wait weeks and months for insights from stale, historic data. Today, …

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Understanding the Multi-Cloud Idiom: Containers, High Availability, and Data Virtualization

The inexorable march of the enterprise to the cloud is underpinned by the multi-cloud tenet which, when involving hybrid clouds, enables organizations to seamlessly access resources between various clouds as well as on-premise settings. According to Denodo Director of Product Marketing Saptarshi Sengupta, a relatively recent survey regarding enterprise cloud habits indicates multi-cloud adoption is …

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The Next Wave of Cloud Computing: The Influx of Blockchain

The similitude between the cloud’s most pronounced enterprise advantages and those of blockchain is too great to ignore. Both are credible means of storing data. They also excel at exchanging data remotely, facilitating multi-party collaborations, and doing so securely. So why are cloud adoption rates soaring while enterprise adoption rates of blockchain remain modest? According …

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Data Governance for Data Science: The Growing Concern

Once, data science was an untamed, emergent discipline without rules, restrictions, or repercussions. Organizations simply wanted accurate advanced analytics; those fortunate enough to procure scarce data scientists gave them boundless freedom in their sandboxes to manipulate data to achieve that end. Nonetheless, the maturation of data science over the past 10 years has paralleled that …

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