Published By: HPE Intel
Published Date: Mar 15, 2016
Led by experienced technology consultants, Hewlett Packard Enterprise Storage Transformation Workshop Service provides a highly interactive, meaningful half day-long session with a customer's IT, business, and executive stakeholders. Using a series of high-quality (slide-free) discussion panels, HPE TS Consulting will facilitate an exploration of data management transformation journey to business-aligned visions, aligning your specific situation and HPE’s experiences over evolutionary trends in Data Management, Transformation to All Flash and End to End Data Protection. HPE consultants will also lead discussions on the potential implications that a data management transformation may present to IT, your storage and backup staff, and your business.
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.
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?
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.
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
"Your business may be competitive and successful today, but how will it perform in the future? It’s all too easy to be immersed in—perhaps even obsessed by—your daily operations. That’s why thinking about what your business and the market will look like one year, five years, even a decade from now needs to be a critical part of your thinking. It’s all about looking ahead, about being proactive.
Marching confidently into the future requires a solid foundation for that empire you’re building. That’s why your planning efforts need to include a dedicated exploration into the types of innovative, evolving technologies that will accommodate the business of the future.
This eGuide addresses the essential business principles that will always apply regardless of the stage your business is in. This resource helps you focus on the key points that should put you well on the way toward future-proofing your business."
A leader in shale oil and gas production, this global Fortune 500 oil and gas company transformed its business, shifting its focus to oil and gas exploration and production and divesting refineries and gas stations. Central to this transformation was to improve operating performance to deliver more value to shareholders.
Published By: Pentaho
Published Date: Mar 08, 2016
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.
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.
Distributed systems enable different areas of a business to build specific applications to support their needs and drive insight and innovation. While great for the business, this new normal can result in development inefficiencies when the same systems are reimplemented multiple times. This free e-book provides repeatable, generic patterns, and reusable components to make developing reliable systems easier and more efficient—so you can free your time to focus on core development of your app.
In this 160–page e-book, you’ll find:
An introduction to distributed system concepts.
Reusable patterns and practices for building distributed systems.
Exploration of a platform for integrating applications, data sources, business partners, clients, mobile apps, social networks, and Internet of Things devices.
Event-driven architectures for processing and reacting to events in real time.
Additional resources for learning more about containers and container orchestration systems.
“There are more
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.
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization.
This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
If you thought HPC solutions are only useful for research or academia, think again! Lenovo HPC solutions, powered by Intel® technology, can be specifically built and optimized for your business needs. They can help to accelerate innovation, whether it’s precisely modelling a new drug, driving simulations to improve manufacturing, improving the efficiency and success rate of explorations, achieving greater manufacturing efficiency, or gaining new insights into IoT data. This best-practice guide will help you evaluate and consider the best approach to adopt HPC for your business needs, as well as the solution components to be considered in its implementation.
Get the eBook.
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.