business analytics projects

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IoT Analytics in Practice

Published By: SAS     Published Date: Oct 22, 2015
The Internet of Things (IoT) presents an opportunity to collect real-time information about every physical operation of a business. From the temperature of equipment to the performance of a fleet of wind turbines, IoT sensors can deliver this information in real time. There is tremendous opportunity for those businesses that can convert raw IoT data into business insights, and the key to doing so lies within effective data analytics. To research the current state of IoT analytics, Blue Hill Research conducted deep qualitative interviews with three organizations that invested significant time and resources into their own IoT analytics initiatives. By distilling key themes and lessons learned from peer organizations, Blue Hill Research offers our analysis so that business decision makers can ultimately make informed investment decisions about the future of their IoT analytics projects.
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SAS

Making IoT Connectivity Secure and Simple for Retailers

Published By: Intel     Published Date: Apr 11, 2019
The Internet of Things (IoT) unleashes valuable business insights through data that’s gathered at every level of a retail organization. With IoT and data analytics, retailers now have the capability to gather insight into customer behavior, offer more personalized experiences, achieve better inventory accuracy, create greater supply chain efficiencies, and so much more. But with data comes great risk. A recent report by security firm Thales and 451 Research found that 43 percent of retailers have experienced a data breach in the past year, with a third reporting more than one breach.1 Intel® technology-based gateways and Asavie, a provider of next-gen enterprise mobility management and IoT connectivity solutions, offer a security connectivity solution that minimizes the effort and cost to businesses to ensure safety from cybersecurity attacks. In addition, the Intel/Asavie IoT solution provides retailers with a solid basis to build their smart, connected projects:
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Intel

Modernizing Data Protection for the Digital Business

Published By: Veritas     Published Date: Jan 04, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands. You will learn: The key trends that are driving change in the digital business The most common causes of data loss in tomorrow’s multi-cloud data centers How to protect an increasingly diverse environment with minimal operational overhead
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Veritas

Big Data, Bad Data, Good Data - The Link Between Information Governance and Big Data Outcomes

Published By: IBM     Published Date: Jul 08, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, data management
    
IBM

Big Data, Good Data, Bad Data-the link between information governance and Big Data outcomes

Published By: IBM     Published Date: Feb 24, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, ibm, big data outcomes, information governance, big data analytics, it management, data management, data center
    
IBM

Big Data, Bad Data, Good Data - The Link Between Information Governance and Big Data Outcomes

Published By: IBM     Published Date: Oct 18, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, enterprise applications
    
IBM

Building your data and analytics strategy

Published By: SAS     Published Date: Mar 20, 2019
What’s on the chief data and analytics officer’s agenda? Defining and driving the data and analytics strategy for the entire organization. Ensuring information reliability. Empowering data-driven decisions across all lines of business. Wringing every last bit of value out of the data. And that’s just Monday. The challenges are many, but so are the opportunities. This e-book is full of resources to help you launch successful data analytics projects, improve data prep and go beyond conventional data governance. Read on to help your organization become truly data-driven with best practices from TDWI, see what an open approach to analytics did for Cox Automotive and Cleveland Clinic, and find out how the latest advances in AI are revolutionizing operations at Volvo Trucks and Mack Trucks.
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SAS

Laying the Foundation for Successful Business Analytics Solution Deployments

Published By: IBM     Published Date: Apr 18, 2014
This IDC paper discusses the critical role of hardware infrastructure in business analytics deployments, citing best practices, the IDC's decision framework, and four client case studies - Wellpoint, Vestas, AXTEL, and Miami-Dade County. IDC's recommendation is that "infrastructure cannot — and should not — be an afterthought". Hardware infrastructure and software requirements must be determined in parallel to maximize the success of business analytics projects.
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ibm, business analytics, deployment, hardware, infrastructure, business analytics solutions, workload, server hardware
    
IBM

Laying the Foundation for Successful Business Analytics Solution Deployments

Published By: IBM     Published Date: Oct 07, 2014
This IDC paper discusses the critical role of hardware infrastructure in business analytics deployments, citing best practices, the IDC's decision framework, and four client case studies - Wellpoint, Vestas, AXTEL, and Miami-Dade County. IDC's recommendation is that "infrastructure cannot — and should not — be an afterthought". Hardware infrastructure and software requirements must be determined in parallel to maximize the success of business analytics projects.
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ibm, idc, hardware infrastructure, business analytics deployments, software, software requirements, business analytics projects, infrastructure management
    
IBM

Big Data, Good Data, Bad Data -- the Link Between Information Governance and Big Data Outcomes

Published By: IBM     Published Date: Apr 06, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, analytics, unstructured content, enterprise information, ibm, security, it management, knowledge management
    
IBM

An Early Adopters Guide to Hadoop

Published By: SAS     Published Date: May 04, 2017
Should you modernize with Hadoop? If your goal is to catch, process and analyze more data at dramatically lower costs, the answer is yes. In this e-book, we interview two Hadoop early adopters and two Hadoop implementers to learn how businesses are managing their big data and how analytics projects are evolving with Hadoop. We also provide tips for big data management and share survey results to give a broader picture of Hadoop users. We hope this e-book gives you the information you need to understand the trends, benefits and best practices for Hadoop.
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SAS

Blue Hill Research: IoT Analytics in Practice

Published By: SAS     Published Date: Jun 05, 2017
It’s there for the taking – real-time information about every physical operation of a business. All you need is a key: data analytics.  This paper is based on Blue Hill Research’s interviews of three organizations – a US-based oil and gas company, a US municipality and an international truck manufacturer – each of which heavily invested in IoT analytics. Focusing on the key themes and lessons learned from their initiatives, this paper will help business decision makers make informed investment decisions about the future of their own IoT analytics projects.
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SAS

KONE Implements Predictive Maintenance with IBM Watson IoT

Published By: IBM     Published Date: Dec 05, 2016
Learn directly from KONE's expert about their recent IoT experience in implementing predictive maintenance (PMQ) and IoT. The session will cover: 1) KONE's business area that the PMQ and IoT solution is supporting, and the metrics used to measure success; 2) KONE's Predictive Maintenance and IoT Platform use case, key personas, savings and benefits realized; and 3) Observations from implementation, including: a) The analytics journey at KONE; b) Organizational change (culture, processes, etc.); c) Measurable maintenance benefits; d) Implementation considerations, learnings, going forward; and e) Future projects and capabilities.
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ibm, leadership, watson, watson iot, predictive maintenance, enterprise applications, business technology
    
IBM

Modernizing Data Protection for the Digital Business

Published By: Veritas     Published Date: Jan 03, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands. You will learn: The key trends that are driving change in the digital business The most common causes of data loss in tomorrow’s multi-cloud data centers How to protect an increasingly diverse environment with minimal operational overhead
Tags : 
    
Veritas
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