Published By: Aberdeen
Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
Discover how to revolutionize processing performance, data intelligence, customer experiences, and GRC.
The future of financial services will belong to those who can capture and capitalize on data. And it all begins with employing modern data strategies in four critical areas.
You’ll learn how to:
Leverage AI, machine learning and predictive analytics.
Get scalable, high-speed access to vast amounts of data.
Respond faster, become more competitive, and attract new customers.
Getting complex decisions right across complicated operational networks is the key to optimum performance. Find out how one of the UK’s biggest bus operators is using data and analytics to make better decisions and optimise the use of resources across their network.
Read this story to discover:
• how data and analytics can transform operational performance
• the benefits of using decision-support tools in the middle office
• key lessons for getting your plans for digital transformation right.
What if you could use just one platform to detect all types of major financial crimes?
One platform to handle the analytical tasks of fraud detection, including:
Data processing and aggregation
Statistical/mathematical/machine learning modeling
One platform that could successfully reduce complex and time-consuming fraud investigations by combining extremely different domains of knowledge including Business, Economics, Finance, and Law. A platform that can cover payments, credit card transactions, and know your customer (KYC) processes, as well as similar use cases like anti-money laundering (AML), trade surveillance, and crimes such as insurance claims fraud.
Learn more about TIBCO's comprehensive software capabilities behind tackling all these types of fraud in this in depth whitepaper.
AA Ireland specializes in home, motor, and travel insurance and provides emergency rescue for people in their homes and on the road, attending to over 140,000 car break downs every year, 80% of which are fixed on-the-spot.
“In each of the last five years, the industry lost a quarter billion in motor insurance," says Colm Carey, chief analytics officer. "So, there's a huge push for new data, models, ways to segment and pick profitable customer types—and get a lot more sophisticated. Our goal is to optimize pricing, understand the types of customers we're bringing, and the types we're trying to attract. We would like to tie that across the business. Marketing will run a campaign, trying to attract a lot of customers, but maybe they're not the right type. "We wanted to step away from industry standard software and go with something that was powerful and future-proof. In 2016, we had an opportunity to analyze all software.
We chose the TIBCO® System of Insight with TIBCO BusinessWorks™ i
Today, you can improve product quality and gain better control of the entire
manufacturing chain with data virtualization, machine learning, and advanced
data analytics. With all relevant data aggregated, analyzed, and acted on, sensors,
devices, people, and processes become part of a connected Smart Factory
•? Increased uptime, reduced downtime
•? Minimized surplus and defects
•? Better yields
•? Reduced cost due to better quality
•? Fewer deviations and less non-conformance
Global producer of polycrystalline silicon for semiconductors, Hemlock Semiconductor needed to accelerate process optimization and eliminate cost. With TIBCO® Connected Intelligence, Hemlock achieved centralized, self-service, governed analysis; revenue gains; cost savings; and more.
Fueled by double-digit growth in the markets it serves, Hemlock Semiconductor is adapting to the increasing commoditization within the polysilicon industry and better positioning itself to compete. A key factor in this plan is to equip process-knowledgeable personnel with the skills and tools to accelerate delivery of process optimizations and associated cost elimination.
Hemlock turned to a TIBCO® Connected Intelligence solution to address the challenges. By implementing TIBCO Spotfire® and TIBCO® Streaming analytics, TIBCO® Data Science, and TIBCO® Data Virtualization, the company created more self-service analytics. Adding TIBCO BusinessWorks™ integration let the company realize the vision of connect
"Considering switching to a single system for finance, planning, and analytics? These leading insurance companies did just that—and they achieved amazing results.
This infographic shows how Workday helped them stay competitive, deliver a customer experience like no other, and ensure compliance as well as:
Save $400,000 annually with better transactional control
Reduce time spent on manual processes, such as quarterly reports
Spend more time analyzing data than gathering it
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
I Big Data e gli analytics workloads sono la nuova frontiera per le aziende. I dati vengono raccolti da fonti che non esistevano 10 anni fa. Tutti i dati dei telefoni cellulari, i dati generati dalle macchine e i dati relativi all’interazione con i siti vengono raccolti e analizzati. Inoltre, con i budget IT sempre più sotto pressione, l’impatto ambientale dei Big Data non fa che aumentare e pone grandi sfi de per i sistemi storage.
Questo documento fornisce informazioni sulle problematiche che le applicazioni dei Big Data pongono sullo storage e su come scegliere le più corrette infrastrutture per ottimizzare e consolidare le applicazioni dei Big Data e degli analytics, senza prosciugare le fi nanze.
More data means more opportunities to discover powerful, actionable insights around customers, internal processes, and the broad market. Unfortunately, legacy IT architectures and approaches can block progressive analytics efforts.
That leaves a lot of room for improvement, and the learning and investment curve for starting in analytics can be steep. However, tending to three core steps will prove immensely helpful on this journey:
• Establishing an organizational foundation
• Mapping the data pipeline
• Transitioning analytics proofs of concept into production
Published By: Datastax
Published Date: Oct 21, 2019
A recent Gartner survey found that 80% of respondents using public cloud are using more than one cloud service provider (CSP). As databases move to the cloud, the database management service vendor landscape is shrinking, and data governance and integration are becoming more complex.
Are you ready for the impact of multicloud deployments on data and analytics strategies in your own organization?
This new Gartner report on multicloud and intercloud data management provides an impact appraisal along with recommendations for leaders planning to incorporate multiple CSPs into their data management strategies.
Published By: Datastax
Published Date: Oct 21, 2019
The majority of enterprises using cloud will be living in a hybrid deployment world for the foreseeable future. Data and analytics leaders must understand the risks and benefits in using the primary scenarios for hybrid cloud DBMS, and how they align with core use cases and architectures.
Read this Gartner report to learn more about how you can evolve your data management strategy to reflect the latest trends and technologies.
This webinar demonstrates how connected data changes everything. In an autonomous world you’ll take the lead, becoming central to your company’s success. Machine learning will take care of the day-to-day maintenance leaving you to focus on design, analytics and strategy. And crucially, you’ll find yourself empowering and inspiring your colleagues to gain their own insights from real- time data.
Organizations are charging ahead with investments in cloud and analytics to deliver agility, scalability and cost savings. With computing power advancements and continuous growth of data, cloud provides the elastic workloads and flexibility required for modern business. However, the environment of flexibility and choice that cloud provides also creates complexity and challenges.
In this white paper, learn how organizations are applying expertise and using the latest methods to move analytics to the cloud, including:
• Why are organizations moving analytic work to the cloud?
• What are the key challenges and misconceptions?
• How do IT leaders provide choice while maintaining control?
Envision this situation at a growing bank. Its competitive landscape demands an agile
response to evolving customer needs. Fortunately, analytically minded professionals in
different divisions are seeing results that positively affect the bottom line.
• A data scientist in the business development team analyzes data to create customized
• experiences for premium customers.
• A digital marketer tracks and influences the customer journey for prospective
• mortgage customers.
• A risk analyst builds risk models for the bank’s loan portfolios.
• A data analyst examines data about local customers.
• A technical architect defines a new system to protect bank data from internal and
• external cyberthreats.
• An application developer builds a new mobile app for online customer portfolio
Between them, these employees might be using more than a dozen packages for
analytics and data management.
Companies that put data at the centre of their business gain better insights and deliver more effective marketing. Data centricity at an organisational level is the priority for larger companies, mindful of the opportunities afforded by more scientific commercial decision-making and data-driven marketing. A focus on data alone in the context of customer analytics is not enough, however. Companies require insights from their data to deliver first-class customer experiences that give them a competitive advantage.
Our global survey of more than 1,000 business respondents shows that companies are rightly focused on activities powered by actionable insights as opposed to focusing on data for its own sake. More effective segmentation and targeting (65%), and better marketing attribution (52%), are the top data-related priorities for marketers, while ‘technologists’ (including analysts, ecommerce, and IT professionals) are primarily focused on making their organisations more data-centric (50%
Like the oxygen we breathe, journey analytics brings life to the customer behind those devices, over time getting to know their favorite pastry choice, when they’re most likely to buy gas, and how long they stay online while in the station’s café.
WHY SHOULD THE TARGET AUDIENCE CARE?
Business struggle to gain a holistic customer view — the skills to identify actionable insights from multichannel data are in short supply. If they could gain a holistic view of customer attributes and behaviors, they could make sure they get the right content at the right time.
If you want your customers to enjoy seamless, personalized experiences, you need to treat them like people. That means marketing to the person — not the device. When you know a customer’s interests, wants, and needs — perhaps even before they do — you’ve succeeded at becoming a true experience business. For some, this may require a shift from analytics as a tool to analytics as a way of life. It may also m
To stay ahead of the competition in a global marketplace, firms are increasingly speeding up operations, in many cases adopting real-time systems and tools to allow for instant decision-making and faster business cycles. Download here to learn how.
What’s the best way for businesses to differentiate themselves today? By delivering a unique, real-time customer experience across all touch points—one that is based on a solid, connected business strategy driven by data and analytics insights. We believe brands that gain the ultimate analytical advantage—by unifying the analytics life cycle from data to discovery to deployment—will also gain the ultimate competitive advantage through brand preference.
The spatial analytics features of the SAP HANA platform can help you supercharge your business with location-specific data. By analyzing geospatial information, much of which is already present in your enterprise data, SAP HANA helps you pinpoint events, resolve boundaries locate customers and visualize routing. Spatial processing functionality is standard with your full-use SAP HANA licenses.
There’s strong evidence organizations are challenged by the opportunities presented by external information sources such as social media, government trend data, and sensor data from the Internet of Things (IoT). No longer content to use internal databases alone, they see big data resources augmented with external information resources as what they need in order to bring about meaningful change. According to a September 2015 global survey of 251 respondents conducted by Harvard Business Review Analytic Services, 78 percent of organizations agree or strongly agree that within two years the use of externally generated big data will be “transformational.” But there’s work to be done, since only 21 percent of respondents strongly agree that external data has already had a transformational effect on their firms.
Artificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
"Cloud-based predictive analytics platforms are a relatively new phenomenon, and they go far beyond
the remote monitoring systems of a prior generation. Three key features differentiate cloud-based
predictive analytics — data sharing, scope of monitoring, and use of artificial intelligence/machine
learning (AI/ML) to drive autonomous operations. To help familiarize the uninitiated with specifically
what types of value these systems can drive, IDC discusses them at some length in this white paper."
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology