Published By: Sitecore
Published Date: Nov 04, 2009
This report highlights the strategic value of a next generation web content management system integrated with lead scoring, email marketing, customer relationship management, and web analytics. The report links the technology and practices of Best-in-Class organizations to engage customers, provide personalized experiences and manage the lead lifecycle.
Published By: Sitecore
Published Date: Jan 06, 2009
This guide offers insight into the WCM technology choices available today, discusses some of the requirements both IT and business users should consider when selecting a WCM solution, and includes advice for ensuring a successful evaluation process.
Published By: Sitecore
Published Date: Jul 08, 2009
This whitepaper discusses the need to target outcomes and focus on building a complete, 360-degree view of your customers -- who they are, where they came from, what they do, and how you can best meet their needs.
Published By: LogRhythm
Published Date: Feb 22, 2018
Security and risk management leaders are implementing and expanding SIEM to improve early targeted attack detection and response. Advanced users seek SIEM with advanced profiling, analytics and response features.
Published By: Engagio
Published Date: Feb 05, 2018
Be the first to read Engagio’s brand-new guide that tells you everything you need to know about ABM Analytics.
Great Marketing, Great Marketing Analytics
Account Based Analytics Are Different
3 Styles of ABM
A Framework for ABM Measurement
Journey and ROI Analytics
6 Attribution Models
The New Metrics for ABM
The Clear & Complete Guide to ABM Analytics is your guide to becoming a world-class B2B marketer!
Today's artifical intelligence (AI) solutions are not sentient in the manner popularized in science fiction by scores of self-aware and typically nefarious androids. Even so, the ability to arm such systems with the ability to directly sense and respond to their in situ environment is critical. Why? In the future, our experiences will be smart, intuitive and informed by analytics that are not seen
but felt via new business, personal and operational engagement models. Enabling this interaction requires AI applications that can sense, analyze and respond to their environment in an intelligent
and interactive manner. Without requiring the end user to write, understand or interpret code.
“Sensitive” artificial intelligence enables:
• More productive use of expanded (big, often unstructured) information sources
• Intuitive man-machine interactions (no code-speak here!)
• Adaptive, immersive experiences and environments
As frequently touted on the nightly news, AI’s popularity is clear. Ho
There is a lot of excitement in the market about artificial intelligence (AI), machine learning
(ML), and natural language processing (NLP). Although many of these technologies have been
available for decades, new advancements in compute power along with new algorithmic
developments are making these technologies more attractive to early adopter companies. These
organizations are embracing advanced analytics technologies for a number of reasons including
improving operational efficiencies, better understanding behaviors, and gaining competitive
What management and leadership challenges will the next wave
of analytic technology bring? This Insight Center on HBR.org went
beyond the buzz of what artificial intelligence can do, to talk about
how it will change companies and the way we manage them.
With decisions riding on the timeliness and quality of analytics, business stakeholders are
less patient with delays in the development of new applications that provide reports, analysis,
and access to diverse data itself. Executives, managers, and frontline personnel fear that
decisions based on old and incomplete data or formulated using slow, outmoded, and limited
reporting functionality will be bad decisions. A deficient information supply chain hinders quick
responses to shifting situations and increases exposure to financial and regulatory risk—putting
a business at a competitive disadvantage. Stakeholders are demanding better access to data,
faster development of business intelligence (BI) and analytics applications, and agile solutions in
sync with requirements.
Business intelligence has come a long way ? from assistance with report generation to self-service platforms for discovery and analytical
insight. As technological capabilities and business aptitude with information continue to advance, the next generation of BI will be even
more capable and valuable to the enterprise. To discuss today’s success factors and tomorrow’s opportunities, IIA spoke with Rick Styll,
Senior Manager, Visual Analytics Product Management at SAS, and Tapan Patel, Principal Product Marketing Manager at SAS.
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.
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and
analytics. And you can do it through an interface that anyone can use.
At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days.
Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
Digital transformation is a reality for marketers that is wrapped in both opportunities
and headaches. Marketers understand the choices and expectations that their
customers now have, and they are up for the challenge. But marketers also have many
obstacles to overcome to deliver the consistently good, timely and engaging customer
experience, across devices, that customers demand.
The good news is that the marketing technology industry is rapidly evolving to address
these challenges. And in the same way that consumers have an abundance of choices,
marketers also have many options when it comes to choosing partners to help. But
where to start, and how to choose the right partners?
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
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.
Industry leaders from the banking and vendor landscape are working to streamline the
customer experience while closing the opportunities for fraud and exposure. Balancing
security and convenience will require an approach that combines consumer-facing
authentication (such as passwords, PINs and biometrics) with background security
measures (such as transaction and session-behavior analytics).
These emerging technologies and solutions certainly are not unique to financial services. But Stewart, a business director of security intelligence solutions within the SAS Security Intelligence
Practice, sees particular interest and application in AML circles.
"There remain a good number of manual processes within financial crimes departments in financial institutions, and AI can help automate some of those rote tasks such as document review or alert triage," he says. "Due to investments in technology, there is a lower barrier of entry for midsized institutions. "And finally, there's this anxiety over the unknown - those risks they are not able to detect, that may be hidden using traditional techniques - so they're hoping that more advanced, unsupervised learning techniques can be used to identify those edge cases or behaviors that are out of norm." In an interview about analytics and the AML paradigm shift, Stewart discusses:
• The new industry intrigue with artificial intelligence a
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses.
SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
“If we had done anything differently in Washington state, we would have done it faster,” said Hammersburg. “The key message is that fraud prevention – dealing with risk and program integrity – is not a cost issue, it’s a saving. When you can truly quantify the
positive impact to the bottom line of a company or government agency, you shift the recognition that this is not an expense but that it’s a saving.”
Some government organizations may be concerned that a rigorous program to shine a light on the underground economy will shine a brighter light on how much they didn’t know until now. Don’t let that stop you, said Hammersburg. “You have the opportunity
to really get ahead of it now. Turn a risk into an opportunity going forward.”
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud
and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes
need not stand in the way. To explore the challenges, opportunities, and value of tax fraud analytics, IIA spoke with Deborah Pianko, a
Government Fraud Solutions Architect within the SAS Security Intelligence practice.
The headlines and major company announcements share a common theme:
Competitive disruption is reshaping business models and organizations’ very futures.
Around the global automotive industry, component and original equipment
manufacturers (OEMs) are taking a hard look at where their future growth will come
from ? and it’s not all based on their core businesses. New technology has opened the
door for new services and revenue streams.
The most recent decade has seen rapid advances in connectivity, mobility, analytics, scalability, and data, spawning what has been called the fourth industrial revolution, or Industry 4.0. This fourth industrial revolution has digitalized operations and resulted in transformations in manufacturing efficiency, supply chain performance, product innovation, and in some cases enabled entirely new business models.
This transformation should be top of mind for quality leaders, as quality improvement and monitoring are among the top use cases for Industry 4.0. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and business models. However, much of the market isn’t focusing on Quality 4.0, since many quality teams are still trying to solve yesterday’s problems: inefficiency caused by fragmented systems, manual metrics calculations, quality teams independently performing quality work with minimal cross-functional own
The Internet of Things enables retailers to do three basics better
1) Sensing who customers are and what they’re doing,
2) Understanding customer behavior and preferences, and
3)Acting on that insight to create a more engaging customer
- There are high-potential IoT applications in supply chain, in
“smart store” operations, and especially in providing an engaging
experience to the “connected customer.” IoT data can anticipate
where the customer is headed and how to meet her there.
- Much of the IoT ground, in both data management and analytics,
may be unfamiliar. Retailers and their IT organizations have to be
realistic about the technological challenges, their own capabilities,
and where they need assistance.
- To differentiate through IoT, focus on the analytics. Devices and
their data — and even their platforms — are commodities.
Advantage goes to the retailer who does the most with the data to
engage the connected customer.
This white paper examines the true costs associated with the Tagging of websites to implement and track the performance of online marketing technologies such as web analytics, display ad serving and search marketing. This research was conducted by TagMan, a smart container tag with real-time attribution and web-page acceleration.
Published By: Skillsoft
Published Date: Sep 25, 2013
HR execs are not finding their budgets rising at the same levels as their workforce requirements. By 2020, the global labor market faces a shortage of 38-40 million college educated workers and 45 million secondary educated workers1. Despite the change catalyst that the mounting crisis demands, many organizations have not adjusted their strategies. Why would an organization race to develop skills by investing the same portion of their budget in classroom approaches and build training that they could buy? To help organizations find a smarter way out of the talent crisis, the Director of Research at the world’s leading learning analytics firm compared the results of Skillsoft elearning to classroom and internally developed elearning benchmarks. The results were clear; Skillsoft provides a solid answer to the global talent imperative.