From intelligent automation to advanced analytics, disruptive technology will enable the finance function to provide all of its services more effectively and efficiently. Find out how technology will change the way finance delivers transactional services, expert services and business partnering,
Read this report to discover:
• how automation in finance can deliver expert services more reliably and with smaller workforces
• how to design an agile workforce of humans and technologies
• how the finance function will shift to be part of a cross-functional analytics model that provides key insights to the business
IBM has been named a Leader in Gartner's Magic Quadrant for Data & Analytics Services.
According to the report, by 2022, 90% of corporate strategies are expected to view information as a critical enterprise asset, and analytics as an essential competency. Data and analytics service providers can help leaders accelerate and transform their ability to deliver data-driven insights and innovation solutions to businesses. Get complimentary access to Gartner's latest Magic Quadrant report for Data & Analytics Service Providers.
Published By: HPE Intel
Published Date: Mar 15, 2016
As more enterprises adopt technologies such as cloud, mobile, and analytics to help achieve strategic competitive advantage, CIOs and IT managers must support business-critical processes at a very high level across the enterprise. At the same time, IT organizations must manage complex hybrid IT infrastructures that include both cloud and on-premises technologies from multiple vendors and support providers. IDC believes that to tackle these challenges, IT organizations should look to support
providers for comprehensive offerings to help optimize IT operations and improve the efficiency of IT service delivery. In addition, IDC recommends that IT organizations looking to manage rapid change in today’s IT landscape consider support providers with a record of innovative support services and a focus on advanced technology in support delivery.
Published By: LogMeIn
Published Date: Mar 19, 2015
Remote support technology, including remote control, desktop sharing, and web collaboration, is one of the most popular platforms used across TSIA service disciplines. Today’s remote support solutions offer much more than just remote control for PCs, their functional footprint is expanding to include support for more devices and richer analytics for trend analysis and supervisor dashboards. Remote support solutions are typically well regarded by users, consistently delivering one of the highest average satisfaction scores in TSIA’s annual Global Technology Survey. Service executives should acquaint themselves with the new features and capabilities being introduced by leading remote support platforms and find ways to leverage the capabilities beyond technical support. Field services, education services, professional services, and managed services are all increasing adoption of these tools to boost productivity and avoid on-site visits. Download this white paper to learn more.
Watch our webinar, 4 Steps to Building a Customer Satisfaction Engine. SurveyMonkey's Director of Customer Success, Jeffrey Coleman, will show you how to:
- Ask questions that yield actionable data
- Scale follow-up actions and improvements
- Analyze survey data and get key metrics
- Close the loop by turning data into action
Adobe sits at the nexus of content, media, and marketing.
Adobe provides an EMSS offering spanning marketing, advertising, analytics, and content management capabilities. Of the vendors included this study, Adobe maintains the highest degree of overall strategic focus on marketing and consumer engagement. Adobe is investing heavily in its platform’s core services to unify data, content, and shared functionality across products. Adobe stands out for its digital intelligence, content handling, and aggressive rollout of AI features. Reference clients praise Adobe for their application usability and account management.
Adobe article that condenses/highlights key findings from the Econsultancy Digital Marketing in the Financial Services and Insurance
Sector 2017 Study, an in-depth, 5000+ word report covering FSI executives’ opinions on:
– General trends in retail banking, investment banking, and insurance
– Internal structures their companies are using to execute digital transformation
– The biggest threats/disruptions in the industry
– The biggest priorities in 2017 (leaders are focusing on both customer retention and customer acquisition, mainstream is focusing just
on customer retention)
– Main sources of sales and leads (digital + mobile are steadily increasing sources)
– Digital marketing budgets & investment areas (leaders are investing more in digital marketing automation and analytics)
– Use of the cloud and AI to automate analysis and marketing
– The importance of multichannel personalization
– Innovation in the types/formats of products/services provided (leaders are focusing on i
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.
Enterprises use data virtualization software such as TIBCO® Data Virtualization to reduce data bottlenecks so more insights can be delivered for better business outcomes. For developers, data virtualization allows applications to access and use data without needing to know its technical details, such as how it is formatted or where it is physically located. For developers, data virtualization helps rapidly create reusable data services that access and transform data and deliver data analytics with even heavylifting reads completed quickly, securely, and with high performance. These data services can then be coalesced into a common data layer that can support a wide range of analytic and applications use cases. Data engineers and analytics development teams are big data virtualization users, with Gartner predicting over 50% of these teams adopting the technology by 202
Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry
The democratization of financial services data and technology, together with more intense competition, makes the needs of today’s market participants vastly different from those of previous generations. Firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments.
By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models.
View our research report to learn how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.
Stories and statistics behind successful analytics projects
The adoption of analytics across the enterprise is accelerating, and with good reason. Analytics can offer a competitive advantage by helping to identify growth opportunities, circumnavigate risk and improve customer relationships. These insights are becoming crucial parts of the business strategy for executives representing a wide array of industries.
Check out our latest eBook to see how some of the world’s leading companies are using analytics to meet their needs. You’ll receive diverse examples of how organizations applied the latest statistical methodologies, such as: scorecard build, regression, decision trees, machine learning and material change to uncover meaning in data.
The examples represent global brands across critical industries – Financial Services, Insurance, High-Tech, Aerospace, Manufacturing and others – where analytics helped answer their most challenging questions.