Solution Overview

SAS® Data ManagementExtend your data foundation and unleash innovation

• Take charge of your data wherever it lives, as fast as it comes.

Forget about boundaries. Now you can access and apply data

management techniques anywhere, whether it’s in-stream, in-data-

base, in-memory, in-cloud or elsewhere. Getting closer to your

source data means better speed, productivity and performance.

• Awaken your data with analytics that generate new awareness.

Give your data The Power to Know® – while freeing IT to do more

– as you automate and augment manual tasks with machine

learning and proven statistical techniques that reveal new insight

into hidden problems, discrepancies, and issues with data

elements and processing.

• Act confidently, knowing your analytically based decisions are

built on a trusted data foundation. With best practices

embedded into every element of data management, your data

will be impeccably consistent, accurate and analytically valid,

even for the most complex investigations.

OverviewData is the heart and soul of every organization, regardless of industry or expertise. And data management underlies virtually every process organizations depend on. So, how important is data management technology? Consider your answer after reviewing this example.

Let’s say you’re in a hospital’s critical care unit. You expect top-notch care – right? The science of modern medicine continually evolves, and you trust that doctors and nurses have worked hard to keep abreast of the newest methods so they can deliver the best possible patient care. But providing nonstop exceptional care is grueling.

Here’s one reason why: Clinicians use many physiological monitoring systems to monitor a patient’s condition. These devices generate data during the ambulance ride and then continuously from the patient’s bedside. They measure blood pressure, heart rate and oxygen level, and they regulate breathing and medicine pumps, stimulate blood flow, and perform many other crucial measurements and functions.

Each monitoring system produces data at various rates and in different formats. Whenever the data signals a change in a patient’s condition, it triggers an alarm. Patients frequently have many different systems connected to them, each with a specific monitoring function. Often, alarms are false positives – because connections are lost, a battery needs to be replaced or some other non-life-threatening situ-ation occurs. With all these alarms, imagine how overwhelmed the care team could become.

“Alarm fatigue” is a common condition that affects clinicians when they become desensitized because there are too many alarms. Too many false positives can cause clinicians to delay responses or even neglect true alarms. The ECRI Institute, an independent, nonprofit organization dedicated to the best approaches to improving safety, quality and cost-effectiveness of patient care, produces a “Top 10 Health Technology Hazards” list each year. For the last four years, alarm hazards has been rated No. 1 on the list.1

With SAS® Data Management, you can ...

Solution Overview

1 Barbara J. Drew et al., “Insights into the Problem of Alarm Fatigue with Physiologic Monitor Devices: A Comprehensive Observational Study of Consecutive Intensive Care Unit Patients,” PLoS ONE (2014), accessed Nov. 25, 2015, e110274. doi: 10.1371/journal.pone.0110274

SAS Data Management can help fight alarm fatigue by targeting the alarm’s root cause in a coordinated way. It works by combining multiple streams of sensor readings to detect meaningful patterns of interest, rather than examining just one alarm without context. As a result, clinicians can monitor the patient’s condition more thoroughly.

Here’s how it works. Domain experts define patterns of interest based on comprehensive historical data – data that SAS can stream into a data lake while integrating, transforming and cleansing on the fly with embedded SAS Data Management capabilities. SAS Analytics, inserted in streaming sensor data, can then analyze these patterns of interest as new data arrives. Clinicians can use this information to create customized settings for each patient and adapt them as the patient’s condition evolves. The customized settings, in turn, help ensure that alerts truly require a response. And clinicians – freed from alarm fatigue – can focus on providing excellent patient care.

A New ApproachMany organizations recognize the path of differentiation they can achieve with the right data at hand. This recognition, in turn, drives demand for more varied and accessible data.

But the data needed for analytics has specific requirements.2 Meeting the demand for data that supports analytics – and sustains its expan-sion – can be a perplexing dilemma.

Organizations know they must quickly adopt new technologies to develop an agile business in the face of changing markets and inno-vations (like mobile, cloud, streaming data, the Internet of Things [IoT], services, etc.). And to build enduring operational excellence while extending existing functions to address new business strategies like data monetization, many organizations will have to modernize their IT infrastructures.

For most IT departments, the well-honed disciplines of managing data for repeated use, monitoring data to consistently validate processing, and governing data for enterprise consumption and orchestration are becoming foundational routines. Yet refining these activities to drive better performance and responsiveness, while taking advantage of changing data delivery and data consumption practices, is a balance that can be hard to strike. Especially given the need for IT to constantly improve, and do so with less.

SAS takes an innovative approach that extends your data manage-ment beyond traditional boundaries to help you make better deci-sions faster, while staving off competitive threats. Encompassing comprehensive data discovery, access, quality, integration, federation, governance and mastering capabilities that work consistently across all types of data environments, SAS Data Management provides clean, trusted data. Data that’s pervasive across the organization, thor-oughly prepped, fully governed and ready to use for any purpose that arises.

With many data management tasks placed directly in the hands of technologically savvy business users, you won’t have to depend on specialists to manage routine activities. Because you can manage data at its source, even while it’s in motion, you can improve both productivity and performance. As you automate and supplement manual tasks, you’ll benefit from data that’s informed by analytics. Through machine learning and well-defined statistical techniques, your data can develop previously unknown insight to direct actions for transformations, adjustments and process refinement. So you can get a more comprehensive understanding of the data management tasks that need to be performed.

Challenges • Counterproductive business and IT efforts. Business

and IT don’t speak the same language, which makes it nearly impossible for everyone to focus on the same long-term, strategic goals or even agree on how to prioritize day-to-day activities.

• Fragmented systems wreak havoc on processes. New data is often stored in silos outside of the managed envi-ronment, so it has variable structures, quality levels and process definitions.

• Wasted time. Manual coding that’s used to reconcile different data quality definitions and structures causes bottlenecks between teams.

• Hampered creativity. Because IT has to spend so much time maintaining the status quo, efforts to improve, automate and be strategic to the business are stifled.

}}A 31-day study was conducted to document alarms for arrhythmia. “88.8% of the 12,671 annotated arrhythmia alarms were false positives.”3 These false positives triggered audible alarms that required a clinician to make a decision and take action. This is just one example from one system. Imagine the effect multiple false alarms from multiple systems would have on care teams.

2 sas.com/en_us/whitepapers/data-management-for-analytics-best-practices-107769.html

3 Barbara J. Drew et al.

With a single, integrated suite of capabilities, SAS helps

organizations manage all their data and turn it into a

valuable business asset. SAS Data Management

software provides:

• Data integration. Break down data silos with the

industry’s leading integration technology. From

legacy systems to Hadoop, you can access any data

you need quickly, whether it’s streaming, real-time or

batch. An intuitive interface with a single point of

control makes it easy for business users to work inde-

pendently, freeing IT for other tasks.

• Data quality. Rely on SAS to embed data quality

into every data process. No need to move or extract

data. SAS profiles, standardizes, monitors and verifies

data where it exists, in motion or at rest. You can

customize, automate and reuse data quality business

rules within process job flows. And establish repeat-

able processes to build and maintain high-quality

data across the entire data life cycle.

• Data preparation for Hadoop. Access, manipulate

and use data stored in Hadoop with an intuitive

interface – no special coding skills required. You

can run existing SAS Data Management technolo-

gies natively inside the Hadoop environment. Add

big data to existing IT processes using technologies

like Impala or Pivotal HAWQ. Or manage the data

where it lives with transformations for MapReduce,

Pig and Hive.

• Data governance and master data management. Promote better collaboration with an integrated

business data glossary, SAS and third-party

metadata management, and lineage visualization.

Set and enforce policies and establish a consistent

view of data using a web-based environment and

dashboards. And create a repository of master terms

and sources to use across any system, enhancing

stewardship and enforcing governance. With SAS

Visual Analytics reporting engine (included), you

can visually and interactively flag issues, route them

to the appropriate person and make sure they

get fixed.

• Event stream processing. Analyze streaming data

– from operations, transactions, sensors and devices

– while it’s in motion. SAS determines which data

requires immediate attention, what you can ignore

and what can be stored for later use. It also provides

in-stream data quality and includes a single, intuitive

interface to let you define patterns and address

scenarios from any aspect of your business.

• One version of metadata. Improve trust in data by

providing complete transparency. Visually trace your

data’s journey – both in and out of the SAS environ-

ment – with SAS Lineage. And keep a common

glossary to ensure that everyone is on the same

page when discussing access to new sources and

data requirements for meeting business objectives.

SAS® Data Management Core Capabilities

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The SAS® DifferenceData processing at the sourceMoving routine execution closer to the source of the data takes advantage of existing hardware, minimizes data movement and simplifies access. In turn, processing is much faster, and downstream use becomes more consistent, requiring fewer computing resources. Consider streaming data from the IoT. By embedding in-stream data management and normalization functions before data is stored, you can identify what’s worth keeping – correcting it as needed before you incur unnecessary, incremental storage costs. Along the way, this helps modernize your infrastructure to meet emerging needs, so you can continue to take advantage of investments in legacy technology.

Data management for business usersFinding the right data source and shaping it to meet project-specific needs should be easy. SAS presents pertinent suggestions to users to help them fine-tune and understand what changes need to be made to data for it to be useful. Easily combining data from both legacy and modern sources, SAS enables you to use Hadoop without learning new coding languages. And because SAS can alert both data scientists and business users when changes are made or need to be made, it helps streamline workflows and keep results current.

A data management foundation empowered by built-in analyticsWith SAS, self-tuning systems can make automatic adjustments to maximize the computing infrastructure and optimize processing. Algorithms can be used to discover and curate business rules that exist within operational data. Parallel profiling discovers and identifies new metadata insights – giving informed direction for data manage-ment transformation and cleansing tasks. And by integrating learning models in data streams, SAS can define groups of similar data events to be assessed for common patterns.

Learn More Find out more about how SAS helps organizations around the world manage their data right. Visit sas.com/data.

Read what analysts say about SAS solutions for data integration, data quality and analytics.