-
IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements
The Internet of Things (IoT) is a computing paradigm where a widening range of physical devices—including smartphones, vehicles, shipping pallets, kitchen appliances, manufacturing robots, and anything fitted with a sensor—can transmit data about their location, state, activity, and surroundings. Depending on the device type, some may also receive data and instructions that control device behavior.
more
-
Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures
To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.
more
-
Defining a Multiplatform Data Architecture and What It Means to You
A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone.
more
-
Accelerating the Path to Value with Business Intelligence and Analytics: A TDWI Best Practices Research Report
Organizations of all sizes are in competition to realize value from data – and to realize it faster. To do so, they increasingly need flexible and agile business intelligence(BI), analytics, and data infrastructure, not systems that take too long to develop and do not give users the dynamic, iterative, and interactive access to data that they need. Fortunately, technology developments are trending in a positive direction for organizations seeking to accelerate their path to value with BI, analytics, and the critical supporting data infrastructure. These include self-service BI and visual analytics, self-service data preparation, cloud computing and software as a service(SaaS), and new data integration technologies.
more
-
Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment
One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs).
more
-
Metadata Discovery Doesn’t Have to be Painful
Today’s BI and analytics landscape is more complicated than it ever has been. Most organizations have repositories of data everywhere, many BI and analytics technologies, and multiple streams of data integration and/or data preparation – all with their own sets of metadata. No wonder BI implementers and business users are frustrated, confused, and lost when it comes to using these critical assets.
more
-
Data Management for Big Data, Hadoop, and Data Lakes
A perfect storm of data management trends is converging.
First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage.
more
-
Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World
In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming.
more
-
Database Strategies for Modern BI and Analytics
The data universe has changed. Big data, cloud computing, and open source have dramatically expanded the number of data warehousing offerings available to today’s businesses. An increasing number of companies are implementing self-service business intelligence (BI) and visual analytics tools to access and make sense of all of the new and diverse sources of data their teams are consuming. Data literacy is changing equally fast as an increasing number of “data consumers” want to interact with data on their own rather than through IT.
more
-
Data Lakes: Purposes, Practices, Patterns, and Platforms
We’re experiencing a time of great change, as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (via advanced analytics and data integrated across multiple analytic and operational applications). To capture new big data, to scale up burgeoning traditional data, and to leverage both fully, users are modernizing their portfolios of tools, platforms, best practices, and skills.
more
-
Creating Value with Unified Governance
Certainly, every business leader wants to have trusted, secure, consistent and usable information. But data volumes and systems complexity has been increasing for years and most organizations rarely prioritize data governance, so why care now? We’re at the brink of a perfect storm of unprecedented IT megatrends. The convergence of Cloud, Social, Mobile and Big Data foreshadows the upcoming tsunami of data ripe with potential business value. But it will also make the frustrating complexity of your traditional on-premises transactional data management challenges appear amazingly “manageable” in contrast.
more