In today's business environment, organizations of all sizes are struggling to maintain the advanced IT capabilities they need to be competitive while reining in cost and complexity. Shifting to off-premise hosted models, such as software-as-a-service (SaaS) and business process outsourcing, is a common activity undertaken to manage costs.
One of the main challenges businesses face in adopting cloud and SaaS delivery models is the task of synchronizing data and integrating the multitude of systems already in datacenters with new cloud-based applications, not to mention within the cloud itself. Traditionally, this required organizations to leverage existing tools as well as custom development.
This white paper looks at how two enterprises encountered problems with cloud integration and adopted IBM WebSphere Cast Iron to solve their immediate problems and extend use more broadly across their organizations.
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
As the approach to strategic business decision making becomes more and more data driven, a method for consolidating our various data sets, which are often spread across multiple systems becomes exceedingly important.
Two of the biggest players in data driven decision making are website analytics platforms and customer relationship management systems. The former includes accumulating data on top of the funnel behavior such as site traffic origins, lead generation, content consumption tracking, device usage, and overall site behavior. While the latter has a focus more on bottom of the funnel activity such as lead nurturing, customer status, lifetime value, etc.
Lastly, without communication between these two essential platforms, a complete understanding of your customers, from lead to longtime client, may never be possible. A web analytics (Google Analytics) and CRM integration provides you with a 360 degree view of your customer base, so that you can understand not just what PPC efforts
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customers’ datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones.
More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneider’s long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
Location analytics is the process of
integrating geographical data into business intelligence (BI) and analytics-led decision
making. Location analytics creates meaningful insight from relationships found in
geospatial data to solve a broad variety of business and social problems.
Location data is found everywhere – with an item or a device, in a conversation or
behavior, in machines or sensors, tied to a customer or competitor, attached to a
database record or recorded from vehicles or other moving objects. Organizations
want to take advantage of location data to improve decisions, create better customer
engagement and experiences, reduce risks and automate business processes.
Published By: Gleanster
Published Date: Nov 10, 2011
This Gleansight benchmark report is based on the experiences of 387 companies and includes the following sections: Reasons to Implement, Challenges, Value Drivers, Core Technologies and Success Story. It also includes a Vendor Landscape with descriptions, rankings and analysis of 39 solution providers.
Social intelligence is being increasingly used today to describe the next rung on the evolutionary ladder of listening to and acting upon consumer conversation on the social web. That rung maps to a number of technology innovations. Chief among them are improved capabilities around analyzing and integrating all sources of voice-of-the-customer data to generate more actionable insights. Social intelligence also speaks to an emerging corporate mindset regarding the strategic importance of social data and the need to better capitalize upon it. This Gleansight benchmark report reveals how Top Performers are achieving success when it comes to the incessant quest to extract customer insights and take actions that ultimately translate into revenue growth, cost reduction, risk reduction and relationship enhancement.
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
This ebook explores:
1. Typical gap areas between revenue & expense operations
2. Strategy for what to address and in what order
3. Leveraging the majority of existing tools & systems
4. Benefits of integrating revenue & expense data
"DBAs need help with tackling significant issues, including doing more with fewer resources, quickly responding to upgrade and patch needs, while reducing the time spent on cloning databases. Finally, they need help eliminating the time they spend integrating, testing, and deploying full-stack cloud solutions. How can DBAs reconcile these competing demands?
Learn more about expanded offerings in a database appliance that are delivering numerous advantages. These new configurations have the potential to help IT derive new benefits in its efforts to serve the business.
As the pace of business continues to accelerate, forward-looking organizations are beginning to
realize that it is not enough to analyze their data; they must also take action on it. To do this, more
businesses are beginning to systematically operationalize their analytics as part of a business process.
Operationalizing and embedding analytics is about integrating actionable insights into systems and
business processes used to make decisions. These systems might be automated or provide manual,
actionable insights. Analytics are currently being embedded into dashboards, applications, devices,
systems, and databases. Examples run from simple to complex and organizations are at different
stages of operational deployment. Newer examples of operational analytics include support for
logistics, customer call centers, fraud detection, and recommendation engines to name just a few.
Embedding analytics is certainly not new but has been gaining more attention recently as data
volumes and the freq
Organizations are increasingly adopting a hybrid cloud computing strategy to realize the benefits without compromising on security and control. Learn of the 3 categories of solutions focused on integrating data centers with cloud.
Self-service analytics implies that users design and develop their own reports and do their own data analysis with minimal support by IT. Most recently, due to the availability of tools, such as those from Qlik, Spotfire, and Tableau, self-service analytics has become immensely popular. Besides powerful analytical and visualization capabilities, they all support functionality for accessing and integrating data sources. With respect to this aspect of data integration four phases can be identified in the relatively short history of self-service analytics. This whitepaper describes these four phases in detail and shows how the tools Cisco Data Preparation (CDP) and Cisco Information Server (CIS) for data virtualization can strengthen and enrich the self-service data integration capabilities of tools for reporting and analytics.
While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.
Future workplace structures are envisioned to be a scenario wherein employees are likely to use a multitude of connected mobile devices and apps that will offer compelling new work style experiences integrating voice, video and data access services.
Spatial data warehouses are becoming more common as government agencies, municipalities, utilities, telcos and other spatial data users start to share their data. This paper illustrates some of the issues that arise when undertaking data replication and data sharing.
VPs of Infrastructure and Operations are under constant pressure to more rapidly provision infrastructure to support applications, hit cost reduction targets, and maintain or improve application availability for network attached storage. To meet these goals, they are the directors of a carefully orchestrated dance of network, server, storage and datacenter operation teams.
Network management systems must adapt to meet the requirements of next generation networks. For example, a user interfaces may need to be refreshed with a state-of-the-art Web interface or support for the NETCONF protocol may be required to help customers deploy scalable networks. Adding functionality and integrating with existing applications and data stores has multiple trade-offs in terms of development time and system efficacy.
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment.
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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