Your business is changing. As a finance leader, you know that accounting is a labour-intensive, costly process where
systems often don’t allow for expedient exception handling and many days are fraught with difficulty in matching
invoices to other databases for reconciliation. Like most companies, you know where you want to go but may not have
infrastructure or internal expertise to handle electronic fund transfers, credit card payments or cheque processing— all
the pieces required to make your vision for an efficient, integrated operation a reality.
Get practical advice from IT professionals on how to successfully deploy all-flash arrays for Oracle, SAP and SQL Server workloads in a SAN environment. You'll explore topics such as transaction processing speed, storage management, future requirements planning, workload migration and more.
Big data has raised the bar for data virtualization products. To keep pace, TIBCO® Data Virtualization added a massively parallel processing engine that supports big-data scale workloads. Read this whitepaper to learn how it works.
"Many financial services firms have automated the vast majority of key processes and customer experiences. However, the “last mile” of most transactions – completing the agreement– far too often relies on the same inefficient pen-and-paper processes of yesteryear.
Digitising agreements using DocuSign lets you keep processes digital from end to end. Completing transactions no longer requires documents to be printed and shipped, and re-keyed on the back end.
Read the whitepaper to learn how leading financial services organisations use straight-through processing by automating the last mile of business transactions to:
- Speed processes by 80% or more, often going from days or weeks to just minutes
- Reduce NIGO by anywhere from 55% to 93%
- Achieve a 300% average ROI "
By processing real-time data from machine sensors using artificial intelligence and machine learning, it’s possible to predict critical events and take preventive action to avoid problems. TIBCO helps manufacturers around the world predict issues with greater accuracy, reduce downtime, increase quality, and improve yield.
Read about our top data science best practices for becoming a smart manufacturer.
Written by: IDC Abner Germanow, Jonathan Edwards, Lee Doyle IDC believes the convergence of communications and mainstream IT architectures will drive significant innovation in business processes over the next decade.
Continuous member service is an important deliverable for credit unions, and. the continued growth in assets and members means that the impact of downtime is affecting a larger base and is therefore potentially much more costly. Learn how new data protection and recovery technologies are making a huge impact on downtime for credit unions that depend on AIX-hosted applications.
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.
To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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 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.
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
What if you could reduce the cost of running Oracle databases and improve database performance at the same time? What would it mean to your enterprise and your IT operations?
Oracle databases play a critical role in many enterprises. They’re the engines that drive critical online transaction (OLTP) and online analytical (OLAP) processing applications, the lifeblood of the business. These databases also create a unique challenge for IT leaders charged with improving productivity and driving new revenue opportunities while simultaneously reducing costs.
The Cisco® Hyperlocation Solution is the industry’s first Wi-Fi network-based location system that can help businesses and users pinpoint a user’s location to within one to three meters, depending on the deployment. Combining innovative RF antenna and module design, faster and more frequent data processing, and a powerful platform for customer engagement, it can help businesses create more personalized and profitable customer experiences.
Published By: OpenText
Published Date: Mar 02, 2017
Watch the video to learn how Procure-to-Pay (P2P) solutions automate B2B processes to help you gain better visibility into transaction lifecycles, improve efficiency, and increase the speed and accuracy of order, shipping, and invoice processing.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making.
Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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: Infosys
Published Date: May 30, 2018
Customers today are far more concerned about the contents and origin of a product than ever before. in such a scenario, granting them easy access to product information, via digital initiatives such as SmartLabel™, goes a long way in strengthening customer trust in a brand. But it also means expending several man-hours of effort processing unstructured data, with the possibility of human error.
Intelligent automation can help save effort and time, with virtually error-free results. A consumer products conglomerate wanted a smart solution to implement SmartLabel™ compliance. See how Infosys helped and the five key takeaways from the project.
Published By: IBM APAC
Published Date: Apr 27, 2018
While relying on x86 servers and Oracle databases to support their stock trading systems, processing rapidly increasing number of transactions fast became a huge challenge for Wanlian Securities. They shifted to IBM FlashSystem that helped them cut average response time for their Oracle Databases from 10 to less than 0.4 milliseconds and improved CPU usage by 15%.
Download this case study now.
As of May 2017, according to a report from The Depository Trust &
Clearing Corporation (DTCC), which provides financial transaction and data processing services for the global financial industry, cloud computing has reached a tipping point1. Today, financial services companies can benefit from the capabilities and cost efficiencies of the cloud. In October of 2016, the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of Currency (OCC) and the Federal Reserve Board (FRB) jointly announced enhanced cyber risk management standards for financial institutions in an Advanced Notice of Proposed Rulemaking (ANPR)2. These proposed standards for enhanced cybersecurity are aimed at protecting the entire financial system, not just the institution. To meet these new standards, financial institutions will require the right cloud-based network security
platform for comprehensive security management, verifiable compliance and governance and active protection of customer data
The purpose of IT backup and recovery systems is to avoid data loss and recover
quickly, thereby minimizing downtime costs. Traditional storage-centric data protection
architectures such as Purpose Built Backup Appliances (PBBAs), and the conventional
backup and restore processing supporting them, are prone to failure on recovery. This
is because the processes, both automated and manual, are too numerous, too complex,
and too difficult to test adequately. In turn this leads to unacceptable levels of failure for
today’s mission critical applications, and a poor foundation for digital transformation
Governments are taking notice. Heightened regulatory compliance requirements have
implications for data recovery processes and are an unwelcome but timely catalyst for
companies to get their recovery houses in order. Onerous malware, such as
ransomware and other cyber attacks increase the imperative for organizations to have
highly granular recovery mechanisms in place that allow
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.