Financial institutions run on data: collecting it, analyzing it, delivering meaningful insights, and taking action in real time. As data volumes increase, organizations demand a scalable analytics platform that can meet the needs of data scientists and business users alike. However, managing an on-premises analytics environment for a large and diverse user base can become time-consuming, costly, and unwieldy.
Tableau Server on Amazon Web Services (AWS) is helping major Financial Services organizations shift data visualization and analytics workloads to the cloud. The result is fewer hours spent on manual work and more time to ask deeper questions and launch new data analyses, with easily-scalable support for large numbers of users. In this webinar, you’ll hear how one major asset management company made the shift to cloud data visualization with Tableau Server on AWS. Discover lessons learned, best practices tailored to Financial Services organizations, and starting tactics for scalable analytics on the cloud.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
Digital transformation (DX) is a must for midsize firms (those with 100 to 999 employees) to thrive in the digital economy. DX enables firms to increase competitive advantage through initiatives such as automating business processes, creating greater operational efficiencies, building deeper customer relationships, and creating new revenue streams based on technology-enabled products and services. DX is a journey, and it starts with firms embracing an IT-centric vision that guides a data-driven, analytics-first strategy. The outcome of DX initiatives depends on the ability of a firm to efficiently leverage people (talent), process, platforms, and governance to meet the firm’s business objectives.
Published By: Red Hat
Published Date: Aug 22, 2018
In the emerging digital enterprise, there’s a good chance some application development will be taking place outside the information technology department. It’s not that the role of IT is in any way being diminished – in fact, IT managers are getting busier than ever, overseeing the technology strategies of their enterprises. Rather, the pieces are in place for business users to build and configure the essential business applications they need, on a self-service basis, with minimal or no involvement of their IT departments.
As the world moves deeper into an era of ongoing disruption from digital players – be they startups, or teams within established enterprises – technology has become an essential part of every job, from the boardroom to the boiler room. Accordingly, the discipline of IT is no longer confined to the data center or development shop. Many business managers and professionals are building, launching or downloading their own applications to achieve productivity and respond
Published By: OpenText
Published Date: Mar 02, 2017
Watch this webinar with IDC supply chain experts to learn how embedded analytics can provide deeper supply chain intelligence and help you extract maximum value from data for your supply chain operations.
Published By: Gigamon
Published Date: Oct 19, 2017
Read the Joint Solution Brief Gigamon Improves Security Visibility with Splunk Enterprise to see how to effectively analyze network events for security threats. Benefits include enhanced visibility and deeper, faster security analytics and intelligence based on all machine data (not just security events), among many others. Download now!
Published By: Gigamon
Published Date: Oct 25, 2017
Read the Joint Solution Brief Accelerate Threat Detection and Response to learn how Gigamon helps Splunk Enterprise users effectively analyze and remediate network security threats. Benefits include enhanced visibility and deeper, faster security analytics from precise, targeted network metadata generated from the traffic flowing in your network. Also learn how automation of common security tasks, across the Gigamon platform and third-party security tools, from within the Splunk platform helps increase analyst efficiency and reduce errors.
Published By: IBM APAC
Published Date: Nov 22, 2017
Using IBM Watson’s cognitive capabilities, companies can quickly differentiate their customer service quality by being more pro active and responsive to customer needs. Simply put, chatbots and virtual agents are the future of customer interactions. Building apps from scratch that incorporate natural language processing, speech to text recognition, visual recognition, analytics, and artificial intelligence requires broad expertise in these disciplines, large staffs, and a huge financial commitment. Making use of IBM Watson cognitive services brings these capabilities in-house quickly and without the capital investment that would be needed to develop the technologies within an organization.
Published By: HP Inc.
Published Date: Feb 03, 2016
Almost every organization takes its desktop/laptop security seriously – but most simply rely on software-level solutions, missing out on a deeper level of protection. Are these traditional software solutions keeping data safe enough? Not according to a recent Spiceworks survey, in which only about half of IT professionals in the Americas feel like their current, software-level solutions are very effective. Where does your organization rank?
Hyperconvergence has been receiving a tremendous amount of attention because it represents the next step in the evolution of IT resource delivery. This technology takes the idea of integrating compute, storage and networking that started with converged systems design and has improved on those architectures by adding deeper levels of abstraction and automation. Hyperconverged infrastructure (HCI) vendors promise simplified operation and the ability to quickly and easily expand capacity by deploying and launching additional modules; simplicity has been the key selling point for the HCI pioneers.
As HCI ventures even deeper into the enterprise and cloud environments, the architectures will need to become more efficient, agile and adaptable to help IT professionals shoulder the burden of rapidly growing data sets and workloads. This report discusses the benefits of HCI and the enhancements that must be
made to expand HCI deeper into the mainstream enterprise datacenter.
The headlines are ablaze with the latest stories of cyberattacks and data breaches. New malware and viruses are revealed nearly every day. The modern cyberthreat evolves on a daily basis, always seeming to stay one step ahead of our most capable defenses. Every time there is a cyberattack, government agencies gather massive amounts of data. To keep pace with the continuously evolving landscape of cyberthreats, agencies are increasingly turning toward applying advanced data analytics to look at attack data and try to gain a deeper understanding of the nature of the attacks. Applying modern data analytics can help derive some defensive value from the data gathered in the aftermath of an attack, and ideally avert or mitigate the damage from any future attacks.
Published By: Carbonite
Published Date: Oct 10, 2018
Organizations still struggle with communication between data owners and those responsible for administering DLP systems, leading to technology-driven — rather than business-driven — implementations.
Many clients who deploy enterprise DLP systems struggle to get out of the initial phases of discovering and monitoring data flows, never realizing the potential benefits of deeper data analytics or applying appropriate data protections.
DLP as a technology has a reputation of being a high-maintenance control — incomplete deployments are common, tuning is a never-ending process, lack of organization buy-in is low, and calculations of ROI are complex.
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements.
To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
Vast resources of data are increasingly available, but the sheer volume can overwhelm human capability. By implementing the cognitive system of IBM Watson Discovery into their infrastructure, businesses can extract deeper and more accurate insights by efficiently identifying, collecting and curating structured and unstructured data.
Watson Discovery, also capable of creating content collections and custom cognitive applications, can transform organizational processes to extend proprietary content and expert knowledge faster and at greater scales.
Read more to learn how Watson Discovery can keep your organization evolving ahead of the competition.
Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
Published By: Workday
Published Date: Jan 16, 2018
The shift from a product- to a service-driven economy—mixed with an uncertain economic climate—has given finance teams the opportunity to become strategic business partners capable of shaping and guiding organisational decision-making. This partnership is only possible by creating a deeper understanding of the contextual factors that influence revenue and profit and loss, and making this analytical data available to the right stakeholders when they need it. That requires a fundamental change in the way finance teams think about technology.
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
Customer Identity Management provides the tools you need to drive registrations, manage customer data and use it to improve cross-channel customer experiences and relationships.
Know your customers on a deeper level
Get a single customer view across channels
Turn data into relationships and results
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. 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 q
Workforce analytics is a very significant development in human
resources. It promises the potential for deeper understanding
of the ways workers contribute to organizational performance.
However, workforce analytics is not just about analyzing data
to reveal exciting insights; it also requires the active
involvement of a firm’s workers if the potential of analytics is
to be fully realized. Without active employee participation,
workforce analytics efforts face at best, restricted data sources
and data sets that are incomplete and at worst, the risk of
damaging employee relations and, ultimately, productivity.
This white paper summarizes recommendations that will
encourage enthusiasm for workforce analytics and active
employee participation, using the FORT (Feedback, Opt-in,
Reciprocal, Transparent) framework. The FORT criteria could
prove particularly useful in European countries. This is
because the 1995 European Union Data Protection Directive,
along with certain local legislative pr
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it
removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility.
SAS adheres to five data management best practices that support advanced analytics
and deeper insights:
• Simplify access to traditional and emerging data.
• Strengthen the data scientist’s arsenal with advanced analytics techniques.
• Scrub data to build quality into existing processes.
• Shape data using flexible manipulation techniques.
• Share metadata across data management and analytics domains.
The demand for new data about customers, customer behaviour, product usage, asset performance, and operational processes is growing rapidly. Almost every industry wants new data. Some examples of this are:
• Financial services organisations want more data to improve risk decisions, for ‘Know Your Customer (KYC) compliance and for a 360 degree view of financial crime.
• Utilities companies want smart meter data to give them deeper understanding of customer and grid usage and to allow them to exploit pricing elasticity. They also want sensor data to monitor grid health, to optimise field service and manage assets.
Download now to learn more!
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
Published By: Quocirca
Published Date: Dec 02, 2008
The need to share information has never been greater as cross-organizational business processes become deeper and more complex. The movement of digital information, both within a business and across its increasingly porous boundaries to external individuals and organizations, carries more and more risk as regulations are tightened around data protection and personal privacy.