operational analysis

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By: SAS     Published Date: Jan 17, 2018
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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     SAS
By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracle’s Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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     Oracle PaaS/IaaS/Hardware
By: Anaplan     Published Date: Apr 06, 2016
A Harvard Business Review Analytics Services White Paper Finance is constantly tested to keep pace in today’s business environment. To keep up, planning needs to become a continuous process that spans departmental boundaries and enables managers to collectively realign resources to respond to market changes. Organizations must streamline disparate sales and operational planning with traditional financial planning and analysis by using technology to connect people, data, and processes across the organization. Download this white paper to discover the three steps to moving towards finance-led integrated business planning recommended by the Harvard Business Review.
Tags : analytics, planning, cfo, operations, business practices, revenue, growth, enterprise business
     Anaplan
By: Reputation.com     Published Date: Oct 02, 2017
1.Meet the new consumer The migration to mobile and social media will challenge — and change — everything we know about consumer marketing. 2. Who owns your brand? Brand equity can no longer be bought. Online reviews now generate total market transparency for location-based businesses. Reviews tilt the balance of branding power away from companies and into the hands of customers. 3. The battle for brick-and-mortar customers is won or lost on the social web. To win, marketers must actively enlist customers as online advocates. Those who scale online review volume and quality will be rewarded with higher search visibility and more business at street level. 4. “Dark data” provides priceless operational insights Vast amounts of unstructured, unmined sentiment data on social media provides feedback about the customer experience that you can filter using thematic analysis and use to improve operations at the national or location level. 5. Business implications Online reputation stands betwee
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     Reputation.com
By: IBM Watson Health     Published Date: Nov 21, 2017
The shift to value-based care means that healthcare organizations should expand their concept of return on investment (ROI) to include the ability of solutions to increase efficiency and contain healthcare costs. Data analytics and automation capabilities have become important tools for providers aiming to maximize value-based payments. Learn from this whitepaper about the best ways for healthcare organizations to measure health IT ROI in the value-based environment, including specific examples of how certain providers are approaching this challenge.
Tags : value-based care, healthcare costs, health outcomes, health it, health information technology, hospital performance, operational performance, hospital performance improvement
     IBM Watson Health
By: Visual Factories     Published Date: Mar 15, 2019
Manufacturers are looking for ways to optimize their factory floor and machinery processes to reach their full production potential. Whether it is because of equipment issues or the personnel running the machines, the average OEE machine is running at 60%. With Visual Factories’ plug-and-play cloud-based solution, factory management get enhanced visibility into their manufacturing processes, helping to identify operational inefficiencies and to increase their OEE. Real-time reports for middle management deliver root cause analysis that will increase machine efficiency. Top management use the business insights for maximizing profits and reducing costs. The solution is quick and easy to install and can be set up by on-site maintenance staff. Visual Factories' solution is compatible with any type of production machine in any market segment and has been deployed at metal working and medical tooling plants in the aerospace, automotive and medical industries around the world. Customers incl
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     Visual Factories
By: Cloudian     Published Date: Feb 15, 2018
We are living in an age of explosive data growth. IDC projects that the digital universe is growing 50% a year, doubling in size every 2 years. In media and entertainment, the growth is even faster as capacity-intensive formats such as 4K, 8K, and 360/VR gain traction. Fortunately, new trends in data storage are making it easier to stay ahead of the curve. In this paper, we will examine how object storage stacks up against LTO tape for media archives and backup. In addition to a detailed total cost of ownership (TCO) analysis covering both capital and operational expenses, this paper will look at the opportunity costs of not leveraging the real-time data access of object storage to monetize existing data. Finally, we will demonstrate the validity of the analysis with a real-world case study of a longstanding network TV show that made the switch from tape to object storage. The limitations of tape storage go way beyond its lack of scalability. Data that isn’t searchable is becoming
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     Cloudian
By: AWS     Published Date: Oct 30, 2018
As cybercriminals look for new ways to break through defenses, it’s vital that organizations have access to real-time operational intelligence across their applications, services, and security infrastructure. As Recreational Equipment, Inc. (REI) migrated applications to Amazon Web Services (AWS), it needed to expand security capacity for edge protection of its AWS VPCs. REI chose Splunk for security monitoring, historical analysis, and data visualization in real time, to help it investigate events and mitigate operational risks.
Tags : rei, intelligent, threat, detection, edge, protection, aws, splunk
     AWS
By: Schneider Electric     Published Date: Aug 15, 2017
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customers’ datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones. More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneider’s long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
Tags : incident tracking, historical trending, troubleshooting, operational analysis, prediction model, schneider equipment, maintenance, firmware updates
     Schneider Electric
By: Evergreen Systems, Inc.     Published Date: Dec 16, 2008
This comprehensive white paper applies automation and ITIL best practices to the data center and reviews current industry trends, server automation energy usage issues and a variety of optimization strategies for data center improvement.  The effects of virtualization are explored in-depth.  Includes detailed sections on increasing operational efficiency using workflow analysis, automating and optimizing server change management, reducing infrastructure complexity and developing security, disaster recovery and business continuity procedures.  Step by step instructions for developing metrics and a business case to justify data center and server automation are included.
Tags : evergreen system, data center automation, data center automation manager, network automation, server automation, server virtualization, itil, data center optimization
     Evergreen Systems, Inc.
By: SAS     Published Date: Mar 14, 2014
This paper will discuss the barriers to data-driven decision making for midsized businesses, and how experts and non-experts alike can use SAS Visual Analytics to unlock the value of data – including big data – to increase revenue, cut operational costs and better manage their business.
Tags : sas, bottom line, midsized businesses, leveraging data, data management, internal and external, decision making, big data
     SAS
By: IBM     Published Date: Dec 15, 2016
The IBM DataPower Operations Dashboard provides the most robust solution in the marketplace for monitoring DataPower gateways and performing trouble-shooting & analysis. Administrators can view operational data with full-text search to immediately diagnose errors and highlight impact analysis, DevOps can view and troubleshoot their own DataPower services, and business owners can generate automated reports to meet SLA and compliance requirements without assistance from centralized IT.
Tags : ibm, middleware, ibm datapower gateways, ibm datapower operations dashboard, enterprise applications
     IBM
By: IBM     Published Date: Mar 05, 2014
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
Tags : ibm, big data, data, big data platform, analytics, data sources, data complexity, data volume
     IBM
By: Domino     Published Date: Feb 25, 2019
Food and beverage manufacturers today face a host of challenges— SKU proliferation, more frequent line changeovers, finding and retaining skilled employees — all while handling the day-to-day challenges of keeping the line running smoothly in the first place. This demand for continuous improvement has plant managers and line engineers examining every aspect of the production and packaging line for ways to squeeze every bit of performance from every machine, every process, and every employee This white paper from Domino takes a look at how conducting a value stream analysis with a third-party provider can uncover a wide array of opportunities to improve productivity and efficiency. From freeing up labor and reducing errors to shortening changeover time and sharing critical operational knowledge among all stakeholders, a value stream analysis provides a collaborative opportunity to learn and discover new ways to boost productivity without major equipment investments.
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     Domino
By: SAS     Published Date: Jun 05, 2017
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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     SAS
By: IBM     Published Date: Feb 10, 2014
Manage physical and virtual assets, maintain your infrastructure and capital equipment and maximize the efficiency of your people, processes and assets with predictive analytics.
Tags : ibm, ibm business analytics, operational analytics, predictive maintenance, insights, data analysis, roi, operational value cycle
     IBM
By: IBM     Published Date: Feb 10, 2014
Predictive maintenance taps into the sets of structured and unstructured data that organizations already have available.
Tags : ibm, business analytics, operational analytics, predictive maintenance, predictive analytics, data analysis, efficiency, roi
     IBM
By: IBM     Published Date: Feb 10, 2014
A car company thought they had a problem with their brake lights. Predictive analytics helped reveal the true culprit.
Tags : ibm, ibm business analytics, operational analytics, predictive maintenance, insights, data analysis, roi, asset management
     IBM
By: Anaplan     Published Date: Apr 14, 2016
Finance is constantly tested to keep pace in today’s business environment. To keep up, planning needs to become a continuous process that spans departmental boundaries and enables managers to collectively realign resources to respond to market changes. Organizations must streamline disparate sales and operational planning with traditional financial planning and analysis by using technology to connect people, data, and processes across the organization.
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     Anaplan
By: Pure Storage     Published Date: Jul 03, 2019
Financial services businesses face unprecedented market challenges. Disruption from Fintech firms, increased local and international regulation, geo- political upheavals and wavering customer loyalty. The need to fully understand the market, to innovate, to reduce costs and be more competitive has never been greater, and this is where AI can help. According to one fintech research company, by 2030 the financial services sector could reduce operational costs using AI, by as much as 22%. It suggests that will equate to around $1 trillion in efficiencies. So, from a purely operational point of view, doing nothing is not really an option for companies that want to remain competitive. Today, financial services firms across the board need to rejuvenate customer experience to protect against client attrition, and protect those customers against risk. While data analysis and visualization are key to making sense of data, the fundamental challenge for all businesses is building an infrastructur
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     Pure Storage
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