Sponsor: SAS Institute

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.

Banks have been using credit scoring models for decades, but since the financial crisis of 2008, regulators have formalized the discipline of model risk management (MRM), driving the need for more rigorous, enterprise-level model information management. Regulators now want to evaluate bank models to access their trustworthiness – not blindly accept the numbers they generate. This paper explores how next-generation MRM is integral to successfully running a financial services business – both for compliance and decision making purposes. Learn why decision makers today are judged not just on outcomes, but on the processes and decision support tools they use to realize them. And see why it’s absolutely critical that your firm be able to manage ever-growing numbers of models – what’s needed to do that effectively.

Many analytics and BI tools limit your ability to get insight in time to make a critical business decision. Once you detect a pattern, you have to work with a data scientist to choose data sets for more analysis, clean the data of noise, and code a query, all while the data becomes less and less relevant with passing time.

This resource explains streaming analytics and describes how it can enable real-time decision-making based on current evidence. Learn how you can resolve business problems more quickly and make data-driven decisions.

“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. In fact, most data scientists spend 50 to 80 percent of their model development time simply preparing data. SAS adheres to five data management best practices that provide access to all types of raw data and let you cleanse, transform and shape it for any analytic purpose. As a result, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes across your business.

The IDMP data chasm is a comprehensive and demanding challenge – but it can be crossed with the right preparation, approach and solution. This white paper highlights eight IDMP challenges, and how they can be addressed with an IDMP data hub solution from SAS. As a trusted advisor, it includes the SAS recommendations on IDMP in an MDM context, and finally broadens the perspective by looking beyond the approach of solely adopting IDMP for compliance.

Terms such as Big Data and Internet of Things, unknown to most just a few years ago, are now commonly used not only by top managers and entrepreneurs, but also by the wider corporate pyramid.

We are often surprised by how things change at a speed which surpasses our ability to learn and to understand. And just as often, corporate dynamics and the context within which we move appear too complicated, too costly, too complex. All too much.

We all know that good, clean water is core to life. Without it, we can only survive for around three days. So what happens if your water source is polluted? Well, unless you filter the water sufficiently, there will definitely be some negative consequences. To get better results, you could enrich the water with fluoride, filter out the arsenic, and deliver it at the right pressure and temperature.

In a similar way, “unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics brings with it different requirements than storing data in a warehouse. How difficult is it to manage unfiltered data and get it ready for analytics?

The power of Hadoop is that it utilises schema on read. With a data warehouse, you often have to know what the tables look like before loading data. With Hadoop, you can pull data from any source or type and then figure out how to organize it. Organisations are beginning to use Hadoop as a dumping ground for all kinds of data because it is inexpensive and doesn’t require a schema on write. Such storage is often referred to as a Hadoop “data lake.” On the flip side, the Hadoop/MapReduce engine is not optimised for the iterative processing that analytics often requires. It is best suited to batch processing.

Organizations have finally recognized the importance of big data. They know it can be used for analytics and other advanced technologies, so they’ve harnessed and stored it in systems such as Hadoop.