Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
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.
The more you know about your people, the more you can enable them to do their best work. And in turn, the greater the chance of business success. Yet, a rapidly changing world of work makes it difficult for companies to achieve this. There is a growing global skills crisis, and it’s getting worse. A shortage of skilled people makes it tough to find and attract the people you need — and it’s even tougher to get them through the door once you find them. To win the war for talent, you need to understand and engage with your candidates better than ever before.
Data driven tools and analytics can uncover a wealth of new savings opportunities for surgery centers and surgical hospitals. And you usually don’t have to search far to uncover the savings. You can immediately tap into one source of data to find these opportunities. Every Electronic Health Record (EHR) and Practice Management System offers a set of standard reports. Stored inside are at least five ways to save. Download this whitepaper to learn about these five opportunities
SecureWorks provides an early warning system for evolving cyber threats, enabling organisations to prevent, detect, rapidly respond to and predict cyber attacks. Combining unparalleled visibility into the global threat landscape and powered by the Counter Threat Platform — our advanced data analytics and insights engine —SecureWorks minimises risk and delivers actionable, intelligence driven security solutions for clients around the world.
Data is the hottest topic in business today. In discussions that range from
understanding performance to predicting future outcomes, data is at the core.
However, data has a bad reputation. Because businesses have been collecting data for
decades, the amount that we must analyze can seem insurmountable. Simply saying
“data” is enough to conjure images of someone poring over a thick stack of
spreadsheets, manually going through row after row to identify performance, trends
and figure out what to do with them. This intimidating view is all too common.
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements.
To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
Published By: QASymphony
Published Date: Jan 08, 2018
Data. It seems to be everywhere today and yet we can never get
enough of it. But as it turns out, a lack of data isn’t our problem
-- our problem is the difficulty piecing together, understanding and
finding the story in all the data that’s in front of us.
In software testing in particular, the need for consolidated,
meaningful test metrics has never been higher. As both the pace of
development and the cost of delivering poor quality software
increase, we need these metrics to help us test smarter, better and
Fortunately, business intelligence now exists to make this goal a
reality. The analytics these tools provide can help drive efficient
and effective testing by providing teams with insight on everything
from testing quality and coverage to velocity and more. And this
knowledge can position the QA team as trusted experts to advise
the entire software development team on steps that can ensure a
better quality end result.
Whether you’re onboarding new customers, cross- or up-selling, getting your supply chain or logistics right, or even collecting unpaid debt, making the best choice of decisions means weighing not just what’s right for your department – but what is best for the business overall. Not to mention what is optimal for your customers and partners.
And let’s face it, even with the availability of business intelligence and other analytic tools, it’s hard to know what constitutes the right actions to take in an era where Big Data consistently throws you curveballs. Prescriptive Analytics can help – but for most organizations, there are more questions and concerns than answers about how to implement it successfully.
Read our white paper on how Prescriptive Analytics can transform your business decisions and actions – leveraging your existing analytics investment and organizational DNA while helping you drive transparency, customer experience, and profits
Analyst firm, Enterprise Strategy Group, examines how companies can leverage cloud-based data lakes and self-service analytics for timely business insights that weren’t possible until now.
And learn how IBM Cloud Object Storage, as a persistent storage layer, powers analytics and business intelligence solutions on the IBM Cloud.
Complete the form to download the analyst paper.
Published By: Mindfire
Published Date: May 07, 2010
In this report, results from well over 650 real-life cross-media marketing campaigns across 27 vertical markets are analyzed and compared to industry benchmarks for response rates of static direct mail campaigns, to provide a solid base of actual performance data and information.
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.