Published By: Prophix
Published Date: Jun 03, 2016
For an increasing number of organizations, enterprise performance management (EPM) tools are enabling senior finance executives to integrate plans, understand where they're losing money, move from annual budgets to rolling forecasts, and identify opportunities for strategic improvements. During this Webcast, a panel of experts will explore: • Why business intelligence and business analytics are each important to your business; • How Big Data and analytics can help your organization answer more questions and ask even better ones; • The capabilities that enterprise performance management software offers organizations; and • How to evaluate what your organization can gain by implementing enterprise performance management software.
Analyst Mike Ferguson of Intelligent Business Strategies writes about the enhanced role of transactional DBMS systems in today's world of Big Data. Learn more about how Big Data provides richer transactional data and how that data is captured and analyzed to meet tomorrow’s business needs. Access the report now.
Is your data architecture up to the challenge of the big data era? Can it manage workload demands, handle hybrid cloud environments and keep up with performance requirements? Here are six reasons why changing your database can help you take advantage of data and analytics innovations.
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Published By: Pentaho
Published Date: Apr 28, 2016
As data warehouses (DWs) and requirements for them continue to evolve, having a strategy to catch up and continuously modernize DWs is vital. DWs continue to be relevant, since as they support operationalized analytics, and enable business value from machine data and other new forms of big data. This TDWI Best Practices report covers how to modernize a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics.
This report covers:
• The many options – both old and new – for modernizing a data warehouse
• New technologies, products, and practices to real-world use cases
• How to extend the lifespan, range of uses, and value of existing data warehouses
Published By: Pentaho
Published Date: Apr 28, 2016
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
This white paper describes how IBM’s Pure Data System for Analytics delivers speed and simplicity to help organizations become more responsive and agile in today’s increasingly mobile and data-driven market.
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
The use of analytics will define the successful midmarket business of the next decade, just as it will define the successful larger organization in this information age.
The past decade has seen rapid change in the business climates with an explosion of global competition and economic uncertainty. Despite this, many midmarket companies have thrived by using their size and agility as a competitive advantage. Certainly they have fewer resources than large corporations who are able to invest large sums in IT. However, small yachts can turn and adjust their course much quicker than large cruise ships. The same came be said for midmarket companies.
The most successful organizations are ones that can react the quickest to changes in the market place. Midmarket companies can take advantage of their smaller size by being more agile and quicker to change, or even reinvent themselves, to respond to the market.
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
By using InfoSphere Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of-impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time.
Ziff Davis Custom Whitepaper: Analytics relies on BI, Big Data, and data discovery to provide reporting, trend analysis, and what-if analysis.iii Analytics is defined as the scientific process of transforming data into insight for making better decisions.
The advent of big data revolutionized analytics and data science and created the concept of new data platforms, allowing enterprises to store, access and analyze vast amounts of historical data. The world of big data was born. But existing data platforms need to evolve to deal with the tsunami of data-in-motion being generated by the Internet of Anything (IoAT).
The IBM Enterprise Health Analytics solution delivers immediate value for an organization’s specific business analytics while laying the foundation for future analytics capabilities that may be added as needs evolve.
It is important for healthcare organizations to become data driven. IBM can help organizations leverage a wide range of big data to deliver clinical and financial benefits, and the provide the steps organizations should take to become data driven.
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care—from near-term demands for regulatory and quality reporting to transforming care delivery.
Big data. We've heard the phrase for quite some time, but how can human resource leaders get into the action? One way is through the development and implementation of talent analytics strategies. Talent analytics is fundamentally changing the way organizations and practitioners are thinking about the role of HR and organizations uncovering never before seen insights.
Published By: Pentaho
Published Date: Mar 08, 2016
If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals.
Read the Buyer’s Guide to Big Data Integration by CITO Research to learn:
• What tools are most useful for working with Big Data, Hadoop, and existing transactional databases
• How to create an effective “data supply chain”
• How to succeed with complex data on-boarding using automation for more reliable data ingestion
• The best ways to connect, transport, and transform data for data exploration, analytics and compliance
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
Download this eBook to learn the steps you can take now to prepare for the all flash data center.
flash storage, SSD, all flash data centers, nimble storage, predictive flash platform, application perfomance, data velocity
Advanced analytics can provide extremelyvaluable insight into today’s media viewers. This must-read report details the top 10 best practices for successfully implementing data analytics for driving profit, attracting new viewers, and increasing viewer loyalty.
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