What is a Data Lake?
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
Defining the Data Lake
“Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
Published By: Aberdeen
Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
Today’s smart computers can beat board game champions, master video games, and learn to recognize cats. No wonder artificial intelligence has captured the imaginations of business and IT leaders. And indeed, AI is starting to transform processes in established industries, from retail to financial services to manufacturing. Read this guide from Google Cloud and learn how you can unlock the transformational power of information and get useful insights from a vast and complex landscape of data.
Whilst businesses of all kinds are utilizing data analytics, many are still only using it to make simple changes that lead to a set of rigid processes. Whereas the more customer-focused organizations are realizing that to deliver exceptional experiences, they need to be able to react to customer data in real-time and predict what might happen next. And that means going beyond simple analytics.
Read our whitepaper to discover what analyst firm Forrester has identified as the Enterprise Insight Platform, technology designed to enable companies to transform into truly data-driven businesses.
With data and analytics the new competitive battleground, businesses that take advantage will be the leaders; those that do not will fall behind. But with data so distributed, gaining this advantage is a huge challenge. Unless you have data virtualization, a better, faster way to meet your analytic data needs. Read this white paper to learn who needs data virtualization and what kinds of benefits others have achieved.
Despite being knowledgeable about their industry and experienced in running their organizations, the majority of business users lack expertise in analytics and visualization techniques—but that doesn't stop them from wanting to have a go. But making tools easier and more widely accessible is only part of the answer. A better approach is to work both sides of the gap. To make tools that can empower business users to discover and unlock value in their data—and that extend capabilities for experts, so they can share the analytics workload, improve efficiency, and focus on higher level work.
TIBCO Spotfire is the premier data discovery and analytics platform, which provides powerful capabilities for our customers, such as dimension-free data exploration through interactive visualizations, and data mashup to quickly combine disparate data to gain insights masked by data silos or aggregations.
Expanding analytic capabilities are critical to digitizing the business, optimizing costs, accelerating innovation, and surviving digital disruption
Historically, manufacturers were almost solely focused on reducing costs by applying automation and analytics to engineering, R&D, manufacturing operations, and quality organizations. Even though the strategies used within these areas are still needed, they are not sufficient to ensure business survival and continuity in the age of Industry 4.0 and the IoT.
Today, it is paramount that smart manufacturers broaden their scope because disruptive innovations in data acquisition, storage, and analytics technology have enabled an entirely new degree of automation and virtualization, promising a complete 360-degree high-fidelity virtual data-driven integrated views of all operations—from suppliers and supply chains, through equipment, processes, and manufacturing practices, to final product testing and customer satisfaction.
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TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential.
Download this white paper to learn:
*How organizations can answer critical questions that have been impeding business success
*How the financial services industry can make great strides in security, compliance and IT
*Common machine data sources in financial services firms
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers.
For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy.
Download this report to learn:
How to reduce monitoring blind spots when creating an ITOM strategy
How to address ITOM requirements across IT and non-IT groups
Distinct layers across ITOM Potential functionality gaps with domain-specific products
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
"The appearance of your reports and dashboards – the actual visual appearance of your data analysis -- is important. An ugly or confusing report may be dismissed, even though it contains valuable insights about your data. Cognos Analytics has a long track record of high quality analytic insight, and now, we added a lot of new capabilities designed to help even novice users quickly and easily produce great-looking and consumable reports you can trust.
Watch this webinar to learn:
• How you can more effectively communicate with data.
• What constitutes an intuitive and highly navigable report
• How take advantage of some of the new capabilities in Cognos Analytics to create reports that are more compelling and understandable in less time.
• Some of the new and exciting capabilities coming to Cognos Analytics in 2018 (hint: more intelligent capabilities with enhancements to Natural Language Processing, data discovery and Machine Learning)."
"Today’s business users want to use all types of data to create compelling, shareable visualizations. But charts and graphs alone may not convey all the information, especially when they are part of a complex series. An audience can best understand analytic results when those results tell a story that connects all the pieces together. The right visuals can also reinforce the lessons buried in the data.
Stories are powerful mechanism to communicate with people. Stories stick and make insights actionable, so it goes without saying that storytelling is a very powerful (soft) skill. In this webinar, you'll learn how to effectively apply storytelling best practices to get your message across. Especially in the world of BI, it is getting more and more important to effectively communicate business results.
Watch this webinar to learn how to use IBM Cognos Analytics to:
· Create the important elements of a good story
· Put the data in context
· Select the best type of ch
"What would you do if you didn’t have to rely on disparate analytics solutions to meet the needs of business users while following the rules of IT?
View this 'Charting Your Analytical Future' webinar to learn about a world of innovation and independence for users that does not limit the confidence and controls of IT.
With the cognitive-guided self-service features available in IBM business analytics solutions, more users than ever before can get the answers they need. Next-generation business analytics capabilities make it possible to access relevant data, prepare it for analysis and understand performance. But it doesn’t stop there. Users can package the results in a visually-appealing format and share them throughout the organization.
Don’t miss this opportunity to hear how you can:
* Benefit from advanced analytics without the complexity
* Operationalize insights and dashboards from a collection of trusted data sources
* Tell your story with rich visualizations and geospati
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly
every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile,
social business, cloud, and big data analytics as the pillars. In this new environment, business leaders
are facing the challenge of lifting their organization to new levels of competitive capability, that of
digital transformation — leveraging digital technologies together with organizational, operational, and
business model innovation to develop new growth strategies. One such challenge is helping the
business efficiently reap value from big data and avoid being taken out by a competitor or disruptor
that figures out new opportunities from big data analytics before the business does.
From an IT perspective, there is a fairly straightforward sequence of applications that businesses can
adopt over time that will help put direction into this journey. IDC outlines this sequence to e
Published By: ServiceNow
Published Date: Sep 18, 2018
What is a performance based business? A performance-based business is an organization guided by data-driven decisions. It is proactive, self-aware, and highly competitive. Data isn’t siloed in a business analytics department. Instead, the right people have the right data at the right time and in the right context.
The top 5 reasons to become a performance-based business:
1. Get better results.
2. Align your entire business.
3. Make data-driven decisions.
4. Manage change more effectively.
5. Spot trends faster.
Discover how ServiceNow Performance Analytics could benefit your business by downloading this eBook.
Published By: ServiceNow
Published Date: Sep 18, 2018
Worldpay deployed ServiceNow Performance Analytics to replace multiple data tools and promote the use of analytics throughout its organization for improved decision making. Nucleus found the project enabled the company to boost analyst productivity, while simultaneously increasing data accessibility and engagement for hundreds of additional employees.
Download this case study to learn more
Ongoing digitization has created vast streams of data, forcing businesses to become more data-driven than ever
before. While the benefits of being a data-driven organization are clear (improved performance, more profitability,
stronger innovations), there are still some technical and business challenges to overcome. Thanks to technological
advancements in data analytics, companies in all types of industries can become data-driven. Read this e-book to
discover what it means to be a data-driven organization and to learn the basic do’s and don’ts of how to get there.
For 20 years running, SAS has landed a coveted spot on Fortune’s 100 Best Companies to Work For list. Our HR department plays a critical role in keeping current employees engaged and productive as well as anticipating and preparing for future workforce needs. How? One reason is our use of data and analytics to drive HR decision making. It would be easy to assume other companies see the value of analytics; however, a Deloitte survey found 75 percent of HR leaders rate analytics as a priority, yet only 8 percent say their HR organization has a strong analytics capability.
I had the pleasure of meeting David Harcourt, Associate Manager of Employee Insights at Yum! Brands when he spoke at the Analytics Experience 2016 conference in Las Vegas. His session, “HR Analytics from Scratch: 8 Lessons Learned in the First Year,” provided valuable insight into what it takes to build HR analytic competency from scratch. His mission is to share with others what he wishes he’d known when he started bui
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