The Internet of Things (IoT) presents an opportunity to collect real-time information about every physical operation of a business. From the temperature of equipment to the performance of a fleet of wind turbines, IoT sensors can deliver this information in real time. There is tremendous opportunity for those businesses that can convert raw IoT data into business insights, and the key to doing so lies within effective data analytics.
To research the current state of IoT analytics, Blue Hill Research conducted deep qualitative interviews with three organizations that invested significant time and resources into their own IoT analytics initiatives. By distilling key themes and lessons learned from peer organizations, Blue Hill Research offers our analysis so that business decision makers can ultimately make informed investment decisions about the future of their IoT analytics projects.
In a panel discussion at the 12th annual SAS Health Analytics
Executive Forum in May 2015, leaders from Dignity Health,
Horizon Blue Cross Blue Shield of New Jersey, Janssen
Pharmaceuticals and SAS shared what they have done to prove
the value of analytics to their business leaders – and what has
worked for them as they developed an analytic culture in their
organizations and put analytic insights to work.
If you are working with massive amounts of data, one challenge
is how to display results of data exploration and analysis in a
way that is not overwhelming. You may need a new way to look
at the data – one that collapses and condenses the results in an
intuitive fashion but still displays graphs and charts that decision
makers are accustomed to seeing. And, in today’s on-the-go
society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time.
SAS® Visual Analytics is a data visualization and business
intelligence solution that uses intelligent autocharting to help
business analysts and nontechnical users visualize data. It
creates the best possible visual based on the data that is
selected. The visualizations make it easy to see patterns and
trends and identify opportunities for further analysis.
This paper is divided into two parts. The first part provides some background and a comparison of the types of episode analytics. Part two explores the real-world experiences of payers and providers in using episode analytics for payment bundling and other purposes.
Finally, we offer some recommendations on how to use episode analytics to reduce variations and manage contracts that involve financial risk.
With new technologies, new opportunities often emerge, especially in business. The advent of innovations, such as social media and mobile devices, is changing the ways businesses interact with customers and the ways in which customers desire to be engaged. Opportunities arising from the benefits of salesforce automation, business intelligence (BI), and customer relationship management (CRM) applications are providing new levels of insight, helping businesses acquire customers more efficiently and retain those customers longer. As a direct result, organizations that invest in better understanding potential customers are likely to see higher returns than those organizations that possess a more limited understanding of their customer base. Seeking the competitive advantage resulting from improved customer focus, IT organizations have increased investment in business intelligence and analytics and the underlying infrastructure to support those applications.
Diagnosoft selected Caristix software and consulting. Consulting work included a hands-on workshop to co-design the interface deployment workflows that Diagnosoft customer engagements would require.
While just about everyone is writing about how IT and the businesses it serves need to be transformed, the actual industry answers to both digital and IT transformation remain unclear at best. Are transformational initiatives all about analytics and big data? Or are they about the move to cloud in all its varieties? Support for mobile? More agile ways of working and developing software? Or are they actually all about crafting teams to promote more proactive dialog between the business and IT?
The truth is, of course, digital and IT transformation depend on all of the above and more. They also depend on a resilient infrastructure that’s easily adapted to changing business priorities without requiring long hours spent on maintenance, updates, and addressing problems of service availability. But making all this work clearly and cohesively is far beyond the purview of almost any solution today—whether from a software management perspective or from a hardware infrastructure perspective.
Is turning data into information still a challenge for your company? If so, you are not alone. View this on-demand Webinar with SAP and HP to learn how you can manage ever-increasing amounts of data; provide this data to business users to make critical decisions in a timely fashion; and enable true self-service business intelligence (BI) for business users.
Business intelligence technology must meet the demands of tomorrow's "digital natives"; integrate seamlessly with cloud data and platforms; align people, conversations, and data with business strategy; and make the most of the infrastructures we have today.
Malicious botnets present multiple challenges to enterprises — some threaten security, and others merely impact performance or web analytics. A growing concern in the bot environment is the practice of credential stuffing, which capitalizes on both a bot’s ability to automate repeat attempts and the growing number of online accounts held by a single user. As bot technologies have evolved, so have their methods of evading detection. This report explains how the credential stuffing exploit challenges typical bot management strategies, and calls for a more comprehensive approach.
Organizations today collect a tremendous amount of data and are bolstering their analytics capabilities to generate new, data-driven insights from this expanding resource. To make the most of growing data volumes, they need to provide rapid access to data across the enterprise. At the same time, they need efficient and workable ways to store and manage data over the long term.
A governed data lake approach offers an opportunity to manage these challenges. Download this white paper to find out more.
Forward-thinking enterprises understand what it takes to be successful in this data-rich, increasingly automated economy. According to the Harvard Business Review Analytic Services research report The Rise of Intelligent Automation: TurningComplexity into Profit, sponsored by Oracle, at least 7 in 10 executives understand that predictive analytics (80%) and AI and machine learning (68%) are important for the future of the business.
Even as executives recognize the vital role data plays in their businesses, many are unable to take advantage of the value residing in their data. The old ways of collecting, managing, storing, and analyzing data are no longer effective, and are preventing businesses from extracting potential value. Many simply can’t execute on a data-driven vision.
Oracle Autonomous Data Warehouse Cloud is more than just a new way to store and analyze data; it’s a whole new approach to getting more value from your data.
Market leaders in every industry depend on analytics to reach new customers, streamline business processes, and gain a competitive edge. Data warehouses remain at the heart of these business intelligence (BI) initiatives, but traditional data-warehouse projects are complex undertakings that take months or even years to deliver results.
Relying on a cloud provider accelerates the process of provisioning data-warehouse infrastructure, but in most cases database administrators (DBAs) still have to install and manage the database platform, then work with the line-of-business leaders to build the data model and analytics. Once the warehouse is deployed—either on premises or in the cloud—they face an endless cycle of tuning, securing, scaling, and maintaining these analytic assets.
Oracle has a better way. Download this whitepaper to f
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.
Download to find out more now.
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.
Do you know your people as well as you know your customers?
Your people’s expectations and the way they work is changing. Employees are more diverse, mobile and technologically-savvy than ever before. HR processes are changing from focusing on transactions to knowing and engaging people.
Just as sales and marketing teams use data to develop actionable and informed insights about their customers, you need to do the same in HR to know your people.
Everything, from attracting and keeping the best talent, to creating better workplace experiences and increasing employee engagement and productivity, depends on smarter decisions. These in turn rely on more actionable insights.
These are only possible through accurate HR data and analytics. They are vital to address the people challenges you face, so you can make smarter decisions.
Discover in this guide how to improve visibility of your workforce with data-driven and actionable insights. Ultimately, it will help you know your people
In our latest survey report, we explore the growth challenges facing businesses and HR leaders in a rapidly changing landscape.
We surveyed over 500 HR leaders in leading organisations to explore their views on these challenges, and to find out how they are supporting people and leveraging people data to help them achieve their growth goals.
The survey revealed that:
• It’s the war for talent, again. The greatest challenges for growing companies are winning the war for talent, growing productivity and improving workforce visibility.
• Fast-growth companies share common traits in the way they manage and engage their people—we call this being a People Company.
• There’s a disconnect between managers and employees in terms of what being a People Company means.
• Becoming a People Company is a journey, with many organisations some way from embracing all aspects.
• People Science is a thing: there’s an appetite to leverage people data and analytics, but there are blockers in the way.
Re
Do you know your people as well as you know your customers? Your people’s expectations and the way they work is changing. Employees are more diverse, mobile and technologically-savvy than ever before. HR processes are changing from focusing on transactions to knowing and engaging people. Just as sales and marketing teams use data to develop actionable and informed insights about their customers, you need to do the same in HR to know your people. Everything, from attracting and keeping the best talent, to creating better workplace experiences and increasing employee engagement and productivity, depends on smarter decisions. These in turn rely on more actionable insights. These are only possible through accurate HR data and analytics. They are vital to address the people challenges you face, so you can make smarter decisions. Discover in this guide how to improve visibility of your workforce with data-driven and actionable insights. Ultimately, it will help you know your people better an
As your organization’s finance leader, the opportunity to better understand the landscape and your business has never been greater. Advances in analytics—powered by digital technologies, such as automation and machine learning—give finance teams deeper business insights. Read the story.
This IDC Government Insights White Paper discusses government's new efforts to achieve its goals regarding service delivery to citizens, businesses, and other government agencies.
You need a path through this madness.In this recorded webicast, Steve Muran will take you through a case study of how he methodically built and managed a cross-enterprise, multichannel program that deepened the customer wallet share. Steve will discuss the why, where, and how his lead generation capabilities made customer cross-sell, up-sell, and retention easier by highlighting the critical importance of customer data and analytics.
For CDS Global, Infor CRM Epiphany Interactive Advisor is being used to create real-time customer profiles based on historical, personal, and contextual data. Using a combination of real-time analytics and business rules, the data is analyzed to identify the highest-impact offers in real time so that the CSR can make an attractive offer during the call.
