WHITE PAPER

SAS® Information ManagementEnd-to-End Continuity, Cohesion and Governance for the Entire Information Path – from Raw Data to Analytic Insight Delivered at the Point of Decision

SAS White Paper

Table of Contents

The Intensifying Data Imperative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Manage Data as a Valued Strategic Asset . . . . . . . . . . . . . . . . . . . . . . . 1

Optimize Decision-Making Processes for Competitive Advantage . . . . 1

Continuously Monitor and Improve Decision-Making Processes . . . . . 1

Reduce and Manage IT Costs While Processing More Workload . . . . . 1

An Evolution in Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Redefining the Concept of Information Management . . . . . . . . . . . . . 5

Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Analytics Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Decision Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Information Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Closing Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Contributor: Mark Troester, IT/CIO Thought Leader and Strategist, SAS

Troester oversees the company’s marketing efforts for information management and for the

overall CIO and IT vision. He began his career in IT and has worked in product management and

product marketing for a number of start-ups and established software companies.

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SAS® Information Management

The Intensifying Data ImperativeRemember the days when successful, leading organizations could make decisions based on gut instinct and intuition, without having to consult the data? Neither do we .

In fact, organizations are under more pressure than ever to make data-driven decisions and to manage information assets more effectively . Yet the rate, diversity and complexity of data coming at organizations are unrelenting . So too is the imperative to transform this data into business insight – and to deliver it where and when it is needed, often immediately .

The information management framework is no longer an adjunct support structure; it is the essential foundation for organizational performance . How information is obtained, validated, stored, accessed, distributed, applied . . . these issues are central to your survival and profitability .

On the quest to extract the most business value from data, you should designate four goals as paramount:

Manage Data as a Valued Strategic Asset

The decision insight locked in organizational databases is too important to be managed by spreadsheet renegades or in departmental silos without enterprisewide visibility, quality control and governance .

Optimize Decision-Making Processes for Competitive Advantage

From day-to-day tactical decisions to far-reaching strategic directions, all decisions count . All organizations benefit by making better decisions faster . This goal requires analytics that transform data into meaningful insights, including a way to get those insights to the point of decision at the right time .

Continuously Monitor and Improve Decision-Making Processes

It’s not enough to build and use analytic models; you have to see that they remain relevant . Are predictive models performing well, or should they be revisited or retired? Have market conditions changed in ways that affect results? Are the results of business decisions, such as marketing campaigns, folded back into continuous improvement?

Reduce and Manage IT Costs While Processing More Workload

IT is constantly pushed to manage and reduce data costs, even as business needs escalate . The pressure is to do more with less – to manage more data, more models and more decisions, even when computing power and human resources are finite .

For many, these four goals will require a new way of looking at how they manage data and use it for decision support .

Business units are looking to IT

to deliver game-changing value,

insisting that the information

framework be able to harvest

new sources of data, support all

forms of devices and delivery

vehicles, and deliver insight in

real time to the point of decision.

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SAS White Paper

Gartner sums it up well in recent research:

“ Leveraging information will continue to fuel business success. But the growth in information volume, velocity, variety and complexity and the new information use cases make information management infinitely more complex than it has been in the past.

“ In addition to the new sources and the increased demand for multiple context delivery, share-ability and reuse, practically all information assets must be available for delivery through varied, multiple and concurrent channels and mobile devices.

“ To deal with these new demands, the IT organization needs to dramatically modernize its IT systems, transforming outdated data management infrastructure and replacing it with a more up-to-date and superior information environment able to support an entirely new set of requirements.”1

Why now? Growing complexity in the data environment is driving the pressure to adopt more efficient and cohesive information practices . Some of these driving forces will no doubt look familiar:

• Fast-growing volume, velocity and variety of data . Organizations need to think about all the data at their disposal and how demands for storage, processing and management will grow in the future .

Data management frameworks must be adept at handling structured and unstructured data, streaming data and data at rest, internal and external data – plus descriptions about the data (metadata) and the data needed to govern and analyze the information assets .

• Radically altered data consumption patterns . An information management strategy needs to support an increasingly diverse set of devices and form factors, from PCs and tablets to smart phones and email, from meters and sensors to industrial devices – automated or interactive, delivered to humans or machine-to-machine .

• Expanded requirements for data management . Users or applications might require data migrations from disparate systems, consolidation for an enterprisewide view, unique data preparation for analytics, integration of vendor or customer data, etc .

• More applications hungry for data . The information management strategy has to support both analytical applications (business intelligence, data mining, text analytics, forecasting, optimization, etc .) and operational/transactional applications, such as enterprise resource planning (ERP) and customer relationship management (CRM) systems .

It also has to support all stages of the application life cycle, from development, test and production phases for operational applications – as well as the iterative sandbox environment and production cycle associated with analytical applications .

1 Beyer, Mark A., Regina Casonato, Ted Friedman, Yvonne Genovese and Anne Lapkin. Information Management in the 21st Century. Gartner Inc., Sept. 2, 2011.

“Some companies attempt

to use traditional data

management practices on big

data, only to learn that the old

rules no longer apply.”

Dan Briody in Big Data: Harnessing a Game- Changing Asset

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SAS® Information Management

• Temporal diversity . The information management strategy must address varying expectations for performance, from batch processing in tighter time frames to real-time or near-real-time speed .

• Varying IT platforms . The IT architecture could be deployed on-premise on commodity hardware or on dedicated, purpose-built appliances, or off-premise in a cloud (public, private or hybrid) or as a hosted service, such as a software-as-a-service (SaaS) offering .

• Diverse organizational needs . Whether centralized or distributed, with business unit autonomy plus enterprise-level visibility and governance, the information management strategy must support multiple levels of use as well as different types of users, from data stewards to IT professionals, analysts, business users and executives .

Satisfying these requirements is a tall order, especially in the face of fast-growing data volumes and processing requirements – or, big data . Everything that was important before is more important now . Everything that was problematic before will just be bigger now .

“Because the shifts in both the amount and potential of today’s data are so epic, businesses require more than simple, incremental advances in the way they manage information,” wrote Dan Briody in Big Data: Harnessing a Game-Changing Asset (Economist Intelligence Unit, 2011) .

“Strategically, operationally and culturally, companies need to reconsider their entire approach to data management, and make important decisions about which data they choose to use, and how they choose to use them . … Most businesses have made slow progress in extracting value from big data . And some companies attempt to use traditional data management practices on big data, only to learn that the old rules no longer apply .”

Figure 1. Eight key forces are driving the need for more cohesive and strategic information management practices.

It is not about the data or how

much of it you can process.

It’s about capitalizing on all the

data assets that are available

to the organization to provide

insight and drive fast and

accurate decisions.

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SAS White Paper

An Evolution in Data ManagementWhat exactly are you doing with your data? What is the ultimate goal? Unless your organization makes its money selling data, it is trying to transform that data into insight to drive business decisions . If it does that well, information becomes a strategic asset that leads to competitive differentiation .

But has it? It is common to focus on data management disciplines such as data integration and data quality . These activities are important – instrumental building blocks – but a more comprehensive approach is needed .

Yes, the data integration market has evolved in recent years to take a more comprehensive view . This may seem like old news, because many vendors have moved (or at least say they have moved) to a unified data management approach that spans data integration, data quality, master data management and more . That’s all well and good, but as Gartner and the Economist Intelligence Unit noted, incremental change within the data management disciplines is not enough to address the challenges of the 21st century .

To begin with, forward-looking organizations are moving away from viewing data integration as a standalone discipline to a mindset where data integration, data quality, metadata management and data governance are designed and used together . They have augmented the traditional extract-transform-load (ETL) data approach with ELT, in-database and in-memory processing that minimizes data movement and improves processing power with that data . And they have expanded the view of data management to address the role of data in analytics and decisions, and conversely, the role of analytics in data management .

Forerunners are also beginning to move from a project-based data management mentality to a holistic, enterprise view that treats data as a core enterprise asset . And finally, they are moving from reactive data management to a managed – and ultimately more proactive and predictive – approach to managing information .

SAS enables organizations to achieve this goal and refers to this approach as information management .

The business and technical

backdrop is compelling leading

organizations to move beyond

traditional, reactive and silo-

based data management

approaches, to a managed,

even predictive approach

that values information

as a strategic asset.

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SAS® Information Management

Figure 2. Organizations are evolving their data management practices from reactive to managed, and ultimately a more proactive and predictive approach.

Redefining the Concept of Information ManagementThe terms “data management” and “information management” are often used interchangeably . SAS has a broader definition of the latter . Whereas data management makes quality data and information more readily available, information management is focused on creating business value through data and information . Think of it as a higher tier on the information continuum, bringing a unity and cohesion to the entire path from data to information to decision insight .

SAS defines information management as the confluence of four important capabilities:

• Data management – The ability to manage and govern the data from a unified platform, including data integration, data quality, data governance, master data management (MDM) and enterprise data access .

• Analytics management – The ability to manage a portfolio of analytic models in a systematic way and to use the results of those models as new information assets .

• Decision management – The ability to embed information and analytical results directly into business applications or processes at the point of decision, and to support a feedback loop as decision outcomes are cycled back into the process .

• Governance – The technologies and tools that facilitate process and collaboration among the key constituents, platforms and entities .

Evolution from Data Management to Information Management•Fromstandalonetointegrated.

•Fromadhoctostrategic.

•Fromreactivetoproactive,evenpredictive .

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SAS White Paper

All of this is supported by a unified platform that provides common services across the information continuum, such as security, metadata, rules, workflow, reporting and monitoring .

Figure 3. Information management brings unity and cohesion to the entire information continuum, from data to information to decision insight.

SAS Information Management extends the traditional view of data management, which has been largely synonymous with data integration . Traditional data management vendors may have the first pillar covered – so does SAS through SAS Data Management – but SAS also brings all four pillars together . Although the mega-vendors have many of the pieces of the four pillars, they don’t deliver an integrated, comprehensive solution – nor can they match the analytic strength of SAS .

Let’s take a high-level look at SAS capabilities for each of the three pillars and information governance that spans the entire life cycle .

Data Management

SAS Data Management provides a unified environment of solutions, tools, methodologies and workflows for managing data as a core asset . Four key components work together:

• Data integration – Improve the flow of accurate information across the organization .

• Data quality – Ensure information integrity and excellence by managing the data quality life cycle .

SAS Information Management

is not a product per se; it is

an umbrella concept that

brings together relevant SAS

software elements in a cohesive

framework, augmented by our

years of experience working

with Fortune 1000 companies

around the world.

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SAS® Information Management

• Enterprise data access – Manage the access and use of data across the enterprise .

• Master data management – Create a single, accurate and unified view of enterprise data .

High-performance, scalable solutions dramatically reduce the time and effort required to filter, aggregate and structure data, even big data, whether in-flight or at rest . By combining data integration, data quality and master data management in a unified development and delivery environment, organizations can maximize each stage of the data management process .

Use analytics to determine upfront which data is relevant . The traditional modus operandi has been to process data after it has been stored; only when you query it do you discover if it is relevant . With SAS, you have a choice . For scenarios where more data would yield better answers, you can manage, process and analyze all the data . Your analysis is not limited to subsets of available data . But where it makes sense to identify the meaningful data upfront, SAS Analytics can support this approach .

A “stream it, score it, store it” approach applies analytics on the front end to identify the meaningful data from the noise, based on enterprise context . The idea is to put analytics in-line to determine the relevance of the data, instead of always landing everything in storage before analyzing it .

For instance, you can analyze all the information within an organization – such as email, product catalogs, wiki articles and blogs – extract important concepts from that information, and look at the links among them to identify and weight millions of terms and concepts . This organizational context is then used to assess data as it streams into the organization, churns out of internal systems or sits in offline data stores .

This analysis can be used to determine which data should be included in analytical processes and which can be placed in low-cost storage, available for later if needed .

Data ManagementUnified data management capabilities that include data governance, data integration, data quality and MDM .

Analytics ManagementComplete analytics management that includes model management, deployment, monitoring and governance of analytics as an information asset .

Decision ManagementThe ability to embed information and analytical results directly in business processes while managing business rules, workflow and event logic .

Information GovernanceCollaboration is facilitated between the constituents involved in the information life cycle while providing effective governance of data and analytic artifacts .

SAS is unique for incorporating

high-performance computing

and analytical intelligence into

the data management process

for highly efficient modeling

and faster results – pinpointing

relevance in a sea of data

volume, variety and complexity.

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SAS White Paper

Figure 4. Analytics applied upfront to the data management process can pinpoint relevant data.

The important thing to note is that SAS provides a choice . Depending on the business question at hand, you can:

• Takeadvantageofhigh-performancetechnologies,suchasagridcomputing,in-database processing and in-memory processing, to analyze massive volumes of disparate data .

• Usethe“streamit,scoreit,storeit”approachtoidentifytherelevantdataupfrontand avoid burdening the system with irrelevant data .

Analytics Management

To some people, analytics means building a model that will reveal new knowledge from data . That’s too narrow a definition . With the increased focus on analytical models as high-value organizational assets comes the realization that predictive models, as well as the underlying data, must be managed for optimal performance, throughout the life cycle, from the discovery/exploration phase to the test-and-learn phase to the inform-and-act phase .

SAS conceptualizes analytics as a closed-loop process of continuous learning and improvement, where each phase is instrumental to the cycle . SAS Analytics Management provides complete lifecycle management of analytic models . Analysts can register, validate, deploy, monitor and retrain analytical models in a minimal amount of time . A more efficient model management process enables organizations to manage a larger number of complex analytical models .

A “stream it, store it, score it”

approach determines the

“1 percent” of your data that

is truly important in all your

organization’s data.

Comprehensive analytics

management capabilities,

from data to decision, make it

possible for organizations to

take advantage of sophisticated

analytical techniques, a large

number of analytical models,

and a virtually unlimited number

of variables and data volumes.

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SAS® Information Management

With a formal model management framework – an analytic “model factory” – it also becomes far easier to document models and collaborate across departments and internal agencies . It becomes clear which models are still adding value and which are no longer working and need to be retired . And it provides a mechanism to feed model results back into the process for continuous improvement .

Figure 5. An analytic model factory formalizes a continuous process for the entire analytics life cycle.

Decision Management

Business insight isn’t much good if it just sits in reports or dashboards, however impressive those reports or dashboards might be . Analytic insights should be embedded very consistently in operational systems, at the point of action .

For example, when a credit card organization processes a card swipe, fraud detection should be embedded in that process . When call center agents have a customer on the phone or tellers have a customer at the counter, analytics behind the scenes should be giving them the information they need to optimize the interaction – immediately .

This ideal has historically been a challenge to implement, because the niche applications used for different business functions have not interfaced with one another .

The leaders will be the ones that

can efficiently transform data

into business insight and then

deliver that insight to the point

of decision – in time for those

decisions to make a difference.

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SAS White Paper

Unlike niche tools, an information management framework makes it easy to deliver the results from analytical processes into operational systems . Data management processes take the information from multiple sources, prepare it and make it available for analytic processes . Insights from analytics can be reported to business systems, on the Web and to mobile devices to get information to decision makers or business solutions when they need it .

The right IT architecture enables organizations to embed analytics into ongoing work processes in three ways:

• Automate decisions that must be made frequently and rapidly without human intervention . This form of embedded analytics is best suited for automated production systems where business rules can be easily coded and conditions are relatively stable, such as for fraud detection, real-time offers, dynamic forecasting and facilities control .

• Deliver analytics results via Web applications or enterprise systems . Analytical business applications are best suited for tasks in which most of the needed information is available electronically, but expertise from a human is required, such as for supply chain optimization, sales forecasting and advertising planning .

• Manage information flow, workflow and collaboration, often drawing information from enterprise systems into desktop productivity tools, such as for case management – and closing the loop by factoring the results of analytics back into the process .

This decision process can make use of business rules to tailor the way information is used . For example, a credit card score that indicates a denial could be overridden by a business rule that indicates that the card holder notified the credit card company that he or she was traveling to a distant location . A call center score could trigger a script to offer a special discount, under specific conditions . In an integrated environment, this decision support can consider the totality of the customer’s relationships with the organization, spanning business units, products or accounts .

Information Governance

SAS solutions provide the management and governance capabilities that enable you to effectively manage the entire life cycle of data management, analytic management and decision management – such as data governance, metadata management, analytical model management, run-time management and deployment management .

SAS also provides the ability to track data lineage from source to analytic result, which is critical in situations that entail strict regulatory or reporting requirements, such as in financial services .

With SAS, this governance is an ongoing process, not just a one-time project . Proven, methodology-driven approaches help you build processes based on your specific data maturity model .

Decision Management:Integrate rich analytical and information services within an operational application .

Provide a structured way to process events and support workflow and case management .

Use workflow and business rules to augment what is done with analytics and information .

Provide a closed-loop decision cycle, where results are fed back into the process .

Although SAS Information

Management represents a

comprehensive approach,

organizations do not have to

embrace it all at once. SAS

capabilities and services can be

provided as needed, deployed

in a step-wise fashion that sets

the stage for future flexibility.

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SAS® Information Management

Bridging the gap . SAS governance capabilities help bridge the familiar gap between IT and the business side . This disconnect is commonplace and only natural . You would expect IT to focus on technology, infrastructure and process, while business users would focus more on their business domains . Nobody is doing anything wrong; these groups just have different backgrounds and missions .

With leading innovators, we see a great deal of teamwork between these groups . They communicate well and work toward common goals . This close collaboration is sometimes formalized in an information management center of excellence (COE) .

These leaders also recognize that information management is not just about software components . They give considerable thought to strategy and implementation, including business, architecture and organizational strategy . Their governance addresses the broader information continuum, such as data stewardship, data remediation processes and more . And of course, all of these activities are bolstered by executive commitment and appropriate involvement or leadership .

Closing ThoughtsWhich of these scenarios looks most familiar to you?

Scenario A: Information is managed at the department or business-unit level . The data tools may be sophisticated, but they are not used consistently nor are they common across the enterprise . There’s a lot of manual intervention, duplicated effort and inefficiency in the information management process . If an information maverick leaves the company, his or her projects are in trouble, because the nuances of the work are difficult to trace or repeat .

Scenario B: Information management processes are unified under a central point of control, which eliminates redundancy and provides end-to-end governance . IT processes are well defined, auditable and repeatable . Analytic insights are available as part of the natural context of business, widely used to make decisions and create value . Due to IT efficiencies, the organization is able to use all relevant data to make the best possible decisions .

Published by SAS, the book, Information Revolution2, defines five levels of maturity in terms of how an organization manages its information, from patchwork to integrated to an optimized ideal . Many organizations are still struggling to achieve Level 3 – Scenario B .

The technology is available, and the benefits of reaching this level substantial . For one, a unified, end-to-end approach to managing and using data drives information efficiencies that reduce costs while delivering better decisions – faster . You gain competitive advantage by automatically embedding information, analytics and business logic at the point of decision .

2 Davis, Jim, Gloria J. Miller and Allan Russell. Information Revolution: Using the Information Evolution Model to Grow Your Business. Wiley and SAS Business Series (Book 4), 2006.

Key TakeawaysAn information management approach:

•Appliesdataquality,datamastering and governance in a pervasive fashion .

•Coverstheentireanalyticslifecycle, from data preparation to model development, deployment and monitoring .

•Expandsdatagovernancetoinclude analytics governance .

•Facilitatescollaborationamongbusiness and IT stakeholders, and across functional areas and business units .

•Embedstheresultsofanalyticsinto operational systems to drive decisions at the point of contact or trigger automated actions .

•Supportsaclosed-loopdecisionprocess, so decisions are constantly validated or modified based on new input .

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SAS White Paper

SAS differentiates itself by offering advantages for any organization that wants to adopt an information management approach:

• SASaugmentsthetraditionaldatamanagementcapabilitiesbyintegratingadvanced analytics, for example, to “stream it, score it, store it” to pinpoint the relevant data instead of just pouring it all into the enterprise data warehouse .

• SASisuniquelypositionedwithourbreadthofproductofferings(mostnotablyadvanced analytics), combined technical and business expertise, and extensive pre- and post-sales support in both business and IT environments .

• SASInformationManagementsupportsalltypesofintegrationscenarios–operational and analytical – from front office to back office, to collaboration applications, analytics and business intelligence .

• SAShasprovenitcanhandlethevolume,varietyandvelocityofbigdata,including nontraditional data sources such as unstructured text from social media, case notes or emails .

• SASprovidesasystematicframeworktomanagethefullanalyticlifecycle,fromdata preparation to model inception to model deployment and ongoing monitoring of model results and performance .

• SASprovidestheabilitytoembedrichinformationandanalyticservicesdirectlyinto operational applications, bringing the value of data directly to the point of decision .

Ultimately, it’s about choice and evolution . If your organization needs to strengthen its core data management capabilities – to ensure that users get timely and trustworthy data – SAS has the data integration, data quality and master data management tools to do that . If your organization wants to more effectively manage its fast-growing data volumes, SAS can supercharge the existing data management framework through the power of analytics . And if your organization is ready to embrace an enterprisewide information management approach, SAS is uniquely positioned to get you there as well .

Whether for enhancing the data management foundation or evolving to a higher level of information management maturity, your goal should be to manage data as the valuable, strategic organizational asset that it is .

For more information, visit our Information Architect blog at: blogs .sas .com/content/datamanagement .

About SASSAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market . Through innovative solutions, SAS helps customers at more than 55,000 sites improve performance and deliver value by making better decisions faster . Since 1976, SAS has been giving customers around the world THE POWER TO KNOW® . For more information on SAS® Business Analytics software and services, visit sas.com .

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