data exploration

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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
By: SAS     Published Date: Mar 14, 2014
Stop to think about how - and how often - your business interacts with customers. Most organizations believe that only a small fraction of data on interactions generated are effectively put to use. Why is that? Check out this whitepaper to see.
Tags : sas, voc, voice of customer, visual text analytics, best practices, customer voice, sound of sentiment, text data, customer data, analytical processing, structured data, enriched dataset, reporting, automatic generation, text analytics, text mining, data exploration
     SAS
By: IBM     Published Date: Apr 07, 2017
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization. This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
Tags : data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations, market research, model management
     IBM
By: SAS     Published Date: Aug 28, 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. 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
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
By: IBM     Published Date: Jan 14, 2015
Big data has been big news in recent years. Organizations recognize that they must now begin to focus on using big data technologies to solve business problems. The pressure is on for organizations to move past the discussion phase toward well-planned projects.
Tags : big data, data management, data exploration, gain visibility, security extension, business intelligence, explore data, data analytics, data center
     IBM
By: IBM     Published Date: Jan 14, 2015
Bloor Research investigated several key critical success factors for big data analytics. Their findings show that governance, data cleansing, and privacy and security have never been more important. Read this paper to see their conclusions.
Tags : big data, data management, data exploration, gain visibility, security extension, business intelligence, explore data, data analytics, data center
     IBM
By: IBM     Published Date: Jan 14, 2015
Explore this interactive infographic to discover how IBM makes it possible to manage all of these priorities while significantly reducing storage and maintenance costs.
Tags : big data, data management, data exploration, gain visibility, security extension, business intelligence, explore data, data analytics, analytics, platform investment, data center
     IBM
By: SAS     Published Date: Apr 25, 2017
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. The heart and soul of SAS Visual Analytics is the SAS® LASR™ Analytic Server, which ca
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     SAS
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
By: SAS     Published Date: Oct 18, 2017
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
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     SAS
By: SAS     Published Date: Aug 03, 2016
Data visualization is the visual and interactive exploration and graphic representation of data of any size, type (structured and unstructured) or origin. Visualizations help people see things that were not obvious to them before. Even when data volumes are very large, patterns can be spotted quickly and easily. Visualizations convey information in a universal manner and make it simple to share ideas with others.
Tags : best practices, data visualization, data, technology
     SAS
By: IBM     Published Date: Apr 18, 2017
Companies in all industries can benefit from a master data management program, advises MDM expert Anne Marie Smith. Learn how to enhance yours.
Tags : data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations, market research, model management
     IBM
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