Unified talent management is a term used for talent management software solutions that handle all pieces of the talent lifecycle in a single platform that shares data across all aspects of an employee’s career.
Unified talent management technology solutions can help organizations define and use the same criteria for selection, performance management, and development planning, providing visibility into the entire talent pool.
This paper will discuss how to judge the quality of your business data and define the characteristics of your target customer and review how to craft marketing messages that resonate with your target customer.
End-user expectations and high levels of performance against Service Level Agreements (SLAs) must be achieved or organizations risk the loss of business. This paper details key capabilities needed for successful end-user monitoring and provides critical considerations for delivering a successful end-user experience.
Organizations are drowning in content. They don't know what they have, and they can't find what they need when they need it. While they spend significant time and money to manage content stored among a host of disconnected systems, their efforts are less than fully effective. The data explosion will continue.
ASG's Business Service Portfolio (BSP) Virtualization Management provides comprehensive oversight, inspections, discoveries, warnings, diagnostics, and reporting for the critical technology and administrative disciplines involved in virtual workload management. This is all done in parallel with physical systems management.
Published By: Attivio
Published Date: Apr 07, 2010
In this paper, well known data management authority Mark Albala proposes replacing the reliance on data models as the fixed navigation scheme used for storing, reporting and analyzing information with a new unified information access capability that enables more agile, insightful and pro-active decisions.
Published By: Attivio
Published Date: Apr 07, 2010
The wealth and volume of content and data, including new applications and data sources, cloud computing, SaaS, email, SharePoint and public content, have made information integration more important than ever.
This white paper demonstrates how companies that have moved their SQL databases to the cloud have overcome past performance and security concerns to increase operational efficiency, improve availability and scalability, reduce costs, gain a faster-time to-market, and achieve a better return on investment.
Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later. Nowadays, executives are being enlightened by the promise of big data technologies and the role data plays in the fact-based enterprise. Leaders in business and IT alike are waking up to the reality that – despite the hype around platforms and processing speeds – their companies have failed to established sustained processes and skills around data.
When Information Revolution1 was published in 2006, no Chinese based companies were among the top 10 largest companies by market capitalization. Apple didn’t sell phones. Facebook was something college kids used to connect with their friends. Back then, we talked a lot about the amount of data coming in and faster processing speed.
What we believed then remains true today: Data, and the decision-making process, can be moved throughout the organization to equip every decision maker (automated, line worker, analyst, executive) to make the best choices. By operationalizing analytics, organizations can identify and quantify both opportunity and risk. Information Revolution highlighted SAS’ Information Evolution Model, which helps organizations understand how they interact with their information and how to extract more value from it through analytics.
In this paper – which is based on a webinar hosted by the Direct Marketing Association (DMA) and sponsored by SAS – we will take a look at how these technological advancements can enable you to become more predictive and prescriptive in your digital and integrated marketing efforts.
As analytics and Big Data have been embraced, analysts are working to become better at communicating the insights from complex analysis. This makes the use of visual analytics increasingly important as a tool to tell compelling stories and to engage decision makers in dialogue. Importantly, the best visual analytics are not necessarily the coolest, most sophisticated, or most complex. Visual analytics are most effective when there is a clear purpose and when data can be visualized and communicated in a way that is easily understandable.
Stop to think about how – and how often – your business interacts with customers. Every day, with each interaction, data is created. What percentage of the data generated by these interactions are you using? There are so many channels for interaction, like social media, call centers, sales staff, help and support resources, marketing and campaigns.
Typically, organizations believe that they are using only a small fraction of it effectively – at best, upwards of 10 percent of all the available data. Why? One reason relates to the difficulties in collecting all this data. This limitation is beginning to wane as commodity hardware becomes increasingly popular for big data storage. But another major inhibitor to examining all customer data has been the inability to examine millions, or even billions, of data points that constitute the customer picture. And much of this is now in the form of unstructured text inputs.
This piece explores some of the social, technological, data, and business trends driving the visual organization. We will see that employees and organizations are willingly representing—or, in some cases, being forced to represent—their data in more visual ways.
In every industry today, businesses feel a fierce urgency to become customer-centric. They want to know what they can do to preserve and expand existing customer relationships and attract the best new customers.
The pace and sophistication of data breaches is growing all the time. Anyone with valuable secrets can be a target, and likely already is. According to the Privacy Rights Clearinghouse, at the time of this writing, 884,903,517 records were breached in 4,621 incidents documented since 2005.
Analytics is more important to success than ever before, and it’s a business practice that has momentum. Fifty-eight percent of the respondents in a recent survey published in the MIT Sloan Management Review stated that the use of analytics gave their companies a competitive advantage, up from 37 percent the prior year. Enterprise-scale companies report dramatic successes with analytics.
This paper examines the barriers to adoption from an IT and
end-user perspective, and shows how self-service analytics in
general – and SAS Visual Analytics in particular – can eliminate
these barriers. Self-service analytics empowers users to truly
exploit the wealth of data available to them, while ensuring
that the IT organization maintains governance and control
over that data.
But if you can’t explain how you got the answer, or what it means, it’s no good. Most self-service BI solutions can only display what has already happened, through reports or dashboards. And most have a predefined path of analysis that gives users very little creative freedom to explore new lines of thought.
To maintain competitive advantage, your BI solution should allow business users to quickly and easily investigate and interrogate the data to find out why something happened – to uncover the root cause behind the “what.”
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.
The heart and soul of SAS Visual Analytics is the SAS® LASR™ Analytic Server, which ca