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Published By: IBM     Published Date: Jan 14, 2015
Cloud-delivered big data analytics presents an enormous opportunity for organizations that want to become more agile, more efficient and more competitive. To capitalize on the full potential of this opportunity, businesses need cloud-enabled big data analysis solutions that are flexible, simple, secure and open.
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big data, data management, the cloud, cloud solutions, data, data analytics, client engagement, computing model
    
IBM
Published By: IBM     Published Date: Mar 18, 2015
More and more companies are moving to the cloud for B2B services, and for good reason. There’s a huge potential for increased visibility and analytics-driven insights to be gained from B2B transactions that can give businesses unprecedented levels of information. But many organizations continue to struggle when it comes to going beyond basic transactional data and historical performance metrics. What does it take to not only report on past activity, but to get real-time alerts, predict future events, manage exceptions, and proactively leverage this wealth of data in order to put it to work? Read this IBM white paper to find out how B2B Services Reporting and Analytics provide new insights into your trading partner relationships and to drive better, more profitable business decisions.
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b2b services, ibm, b2b analytics, b2b reporting, transactional data, trading partner relationships
    
IBM
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
    
IBM
Published By: IBM     Published Date: May 12, 2015
This white paper describes a holistic approach to healthcare cybersecurity, which incorporates sophisticated big data analytics to help better protect and secure the vast array of data healthcare organizations maintain.
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healthcare, cybersecurity, big data, security
    
IBM
Published By: Cornerstone OnDemand     Published Date: Jan 31, 2018
Over the past decade, talent management initiatives have become a critical priority for organizations. While CEOs see the business value of talent management— typically talent acquisition, learning, performance, talent mobility, compensation, and analytics—some organizations have found it challenging to quantify the business impact or return on such investments. If you are looking for help in building a talent management business case, this overview was created for you.
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Cornerstone OnDemand
Published By: IBM     Published Date: Oct 16, 2014
Because all processes should be aligned to customer metrics,process improvement is an important goal for organizations in every industry. This paper illustrates the impact analytics can make on business processes through real-world examples based on IBM client experiences, and describes the steps organizations can take to refine quality, warranty, financial,inventory and other processes that are essential to achieving operational excellence.
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business analytics, organizational processes, customer metrics, process improvement, operational excellence
    
IBM
Published By: SAS     Published Date: Sep 30, 2014
When Information Revolution1 was published in 2006, no Chinese based companies were among the top 10 largest companies by market capitalization. Apple didn’t sell phones. Facebook was something college kids used to connect with their friends. Back then, we talked a lot about the amount of data coming in and faster processing speed. What we believed then remains true today: Data, and the decision-making process, can be moved throughout the organization to equip every decision maker (automated, line worker, analyst, executive) to make the best choices. By operationalizing analytics, organizations can identify and quantify both opportunity and risk. Information Revolution highlighted SAS’ Information Evolution Model, which helps organizations understand how they interact with their information and how to extract more value from it through analytics.
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sas, organizational insights, operationalizing analytics, sas’ information evolution model
    
SAS
Published By: SAS     Published Date: Mar 31, 2016
Digitization creates major opportunities for financial services – automating operations, expanding channels, delivering engaging customer experiences.
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analytics, financial services, operations, digital management, data, best practices
    
SAS
Published By: SAS     Published Date: May 17, 2016
This report provides a guide to some of the opportunities that are available for using machine learning in business, and how to overcome some of the key challenges of incorporating machine learning into an analytics strategy. We will discuss the momentum of machine learning in the current analytics landscape, the growing number of modern applications for machine learning, as well as the organizational and technological challenges businesses face when adopting machine learning. We will also look at how two specific organizations are exploiting the opportunities and overcoming the challenges of machine learning as they’ve embarked on their own analytic evolution.
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oreilly, evolution of analytics, sas, machine learning, analytics landscape
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: Apr 25, 2017
This Checklist explores how AI can be used to enhance marketing analytics and to help companies both better understand their customers and deliver a great customer experience. It also provides practical advice on how organizations can use what they may already be doing to become more effective in marketing.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
One of the biggest inhibitors of analytics success is the delay between developing and implementing models. This paper reviews how an analytics factory, a rapid scoring and model development environment, can help organizations turn models into insight faster than before.
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SAS
Published 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
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
Published By: SAS     Published Date: Jun 05, 2017
The Internet of Things is fast becoming a fixture in some industries, and the technologies for transformative business applications are at hand. Yet many organizations have been slow to recognize and act on these new opportunities. This report from the International Institute for Analytics explores the many business opportunities IoT presents, details its associated implementation challenges and describes how organizations can accelerate their progress so they don’t fall behind.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
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
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SAS
Published By: SAS     Published Date: Oct 18, 2017
Organizations need to accelerate the pace with which they realize business value from data. The focus is on improving “time to value,” which is the length of time it takes from the beginning of a project to the delivery of anticipated business value. This TDWI Best Practices Report focuses on realizing value from BI and analytics and how organizations can accelerate the path to higher value. The report looks at multiple factors impacting the ability of organizations to quickly derive greater value from data and analytics, including the organizational issues, practices, and development methods that are often just as important as keeping pace with technological innovation.
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SAS
Published By: IBM     Published Date: Sep 10, 2013
This white paper will discuss how big data analytics, coupled with the right facilities and asset management software, can provide next-generation opportunities to improve facilities and asset management processes and reutnrs. It will examine how different organizations successfully use big data generated by their facilities and assets to help increase revenue, power operational efficiency, ensure service availability and mitigate risk. Most importantly, this white paper will reveal how your organization can leverage big data analytics to achieve similar benefits and transform the management of your organization's facilities and assets-and ultimately, your business.
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big data, smarter infrastructures, big data, ibm, power operational, leverage big data, analytics, harnessing the power of data
    
IBM
Published By: IBM     Published Date: Feb 10, 2014
The closer your organization gets to your customers, the more successful it will be. Learn how to take customer relationships to a new level of intimacy using a combination of business intelligence and predictive analytics software.
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ibm, king fish media, business analytics, insights, business intelligence software, analytics quotient, roi, predictive analytics
    
IBM
Published By: IBM     Published Date: May 07, 2015
Learn how to build a proactive threat and fraud strategy based on business analytics. You’ll see extensive examples of how organizations worldwide apply IBM Business Analytics solutions to minimize the negative impact of risk and maximize positive results.
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business analytics, risk management, threat management, fraud, proactive threat, analytics solutions, reduce exposure, reduce threats
    
IBM
Published By: IBM Software     Published Date: Oct 26, 2010
Analytics are changing how organizations today operate. Being able to quickly and effortlessly interact with business information is now considered essential to making the best business decisions.
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analytics, business decision making, analytics system
    
IBM Software
Published By: IBM Software     Published Date: Feb 11, 2011
This white paper explores how predictive analytics helps marketing organizations increase ROI by executing highly targeted campaigns focused on high revenue-generating customers and prospects.
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ibm cognos, predictive analytics, marketing campaign management system, roi, customer behaviour
    
IBM Software
Published By: SAS     Published Date: Aug 03, 2016
New analytics tools and services are helping organizations extract exceptional business value from the massive volumes of available data provided by various internal and external sources. Many companies are harnessing these insights to improve operational and business processes, troubleshoot problems, identify business opportunities, and generally compete and innovate better. Now the benefits of analytics in those areas are prompting companies to look to analytics to improve information security. Enterprise security organizations are under tremendous pressure to change. Traditional perimeter-focused security controls and strategies have proved inadequate against modern, highly targeted attack campaigns.
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best practices, technology, security, enterprise, analytics
    
SAS
Published By: SAS     Published Date: Aug 03, 2016
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment.
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best practices, embedding analytics, technology, data, operational analytics
    
SAS
Published By: SAS     Published Date: Jun 27, 2019
In the quest to understand how a therapeutic intervention performs in actual use – in real medical practice outside the controlled environment of clinical trials – many life sciences organizations are stymied. They rely on one-off processes, disconnected tools, costly and redundant data stores, and ad hoc discovery methods. It’s time to standardize real-world data and analytics platforms – to establish much-needed consistency, governance, repeatability, sharing and reuse. The organizations that achieve these goals will formalize their knowledge base and make it scalable, while significantly reducing turnaround times, resources and cost. Learn the seven key components for putting that structure to real-world evidence – and four ways to take it to the next level.
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SAS
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