Published By: BlueData
Published Date: Aug 19, 2015
Big Data is on virtually every enterprise’s to-do list these days. Recognizing both its potential and competitive advantage, companies are aligning a vast array of resources to access and analyze this strategic asset. However, despite best intentions, the majority of these Big Data initiatives are either extremely slow in their implementation or are not yielding the results and benefits that enterprises expect. Download this white paper to learn how to solve the Big Data intention-deployment gap and see how you can make your infrastructure in a flexible, easy-to-use platform that will provide in-depth analytics.
Published By: BlueData
Published Date: Aug 19, 2015
As companies seek to better understand their customers, their opportunities, and themselves, they are embracing new technologies such as Hadoop and NoSQL to better manage and manipulate their data. Yet a complete solution for big data has many moving parts while at the same time these moving parts are continuously evolving. Download this white paper to figure out how to make all the moving parts work smoothly together and see how this will ease frustration with business users and free up your IT teams time to handle other issues.
A modern data warehouse is designed to
support rapid data growth and interactive analytics over a variety of relational, non-relational, and
streaming data types leveraging a single, easy-to-use interface. It provides a common architectural
platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling
organizations to derive deeper business insights.
Key elements of a modern data warehouse:
• Data ingestion: take advantage of relational, non-relational, and streaming data sources
• Federated querying: ability to run a query across heterogeneous sources of data
• Data consumption: support numerous types of analysis - ad-hoc exploration, predefined
reporting/dashboards, predictive and advanced analytics
Data is the new currency. Is your organization capitalizing on the full potential of data analytics? In this big data primer, you will learn about the 3 key challenges facing organizations today: managing overwhelming amounts of data, leveraging new complex tools/technologies, and developing the necessary skills and infrastructure. And since storage is where your organization's data lives, it’s a pivotal part of the infrastructure jigsaw puzzle. Thus with a “tuned for everything” storage solution that is purpose-built for modern analytics, you can confidently harness the power of your data to drive your enterprise forward.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. 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.
Amazon 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. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set:
• Enterprise-class relational database query and management system
• Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools
• Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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 technolog
It’s an exciting yet daunting time to be a security professional. Security threats are becoming more aggressive and voracious. Governments and industry bodies are getting more prescriptive around compliance. Combined with exponentially more complex IT environments, security management is increasingly challenging. Moreover, new “Big Data” technologies purport bringing advanced analytic techniques like predictive analysis and advanced statistical techniques close to the security professional.
Large organizations can no longer rely on preventive security systems, point security tools, manual processes, and hardened configurations to protect them from targeted attacks and advanced malware.
Henceforth, security management must be based upon continuous monitoring and data analysis for up-to-the-minute situational awareness and rapid data-driven security decisions. This means that large organizations have entered the era of data security analytics.
Download here to learn more!
To develop the visibility, agility and speed to deal with advanced threats, traditional security strategies for monitoring, often based around security information and event management (SIEM) systems need to evolve into a central nervous system for large-scale security analytics. In particular, four fundamental capabilites are required:
1. Pervasive visibility
2. Deeper analytics
3. Massive scalability
4. Unified view
Download here to learn more!
Published By: Advizex
Published Date: Sep 25, 2013
The challenge of Big Data is more than a question of size; it’s about time to insight and action. With the exponential growth of unstructured data such as social media, video and the raw data generated by smartphones and other “intelligent” machines, businesses are buried under an avalanche of data that renders even best-effort analytics slow and sometimes unreliable. As many businesses are learning in this age of Big Data, it’s not just what you know, but when you know it and how much you trust it.
Download this white paper and learn that with SAP HANA, companies can react intelligently at the speed of thought to capture new opportunities.
Published By: InfoSys
Published Date: Apr 08, 2013
Big Data: Too Much information, Too Little illumination; As enterprises go about their Big Data adoption journey, there are many pressing questions at hand. What are the Big Data capabilities they desire?
There are many new ways Big Data analytics can significantly boost marketing and promotional efforts through real-time and historical analysis of online data, such as clickstream or purchase transactions. Unstructured data based on social media—even photos and video—offers enormous potential when analyzed with the right tools.
Tired of dealing with ID10T errors? Wouldn't life be easier if you reduced the number of ID10T errors and focused more on the critical issues?
HP Service Anywhere seamlessly connects service quality, customer satisfaction and staff efficiency through its easy-to-use social collaborative capabilities. The only service desk with embedded Big Data technology, HP Service Anywhere delivers connected intelligence to IT and business users to support proactive problem solving and actionable analytics.
"ACG Michigan, a large auto insurance underwriter in the US state of Michigan, needed a user-friendly system that would enable its agents (internal and independent) to churn out precise and consistent policy quotes and underwriting decisions. They turned to FICO Blaze Advisor decision rules management system to create an enterprise decision management framework to execute decisions.
Learn more on how FICO Blaze Advisor helped ACG Michigan automate its underwriting
FICO (NYSE: FICO), formerly known as Fair Isaac, is a leading analytics software company, helping businesses in 90+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company's groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimiz
Published By: Infosys
Published Date: May 21, 2018
In HR, working purely on instinct is dangerous. HR Professionals are highly skilled, and their experienced opinion is extremely valuable when it comes to selecting candidates, assessing performance, and all the other important aspects of this function. But can you rely on their instinct alone? This was the big question that our client, a large CPG company was facing.
When we realized this was the problem, the solution was obvious. Not necessarily easy, but obvious. There was plenty of data, but it wasn't being used to improve HR decision making. We designed an analytics solution that would improve HR Decision making. We designed an analytics solution that would improve the efficiency of data gathering, to make the HR function more effective. We then proposed an additional layer, which would use artificial intelligence (AI) to improve HR Decision-making further.
Published By: Infosys
Published Date: Sep 11, 2018
Infosys has been recognized as a ‘Leader’ in NelsonHall’s Vendor Evaluation and Assessment (NEAT) report on big data and analytics services 2018.We have also been highly rated for our focus on automation. Our ability to meet future client requirements as well as deliver immediate benefits such as analytics, data management and support functions to our clients with a specific focus on process automation enabled us to secure this position.
Siloed data sources, duplicate entries, data breach risk—how can you scale data quality for ingestion and transformation at big data volumes?
Data and analytics capabilities are firmly at the top of CEOs’ investment priorities. Whether you need to make the case for data quality to your c-level or you are responsible for implementing it, the Definitive Guide to Data Quality can help.
Download the Definitive Guide to learn how to:
Stop bad data before it enters your system
Create systems and workflow to manage clean data ingestion and transformation at scale
Make the case for the right data quality tools for business insight
Published By: FICO EMEA
Published Date: Jan 25, 2019
Communications service providers (CSPs) have long recognized the potential of data analytics. Yet their early efforts to pull actionable intelligence from the oceans of data they have access to were largely unsuccessful. Many tried a 'big bang' approach to building a central repository without knowing what they wanted to do with the data in it. The arrival of artificial intelligence (AI) – its machine learning subset in particular – has changed their thinking and approach.
For this Quick Insights report, we surveyed 64 professionals from CSPs around the world who are applying, leveraging and/ or planning to deploy advanced analytics in some capacity at various points across the customer lifecycle.
Starting with a foundational set of data management and analytic capabilities enables organizations to effectively build and scale security management as the enterprise evolves to meet Big Data challenges.
As executives witness data’s proven impact on performance and innovation and recognize its strategic significance, they also realize the growing need for a leader whose primary role is to understand and advocate on behalf of data: The Chief Data Officer.
Credit Union Times is the nation's leading independent source for breaking news and analysis for credit union leaders. For more than 20 years, Credit Union Times has set the standard for editorial excellence and ethical, straight-forward reporting.