TIBCO Spotfire is the premier data discovery and analytics platform, which provides powerful capabilities for our customers, such as dimension-free data exploration through interactive visualizations, and data mashup to quickly combine disparate data to gain insights masked by data silos or aggregations.

When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f

With 50 to 100 billion things expected to be connected to the Internet by 2020, we are now experiencing a major paradigm shift that is revolutionizing business. More and more of the objects we use every day—including those in our factories, utilities, and railroads—are used to capture and distribute information that is helping us know more and do more. The TechWiseTV team and guest experts take an in-depth look at how industries like these are utilizing the data they are gathering from the factory floor all the way out to the field. This exploration into how the Internet of Things actually works in the real world and what your organization must do to take full advantage of it is a great opportunity to understand the practical challenges and specific technology involved in bringing all this potential to life.

What can you see and discover when you’re able to explore trends and make predictions with your organization’s data? If you’re a midsize home delivery business, you can discover new ways to make customers happy. If you’re a local government agency, you can predict where your resources are needed most. And if you’re a growing hospital, you can bring life-changing patient data directly to doctors and nurses. In this e-book, we’ve profiled six organizations that are using self-service visual exploration to make big improvements in the way they work. From college administrators to professional sports teams, everyone makes better decisions with easy access to powerful, interactive analytics.

If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis.

Does your organization struggle to get new business insights from all data types with rapid exploration?

Today, all consumers can obtain any piece of data at any point in time. This experience represents a significant cultural shift: the beginning of the democratization of data. However, the data landscape is increasing in complexity, with diverse data types from myriad sources residing in a mix of environments: on-premises, in the cloud or both. How can you avoid data chaos?

Today’s marketing leaders need sophisticated tools to turn data into cross-channel insights that improve performance. In a new report, Gartner compares 11 digital marketing analytics solutions across five key areas: data integration, exploration, advanced models, platform integrations, and measurement. Selecting the best solution for your team requires thoughtful analysis. How will you determine the best fit? Gartner’s Magic Quadrant can help.

The Bakken is a very large hydrocarbon-bearing subsurface rock formation underneath a large portion of the Williston Basin in North Dakota, Montana, Saskatchewan and Winnepeg. The Bakken has been the scene of many advancements in drilling technology – horizontal drilling, pad drilling and downspacing, to name a few. This first Edition of the DI Expert eBook by Drillinginfo, the premier provider of data and insights for oil and gas exploration decisions, is a collection of articles posted by our staff of engineers, analysts and geologists about the Bakken over the past year.

This second edition of the DI Expert eBook provides a collection of articles written by staff analysts from Drillinginfo, the premier provider of data and insights for oil and gas exploration decisions. These articles focus on the Eagle Ford Shale play in south central Texas. The Eagle Ford Shale boom began in 2008 and has transformed the lives of mineral rights owners, communities and E&P operators throughout Texas and the United States. The Eagle Ford Shale is a cretaceous formation with a favorable brittleness index, which makes the formation ideal for hydraulic fracturing and economical unconventional production.

The Permian basin, a long time oil and gas producing region nestled in west Texas and southeastern New Mexico was once believe to have reached “Peak Oil” extraction levels. A significantly larger play than the booming Eagle Ford Shale, the Permian Basin has reemerged as a production pillar for the US, thanks to advancements in horizontal drilling and hydraulic fracturing. Although still in early stages of new production, the Permian has already transformed the local economies surrounding the play, adding to the energy security of the U.S. In this issue of the DI Expert eBook, by Drillinginfo, the premier provider of data and insights for oil and gas exploration decisions, our expert analysts have delved into the complexities of the geology in the Permian, highlighted the successes of operators in the region and provided in-depth analyses on the play’s re-emergence.

Imagine expanding your business and monetizing your bank's data. Imagine bringing services together and delighting customers. API's can connect your bank to a whole ecosystem of business. With innovative thinking and exploration your bank can capitalize on API's in the new digital economy.

In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.

Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.

The focus of modern business intelligence has been self-service; pushing data into the hands of end users more quickly with more accessible user interfaces so they can get answers fast and on their own. This has helped alleviate a major BI pain point: centralized, IT-dominated solutions have been too slow and too brittle to serve the business. What has been masked is a lack of innovation in data modeling. Data modeling is a huge, valuable component of BI that has been largely neglected. In this webinar, we discuss Looker’s novel approach to data modeling and how it powers a data exploration environment with unprecedented depth and agility. Topics covered include: • A new architecture beyond direct connect • Language-based, git-integrated data modeling • Abstractions that make SQL more powerful and more efficient

Many companies, especially those in the Oil and Gas Industry, need high-density deployments of high performance compute (HPC) environments to manage and analyze the extreme levels of computing involved with seismic processing. CyrusOne’s Houston West campus has the largest known concentration of HPC and high-density data center space in the colocation market today. The data center buildings at this campus are collectively known as the largest data center campus for seismic exploration computing in the oil and gas industry. By continuing to apply its Massively Modular design and build approach and high-density compute expertise, CyrusOne serves the growing number of oil and gas customers, as well as other customers, who are demanding best-in-class, mission-critical, HPC infrastructure. The company’s proven flexibility and scale of its HPC offering enables customers to deploy the ultra-high density compute infrastructure they need to be competitive in their respective business sectors.

Today’s marketing leaders need sophisticated tools to turn data into cross-channel insights that improve performance. In a new report, Gartner compares 11 digital marketing analytics solutions across five key areas: data integration, exploration, advanced models, platform integrations, and measurement. Selecting the best solution for your team requires thoughtful analysis. How will you determine the best fit? Gartner’s Magic Quadrant can help.

If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals. Read the Buyer’s Guide to Big Data Integration by CITO Research to learn: • What tools are most useful for working with Big Data, Hadoop, and existing transactional databases • How to create an effective “data supply chain” • How to succeed with complex data on-boarding using automation for more reliable data ingestion • The best ways to connect, transport, and transform data for data exploration, analytics and compliance

A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics

With 50 to 100 billion things expected to be connected to the Internet by 2020, we are now experiencing a major paradigm shift that is revolutionizing business. More and more of the objects we use every day—including those in our factories, utilities, and railroads—are used to capture and distribute information that is helping us know more and do more. The TechWiseTV team and guest experts take an in-depth look at how industries like these are utilizing the data they are gathering from the factory floor all the way out to the field. This exploration into how the Internet of Things actually works in the real world and what your organization must do to take full advantage of it is a great opportunity to understand the practical challenges and specific technology involved in bringing all this potential to life.

Discover new opportunities for maturing your data practices—and building your business results. You’ll learn how to move beyond mere web analytics to build a more comprehensive marketing analytics approach that includes mobile, social, and offline channels. And you’ll see how your current analytics capabilities compare to those of similar organizations and where you have opportunities for improvement.

IDC's 3rd IT Platform — defined by the nascent and confluent mobile, cloud, social business and big data technologies — will drive 98 % of future IT industry growth over the next seven years. In 2013, industry transition to the 3rd Platform will move from the exploration stage to full-blown high stakes competition, resetting the leadership ranks in the IT sector forever.

Embrace the GDPR with the most complete, secure, and intelligent solution for digital work. The GDPR is compelling every organization to consider how it will respond to today’s security and compliance challenges. This may require significant changes to how your business gathers, uses, and governs data. Microsoft has brought together Office 365, Windows 10, and Enterprise Mobility + Security into a single, always-up-to-date solution called Microsoft 365—relieving organizations from much of the cost and complexity of multiple, fragmented systems that were not necessarily designed to be compliant with current standards Read this white paper for an in-depth exploration of: The GDPR and its implications for organizations. How the capabilities of Microsoft 365 Enterprise edition can help your organization approach GDPR compliance and accelerate your journey. What you can do to get started now.

Better health care at lower costs, for everyone – how do health care providers get there? Understanding the gaps in patient care, patient needs, and the geographic distribution of the patient population are important elements to consider when making decisions about improving the quality of care and reducing its costs. To effectively analyze gaps in patient care, the data needs to be in a single place or system. However, in many organizations, data is spread across a myriad of spreadsheets and database systems. Data not organized for visual exploration and coherent analysis isn’t useful for decision making. Hence the need for visually appealing and scalable analytical tools to help organizations be more efficient, effective and economically successful.

When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da