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
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars.
This paper, based on presentations by SAS Data Scientist Wayne Thompson, introduces key machine learning concepts and describes SAS solutions that enable data scientists and other analytical professionals to perform machine learning at scale. It tells how a SAS customer is using digital images and machine learning techniques to reduce defects in the semiconductor manufacturing process.
Everyone is talking about predictive analytics these days, but what does it mean for B2B marketers? Find out how top demand generation professionals are using predictive applications powered by leading edge data science to optimize all stages of the funnel. Read Lattice Engine’s latest ebook, Decoding Predictive Marketing, and learn where predictive fits into your marketing mix.
Hurwitz & Associates announced the findings of its inaugural Victory Index for predictive analytics. This assessment highlights both the diverse uses of predictive analytics and vendors who make those applications.
Financial and business managers today are seeking a higher level of analytical ability to manage business performance than ever before. But these kinds of analyses often lie beyond the reach of most of today's ERP systems.
Published By: Connectus
Published Date: Aug 21, 2009
This paper discusses both the benefits and the pitfalls of the increased focus on marketing measurement, and describes how a comprehensive Marketing Intelligence strategy can be used to report on the full spectrum of marketing activities.
Today's business drives application use, but it is how these applications are deployed and managed that can deliver differentiation value to the business. Whether your challenge is one of scale, optimization, heterogeneity or complexity – learn how flexibility can be built in at the application layer. Download this analyst bulletin today!
Published By: Winward
Published Date: Aug 21, 2009
To succeed in today's business environment, the enterprise must manage the effects of three realities: the recent, massive investment in technology has significantly increased IT complexity; the Internet has altered customers' expectations of availability, cost, and service; and the new economic climate highlights the need to leverage existing assets and improve the return on investment for new initiatives.
Surprisingly low-cost technology MSSO/SD4E transforms Excel into an incredibly powerful and completely secure Business Intelligence tool capable of quickly and easily manipulating and analyzing live data directly from multiple, disparate data sources, generating even the most complex and sophisticated analytical reports with a simple keystroke.
This White Paper discusses how intelligence agency management can apply machine translation as a much-needed support tool that can make linguists and intelligence analysts more effective in producing meaningful and actionable results.
Business Intelligence Software are applications that build on existing data warehouses and provide analytical processing tools that allow users to more effectively analyze such data. This, in turn, permits businesses to more rapidly develop existing and new analyses and reports for improved decision-making power and information dissemination capacity.
ROI is based on the analysis of differential cash flows. In the case of remote data acquisition and aggregation systems for fuel tank operators, it is based on calculating the cost of acquiring and aggregating the data manually and compared to the total cost of owning, maintaining and operating an automated data acquisition and aggregation system.
Companies that purchase fuels, chemicals, solvents and other products often have to make a choice: either reduce costs by keeping inventory levels low, risking run-outs and lost sales, or keep enough surplus inventories on hand to be prepared for unforeseen spikes in product demand, which tends to drive up inventory costs and market price risks.
Businesses, more than ever before, are relying on fact based decision-making and analytics to compete in this environment. This has given rise to "Business Intelligence," or simply BI, a broad category of applications and technologies for accessing, combining, computing and analyzing data to help enterprise users make better business decisions.
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.
With more data in the hands of more people – and easier access to easy-to-use analytics – conversations about data and results from data analysis are happening more often. And becoming more important. And expected. So it’s not surprising that improved collaboration is one of the most common organizational goals.
Let’s take a look at how you can use results produced by SAS Visual Analytics with Microsoft Office applications. You’ll see how easy it is to combine sophisticated analytic visualizations and reports with Microsoft’s widely used productivity tools – to share insights, improve collaboration and drive increased adoption of analytics and BI across your organization.
In spite of the growth of virtual business activities performed via the World Wide Web, every business transaction or operation is performed at a physical place. And as handheld GPS devices drive a growing awareness of the concept of "location," people are increasingly looking for operational efficiencies, revenue growth, or more effective management as a result of geographic data services and location-based intelligence. In this white paper, David Loshin, president of Knowledge Integrity, Inc., introduces geographic data services (such as geocoding and proximity matching) and discusses how they are employed in both operational and analytical business applications. The paper also reviews analytical techniques applied across many types of organizations and examines a number of industry-specific usage scenarios.
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications.
Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios.
Since starting to work with this technology
Published By: Datastax
Published Date: Aug 15, 2018
Built on a production-certified version of Apache Spark™ and with integrated search and graph capabilities, DSE Analytics provides highly available, production-ready analytics that enables enterprises to securely build instantly responsive, contextual, always-on applications and generate ad-hoc reports. Read this white paper to learn about the specific features and capabilities of DSE Analytics, and why DSE Analytics is designed for the Right-Now Enterprise.
This white paper outlines a framework that emphasizes digitization and business transformation and the new opportunities pull processes bring.
The mechanism of “Pull” processes—those triggered by an actual event instead of a forecast—is nothing new. It is at the heart of many successful manufacturing strategies. Recent technological advances in digitization, including the harnessing of Big Data analytics, the use of the cloud, Business Process Management (BPM), social media, IIoT, and mobility, have extended the power of Pull beyond Lean manufacturing. In the wake of the current technological innovation wave, it is not uncommon for manufacturers to not know what next step to take.
In light of these new developments, this white paper will focus on the mechanism of business transformation enabled by these technologies, which can be attributed to two major forces: the power of Pull and digitization. Nine practical applications are detailed, showing how innovative manufacturers can better
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude.
Asian ICT infrastructure investment is exploding as businesses review and modernise their data-centre architectures to keep up with the service demands of a growing and increasingly sophisticated population.
Demand for cloud services, particularly to support big-data analytics initiatives, is driving this trend. Frost & Sullivan, for example, believes the Asia-Pacific cloud computing market will grow at 28.4 percent annually through 2022. Despite this growth, many businesses are also rapidly realising that public cloud is not the best solution for every need as they do not always offer the same level of visibility, performance, and control as on-premises infrastructure.This reality is pushing many companies towards the middle ground of hybrid IT, in which applications and infrastructure are distributed across public cloud and self-managed data centre infrastructure. Read about Medical company Mutoh and how it took advantage of the latest technology.