self service data

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Published By: GrapeCity, Inc.     Published Date: Feb 25, 2013
Getting ad hoc reporting tools into users’ hands can be a logistical challenge. This white paper offers five tips for easing that process and discusses how to effectively deploy ad hoc reporting for business users.
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ad hoc reporting, ad hoc report designer, self-service bi, business intelligence, reporting, activereports, web-based reporting, mobile bi, end user reporting, dashboards, data visualization, charts, reports, analytics, multi-tenant, self-service reporting, crystal
    
GrapeCity, Inc.
Published By: GrapeCity, Inc.     Published Date: Feb 25, 2013
Encouraging self-service BI adoption is both a technical and a human resource effort. This white paper offers a 10-point checklist for improving this process, and includes these questions and more in this easy-to-read white paper.
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ad hoc reporting, ad hoc report designer, self-service bi, business intelligence, reporting, activereports, web-based reporting, mobile bi, end user reporting, dashboards, data visualization, charts, reports, analytics, multi-tenant, self-service reporting, crystal
    
GrapeCity, Inc.
Published By: GrapeCity, Inc.     Published Date: Feb 25, 2013
When it comes to explaining information, this white paper outlines a few basic data visualization tips to improve the impact of visualizations, ensuring that the story in the data is expressed as effectively as possible.
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ad hoc reporting, ad hoc report designer, self-service bi, business intelligence, reporting, activereports, web-based reporting, mobile bi, end user reporting, dashboards, data visualization, charts, reports, analytics, multi-tenant, self-service reporting, crystal
    
GrapeCity, Inc.
Published By: Rackspace     Published Date: Mar 08, 2016
This whitepaper examines how businesses can leverage the benefits of a Microsoft Private Cloud, and taking advantage of the following functions: •Enabling self-service. •Federating your Microsoft® Cloud Platform •Taking advantage of software-defined networking. •Messaging services. •Database as a Service (DBaaS) and its advantages.
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microsoft private cloud, microsoft cloud platform, rackspace, self-service, software-defined networking
    
Rackspace
Published By: AWS     Published Date: Jul 16, 2019
Ypsomed is a leader in the development and manufacturing of injection and infusion systems. The company is keenly aware of the multi-billion dollar problem of poor medication adherence and the need to measure medicine intake and ensure doses are taken at the correct time. Ypsomed sought to create a digital solution for medication adherence monitoring and smart device management for contract research organizations’ (CROs) use in clinical trials, including self-injection systems for trial participants to administer medications at home. Yet the company faced serious demands for remote device management, global scale, and privacy and security regulations such as HIPAA and GDPR. To solve these challenges, Ypsomed adopted Philips’ HealthSuite digital platform (HSDP), a cloud platform built on Amazon Web Services (AWS). HSDP allows Ypsomed to connect devices to the cloud and remotely manage them; store data; and manage and scale services globally within healthcare regulatory, privacy, and s
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AWS
Published By: AWS     Published Date: Jun 24, 2019
Join our webinar to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS. Webinar attendees will learn: Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights. How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS. How easy it is to get started with machine learning solutions for data wrangling on the cloud.
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AWS
Published By: SAS     Published Date: Mar 06, 2018
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and analytics. And you can do it through an interface that anyone can use. At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days. Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
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SAS
Published By: SAS     Published Date: Mar 06, 2018
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. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
Published By: SAS     Published Date: Jun 20, 2018
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SAS
Published By: IBM     Published Date: Nov 09, 2015
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
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ibm, data, magic quadrant, data management, analytics
    
IBM
Published By: SAS     Published Date: Dec 20, 2018
Think of the self-service things you use in a day. Gas pumps. ATMs. Online apps for shopping. They’re convenient and easy to use. People choose what they want, when they want – without involving others in their minute-to-minute decisions. What if your organization could treat data discovery and analytics the same way? SAS has combined two of its visual solutions to do just that. SAS Visual Analytics and SAS Visual Statistics share the same web-based interface to provide self-service data exploration and easy-to-use interactive predictive analytics in a collaborative environment. This white paper takes a look at this convergence and outlines how these products can be used together so that everyone, even nontechnical users, can investigate data on their own, create analytical models and uncover new insights that drive competitive differentiation. Your analytics journey just got a lot easier.
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SAS
Published By: Cisco     Published Date: Dec 21, 2016
Self-service analytics implies that users design and develop their own reports and do their own data  analysis with minimal support by IT. Most recently, due to the availability of tools, such as those from Qlik,  Spotfire, and Tableau, self-service analytics has become immensely popular. Besides powerful analytical  and visualization capabilities, they all support functionality for accessing and integrating data sources.  With respect to this aspect of data integration four phases can be identified in the relatively short history  of self-service analytics. This whitepaper describes these four phases in detail and shows how the tools  Cisco Data Preparation (CDP) and Cisco Information Server (CIS) for data virtualization can strengthen and  enrich the self-service data integration capabilities of tools for reporting and analytics.  
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Cisco
Published By: SAS     Published Date: May 12, 2016
This paper examines the barriers to adoption from an IT and end-user perspective, and shows how self-service analytics in general – and SAS Visual Analytics in particular – can eliminate these barriers. Self-service analytics empowers users to truly exploit the wealth of data available to them, while ensuring that the IT organization maintains governance and control over that data.
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sas, business analytics, it organization
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
But if you can’t explain how you got the answer, or what it means, it’s no good. Most self-service BI solutions can only display what has already happened, through reports or dashboards. And most have a predefined path of analysis that gives users very little creative freedom to explore new lines of thought. To maintain competitive advantage, your BI solution should allow business users to quickly and easily investigate and interrogate the data to find out why something happened – to uncover the root cause behind the “what.”
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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: Gleanster Research     Published Date: Oct 11, 2012
Authored by two luminary Business Intelligence (BI) industry analysts. This 9 page report exposes best practices on Self-Service BI. The findings come from a Q3'12 survey on BI, which captured the experiences of 327 organizations.
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business intelligence, cio, self-service, business analytics, data warehouse, data, analysis
    
Gleanster Research
Published By: Oracle     Published Date: Mar 18, 2014
Customer relationship management (CRM) deployments are most effective when they legitimately support all three words that make up the acronym itself. Customers of the modern business-to-business (B2B) enterprise benefit when they purchase goods and services from companies who are focused on the buyer’s experience. Internal relationships within the selling organization are more effectively maintained when all customer-facing stakeholders have access to the rich data contained in a well-maintained CRM. And the management of the enterprise providing solutions can run their business like a finely-tuned machine when the maximum levels of visibility into customers and accounts are clear and accurate. This Research Brief combines research from a number of Aberdeen Sales Effectiveness research data sets, to create a holistic view of the most effectively deployed CRM systems.
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oracle, customer relationship management, crm, b2b, internal relationships, sales effectiveness, sales teams, sales strategy, buyer experience, sales
    
Oracle
Published By: SBE     Published Date: Nov 17, 2006
This white paper covers the use of new growth-oriented file systems with snapshot capabilities, iSCSI for network layer independence, and a collection of other technologies provided by various vendors and open source projects to create a multi-tiered storage solution with self-service data restoration, long term growth, and disaster recovery.
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iscsi, storage management, storage scalability, scalable storage, scalability, multi tiered, multitiered, sbe
    
SBE
Published By: SAS     Published Date: Apr 16, 2015
SAS Institute is gearing up to make a self-service data preparation play with its new Data Loader for Hadoop offering. Designed for profiling, cleansing, transforming and preparing data to load it into the open source data processing framework for analysis, Data Loader for Hadoop is a lynchpin in SAS's data management strategy for 2015. This strategy centers on three key themes: 'big data' management and governance involving Hadoop, the streamlining of access to information, and the use of its federation and integration offerings to enable the right data to be available, at the right time.
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SAS
Published By: SAS     Published Date: Jun 08, 2018
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SAS
Published By: IBM     Published Date: Jul 09, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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IBM
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