data analyst

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Published By: IO-Tahoe     Published Date: Jan 15, 2019
The white paper addresses the problem of managing data in an increasingly complex, hybrid and distributed world; acknowledging that it is too great even for highly-skilled analysts and scientists to handle alone. To solve this problem, organizations should understand why a single unified product from smart data discovery to AI-driven data catalog can vastly reduce the time it takes to do the kind of analytics that are necessary for digital transformation.
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IO-Tahoe
Published By: Alation     Published Date: Jan 06, 2017
90% of the time that is spent creating new reports is recreating information that already exists. Without a way to effectively share prior work and identify verified data sources, analysts and other data consumers lack shared context on how to apply data to analytic inquiries and business decision making. Time is wasted tracking down subject matter experts and trying to unearth tribal knowledge. Leading analytic organizations in retail, healthcare, financial services and technology are using data catalogs to help their analysts find, understand and use data appropriately. What are the 5 critical capabilities of a data catalog? Learn more here:
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Alation
Published By: Alation     Published Date: Jan 06, 2017
90% of the time that is spent creating new reports is recreating information that already exists. Without a way to effectively share prior work and identify verified data sources, analysts and other data consumers lack shared context on how to apply data to analytic inquiries and business decision making. Time is wasted tracking down subject matter experts and trying to unearth tribal knowledge. Leading analytic organizations in retail, healthcare, financial services and technology are using data catalogs to help their analysts find, understand and use data appropriately. What are the 5 critical capabilities of a data catalog? Learn more here:
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Alation
Published By: DATAVERSITY     Published Date: May 25, 2014
Deconstructing NoSQL: Analysis of a 2013 Survey on the Use, Production, and Assessment of NoSQL Technologies in the Enterprise This report examines the non-relational database environment from the viewpoints of those within the industry–whether current or future adopters, consultants, developers, business analysts, vendors, or others. This paper is sponsored by: MarkLogic, Cloudant and Neo4j.
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research paper, analysis, nosql, database, nosql database, white paper, nosql white paper
    
DATAVERSITY
Published By: Paxata     Published Date: Apr 02, 2014
Why Sift Through Data Landfills? Better business insight comes from data - but data is often dirty, incomplete and complicated. As any analyst would admit, what passes for data science is more like janitorial work. Find out why that is - and how you can avoid the painful, manual and error-prone processes that have bogged down the analytics process for 30 years.
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data, data management, big data, white paper, paxata, analytics
    
Paxata
Published By: Paxata     Published Date: Nov 29, 2016
Every organization looks for ways to reduce costs and run more efficiently. In fact, those are key drivers for the mainstream adoption of Hadoop and self-service BI tools. And while we can now collect and store more data than ever before, and we have enabled every information worker into a data-hungry analyst, not much consideration has been paid to the cost - including time and effort - of preparing data. Download this report to learn more about the hidden cost of data preparation.
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Paxata
Published By: Paxata     Published Date: Mar 21, 2017
Every organization looks for ways to reduce costs and run more efficiently. In fact, those are key drivers for the mainstream adoption of Hadoop and self-service BI tools. And while we can now collect and store more data than ever before, and we have enabled every information worker into a data-hungry analyst, not much consideration has been paid to the cost - including time and effort - of preparing data. Download this report to learn more about the hidden cost of data preparation.
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Paxata
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
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data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
Published By: Reltio     Published Date: Jan 20, 2017
If you invested in master data management (MDM), you are part of an elite association of those who have been able to afford the time, effort and resources to deploy what has characteristically been a tool, and discipline reserved for only the largest enterprises. Feedback from top industry analysts and companies that transitioned from legacy MDM to modern data management platforms, led to the compilation of a list of 10 warning signs you can use as a handy guide. If one or more of these signs get your attention, it warrants a serious conversation with your current provider about these issues, and how they compare to modern offerings available today.
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Reltio
Published By: Looker     Published Date: Mar 15, 2016
Data centralization merges different data streams into a common source through unified variables. This process can provide context to overly-broad metrics and enable cross-platform analytics to guide better business decisions. Investments in analytics tools are now paying back a 13.01:1 return on investment (ROI), with increased returns when these tools integrate with three or more data sour- ces. While the perks of centralization are obvious in theory, the quantity and variety of data available in today’s landscape make this difficult to achieve. This report provides a roadmap for how to connect systems, data stores, and institutions (both technological and human). Learn: • How data centralization enables better analytics • How to redefine data as a vehicle for change • How the right BI tool eliminates the data analyst bottleneck • How to define single sources of truth for your organization • How to build a data-driven (not just data-rich) organization
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Looker
Published By: Alation     Published Date: Mar 15, 2016
curation (noun): The act of organizing and maintaining a collection (such as artworks, artifacts, or data). Data curation is emerging as a technique to support data governance, especially in data-driven organizations. As self-service data visualization tools have taken off, sharing the nuances and best practices of how to use data becomes ever more critical. Analysts at companies from eBay to Safeway and Square are scaling their data knowledge through curation techniques. What are the 4 steps to successful data curation? Find out here:
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data stewardship, self-service analytics, data curation, data governance
    
Alation
Published By: Alteryx     Published Date: May 24, 2017
Spreadsheets are a mainstay in almost every organization. They are a great way to calculate and manipulate numeric data to make decisions. Unfortunately, as organizations grow, so does the data, and relying on spreadsheet-based tools like Excel for heavy data preparation, blending and analysis can be cumbersome and unreliable. Alteryx, Inc. is a leader in self-service data analytics and provides analysts with the ability to easily prep, blend, and analyze all data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. This paper highlights how transitioning from a spreadsheet-based environment to an Alteryx workflow approach can help analyst better understand their data, improve consistency, and operationalize analytics through a flexible deployment and consumption environment.
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Alteryx
Published By: TopQuadrant     Published Date: Jul 18, 2016
With information streaming in from more varied sources and at a faster pace than ever before, organizations are having an increasingly difficult time deriving accurate meaning from their data. Data governance systems that were once able to organize and process enterprise information are becoming too slow and limited.   Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data.     Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: TopQuadrant     Published Date: Aug 01, 2016
With information streaming in from more varied sources and at a faster pace than ever before, organizations are having an increasingly difficult time deriving accurate meaning from their data. Data governance systems that were once able to organize and process enterprise information are becoming too slow and limited. Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data. Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: Basho     Published Date: Nov 25, 2015
The landscape of Scalable Operational and Analytical Systems is changing and disrupting the norm of using relational databases for all workloads. With the growing need to process and analyze Big Data at Scale, the demand for alternative strategies has grown and has given rise to the emergence of NoSQL databases for scalable processing. Mike Ferguson, Managing Director of Intelligent Business Strategies, is an independent IT Analyst who specializes in Big Data, BI/Analytics, Data Management and Enterprise Business Integration. In this whitepaper he will discuss the movement towards NoSQL databases for scalable operational and analytical systems, what’s driving Big Data analytics from Hadoop to the emergence of Apache Spark, the value of operational analytics and the importance of in-memory processing, and why use Apache Spark as your in-memory analytical platform for operational analytics.
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Basho
Published By: Forcepoint     Published Date: Jan 03, 2019
Once again, Forcepoint has been named a Top Player in this year’s report, citing strengths such as integration with Forcepoint CASB to extend DLP policies into cloud applications and more. Read the report to learn more.
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data protection, data security, analyst report, dlp, cloud apps, data loss prevention, data leakage prevention, cybersecurity, cyber security, casb, radicati, radicati market quadrant
    
Forcepoint
Published By: Forcepoint     Published Date: Jan 07, 2019
Once again, Forcepoint has been named a Top Player in this year’s report, citing strengths such as integration with Forcepoint CASB to extend DLP policies into cloud applications and more. Read the report to learn more.
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data protection, data security, analyst report, dlp, cloud apps, data loss prevention, data leakage prevention, cybersecurity, cyber security, casb, radicati, radicati market quadrant
    
Forcepoint
Published By: BMC ASEAN     Published Date: Dec 18, 2018
400+ IT professionals reveal what’s next for workload automation How are today’s leading companies using workload automation to drive their most important IT initiatives? Find out in this detailed report summary from analyst firm Enterprise Management Associates (EMA). You’ll learn how the role of workload automation is evolving, including: The rise of predictive analytics in WLA Surprising stats on the frequency and ease of migration How big data and cloud impact WLA The use of containers and micro services architectures Workload automation is changing fast. Keep up with the latest analyst research – download the report.
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BMC ASEAN
Published By: SAP     Published Date: May 18, 2014
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools, database development, service oriented architecture
    
SAP
Published By: SAP     Published Date: May 18, 2014
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools, it management, knowledge management
    
SAP
Published By: Hewlett Packard Enterprise     Published Date: Feb 08, 2017
Explore four ways you can empower your data to help you meet your business goals in this analyst white paper from ESG. Download now.
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Hewlett Packard Enterprise
Published By: Carbonite     Published Date: Jul 18, 2018
© 2018 Carbonite, Inc. All rights reserved. Case study Diamond Foods’ Diamond of California® nuts are household staples for shoppers across the U.S. But constantly filling grocery store shelves with snacks requires intricate supply chain management that relies on critical business data, including complex spreadsheets and enterprise resource planning files, to keep production and deliveries on schedule. “If our critical servers go down or we lose important data on employee laptops, it has a direct impact on our bottom line,” says Kentrell Davis, Senior Client Support Services Analyst at Diamond Foods.
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Carbonite
Published By: Adobe     Published Date: Sep 12, 2017
"Read the guide to learn five ways to overcome challenges to programmatic ads, including how to: · Bring marketers, creatives, IT, and data analysts together to ensure success · Save money and time while improving engagement and conversion · Customize multiple versions of one campaign and serve ads with strong creative in real time"
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Adobe
Published By: Gigamon     Published Date: Oct 25, 2017
Read the Joint Solution Brief Accelerate Threat Detection and Response to learn how Gigamon helps Splunk Enterprise users effectively analyze and remediate network security threats. Benefits include enhanced visibility and deeper, faster security analytics from precise, targeted network metadata generated from the traffic flowing in your network. Also learn how automation of common security tasks, across the Gigamon platform and third-party security tools, from within the Splunk platform helps increase analyst efficiency and reduce errors.
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Gigamon
Published By: SAS     Published Date: Nov 04, 2015
If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis.
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data visualization, sas, big data, visual analytics, data exploration, analysis, networking, knowledge management, data management
    
SAS
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