Published By: IBM APAC
Published Date: Nov 22, 2017
Using IBM Watson’s cognitive capabilities, companies can quickly differentiate their customer service quality by being more pro active and responsive to customer needs. Simply put, chatbots and virtual agents are the future of customer interactions. Building apps from scratch that incorporate natural language processing, speech to text recognition, visual recognition, analytics, and artificial intelligence requires broad expertise in these disciplines, large staffs, and a huge financial commitment. Making use of IBM Watson cognitive services brings these capabilities in-house quickly and without the capital investment that would be needed to develop the technologies within an organization.
Published By: Dell EMC
Published Date: Oct 08, 2015
Download this whitepaper to learn how Dell can help you realize the full value of your big data strategy—and to capitalize on all of your data, from enterprise systems to social media and the Internet of things.
Companies are pursuing digital transformation. The goal is to improve customer value, operate with greater efficiency and agility, and increase innovation. But as companies leverage new workflows, security has not kept pace, and cyber criminals are becoming more sophisticated. This white paper describes a security paradigm for today’s hostile environment: zero trust.
Social, Mobile, Analytics and Cloud (SMAC), have broad potential to provide huge business value, while simultaneously presenting potentially overwhelming challenges. The rapid technology changes supporting SMAC and the overall complexity involved demand a systematic approach to building out your SMAC capability.
In July 2013 Acxiom commissioned Forrester Consulting to evaluate how companies use the data they collect from their customers to make better decisions on their marketing campaigns by gauging their experiences and attitudes around their use of and future vision for using customer data across multiple marketing channels. In order to understand this topic, we conducted interviews with 11 executives representing a range of roles and perspectives, including consumer packaged goods companies, financial services organizations, and agencies.
If you are responsible for online advertising, this report is going to change the way you look at your banner ads forever. It did for us. But, step one on the road to this transformation was deciding exactly what to measure. Download this whitepaper to learn about a new measurement approach that proves banner ads boost offline sales.
This report offers recommendations and best practices for implementing analytics in an organization. It provides in-depth analysis of current strategies and future trends for next-generation analytics.
With sophisticated analytics, government leaders can pinpoint
the underlying value in all their data. They can bring it together
in a unified fashion and see connections across agencies to
better serve citizens.
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.
Published By: Dell EMC
Published Date: May 10, 2017
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.
Published By: IBM APAC
Published Date: Jul 09, 2017
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.
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.