This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
Data is the DNA of modern healthcare. As healthcare technology continues to evolve at a rapid pace, and patient data management and security evolve, emerging approaches for disease treatment and prevention—like precision medicine and healthcare content management—are becoming more necessary. Precision medicine is about moving from generic to more precise, population-focused diagnostics and treatment by factoring in data from patients’ genes, environment, lifestyle factors and family history, into clinical decision-making for earlier, more accurate diagnoses, and more effective treatment and prevention. Data is at the heart of enabling doctors and scientists to execute on this mission. Additionally, rapidly changing regulations throughout the world are affecting the management of all healthcare data. Infinidat removes data management barriers from this level of data interaction by removing isolated islands of storage and allowing much more data to reside on a single, high-performance, h
Put your data in the hands of those who need it most, so they can act quickly to deliver great customer experiences. And a better bottom line.
Data is no longer the domain of a few. Everyone in your organization should be thinking about it. And more importantly, acting upon it. This means taking data insights out of the realm of data scientists and making them a natural part of everyone’s workflow — from the marketing department to the executive team.
To seize and capitalize on the data you already have, it’s important for analysts and marketers to rethink data—shifting from a reporting mindset to an action mindset.
74% of organizations want to be data driven, yet only 29% say they’re good at connecting analytics to action.
Businesses are struggling with numerous variables to determine what their stance should be
regarding artificial intelligence (AI) applications that deliver new insights using deep learning.
The business opportunities are exceptionally promising. Not acting could potentially be a
business disaster as competitors gain a wealth of previously unavailable data to grow their
customer base. Most organizations are aware of the challenge, and their lines of business
(LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
Artificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
Increasingly sophisticated location technology makes it possible for data scientists to gain a deeper understanding of their target audiences – and how to reach them. Accurate and precise intelligence can give more timely and complete insights into audiences than ever before.
As one of the world’s leading location platforms in 2018, HERE shares insights and solutions to buying location data for better audience segmentation.
Published By: IBM APAC
Published Date: Mar 06, 2019
The Forrester Study on cost savings and business benefits enabled by Watson Studio and Watson Knowledge Catalog.
Watson Studio provides a suite of tools for data scientists, application developers, and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy machine learning models at scale. The Forrester provides readers a framework to evaluate the potential financial impact of the Watson Studio and Watson Knowledge Catalog investment on their organizations.
Intel's Bob Rogers, chief data scientist for big data solutions, sat down with Dan Magestro, research director at the international Institute of Analytics (IIA), to discuss the power of asking questions when assessing an organisation's analytics maturity.
Read on to find out more.
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.
3TIER helps organizations understand and manage the risks associated with renewable energy projects. A pioneer in wind and solar generation risks analysis, 3TIER uses science and technology to frame the risk of weather-driven variability, anywhere on Earth.
3TIER's unique expertise is in combining the latest weather data with historical weather patterns, and using the expertise of 3TIER's meteorologists, engineers and data scientists to create a detailed independent assessment of the future renewable energy potential of any location.
Published By: Veritas
Published Date: Oct 03, 2016
This benchmark report, the Data Genomics Index, encompasses a community of like-minded data scientists, industry experts, and thought leaders together with the purpose of better understanding the true nature of the unstructured data that we are creating, storing, and managing on a daily basis - a report on real storage environments’ composition.
To keep up with rapid growth and stay ahead, disruptor fintechs must stay agile and go on innovating. Bangkok-based Forth Smart provides payment gateways that turn cash into digital currency via its thousands of vending machines. They needed to approach ITin new ways in order to free up budgets, resources and imaginations to focus on innovation. Oracle Cloud Specialist Marek Winiarski, talked to Forth Smart’s Data Scientist, Pawarit ‘Taa’ Ruengsuksilp about how the company has made cost savings and improved customer experience by adopting Oracle Autonomous Data Warehouse.
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing.
Download this white paper to find out how you can improve your data preparation for business analytics.
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
Between the Internet of Things, customer experience and loyalty programs, social network monitoring, connected enterprise systems and other information sources, today's organizations have access to more data than they ever had before-and frankly, more than they may know what to do with. The challenge is to not just understand that data, but actualize it and use it to recognize real business value. This ebook will walk you through a sample scenario with Albert, a data scientist who wants to put text analytics to work by using the Word2vec algorithm and other data science tools.
Published By: Veritas
Published Date: May 12, 2016
The Data Genomics Index is a first-of-its-kind benchmark analysis of data stored within a typical enterprise environment. This report reveals insights into data growth, data age, and data type thereby providing organizations with the comparison standard for beginning to take action on their data.
In addition to the Index, Veritas has founded the Data Genomics Project. This community of likeminded data scientists, industry experts and thought leaders will come together to surface the true nature of enterprise environments, build the data-genome that matters for information management, and share the discussion with a world struggling to solve tremendous data growth challenges.
It's all well enough for an organization to collect every slice of data it can reach, but having more data doesn't mean you'll automatically get better insights. First, you have to figure out what you want from your data you have to find its value.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.
Published By: Datastax
Published Date: May 14, 2018
"What’s In The Report?
The Forrester Graph Database Vendor Landscape discusses the expanding graph uses cases, new and emerging graph solutions, the two approaches to graph, how graph databases are able to provide penetrating insights using deep data relationships, and the top 10 graph vendors in the market today
Download The Report If You:
-Want to know how graph databases work to provide quick, deep, actionable insights that help with everything from fraud to personalization to go-to-market acceleration, without having to write code or spend operating budget on data scientists.
-Learn the new graph uses cases, including 360-degree views, fraud detection, recommendation engines, and social networking.
-Learn about the top 10 graph databases and why DSE Graph continues to gain momentum with customers who like its ability to scale out in multi-data-center, multi-cloud, and hybrid environments, as well as visual operations, search, and advanced security."
The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process.
Download The Definitive Guide to Self-Service Data Analytics to:
Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you
Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require
See how Alteryx stacks up against traditional data prep and analytics tools
Data Analytics has become critical for many business decision makers. However, many of these managers and data analysts still rely on spreadsheets and other legacy-era tools that fall far short of current needs. As a result, they also rely heavily on a virtual army of data specialists and scientists, working under the auspices of a centralized analytics group, to prepare, blend, analyze, and even report on the critical data they need for decision making.
Download this new paper to get the details behind self-service data analytics, and how it lets business analysts:
Take charge of the entire analytical process, instead of relying on other departments
Overcome limitations of legacy tools to save time and prevent errors
Make more comprehensive and insightful business decisions at speed
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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