"The appearance of your reports and dashboards – the actual visual appearance of your data analysis -- is important. An ugly or confusing report may be dismissed, even though it contains valuable insights about your data. Cognos Analytics has a long track record of high quality analytic insight, and now, we added a lot of new capabilities designed to help even novice users quickly and easily produce great-looking and consumable reports you can trust.
Watch this webinar to learn:
• How you can more effectively communicate with data.
• What constitutes an intuitive and highly navigable report
• How take advantage of some of the new capabilities in Cognos Analytics to create reports that are more compelling and understandable in less time.
• Some of the new and exciting capabilities coming to Cognos Analytics in 2018 (hint: more intelligent capabilities with enhancements to Natural Language Processing, data discovery and Machine Learning)."
"What would you do if you didn’t have to rely on disparate analytics solutions to meet the needs of business users while following the rules of IT?
View this 'Charting Your Analytical Future' webinar to learn about a world of innovation and independence for users that does not limit the confidence and controls of IT.
With the cognitive-guided self-service features available in IBM business analytics solutions, more users than ever before can get the answers they need. Next-generation business analytics capabilities make it possible to access relevant data, prepare it for analysis and understand performance. But it doesn’t stop there. Users can package the results in a visually-appealing format and share them throughout the organization.
Don’t miss this opportunity to hear how you can:
* Benefit from advanced analytics without the complexity
* Operationalize insights and dashboards from a collection of trusted data sources
* Tell your story with rich visualizations and geospati
As the information age matures, data has become the most
powerful resource enterprises have at their disposal. Businesses
have embraced digital transformation, often staking their
reputations on insights extracted from collected data. While
decision-makers hone in on hot topics like AI and the potential of
data to drive businesses into the future, many underestimate the
pitfalls of poor data governance. If business decision-makers can’t
trust the data within their organization, how can stakeholders and
customers know they are in good hands? Information that is not
correctly distributed, or abandoned within an IT silo, can prove
harmful to the integrity of business decisions.
In search of instant analytical insights, businesses often prioritize data
access and analysis over governance and quality. However, without
ensuring the data is trustworthy, complete and consistent, leaders
cannot be confident their decisions are rooted in facts and reality
Published By: HireVue
Published Date: May 16, 2018
Unilever is a global player in the fast-moving consumer goods (FMCG) sector, with nearly a third of the world’s population using its products every day. The company has more than 169,000 employees working around the world, and its leaders estimate that within three years, as many as 60 percent of those employees will be Millennials. Critical to Unilever’s ongoing success will be its ability to attract these recent college graduates.
For its signature Future Leaders Programme, the company wanted its recruitment efforts to get ahead of the curve. The existing process was rooted in paper-based applications, phone interviews with recruiters, and manual assessment tests. It took four to six months to sift through 250,000 applications and ultimately hire 800 individuals. The company sought to radically transform this process using online gamification, digitally recorded interviews, and science-based assessment tests and data analysis.
In this case study, we:
• Explain the chal
Today, when you make decisions about information technology (IT) security priorities, you must often strike a careful balance between business risk, impact, and likelihood of incidents, and the costs of prevention or cleanup. Historically, the most well-understood variable in this equation was the methods that hackers used to disrupt or invade the system.
The Business Case for Data Protection, conducted by Ponemon Institute and sponsored by Ounce Labs, is the first study to determine what senior executives think about the value proposition of corporate data protection efforts within their organizations. In times of shrinking budgets, it is important for those individuals charged with managing a data protection program to understand how key decision makers in organizations perceive the importance of safeguarding sensitive and confidential information.
Published By: Adverity
Published Date: Jun 15, 2018
In this whitepaper, we take a closer look at some of the biggest challenges facing e-commerce businesses, namely understanding your data in general and, more precisely, your customer acquisition costs (CAC).
It's full of inspiration, useful tips and actionable insights for you to step up your marketing game.
In order to reap the fruits from your data seeds, you have to make sure you tackle these five challenges full-frontal:
Knowing what data to capture
Understanding customer behaviour
Finding your technology solution
Ensuring analysis is impartial
Optimising website content - especially for your offline users
The SAP HANA platform provides a powerful unified foundation for storing, processing, and analyzing structured and unstructured data. It funs on a single, in-memory database, eliminating data redundancy and speeding up the time for information research and analysis.
The operation of your organization depends, at least in part, on its data.
You can avoid fines and remediation costs, protect your organization’s reputation and employee morale, and maintain business continuity by building a capability to detect and respond to incidents effectively.
The simplicity of the incident response process can be misleading. We recommend tabletop exercises as an important step in pressure-testing your program.
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
Powered by data from 451 Research, the Right Mix web application benchmarks your current private vs public cloud mix, business drivers, and workload deployment venues against industry peers to create a comparative analysis. See how your mix stacks up, then download the 451 Research report for robust insights into the state of the hybrid IT market.
Published By: Pentaho
Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
The demands on IT today are staggering. Most organizations depend on their data to drive everything from product development and sales to communications, operations, and innovation. As a result, IT departments are charged with finding a way to bring new applications online quickly, accommodate massive data growth and complex data analysis, and make data available 24 hours a day, around the world, on any device. The traditional way to deliver data services is with separate infrastructure silos for various applications, processes, and locations, resulting in continually escalating costs for infrastructure and management. These infrastructure silos make it difficult to respond quickly to business opportunities and threats, cause productivity-hindering delays when you need to scale, and drive up operational costs.
Patients are going digital — and taking the healthcare system with them. Learn how in the 2017 Digital Trends in Healthcare and Pharma report.
Download it now to learn:
Why two-thirds of healthcare companies are investing in data analysis.
How they’re building content marketing programs to boost patient knowledge.
What they plan to do with virtual and augmented reality this year and beyond.
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
Published By: Oracle CX
Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s
databases, accessing and using the right information at the right time has
become increasingly critical. Real-time access and analysis of operational
data is key to making faster and better business decisions, providing
enterprises with unique competitive advantages. Running analytics on
operational data has been difficult because operational data is stored in row
format, which is best for online transaction processing (OLTP) databases,
while storing data in column format is much better for analytics processing.
Therefore, companies normally have both an operational database with data
in row format and a separate data warehouse with data in column format,
which leads to reliance on “stale data” for business decisions. With Oracle’s
Database In-Memory and Oracle servers based on the SPARC S7 and
SPARC M7 processors companies can now store data in memory in both
row and data formats, and run analytics on their operatio
Published By: Oracle CX
Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been
top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar
database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as
quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data
from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure
1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP
database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks
can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data.
In-memory databases have helped address p
Although AI research has been ongoing for decades, the past few years have seen
a leap in practical innovations, catalyzed by vast amounts of digital data, online
services, and enormous computing power. As a result, technologies such as
natural-language understanding, sentiment analysis, speech recognition, image
understanding, and machine learning have become accurate enough to power
applications across a broad range of industries.
Published By: Delphix
Published Date: May 03, 2016
Data security is a top concern these days. In a world of privacy regulation, intellectual property theft, and cybercrime, ensuring data security and protecting sensitive enterprise data is crucial.
Only a data masking solution can secure vital data and enable outsourcing, third-party analysis, and cloud deployments. But more often than not, masking projects fail. Some of the best data masking tools bottleneck processes and once masked, data is hard to move and manage across the application development lifecycle.
Published By: Dell EMC
Published Date: Oct 08, 2015
Download this whitepaper for:
• An overview of how manufacturing can benefit from the big data technology stack
• A high-level view of common big data pain points for manufacturers
• A detailed analysis of big data technology for manufacturers
• A view as to how manufacturers are going about big data adoption
• A proven case study with: Omneo
Forrester presents the relevant endpoint security data from their most recent surveys, with special attention given to those trends affecting SMBs (firms with 20 to 999 employees) and enterprises (firms with 1,000+ employees), along with analysis that explains the data in the context of the overall security landscape. As organizations prepare for the 2015 budget cycle, security and risk (S&R) professionals should use this annual report to help benchmark their organization’s spending patterns against those of their peers — while keeping an eye on current trends affecting endpoint security — in order to strategize their endpoint security adoption decisions. Please download this Forrester Research report, offered compliments of Dell, for more information.
The explosion of Big Data represents an opportunity to leverage trending attitudes in the marketplace to better segment and target customers, and enhance products and promotions. Success requires establishing a common business rationale for harnessing social media and determining a maturity model for sentiment analysis to assess existing social media capabilities.
With sophisticated analytics, government leaders can pinpoint
the underlying value in all their data. They can bring it together
in a unified fashion and see connections across agencies to
better serve citizens.