White Paper: Sisense
To capitalize on the value of their information, many companies today are taking an embedded approach to analytics and delivering insights into the everyday workflow of their users through embedded analytics and business intelligence (BI). However, in order to successfully expose analytics to customers and partners, companies are faced with three many challenges such as manage complex data quickly, sharing data and insights securely. This guide includes a general overview of BI & Embedded analytics, the different approaches to embedding BI and analytics, and the benefits and challenges of the most popular BI solution technologies that offer OEM partnerships. Key takeaways form this white paper: An overview on embedded business intelligence & analytics How to Make Analytics Your Competitive Differentiator Top Approaches to Embedding Analytics Comparing Embedded Analytics Partners Unique Technical Benefits of Sisense OEM Solution
White Paper: Systech
The main aim of this research paper is to understand the term “ Offshore Outsourcing ” and to realize the worth of Offshore Outsourcing, which stands of prime importance for every organization in U.S.A today. This white paper discusses how outsourcing has grown from just a business aid into a complete business in itself.
White Paper: Intetics
Big data is everywhere, but how are companies actually using it? Whether you want it to or not, the tech world is transitioning into a data-driven age. With these changes new technologies are taking hold, and companies are finding new and exciting ways to implement ideas and bring innovation to their businesses. This presentation brings forth the most transformative and pressing ideas for managing big data. It explores how technology transforms business and how data is helping drive the change. The focus is on real-life examples of how companies are implementing location-based services, Internet of Things, and omni-channel systems technologies and what benefits these technologies are bringing.
Where Big Data begin: Paperclip Mojo
White Paper: PaperClip Inc.
Everybody talks about big data with many uses starting with predictive analytics of this big data. Today’s challenge is the way in which we approach data standardization and how to manage data and engage Crowd Sourcing for Big Data processing. It is very important to find a way to standardize the data, one integration point to exchange with trading partners and open up collaborations with different industries to begin building a true Big Data platform. Leveraging the best use of technology interacting with people can provide an economical service of data capture, data warehousing and predictive analytics. This whitepaper provides insights on the ways to transcribe, translate and interpretation of Big Data faster and more accurate than ever. What’s Inside this Big data Whitepaper? Shredded Data Technology Complete Application Data Capture enabling Big Data processing Timely, Accurate and Secure Data Standards Based Data Dictionary Clippers Processing Forms Combination of Crowd Sourcing and Imaging Technology
Enhanced Lambda Architecture in AWS using Apache Spark
White Paper: DataFactZ Solutions
Lambda architecture can handle massive quantities of data by providing a single framework. Through Amazon Web Services, we can quickly implement the Lambda Architecture, reduce maintenance overhead and reduce costs. Lambda Architecture also helps in reducing any delay between data collection and availability in dashboards using Apache Spark. This whitepaper discusses about the benefits of enhanced Lambda Architecture in AWS using Apache Spark. Key takeaways from this whitepaper: Traditional Lambda Architecture Three Processing Layers of Lambda Architecture Components of Lambda Architecture on Amazon Web Services
Data Visualization: Creating Impactful Reports
White Paper: DataFactZ Solutions
Data visualization is an effective way to create impactful reports, dashboards that improve decision making, enhanced ad-hoc data analysis, better information sharing, increased ROI, time saving and reduced burden on IT. Data visualization is an essential component in the era of big data, enabling users to see trends and patterns that provide actionable intelligence. This white paper talks about data visualization techniques that enable the user to make faster decisions and a better technological investment. Inside this Visualization White Paper: Interactive visualization: Clear and effective in communicating the content List of DOs and DON’Ts in data visualization Guide to choose the right chart type for successful data visualization
White Paper: DataFactZ Solutions
Apache Spark is the next-generation distributed framework that can be integrated with an existing Hadoop environment or run as a standalone tool for Big Data processing. Hadoop, in particular, has been spectacular and has offered cheap storage in both the HDFS (Hadoop Distributed File System) and MapReduce frameworks to analyze this data offline. New connectors for Spark will continue to emerge for extracting data from various data sources. This whitepaper provides insights on how Apache Spark stands out as a next-generation super-fast distributed computing engine for Big Data applications. White Paper Discusses About: Inception of Apache Spark Spark Ecosystem: A unique API library design Range of libraries supported by Spark
White Paper: Avere Systems
Today, firms are increasingly aggregating data and analyzing disparate sources of internal and external information in an effort to uncover new patterns of activity and relationships that may be signs of financial fraud. How to take advantage of the power of the cloud in lowering operational risks? In this white paper, we discuss how firms are using aggregated internal and external data as advanced analytics to perform sophisticated analytics and simulations to help maximize investment returns and performance and to help mitigate risk. It discusses: Obtaining resources "on-demand" helps scale infrastructures during peak times Critical infrastructure for critical middle-office analytics Technologies financial organizations are evaluating in order to manage growing critical infrastructures Portfolio risk and investment analysis
White Paper: Intersec Group
The advent of open‐source technologies fueled big data initiatives with the intent to materialize new business models. The goal of big data projects often revolves around solving problems in addition to helping drive ROI and value across a business unit or entire organization. It’s often difficult to launch a big data project quickly due to competing business priorities; the myriad of technology choices available as well as, the sheer size, volume, and velocity of data. Key questions from this whitepaper: What are the common questions and challenges that the operators are facing when starting a Big Data project? What are the best practices to avoid being trapped in the ever‐lasting big data project that fails to generate any revenue? Should the big data project be carried out by the IT department or should it be led by a dedicated organization, under a new function like a Chief Data Officer, distinct from traditional IT?
White Paper: Inspur Group Co. Ltd
Apache Spark is a fast and general engine for large-scale data processing. To handle increasing data rates and demanding user expectations, big data processing platforms like Apache Spark have emerged and quickly gained popularity. This whitepaper on “Optimizing Apache Spark with Memory1”demonstrates that by leveraging Memory1 to maximize the available memory, the servers can do more work (75% efficiency improvement in Spark performance), unleashing the full potential of real-time, big data processing. This Technology Whitepaper Covers: How Spark works? What are the issues that subvert the full potential of Apache Spark’s disaggregated approach? How to simulate the critical demands of a typical Spark operations workload? How to eliminate the hardware cost concerns traditionally faced in multi-server Spark deployments? What efficiency metrics are involved in the Spark operations? Key issues faced by Spark in traditional, DRAM-only deployments How to Avoid the high cost of DRAM-only implementations in Apache Spark architects
Decisions from Data: Shortest Path to Data-driven Decision Making
White Paper: Fusionex International
Decisions from Data: What is the shortest path ? Making data-driven decision in a complex big data environment presents many challenges for organizations . What is driving organizations to rethink new approaches to shorten the path to data-driven decisions making?? This whitepaper highlights the top priorities as well as challenges that hinders wider adoption of data-driven decision making. It addresses questions like: What challenges organizations are facing in making data -driven-decisions ? What are the key offerings that address the organizations' needs in this process of driving decisions from data? What are the considerations CXOs need to take into account when implementing infrastructure and tools to support the data-driven decision making path? What are the benefits of leading with data-driven decisions in solving business problems? This informative whitepaper comprehensively explains the key trends impacting big data and analytics including: Digitization of Everything: Managing and analyzing new sources of data effectively Security and compliance regulations in the process of driving decisions from data New expectations for information access in making decisions based on data
The Total Economic Impact of Saama Fluid Analytics
White Paper: Saama Technologies
This Total Economic Impact (TEI) study was conducted to examine the potential return on investment (ROI) enterprises may realize by deploying Fluid Analytics which is commissioned by Saama technologies and delivered by Forrester Consulting. Fluid Analytics enables organizations to inherently accelerate their time-to-value on analytics and BI initiatives by building out their very particular modern analytics platform or system of insight as well as unlocking valuable customer data. This Whitepaper from Saama Technologies on ‘’The Total Economic Impact of Saama Fluid Analytics’’ highlights: •An Overview of Saama Fluid Analytics •What are the risks involved in the Fluid Analytics Implementation that can affect financial costs • Four components of Total Economic Impact methodology to evaluate investment value •Determining the costs associated with the Fluid Analytics solution through economic impact study Download this whitepaper that represents multistep approach taken by Forrester for the ''Total Economic Impact analysis'' which examines the potential return on investment (ROI) that enterprises can achieve by deploying Saama Fluid Analytics Engine.
White Paper: Xoomworks BI
Have you identified Business Intelligence and Analysis as an important investment? 90% of survey report states Business intelligence as a prevalent tool to improve business. XoomWorks, an expert in Business Intelligence and Analytics gave a statistic report on how Business Intelligence, Data Management and Data Security remain a high priority for large enterprises. Based on survey done by XoomWorks:What are the main reasons for Business Intelligence project failures? Data quality, Data governance, performance, user adoption change management and lack of skills, which tools to be used, are the main causes for project failures. Has your company identified Dashboards and Story boards as an important investment? How important is the control over access to sensitive data to your organization? This white paper gives a deep insight on proper methodology and business intelligence strategies including: Business Intelligence Tool Selection Management Information ,Business Intelligence Competence Centres, Centres of Excellence How Database Governance and Data Security is an important investment to lead in Business Intelligence? Download this white paper to learn more about trending Business Intelligence issues and implemention of self-service technologies to ensure high quality data which results creating a visual thinking culture.
White Paper: AvidBeam Technologies
Why You Should Use intelligent Video Analytics platform?? An intelligent video analytics platform can help you to handle massive amounts of video or image input data and produce results in real time. The complex process of converting unstructured video data to a structured visual realization in a parallel context can be done with Big Data Video Analytics easily. So would you like to employ an intelligent big data video analytic tool and cloud based platforms to scale up the computer vision algorithms? If Yes! Read this whitepaper that gives you a brief insight of video analytics platform for big data for better business intelligence. Why intelligent video analytics platform will be the next big thing in big data? Why intelligent video analytics platform is the most untapped source of data? What are benefits of implementing intelligent video analytics platform? How to select the best platform for performing Video Analytics? How to handle Video Processing with Big Data Tools?
White Paper: Affirma Consulting
Business Intelligence derived Information and analysis can lead to a tremendous return on investment (ROI) if implemented correctly. You can improve the decision making processes at all levels of management and improve your tactical and strategic management processes with it. Do you have quick access to actionable data? Would you like to increase collaboration and unlock insights from your business systems? If yes! Read this whitepaper that addresses the following questions: What is Business Intelligence and why do organizations need it? Is it the right time to implement Business Intelligence for your organization? How to select the correct Business Intelligence solution for your business needs?
White Paper: Datapine GmbH
Self-service BI(business intelligence) is growing with major shift from IT driven process to self service approach in Business intelligence. Instant insights with self-service reports are demanded by decision makers, as they need right data at the right time to make the right decision. Why are organizations adopting to self-service business intelligence (BI) tools ? What advantages do self-service BI offer over traditional BI? What's the difference between "self-service" and "traditional" business intelligence? How can self-service BI improve your business? What are potential advantages and challenges of self-service business intelligence tools? So if you want to eliminate the traditional time-consuming reporting process, self-service business intelligence tools can help you analyze what is happening in real-time. Check out this whitepaper outlining ten reasons why you need a true self-service BI solution, and what that solution should offer.
White Paper: InfoTrellis
With technologies like Customer 360 and Mobile Device Management (MDM), you can obtain an accurate assessment of customers by extracting customer details from all possible data sources. Customer data management is becoming more complex than ever. Hence, it is recommended to choose the right technology for the most comprehensive view of the customer and to integrate the data within the various technologies. Read more about MDM and Customer 360 in this report that includes: 1. The Purpose of MDM and Customer 360. 2. The Benefits of Customer 360. 3. The biggest challenges in developing a 360-degree view of the customer. 4. The Challenges of Unstructured Interaction Data.
White Paper: Kavi Global
Analytics strategy has to be very closely aligned with the business strategy as business transformation is not an isolated activity. An analytics strategy can identify the gaps in terms of organization and people, processes, technology and data are and proactively address them to be successful. So, what really is an analytics strategy? The key to success is a clearly defined vision and an analytics strategy. Whether you are planning an enterprise wide analytics program to transform the business or an analytics initiative that would benefit a single department, a well-planned Analytics Strategy can make all the difference by proactively addressing the reasons for the failures. Are you struggling to bring analytics to the business strategy?? Then read this whitepaper that explores enterprise data analytics strategy, best practices, risks associated and how to mitigate them. It highlights: How to successfully execute an enterprise analytics strategy? Where in the business processes can analytics strategy be applied? What are the quantifiable benefits of doing so? What are the best known strategies to prepare data for analytics? What are the keys to develop a successful analytics strategy?
How to Make the Most of Your Data-Science Dollar
White Paper: Mosaic Data Science
Data scientists are a scarce commodity, and are likely to remain so for years to come.i At the same time, data science can create a substantial competitive advantage for early adopters who make the best use of their scarce data-science resources. data scientists often struggle to recognize that every data-science project is an optimization problem.iii In business the objective function is usually expected value, but sometimes expected value is and should be traded away to avoid the possibility of catastrophic losses. Data science has a long, highly technical learning curve. An inexperienced team with limited background has two ways to shorten its learning curve. The first is training. For example, Coursera offers a series of online classes designed to train data scientists in specific computational techniques and tools. This paper explains how management can make the most of data-science resources and opportunities, to achieve data-driven competitive advantage.
Making Sense of Big Data in Insurance
White Paper: MarkLogic
The insurance industry has been struggling to get a good handle on its data for decades, both on the transactional and the risk management sides. And the recent emphasis on utilizing new sources of data that extend beyond traditional sources, often referred to as Big Data, has created renewed interest in data management across the industry. Data variety and diversity in particular are pushing the traditional, relational database management technologies to their limits, and are raising more and more interest in new approaches to data management. Some commentators and vendors have attached great promise to the capabilities of Big Data, even arguing that other data types are no longer necessary. According to Gartner, Big Data has reached the peak of its Hype Cycle this year. “Hype” connotes an over-selling of potential and leads to inflated expectations. So, are relational databases on their way to extinction? Not anytime soon. They are still the most efficient way to handle highly structured tabular data, especially in the context of Online Transaction Processing. Instead, companies should look to benefit from new technologies associated with Big Data where these can provide value beyond the core transaction processing associated with policy administration and claims management.
Big data, Real-time Marketing Requires Strategic Alliances, Cutting-edge Technologies
White Paper: TURN
Sophisticated digital marketing technology, and the dramatic increase in always-connected consumers, is increasingly pushing marketing onto the center stage of corporate strategy. Ad campaigns launched on advanced technology platforms, which include tools to perform instant analysis of huge amounts of data, enable marketers to target campaign results and ROI in ways they could only dream about just a few years ago. They also give the entire enterprise real-time business intelligence to drive strategy at the moment of decision. These same technologies also turn the spotlight on IT and the CIO, who now have to deliver a range of advanced new data services to the enterprise, well beyond what they’ve had to do before. Marketers who have embraced their new strategic role share some common characteristics. They understand that navigating today’s rapidly evolving digital world requires an ecosystem of partners, both inside and outside their company walls. They seize the marketing opportunities that an interconnected, real-time digital landscape presents. And they take advantage of a new generation of tools that instantly orchestrate the massive amounts of data and the many moving parts of a modern media campaign. It’s now critically important that the CMO work strategically across department lines with CIOs and IT. CMOs need to evangelize their goals, strategies, and methods with the CIO to be sure IT can effectively align behind them. And the CIO needs to help marketing understand the capabilities and services they can provide and how to most effectively interface with IT processes. And, further, the CIO needs to be sure to have preexisting relationships with the right technology providers and Systems Integrators (SIs) to be sure they can quickly deliver new capabilities as dictated by rapidly evolving strategic goals.
White Paper: CORE SECURITY
How to empower business users with information based user interface? Nowadays, organizations are looking for a way to more effectively empower business users to create, manage, and optimize the online experience for their visitors in a fiercely competitive environment. This whitepaper outlines the key capabilities that can empower business users to drive effective value on the web through a contextual approach and contextual digital online strategy. It also addresses: How can you empower business users to address the increasing demands of the evolving web? What are the five fundamental prerequisites essential for content creation and management in user interface? What are the benefits of information based user interface over traditional WCM system? Why traditional WCM editorial tools are falling short of meeting firms needs? How to use advanced web user interface to enhance productivity and empower business users?
White Paper: Metric Insights
Many healthcare organizations have implemented Business Intelligence (BI) tools for healthcare analytics in the hopes of making their operations data-driven and therefore more effective.Yet,despite the investment of billions in these tools, the technology has under-delivered on its promise. This white paper addresses seven areas on which a healthcare IT organization should focus to ensure success in implementing Push Intelligence: • Provide insights that find the practitioner, not the other way around • Never require users to wait • Personalize delivery of Metrics • Allow Context and Collaboration • Remove training from the equation • Deploy quickly and iterate • Seamlessly interface with existing applications
White Paper: Actian
Four undeniable trends shape the way we think about data – big or small. While managing big data is ripe with challenges, it continues to represent more than $15 trillion in untapped value. New analytics and next-generation platforms hold the key to unlocking that value. Understanding these four trends brings light to the current shift taking place from Big Data 1.0 to 2.0, a cataclysmal shift for those who fail to see the shift and take action, a catalyst for those who embrace the new opportunity. Key take aways from this white paper: 5 Advancements of Big Data 1.0 5 Challenges of Big Data 1.0 Big Data 2.0 – Transformational Value Big Data 2.0 Means Big Value Times Ten
White Paper: XPatterns
Building successful Big Data solutions is all about taking advantage of volume, velocity, variety and visualization through analytics and making it accessible to all. Building successful Big Data solutions is all about taking advantage of volume, velocity, variety and visualization through analytics and making it accessible to all. This Big Data whitepaper addresses queries like: Does your Big Data solution secure data out of the box? What are the big data opportunities and challenges from data analytics perspectives? What are the essential steps and mindsets you must consider when implementing big data solution for your business? How xPatterns helps in mitigating the challenges faced by the big data and advancing the insight? Explore this whitepaper on Big Data to learn more about: Building blocks of a successful big data deployment Advanced big data analytical tools and techniques Business opportunities to leverage Big Data through advanced analytics Lower the barrier of entry for any enterprises or application to take advantage of Big Data opportunities
The Total Economic Impact of Economic Sight's Behavioral Analytics
White Paper: Forrester Consulting
In a comprehensive six month study conducted in 2012, Mattersight Corporation ("Mattersight") commissioned Forrester Consulting to examine the total economic impact and potential return on investment (ROI) enterprises may realize by deploying its Behavioral Analytics platform. The Behavioral Analytics platform captures customer and employee interactions, and then automatically analyzes those interactions using proprietary algorithms and unique behavioral models. The output of this analysis is new data attributes on every interaction measuring customer expectations and behaviors, as well as employee performance. This data is then leveraged by three primary products which comprise Mattersight's Behavioral Analytics platform to drive significant business value - Predictive Behavioral Routing, Predictive Customer Analytics and Employee Performance Management.
Hadapt: Technical Overview
White Paper: Hadapt
Today, business leaders see big data as a valuable competitive advantage. High-volume, disparate data - particularly internet- and social media based data - is increasingly important for enterprises as they seek to glean insights about their globally dispersed workforces and customers. Yet one principal challenge remains: how to derive timely and meaningful value from the growing masses of structured and unstructured data, when traditional platforms lack flexibility and demand significant capital expenditure. Hadapt is ushering data analytics into the future with its Adaptive Analytical Platform, an extensible, interactive SQL interface to Apache Hadoop. While most businesses are heavily invested in effective, well-established data warehouses, operational data stores, data marts, and business intelligence tools to mine and analyze structured data, many still do not have a clear approach for analyzing unstructured and semi-structured data such as audio, clickstreams, graphics, log files, raw text, social media messages, and video. Organizations understand that legacy relational database approaches are neither cost-effective nor efficient for unlocking the wealth of information in unstructured data. Yet companies continue to devote the majority of their resources to managing the smallest portion of their data (structured), while moving the majority of their data (unstructured) into special-purpose databases or content management systems with little or no analytic capabilities.
White Paper: Decision Management Solution
Standards play a central role in creating an ecosystem that supports current and future needs for broad, real-time use of predictive analytics in an era of Big Data. Just a few years ago it was common to develop a predictive analytic model using a single proprietary tool against a sample of structured data. This would then be applied in batch, storing scores for future use in a database or data warehouse. Recently this model has been disrupted. There is a move to real-time scoring, calculating the value of predictive analytic models when they are needed rather than looking for them in a database. At the same time the variety of model execution platforms has expanded with in-database execution, columnar and in-memory databases as well as MapReduce-based execution becoming increasingly common.
White Paper: Saama
The Power Exchange Informatica Connector for Google Analytics is designed to integrate Google Analytics sources within Informatica PowerCenter installations. With the capabilities provided by the connector, user can extract data from Google Analytics through Informatica PowerCenter platform. It also enables the processing of data stored in Google Analytics using different Informatica transformations. Google Analytics Informatica Connector resides under the Informatica as a Source definition in the Client. The source definition contains the fields (dimensions or metrics) for which the data is to be fetched from Google Analytics Server (Data store). The server component is responsible for actual data transfer from Google Analytics server. The data is fetched from the Google Analytics server for the dimensions and metrics user has opted for. The data is then converted to Informatica understandable format and flushed to the next stage in the Informatica pipeline.