You may know some data management basics, but are you aware of the transformational results that can result from doing data management right? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data.
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security.
Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.
As our corporate data stores have grown in both size and subject area diversity, it has become clear that a strategy to address data is necessary. Yet some still struggle with the idea that corporate data needs a comprehensive strategy.
There’s no shortage of blue-sky thinking when it comes to organizations’ strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations’ strategic plans often generate very few tangible results for organizations – only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a r
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data.
To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever.
Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
You may know some basics about data management, but do you realize the transformational results data-management-done-right can produce? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data. From getting fast, easy access to trustworthy data to making better decisions and becoming a data-driven business, you’ll learn why good data management is essential to success. Multiple real-world examples illustrate how SAS customers have used data management to improve customer experience, boost revenue, remain compliant and become more efficient.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
Some organizations focus on the scary aspects of failing to comply with the EU General Data Protection Regulation. But there are many long-term benefits of following through with plans for sustainable GDPR compliance – such as gaining a competitive edge, or developing new products or services.
To learn how organizations have approached compliance efforts, SAS conducted a global survey among 183 cross-industry businesspeople involved with GDPR. Based on the results, this e-book delves into the biggest opportunities and challenges faced.
Read the e-book to:
• Get advice from industry experts.
• Find out what steps peers have taken.
• Learn how an integrated approach from SAS can continue to guide your journey.
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
Starting data governance initiatives can seem a bit daunting. You’re establishing strategies and policies for data assets. And, you’re committing the organization to treat data as a corporate asset, on par with its buildings, its supply chain, its employees or its intellectual property.
However, as Jill Dyché and Evan Levy have noted, data governance is a combination of strategy and execution. It’s an approach that requires one to be both holistic and pragmatic:
• Holistic. All aspects of data usage and maintenance are taken into account in establishing
• Pragmatic. Political challenges and cross-departmental struggles are part of the
equation. So, the tactical deployment must be delivered in phases to provide quick
“wins” and avert organizational fatigue from a larger, more monolithic exercise.
To accomplish this, data governance must touch all internal and external IT systems and establish decision-making mechanisms that transcend organizational silos. And, it must provi
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast.
In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
Risks have intensified as retailers and financial organizations embrace new technologies to meet customer demands for convenience. The rise of mobile and online transactions introduces new risks – and with that, new requirements for fraud mitigation. This paper discusses key steps for fighting back against fraud risk by establishing appropriate and accurate data, analytics and alert management.
Because terrorists and other criminals are already using technology to carry out their missions, intelligence professionals need to access all available, appropriate information, to extract important elements and process, analyze and disseminate it quickly to keep ahead of potential threats. The scale, complexity and changing nature of intelligence data can make it impossible to stay in front without the aid of technology to collect, process and analyze big data. This paper describes a solution for how this information can be quickly and safely shared with access based on a user's organizational responsibilities and need to know.
Medicaid fraud is prevalent, costly and difficult to prevent. With a combination of more integrated data and advanced analytics, state agencies can turn the tables on fraudsters. They can accelerate the transition from detection to prevention, as new forms of fraud are recognized faster and fewer improper payments go out the door.
This IIA Discussion Summary explores the challenges and opportunities in preventing Medicaid fraud in an interview with SAS’ Ellen Joyner-Roberson, Principal Marketing Manager for Fraud and Security Intelligence, and Victor Sterling, Principal Solutions Architect.
Fraud is costing government programs more than ever. For example, Medicaid lost $36 billion in 2017 – 10 percent of its total expenditures – which represents a 27 percent increase over 2015. As explored in this paper, fraud numbers are on the rise because fraud rings are not only getting more effective at what they do, but because it’s easy for them to find soft targets across multiple government programs simultaneously. Learn about how next-generation analytic tools from SAS cut across data and program silos and empower investigators to go on the offensive with fraud operators – without disrupting the efficient and timely delivery of benefits, services or tax refunds.
Only a decade or so ago, those human resources professionals who hadn't yet found their way onto the Internet were finding themselves increasingly left out in the cold.As we slip swiftly into the second decade of the 21st century, it's those who haven't yet begun to participate in 'social media and networking' that are starting to feel the chill.
Only a decade or so ago, those human resources professionals who hadn't yet found their way onto the Internet were finding themselves increasingly left out in the cold. As we slip swiftly into the second decade of the 21st century, it's those whohaven't yet begun to participate in 'social media and networking' that are starting to feel the chill.
Published By: Zynapse
Published Date: Jun 16, 2010
Data Governance has emerged as the point of convergence for people, technology and process in order to manage the crucial data (information) of an enterprise. This is a vital link in the overall ongoing data management process for it maintains the quality of data and makes it available to a wide range of decision making hierarchy across an organization
Published By: Zynapse
Published Date: Sep 10, 2010
UNSPSC enables preference item management, better spend analysis, supply standardization and information control.
Whether you are deliberating on the need for a common product and classification standard for your company, or are an advanced UNSPSC adopter, we hope that "Adopting UNSPSC" will answer some of your questions and perhaps help you in some way to improve your purchasing and supply management processes.