The term NoSQL has been a misnomer ever since it appeared in 2009 to describe a group of emerging databases. It was true that a lack of support for Structured Query Language (SQL) was common to the various databases referred to as NoSQL. However, it was always one of a number of common characteristics, including flexible schema, distributed data processing, open source licensing, and the use of non-relational data models (key value, document, graph) rather than relational tables. As the various...
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Topics:
Business Continuity,
Analytics,
Data,
Digital Technology,
Digital Business,
data platforms
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
data operations,
data platforms,
AI and Machine Learning
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting...
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Topics:
business intelligence,
Analytics,
Data,
data lakes,
data operations,
data platforms,
AI and Machine Learning
Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to...
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Topics:
Customer Experience,
Voice of the Customer,
Analytics,
Data Integration,
Contact Center,
Data,
agent management,
data operations,
Digital Business,
Experience Management,
Customer Experience Management,
Field Service,
AI and Machine Learning
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management...
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Topics:
embedded analytics,
Analytics,
Collaboration,
Data Governance,
Information Management,
Data,
Digital Technology,
data lakes,
AI and Machine Learning
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility...
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Topics:
business intelligence,
Analytics,
Data Governance,
Data,
Digital Technology,
data operations,
data platforms
The emergence of the Chief Revenue Officer (CRO) has mirrored the adoption of the subscription model and the development of multiple selling and buying channels over and above the traditional direct sales model, referred to as Revenue Management. Supporting the traditional sales team and management was the sales operations team with responsibilities around incentive compensation, territory and quota planning, sales metrics and reporting and sales forecasting as well as sales engagement and...
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Topics:
Sales,
Customer Experience,
Analytics,
Sales Performance Management,
Digital Commerce,
Subscription Management,
partner management,
Revenue Management,
Sales Engagement
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a...
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Topics:
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Data Preparation,
Information Management,
Data,
Digital Technology,
data lakes,
AI and Machine Learning
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As...
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Topics:
Analytics,
Cloud Computing,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
data operations,
data platforms,
AI and Machine Learning
How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but...
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Topics:
Analytics,
Business Intelligence,
Internet of Things,
Data,
Digital Technology,
Streaming Analytics,
AI and Machine Learning