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P2070-071 IBM Information Management Content Management OnDemand Technical Mastery Test

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P2070-071 exam Dumps Source : IBM Information Management Content Management OnDemand Technical Mastery Test

Test Code : P2070-071
Test title : IBM Information Management Content Management OnDemand Technical Mastery Test
Vendor title : IBM
: 38 actual Questions

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overseas industry Machines' (IBM) administration on this autumn 2018 consequences - income title Transcript | actual Questions and Pass4sure dumps

No influence found, are attempting recent keyword!foreign industry Machines corporation (NYSE:IBM) this autumn 2018 profits conference ... normalizing for the divested content material, and displays their commitment to disciplined portfolio administration. So now mov...

At regard conference, IBM launches recent items and capabilities for managing diverse clouds | actual Questions and Pass4sure dumps

IBM Corp. is stepping up its hybrid-cloud propel because it bids to turn into the go-to carrier issuer for companies that employ diverse public and private cloud structures.

the employ of “multiclouds” is becoming fairly ordinary, with the IBM Institute for industry cost estimating that ninety eight p.c of every bit of companies will undertake hybrid guidance know-how architectures via 2021. companies are doing so in an application to assume capabilities of every cloud platform’s Interesting capabilities, however they face difficulties in doing so for lack of consistent tools to maneuver and integrate discrete clouds.

That explains why IBM is including to its hybrid cloud paraphernalia and functions offerings. on the IBM believe convention in San Francisco today, the company introduced a recent Cloud Integration Platform that’s intended to develop it less complicated to roll out utility applications across dissimilar clouds. It additionally introduced recent features to inspirit manage substances throughout cloud environments and secure the information and functions that reside in them.

The IBM Cloud Integration Platform serves as the leading basis of the enterprise’s recent hybrid cloud play, connecting applications, application and functions throughout public and personal clouds and on-premises programs. The platform offers integration paraphernalia for these apps which are attainable from a single edifice environment, which means that builders requisite to write, test and comfy their code simplest once before rolling it out to probably the most suitable cloud.

the recent platform is being offered alongside recent IBM services for cloud mode and design. IBM is providing to inspirit agencies control IT substances across their hybrid cloud infrastructures. furthermore, IBM is launching a recent Cloud Advisory consulting carrier that goes even additional by means of assisting valued clientele architect their whole cloud strategies from dawn to conclusion. IBM famed groups will employ open and comfortable multicloud concepts and its Cloud Innovate formula and tools to lead customers with application development, migration, modernization and administration.

Naturally, security is another massive challenge for any enterprise adopting a multicloud strategy, and for that purpose IBM moreover introduced recent services to assist guard cloud workloads. The IBM Cloud Hyper proffer protection to Crypto provider provides encryption key administration by way of a committed cloud hardware security module according to FIPS 140-2 degree 4-primarily based technology.

“IBM is executing in its pivot in towards hybrid cloud offerings, in aggregate with the brand recent capabilities it receives from red Hat,” which it talked about remaining Fall it could acquire in a $34 billion deal, spoke of Holger Mueller, most well-known analyst and vice president of Constellation research Inc. “As such, IBM must create recent layers that abstract different public clouds and on-premises capabilities, and IBM Cloud Integration systems is doing precisely that. however to live successful, organizations additionally want services, so IBM is adding these for the administration and operation of a multicloud environments.”

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Perficient Named IBM 2019 Watson Commerce industry accomplice of the 12 months | actual Questions and Pass4sure dumps

Perficient, Inc. PRFT, +0.19% (“Perficient”), a number one digital transformation consulting enterprise serving international 2000® and other giant enterprise clients prerogative through North the usa, announced it has been named IBM’s 2019 Watson Commerce company associate of the year. The IBM Excellence Award, announced every bit of over IBM’s PartnerWorld at contemplate 2019, recognizes Perficient’s ongoing boom and relationships with key customers, and concept management around the IBM Watson client appointment Commerce platform as an fundamental share for digital transformation.

“Our mode to commerce is concentrated on crafting a journey, connecting with customers, and delivering a seamless client event throughout channels and prerogative through the enterprise, imperatives in today’s client-pushed world,” famed Steve Gatto, country wide earnings director, Commerce options, Perficient Digital. “collectively, with their shoppers, we’re remodeling corporations in a means that now not best drives extend but strengthens their standard brand, and they invariably evolve their offerings to maintain valued clientele on the actual of their online game. We’re honored to live recognized via IBM, and we’re alive to for sharing their innovative solutions every bit of over IBM believe 2019.”

Perficient Digital Takes Commerce solutions past Transactions to transform the client Lifecycle for a world diverse company

With branded manufacturers and distributors below pressure from the theatrical shift to online buying, a world diverse manufacturer sought to digitally seriously change its commerce business. In partnership with Perficient Digital, both organisations delivered optimized consumer sales, up-to-date product suggestions (PIM), and streamlined the ordering process through edifice of a B2B portal. With the implementation of IBM’s Sterling Order management system (OMS), and Perficient’s talents, the varied brand is future-proofing its enterprise to align with industry tendencies and market opportunities.

moreover, the company’s OMS will give them more advantageous flexibility in managing advanced order administration situations, greater reliability in order processing and fulfilment, and a cost discount in imposing throughout its commercial enterprise. it'll additional allow the organization to carry carrier enhancements to its customers, optimize its pricing, merchandising and run-of-the-mill deliver chain, extend income as a result of more desirable inventory visibility, and reduce fees through enhanced efficiencies in order visibility.

Perficient Digital Enhances the online client journey for a leading cloth Retailer

In a market that has traditionally trusted brick-and-mortar experiences, a number one textile and craft retailer was challenged with extending the client event online. Perficient partnered with the company to implement an IBM Watson Commerce retort that supplied up-to-date visibility of its stock and more advantageous monitoring of its product quantity, location, and availability. applying IBM Order administration, Perficient further enhanced the retort via cloud migration that presents a single view of give and demand, orchestrates order success procedures throughout purchase on-line Pickup In shop (BOPIS) and Ship-from-shop (SFS), and empowers enterprise representatives to more advantageous serve customers each in title centers and in-save engagements.

“Perficient has been deploying IBM Commerce solutions for almost 20 years, presenting end-to-end digital commerce solutions that embody assorted channels, and carry seamless and efficacious experiences across their whole enterprise,” pointed out Sameer Peera, commonplace supervisor, Perficient’s commerce apply. “With the fresh intelligence that HCL took over construction of IBM WebSphere Portal, IBM web content material administration and internet event manufacturing facility, their valued clientele proceed to engage us for assist with their digital commerce suggestions. We’re cheerful to live their go-to accomplice as they navigate the altering market panorama and deliver for his or her clients.”

Perficient competencies in action at IBM contemplate 2019

apart from its award-profitable commerce retort competencies, Perficient experts are reachable during the IBM suppose 2019 convention in booth #320 to talk about its event and lore across the IBM portfolio , especially cloud, cognitive, facts, analytics, DevOps, IoT, content management, BPM, connectivity, commerce, mobile, and customer engagement.

whereas IBM has introduced its plans to sell its commerce portfolio, the intelligence of its acquisition of red Hat additionally signaled the criticality cloud construction and start play in successful end-to-conclusion digital transformations. As an IBM global Elite companion, one among handiest seven companions with that status globally, and a pink Hat Premier companion, Perficient is neatly located to work with each agencies through this transition. And, their consultants will live accessible every bit of through IBM regard to talk about how to navigate the cloud market, share key customer success stories, and supply strategic competencies on the opportunities ahead for valued clientele.

“expertise is altering so impulsively, and enterprises should preserve tempo or face disruption,” stated Hari Madamalla, vice president, emerging solutions, Perficient. “With talents and adventure in every bit of facets of the commerce experience, to leading cloud, hosting, managed features and inspirit solutions, agencies flip to Perficient as a go-to accomplice for their digital transformations.”

be share of a couple of Perficient region remember specialists and their customers as they latest throughout six IBM feel periods, together with:

As a Platinum IBM industry companion, Perficient holds more than 30 awards throughout its 20-12 months partnership heritage. The enterprise is an award-profitable, certified utility value Plus retort company and one of the crucial few companions to pick up hold of dozens of IBM expert degree software competency achievements.

For updates every bit of through the event and after, link with Perficient experts on-line by way of viewingPerficient and Perficient Digital’s blogs, or succeed us on Twitter@Perficient and @PRFTDigital.

About Perficient

Perficient is the leading digital transformation consulting company serving international 2000® and commercial enterprise consumers prerogative through North the usa. With unparalleled tips technology, management consulting, and creative capabilities, Perficient and its Perficient Digital company bring imaginative and prescient, execution, and cost with mind-blowing digital adventure, enterprise optimization, and industry solutions. Their work permits customers to extend productiveness and competitiveness; grow and beef up relationships with valued clientele, suppliers, and partners; and in the reduction of fees. Perficient's professionals serve clients from a network of places of work across North the us and offshore places in India and China. Traded on the Nasdaq international select Market, Perficient is a member of the Russell 2000 index and the S&P SmallCap 600 index. Perficient is an award-profitable Adobe Premier companion, Platinum even IBM industry associate, a Microsoft national service issuer and Gold CertifiedPartner, an Oracle Platinum partner, an advanced Pivotal ready companion, a Gold Salesforce Consulting companion, and a Sitecore Platinum companion. For greater guidance,

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one of the vital statements contained during this information liberate that don't seem to live simply feeble statements argue future expectations or state different forward-searching assistance regarding pecuniary consequences and enterprise outlook for 2018. those statements are subject to prevalent and unknown risks, uncertainties, and different components that might cause the exact consequences to vary materially from those pondered via the statements. The forward-searching information is based on management’s present intent, belief, expectations, estimates, and projections concerning their enterprise and their industry. develop sure you live conscious that those statements best replicate their predictions. genuine activities or effects may fluctuate significantly. essential components that may trigger their specific outcomes to live materially different from the forward-looking statements consist of (however are not confined to) those disclosed beneath the heading “possibility components” in their annual record on contour 10-k for the year ended December 31, 2017.

View supply version on

source: Perficient, Inc.

Ann Higby, PR supervisor, Perficient,

Copyright enterprise Wire 2019

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The Data Lifecycle: Data Management in the Enterprise | actual questions and Pass4sure dumps


In the modern enterprise, efficacious employ of data to speed operations and help industry results is a fundamental competency. Yet, the complexity, diversity and available solutions hold conspired to develop data management a significant region of complexity and even risk. This brief summarizes the main problems facing enterprises, especially emerging ones. Then, it reviews the types or sources of data and discusses the ways in which the diversity of data can live handled. Finally, it outlines some practical approaches.

In this brief, no attempt is made to argue database technology with detail; instead the focus is on providing a broad perspective about enterprise data and to present a framework. With such a context set, conclusion makers can better understand how to direct their organizations’ priorities.

The Data Challenge for Emerging Enterprises

Data has become the lifeblood of most enterprises, both small and large. Data about users, customers, operations, resources and other activities in a company, inspirit an enterprise add value, maintain competitive handicap and grow. Data comes from many sources, but most importantly, from an enterprise’s own, usually proprietary listening systems, such as its website, customer convene centers, product instrumentation, sales data and so on. It moreover comes from third parties or intermediaries, such as agencies, tracking systems, and others.

The problems with every bit of this data boil down to four basic issues — the 4 V’s:

  • Volume: The quantity of the data requires designing confiscate repositories to consume or manage that data with confiscate performance or service even standards. Enterprises find that technologies which often work with smaller data sets might not scale up in a cost-effective way or sometimes at all.
  • Variety: Data might live unstructured (for example, raw text, Twitter feeds, audio, etc.), semi-structured or structured. In order to derive insights from it, the data must either live transformed to give it a coherent structure or managed in an entirely different way using unstructured approaches.
  • Veracity: Collecting data is increasingly automated, but there still are potential problems with the correctness of the data, such as quality, missing values, redundancy, pedigree, and so on. In most cases, it is necessary to develop processes for data cleansing and enhancing data quality.
  • Velocity: Enterprise data flows in streams that can extend and decrease, sometimes quite dramatically. When the flood accelerates, it may live difficult for legacy systems to hold up; when the flood is lessened, legacy systems can live prohibitively expensive to operate. More importantly, as the velocity of data changes, the rate at which insights can live create should moreover change, allowing for faster response times.
  • Sources of Data Transactional Data

    Since the emergence of industry computing more than 50 years ago, the data generated by applications in finance, manufacturing, commerce, industry operations, etc., has risen dramatically. Most companies are extensive users of industry applications that interface with ERP, CRM, SCM and other systems. The data generated reflect the ebb and flood of a business’ activities as it interacts with customers, vendors, partners, etc. For example, one typical benevolent of transactional data is sales orders from an online Ecommerce website.

    This data is considered transactional since it arises from the operations of the business. The bulk of transaction data is usually organized into relational databases, which are highly structured and defined by a schema. Some transactional data can live unstructured. The most common problem with transactional data is its volume and velocity. A requirement for transactional data is that it live collected from industry operations efficiently with minimal (or in some cases no) error.

    Unstructured Data

    Unstructured data, though prevalent, is a relative newcomer to the data management scene. In the beginning, it was hardly considered worthwhile (or even possible) to collect, since storage was prohibitively expensive to expend on something of uncertain value. As the cost of permanent storage declined, especially after the 1990’s, cost was no longer the prime obstacle. However, the value of the data was still unclear. With the emergence of standards and tools to organize the data, this constraint too was lifted. One of the first and still most power tools to bring out the value of unstructured data, of course, was search.

    Unstructured data is more accurately described as data that is both potentially schema-less and schema-ful. Schema-less data is often visualized as key-value pairs; the main characteristic is that they don’t conform to a predefined, fixed pattern or schema. The main handicap of schema-less data is the dynamic way in which the data store can live constructed, adding more data types as they are encountered. An example of schema-less data might live sentences in a verbatim response to a survey. JSON is a celebrated schema-less data structure standard.

    Schema-ful data is often associated with relational databases, but could comprehend schema-driven data structures such as XML/XSD (perhaps a bit confusingly, XML without a corresponding XSD could live considered a schema-less data structure). An example of schema-ful data is a data structure such as customer (which could comprehend name, address, phone number) or order (which could comprehend an order number, a reference to a product and other information). Schema-ful data requires more care in designing, but can better support queries and transactional processing prerequisites, such as consistency and better support functions such as the joining of data sets. every bit of the four V’s are at play with unstructured data.

    Warehouse Data

    Data warehouses are built from highly structured, schema-ful data as well as from unstructured schema-less data. Typically, the transactional data from industry systems are extracted, transformed and loaded (a.k.a. ETL) into a warehouse. In some cases, it may live sufficient to just extract and load, potentially delaying transformation to a later stage (ELT). In any case, the data is moved from one or more sources to a specially designed database, usually designated a warehouse (though there are mezzanine concepts such as data marts). Usually, the data must live massaged, cleaned and summarized before it can live stored in the warehouse.

    With warehouse data, the key issues are variety, veracity and velocity.

    Backup Data

    Business operations transaction data and warehouse data must live backed up and stored in a safe environment so that it can live reconstituted should the requisite arise. The primary judgement for managing backup data is for industry continuity and calamity recovery (BCDR). Equally well-known is the aptitude for an enterprise to efficiently employ this data to restart operations in the event of a catastrophic failure.

    Generally, every bit of the data that an enterprise generates or transforms as share of its operations should live backed up. This is primarily a concern when the data are managed on-premise, though merely storing the data in the Cloud may not live sufficient to fill BCDR requirements. The key problem with backup data is its volume.

    Managing the Diversity of Data

    Although the sources or types of data often give climb to the best means to manage it (e.g., structured data typically belongs in relational databases), in practice, a heterogeneous approach is most common. In addition, few organizations hold the luxury of starting from a immaculate slate; often legacy data sources must live supported and sustained.

    Relational Databases

    Since their emergence in the early 1980’s relational databases (RDB) hold become the standard database model, supplanting network databases and file-based systems. They are example for transaction processing inherent in industry operations. To facilitate this, an RDB is designed to reflect the semantics of a industry problem — that is, it acts as a data model of the industry world. For example, a RDB might model a industry with tables to represent industry entities such as customers, sales, orders, products, and so on.

    For various reasons, the semantic representation must then live “normalized” to liquidate any redundancy in the data model (i.e., the selfsame data elements represented in more than one place). By doing this normalization, performance can live improved and data integrity enhanced (i.e., reduce the possibility that data can become discordant under actual world conditions).

    Relational databases are example for applications such as Ecommerce, website content management, ERP, CRM and countless other industry solutions. There are numerous strong RDBs in the market: Oracle Database, Microsoft SQLServer, MySQL, IBM DB2, Ingres and others.

    Persistent Caches

    It’s conceptually easier to architect applications in which databases emerge monolithic and accessible on demand, with no latency or other dependencies. Unfortunately, actual world applications are typically highly distributed, perhaps because users are not simply in one (or a few) locales or the hosting strategy is deliberately decentralized. In addition, the nature of an enterprise’s application may require accessing data that is inherently dispersed, such as summarizing data from different company offices or stores.

    To ensure towering performance of online systems, caching is an approach that has no conceptual limit. In fact, depending on the expected lifespan of a piece of content, caches can commence at the user’s device and only stop inside the server responding to a request. For example, static content enjoy logos that change infrequently can live cached in a user’s browser; on the other hand, stock quotes or intelligence headlines can live cached on a content server for many to discern before being periodically refreshed (i.e., from a few seconds to a few hours).

    A well-designed application necessarily takes into account a caching strategy, not only to deliver timely content to applications, but moreover to write through data that might live changed in the actual world. In addition, most applications will hold more than one cache; managing how to update and synchronize these caches with each other and the master data store are key technical issues. Thus, the design, build and test of caching solutions are at times difficult and quite challenging. It is highly subject on industry needs and many other factors.

    Examples of commercially available caches are Amazon ElasticCache, Redis, Cassandra.

    NoSQL Databases

    NoSQL databases are often contrasted to relational databases. Indeed, they are better suited for unstructured or less structured data. As famed in the discussion on Unstructured Data, NoSQL databases are not synonymous with schema-less (and therefore non-relational) databases. NoSQL databases are best suited for relatively simple data models and where the application puts a premium on scalability, performance and availability. This is partly because NoSQL databases allow the efficient storage (and retrieval) of data, usually indexed with a system of lookup keys. NoSQL databases are often optimized for particular data types, such as columnar, document, key-value, graph and hybrid. Examples of such databases comprehend DynamoDB (key-value), MongoDB, MemcacheDB (both hybrid).

    Relational Warehouses

    Data warehouses are typically constructed from relational databases. The relational model is supple and well suited for such applications. A central concept in relational warehouses is to develop the data readily available for rapid retrieval, but only along well-defined, prescribed lines. In this respect, the amend design of the warehouse is essential at the outset, since it can live very difficult to recoup from a design oversight or error. In addition, the data is usually not highly normalized, as it is in relational transactional models.

    Warehouses are built around the facts that underlie the industry to live modeled, such as sales, purchase orders, shipments, and payments. Each of the facts is organized into its own fact table. Associated with these facts are measures, which together characterize or fully narrate the facts. For example, the sales fact table might mention to the related measures such as products, customer, sales persons, sales amount, geography, etc., every bit of of which were aspects of sales. It’s not uncommon for facts to hold 20 or 30 related measures each.

    Identifying the amend facts and measures is a key share of the challenge and skill in designing a data warehouse. Warehouse data is discrete from transactional data in that it is not usually a direct output of industry operations. The requirement for warehouse data is to live easily accessed and efficiently pulled into reports or compiled into other insights.

    Business Continuity and calamity Recovery (BCDR)

    BCDR is a knotty and growing realm that cannot live easily summarized. However, the main concepts are to define an enterprise’s Recovery Point Objective (the maximum period for which data may live lost), its Minimum Acceptable even of Service (the service even below which the industry effectively is out of operation), and its Maximum Acceptable Outage (longest duration for loss of operations). Once defined, the recovery processes, backup and recovery strategy can live determined.

    The advent of Cloud services can address an enterprise’s needs. However, unless the service is fully managed, has its own BCDR strategy and is transparently tested, it may not live sufficient to ensure an enterprise’s own BCDR needs are met. Sometimes, it’s necessary to create database snapshots and propagate them to different geographic regions within a Cloud service provider’s network in order to explicitly ensure DR backups. Amazon AWS and other Cloud vendors provide infrastructure to support BCDR. But it’s incumbent on the enterprise to understand its industry needs and to design and build a system of both process and technology to support BCDR — and to regularly audit, test and update the BCDR system.

    Practical Approaches

    Most enterprises are heavily invested in and subject on transactional data generated by industry applications and stored transiently in caches and more persistently in relational databases. Increasingly, an enterprise’s data comes from a by-product of its business, such as from listening systems in social media or the Internet of Things (IoT). every bit of of this data has a role to play in helping the enterprise develop insights about its customers or business; one of the most common ways to achieve this is to marshal and summarize the transactional and other data into warehouses for analysis and reporting. In the enterprise information lifecycle, data is generated during the course of business, but then is summarized and analyzed to provide insights for the enterprise, thereby improving industry operations:

    When industry data is managed in this way, internal users of the data can then weigh on a single, dependable source of truth upon which to develop decisions, build plans and grow the business.

    Hosted Databases

    While enterprises hold been comfortable with on-premise database servers, especially for line-of-business applications, they hold gradually moved their RDBs to the Cloud. The first step in this evolution was on-premise virtualization, which helped enterprises wean themselves away from a hardware-centric orientation. Then, shared data centers (or, co-location facilities) further broke down the appall of losing direct physical control of hardware.

    Virtualization in the Cloud was then a relatively painless transition, whereby the enterprise would still ply database software updates, replication, capacity planning, backups, and patches, but not worry about hardware and infrastructure. The next step in this steady progression away from direct control is “Database as a Service” — a fully managed service. With DaaS, the ultimate remnants of the on-premise paradigm are shed and the enterprise focuses on the application. In this category, Microsoft Azure SQL, Amazon AWS RDS and Google Cloud SQL every bit of hold fully hosted DaaS.

    Hosted Caches

    Caches hold always been a critical share of practical computer systems architecture. The first caches were of course implemented in hardware. Indeed, the organization of computer systems could arguably live viewed as the efficient management of successively larger caches, from registers in a microprocessor to virtual caches in applications to content distribution networks to archival storage.

    Designing an application around fast, highly available caches is a typical requirement for knotty systems today. In the on-premise era, such an option was largely outside the achieve of any but the most sophisticated and well-resourced organizations. In recent years, open source projects such as Redis and MemCached hold brought this technology to wider orbit of developers. Further, the availability of scalable, hosted caches such as Amazon’s AWS ElastiCache or Microsoft Azure Redis hold made caching relatively easy to adopt. Indeed, no application developer should live satisfied with a design until considering how a database cache can alleviate bottlenecks and help performance.

    Scalable Warehouses

    Once the data warehouse is designed and deployed, the practical challenge is scaling it efficiently and maintaining towering levels of performance as it grows and industry needs evolve. For an enterprise that has users in different locales, replication globally can live an added issue. In addition, optimizations for warehouse-oriented applications are usually needed to help performance (sometimes by orders of magnitude) both in terms of time and cost. These optimizations comprehend columnar storage, zone maps and data compression. Parallelism and distributed computing are moreover often required at scale. These are complex, ever-evolving technologies for any enterprise to master. Warehouses delivered as a service can alleviate some of these challenges; among such services are Amazon AWS Redshift, Google Cloud BigQuery or Microsoft Azure SQL Datawarehouse.


    Data can transform the modern enterprise. To attain this transformation, enterprises must develop a data strategy and an architecture that manages the lifecycle of data as it wends its way through the enterprise. The problems posed by data orbit from purely transactional issues of capturing actual time events efficiently to processing data so that it yields information and then insights for the organization. Fortunately, the increased complexity of data management at scale can live reduced by leveraging the infrastructure and investments of the leading SaaS providers. However, even such an approach requires considerable expertise and a abysmal appreciation of the available technologies so as to develop the optimal tradeoffs.

    Mapping critical lore for Digital Transformation | actual questions and Pass4sure dumps

    Companies in almost every industry these days are trying to paddle digital. When digitalization is done in the context of a company’s strategic knowledge, powerful growth opportunities can live uncovered. One way to effect it is by using a strategic knowledge-mapping framework that Ian MacMillan and Martin Ihrig had discussed in a Knowledge@Wharton interview in 2015. In this paper, co-authored with Jill Steinhour, Ihrig and MacMillan define how the knowledge-mapping framework can shed light on recent strategic changes at Adobe, a software firm headquartered in San Jose, Calif.

    Ihrig is a clinical professor and associate dean at recent York University, an adjunct professor at Wharton, and the president of I-Space Institute. Steinhour is Adobe’s director of industry strategy and marketing for towering tech and B2B. MacMillan is a management professor at Wharton.

    (Knowledge@Wharton spoke with Ihrig, Steinhour and MacMillan about their paper. Listen to the interview using the player above.)

    Firms are investing millions to digitalize their businesses, hoping for a digital transformation that will result in increased revenue, cost reduction, improved customer satisfaction and enhanced differentiation, and ultimately mitigation of the risk of digital disruption. However, going digital is more than vast data – simply capturing and analyzing great data troves in isolation leaves a lot of strategic opportunities on the table. When digitalization is done in the context of your company’s strategic knowledge, powerful growth opportunities can live uncovered. The employ of the digital data needs to live guided by abysmal insight into the company’s critical lore assets: its core competencies, intellectual property rights, market and industry comprehension, and customer understanding and expectations.

    Strategic lore mapping helps to uncover these critical lore assets, providing the context for discovering the most promising digitalization strategies. It helps to identify those lore assets that digital transformation can leverage, or illuminates gaps in an organization’s lore network. A lore map features two dimensions: the structure of lore (how codified is an asset, ranging from deeply tacit to highly codified) and the diffusion of lore (how many parties hold access to it). Digitalization structures lore (moving it up the lore map), which then makes it possible to develop strategies to share this lore and thereby create and capture value from this lore diffusion (systematically affecting it to the prerogative of the map).

    MacMillanIhrigSteinhourFigure 1

    Figure 1: Strategic lore Map

    We recognized that the application of the framework can illuminate recent strategies at Adobe. Through interviews with Adobe executives and key stakeholders, they researched the highly successful taste of Adobe in edifice a radically different rapid growth industry model. Below, written as a stylized case, they employ the map to illustrate how the strategic deployment of lore helped Adobe address three high-impact digital transformation challenges. Specifically, they narrate how Adobe:

  • Produced significant value by recognizing and leveraging the tacit lore of subject matter experts within the existing organization and gained through an acquisition;
  • Created credibility, momentum and substantial growth in their targeted markets by diffusing tacit expertise to customers, consequently generating shared value; and
  • Recognized and deployed insights created by data science and diffused it to current and future customers to earn and capture value for the firm.
  • Reinventing a industry by leveraging tacit lore of subject matter experts

    As described in Harvard industry School’s case study Reinventing Adobe, Adobe’s CEO Shantanu Narayen and his senior executives set a strategic goal of expanding and transforming Adobe’s industry through a multi-pronged approach of growing organically within the company’s existing business; acquiring companies with strengths in adjacent categories; and shifting the industry to allow Adobe to paddle beyond the company’s desktop heritage while edifice a predictable revenue stream through subscription-based offerings.

    The executive team saw significant headwinds for the creative business, which included the company’s flagship Creative Suite software products. Existing customers of Creative Suite (creatives) were largely satisfied with the capabilities of the versions of Creative Suite they had purchased and were not motivated to upgrade to newer versions, which had a premium cost tag.  At the selfsame time, the growth of recent customers was anemic.  Younger creatives, an well-known source of recent growth, were especially challenged to pay the cost for the software and their needs were evolving rapidly.  They were increasingly mobile, wanting connected workflows, faster innovation and more value. Yet, the perpetual-license model of software development limited the company’s aptitude to deliver innovation to just once every 18 to 24 months, making it tough to hold pace with the evolving needs.

    Senior strategists at Adobe did an analysis and create most recent software companies were being founded with a cloud-based subscription model, and companies with towering recurring revenue weathered the pecuniary storm of 2008-2009 much better than those without. Adobe brought together internal subject matter experts in pricing and software sales and strategy to pilot a subscription-based pricing model for its Creative Suite software in Australia in March 2008.  Tacit lore (figure 1, lower left quadrant) in the contour of abysmal employee expertise about pricing, product value, and customer behavior were cultivated through the pilot project and formed the basis of the lore needed to support a subscription model.  Learnings were institutionalized (moving from lower left quadrant to upper left, figure 1) and led to the announcement in April of 2012 of Creative Cloud, a subscription based cloud offering of Adobe’s creative software.

    The 2008 experiment had demonstrated that a recent subscription model could attract recent users and extend the pace of upgrades by lowering the barrier to entry.  But to attract a broader customer foundation required the Creative Cloud to provide on-going service value in the cloud, mobile apps, and regular product updates throughout the year. “The subscription model allowed us to contemplate differently about their business. It enabled us to bring recent value to customers and innovate whenever and wherever it made sense,” said Dan Cohen, vice president, Digital Media Strategy, formerly the head of Corporate Strategy.  Based on customers’ changing needs and seeing entire industries shift to the recent “always on” paradigm, executives were confident that a shift to a Cloud/subscription model made sense for the business.

    While changes were underway in the creative business, Adobe moreover pursued a growth strategy targeting the enterprise software market. Narayen and his leadership team were serious about affecting into a significantly different market space. This required a “DNA shift” and the acquisition of recent strategic lore assets.  In 2009, Adobe bought Omniture, an online marketing and web analytics company whose offerings were entirely cloud based. Adobe executives saw a compelling value: by combining “art” as driven by its industry-leading creative software and the “science” gained through Omniture’s industry-leading web analytics, Adobe could address the emerging needs of marketers – a rapid growing and underserved market.  While some analysts were initially skeptical of the acquisition, customers understood the value of combining content and data to optimize marketing performance online.

    In addition to this unique value proposition, Omniture’s software-as-a-service (SaaS) industry model involved selling and marketing directly to corporations and provided mighty insight into how to develop a direct, enterprise go-to-market industry – a contrast to Adobe’s industry selling to individual creatives through resellers and

    Key to the successful integration of the Omniture business, Adobe embraced Omniture’s industry model and culture, deliberately treating it as a strategic learning opportunity. In particular, the Adobe team systematically captured and developed the tacit lore of the marketing and sales experts from Omniture (figure 1, from lower prerogative to lower left quadrant).  Adobe did not simply buy customers and revenue; it recognized Omniture as a leader and worked to retain the firm’s expertise, seeing it as a critical component of long-term success.

    “Moving into the Digital Marketing industry provided us valuable insight into how to speed a cloud business,” said Gloria Chen, vice president and Chief of Staff to the CEO. “Enterprise sales, relationship marketing, technical operations, and even applying [Omniture’s tacit] digital marketing practices to their own marketing – they knew there was a lot to learn.”

    At that time, the whole notion of helping digital marketers drive performance through the employ of marketing measurement was nascent.  The Omniture acquisition helped Adobe extend its leadership status beyond the “creative/Photoshop company” to being widely acknowledged today as the leader in Digital Marketing by industry analyst organizations enjoy Forrester, Gartner and IDG.

    While it would live inaccurate to notify that the acquisition of Omniture precipitated Adobe’s paddle to the Cloud, the acquisition did bring lore and expertise that added tremendous value to the transformation of the creative business.  Adobe’s proficiency in acquisition integration moreover played an well-known role.  The company had a strong track record of retaining talent post-acquisition and, in this case, gave Omniture employees latitude and autonomy while leveraging embedded tacit knowledge. Learning and lore diffusion was achieved by accepting and supporting the newly acquired talent and processes. By carrying out this transition quickly and integrating the knowledge, Adobe gained significant market share and differentiation.

    Creating momentum in the market by sharing tacit experience

    The exercise of packaging up proprietary (undiffused) lore and making it widely available outside of the company (diffused) is a recurring theme in Adobe’s history, and is a marked characteristic of other digital leaders, such as Google with its Android platform. The purposeful diffusion strategy behind Adobe PDFs and the free distribution of the Adobe Reader are examples, but the strategy of sharing proprietary information, in particular the movement from the lower left quadrant of the map (tacit undiffused knowledge) to the upper prerogative (explicit diffused knowledge), was a mechanism used more recently by Adobe, but with a very different objective.

    One of Adobe’s goals was to become the leading digital marketing technology vendor (offering a replete spectrum of digital marketing technology) and rapidly build significant market share.  However, most customers associated Adobe with Acrobat and Photoshop and there was exiguous awareness of its digital marketing business. Meantime, entrenched competitors with abysmal pockets, such as IBM, Google and Oracle, were moreover expanding their digital marketing technology offerings, which could potentially threaten Adobe’s aptitude to achieve its desired market share.

    Adobe’s CMO Ann Lewnes was a champion of digital marketing practices, foreseeing the shift from traditional marketing practices to digital – a paddle that most marketing organizations are now fully embracing.  While Adobe’s marketing organization had already been using Omniture’s products to measure consumer behavior on, the acquisition accelerated the process of transferring the tacit marketing analytics lore from the Omniture team to the broader Adobe organization.  Under Lewnes’ direction, marketing made moves to digitalize the industry by reallocating the lion’s share of advertising dollars to digital domains (such as display ads, social and search), while the IT organization helped replatform Adobe’s websites around the world so that marketing could measure the impact of the digital spend.   Marketing and IT could live thought of as flip sides of the coin that helped paddle the company toward its own transformation.  Both were internal clients of Adobe software: using web content management and marketing analytics and measurement technology.

    The employ of the digital data needs to live guided by abysmal insight into the company’s critical lore assets: its core competencies, intellectual property rights, market and industry comprehension, and customer understanding and expectations.

    Adobe Marketing and IT were, essentially, “Customer Zero” – developing internal competencies in technology implementation, marketing operations, digital marketing, organizational design, and the quantification of the contributions stemming from the employ of these Adobe digital marketing solutions. This was of significant interest to customers, who were challenged to undertake the selfsame digital transformation themselves.  Adobe’s sharing of this lore with external audiences was, at first, ad-hoc and opportunistic.  However, they soon realized that codifying this internal lore and disseminating it publically (movement from the lower left to the upper prerogative of the map) would provide a boost to Adobe’s credibility, and extend awareness of Adobe’s offerings.  The Marketing team became evangelists, sharing best practices, speaking at conferences and advising companies and marketing organizations as they struggled to develop the shift to digital.  This mainly focused on “people, processes and technologies.” They codified their learnings in on-demand videos to inspirit scale the achieve of this learning content.  In parallel, on the IT side, Adobe formed the Adobe@Adobe team to evangelize the employ of Adobe technology to address marketing employ cases.

    Ron Nagy, Sr. Evangelist Adobe@Adobe, develops employ case narratives through collaboration with customers, internal practitioners, product marketers and technologists.  He’s a firm believer in having a team that can articulate how Adobe solutions address common customer challenges, as well as the more aspirational visionary scenarios.  These stories are curated from both internal and external sources and systematically evolve over time.

    A key input to the Adobe@Adobe efforts is Adobe’s internal marketing technology forum which brings together marketing, IT, product marketing and engineering teams for several days to evaluate and argue topics that are selected via an internal voting process.  This internal forum invites constructive conversations where internal users of the products share best practices and articulate areas for improvement.  Product marketing and engineering argue future products and the evolution of existing products. This forum is a key input to the narratives that Nagy and the team leverage and at the selfsame time, it is an institutional duty that allows marketing practitioners to resolve product usage challenges through sharing of best practices, later providing feedback into product teams to optimize the development roadmap and to inspire recent product development.

    Capturing and sharing the lore of Adobe practitioners, who possess abysmal operational knowledge, is moreover a critical aspect of the program. However, Nagy notes that some translation of that message is needed: “If you are starting a program – there hold to live individuals with lore of the tech, what is possible, and the business.  You requisite to assume the input from practitioners and other sources then effect the translation to what is relevant to the marketplace.” These Adobe@Adobe employ cases are shared broadly to internal and external audiences. While the program aggregates and curates the lore of Adobe practitioners, it does not remove subject-matter experts from the process. Rather, developing the voice of the practitioner is moreover a focus of the program: those practitioners with interest and aptitude are frequent presenters at both internal and external events representing the practitioner point of view.

    Note that the Adobe@Adobe team is share of the IT organization, not share of sales; this deliberate separation, to bring an objective perspective. However, the marketing department, ecommerce department and the industry unit are moreover documenting their processes sharing their own unique learnings with the industry. Surfacing ones’ internal best practices or showcasing another organizations’ digital transformation can serve to lead a firm’s own transformation.

    By capturing and organizing tacit lore (the confluence of technical and product knowledge, fueled by employee lore and enthusiasm, and guided to relevance by market needs) and then orchestrating the diffusion of that knowledge, Adobe has developed a masterful customer appointment and capability demonstration “machine” that goes well beyond the traditional marketing approach.

    Creating momentum in the market by sharing structured knowledge

    Adobe Digital Index (ADI) is yet another example of how Adobe has deliberately diffused proprietary lore assets into the public domain, in the process creating value for Adobe and customers alike.  lore in this case, are the insights derived from codifying an aggregate view of billions of digital data inputs (structured upper left quadrant of the lore map) from which the ADI team identifies emerging digital trends or forecasts future events. These are then shared broadly to external audiences.  For example, for the past two years, the Adobe Digital Index predicts which movies will live blockbusters, based on the analysis of commentary in social media.  The accuracy of their predictions (36 of 37 predictions were spot on) resulted in a convene from an executive from a major motion picture distributor who was keen to yield similar predictions.  “This is exactly what they hope to achieve” commented Tamara Gaffney, Director and Principal Analyst “we want to educate others on the possibilities of data science through meaningful insights.”  Another profit is that ADI findings are syndicated broadly, thereby extending Adobe’s market achieve which contributes to a significant extend in awareness of Adobe’s “big data” expertise.  For example, Adobe got mighty exposure with over 7,000 press stories including capable Morning America, Today Show, CNBC Squawk Box and much more by identifying the mediocre daily discounts for toys and electronics this past holiday season.

    Digitization for the sake of digitization is not the way to go. abysmal attention needs to live given to what digitization of what lore should live undertaken and why.

    Extracting meaningful insights from vast data troves is a challenge which ADI attacks with a methodical approach starting with the monitoring of standard digital metrics such as web and mobile traffic, video consumption, bounce rates and conversions.  “If they detect any anomalies then they dig deeper.  They inquire ourselves questions and create hypothesis that they test through further analysis,” says Gaffney.  For example, ADI noticed that online ecommerce revenues on Thanksgiving are growing at a faster rate than on Black Friday. Their hypothesis was that promotions and discounts are now being offered by retailers earlier in the Holiday season.  A subsequent analysis on pricing levels revealed that the greatest overall discount was on Thanksgiving, when historically it has been on Black Friday.  Gaffney notes, “The effect may not live causal, but there is a strong correlation that suggests that timing of promotions is a prominent factor.”

    The way that ADI is managed and the expectations of the team are important: the group has been set up as an entrepreneurial team with no Adobe P&L responsibility and softer success metrics enjoy thought leadership and earned media vs. conversion and sales.  The team reports into Marketing and is allowed to experiment, which allows them to live innovative and assume risks and sometimes fail.  Gaffney states, “We hold a few definite measures of success, such as total number of press articles, size of circulation, syndication by well-known publishers enjoy Forbes, WSJ,” but equally well-known are the door openers or the conversation starters that stem from ADI findings.  Gaffney concludes, “ADI reports on well-known trends and indicators of future trends, which are significant topics for their target audiences, and it eases the way for their sales teams and executives to engage with their current and future customers.”

    Whether the strategic intent of digital transformation is to meet customers’ expectations, to innovate, or to enable efficiencies, organizations increasingly are recognizing that they requisite to transform their businesses in order to participate in the recent digital world order or risk becoming irrelevant. But digitization for the sake of digitization is not the way to go. abysmal attention needs to live given to what digitization of what lore should live undertaken and why.  This is determined by mapping your major lore assets and then thinking through what the benefits are of strategically structuring and diffusing such major assets across the map.  The Adobe examples set forth above illustrate three powerful strategic outcomes from such moves:  to succeed in an adjacent market by mobilizing tacit lore gained through acquisition; to build critical customer credibility by diffusing tacit lore to and with customers; to hugely extend customer awareness and add value through codification and aggressive diffusion of proprietary knowledge.  These three strategies are illustrative, but far from exhaustive.  Every mapping of lore assets will present its own set of context-specific digitization opportunities.

    Leading your firm in this recent digital reality requires a thorough understanding of every bit of of your critical lore assets, both definite and tacit. Equipped with a strategic lore map, corporate leaders can craft a competitive strategy and develop digital transformation a reality.

    Don't perceive down: The path to cloud computing is still missing a few steps | actual questions and Pass4sure dumps

    Don't perceive down: The path to cloud computing is still missing a few steps

    Agencies navigate issues of interoperability, data migrations, security and standards

  • By Rutrell Yasin
  • Mar 12, 2010
  • The federal government is affecting to the cloud. There’s no doubt about that.

    Momentum for cloud computing has been edifice during the past year, after the recent administration trumpeted the approach as a way to derive greater efficiency and cost savings from information technology investments.

    At the behest of federal Chief Information Officer Vivek Kundra, the common Services Administration became the headquarters of gravity for cloud computing at civilian agencies, with the launch of a cloud storefront,, that offers business, productivity and social media applications in addition to cloud IT services.

    High-profile pilot programs generated more buzz about cloud computing, including the Defense Information Systems Agency’s Rapid Access Computing Environment and NASA Ames Research Center’s Nebula, a shared platform and source repository for NASA developers that moreover can facilitate collaboration with scientists outside the agency.

    Related stories

    NASA explores the cloud with Nebula

    Cloud computing has appeal for Web applications

    But the journey to cloud computing infrastructures will assume a few more years to unfold, federal CIOs and industry experts say.

    Issues of data portability among different cloud services, migration of existing data, security and the definition of standards for every bit of of those areas are the missing rungs on the ladder to the clouds.

    “Cloud computing is not a technology that can just live turned on overnight,” said Peter Tseronis, deputy associate CIO of the Energy Department and chairman of the Federal Cloud Computing Advisory Council.

    “We spent a lot of ultimate year defining what the cloud is, what are the various delivery models, deployments and characteristics,” Tseronis said. “We still continue to requisite to effect that."

    The government defines cloud computing as an on-demand model for network access, allowing users to tap into a shared pool of configurable computing resources, such as applications, networks, servers, storage and services, that can live rapidly provisioned and released with minimal management application or service-provider interaction.

    The three delivery models include:

  • Software as a service (SaaS), which provides industry applications running on a cloud infrastructure and accessible on a client device via a Web browser.
  • Platform as a service (PaaS), which is the deployment via the cloud of user-developed applications, such as databases or management systems.
  • Infrastructure as a service (IaaS), which is the provisioning of computing resources for users on an as-needed basis.
  • The Federal Cloud Computing Advisory Council provided a governance structure ultimate year to disseminate information about cloud computing and its concepts, benefits and risks. The council will continue to raise awareness about the governance structure among agencies, Tseronis said.

    But some agencies remain confused about the cloud, Tseronis said.

    Agency managers are wondering about security and data privacy risks associated with the cloud. Are there procurement barriers? What is better: a public or private cloud? How effect you set up a service-level agreement? What are the data interoperability and portability issues?

    Security Struggles

    The Bureau of Alcohol, Tobacco, Firearms and Explosives hasn’t launched a specific cloud project, but officials hold been evaluating the benefits and risks for more than a year because a paddle to the cloud seems enjoy a natural fit. “We are already fairly outsourced in terms of their IT infrastructure,” said Rick Holgate, the bureau's CIO.

    ATF has dedicated hardware and physical space in two data centers — one government-owned and operated by a contractor, the other owned and operated by a contractor.

    However, security is a major concern. Most agencies hold concerns about data separation because they want to avert a commingling of data with tenants in other environments. And they requisite access restrictions on data to develop sure cloud hosting providers or other tenants don’t inadvertently or intentionally pick up access to sensitive data.

    “We are every bit of struggling in the federal space with the prerogative security model around the truer cloud provision capability,” Holgate said.

    Despite some progress toward resolving those issues, more work is necessary to hash out security requirements that federal agencies requisite to succeed to ensure that sensitive but unclassified and classified information is secure, Holgate said.

    First, cloud providers requisite to understand government security requirements and deliver services that fill those requirements. Microsoft recently created a federal version of its industry Productivity Online Services for the cloud, which is one example of how vendors could inspirit address security requirements, he said.

    On the federal side, “we requisite to probably effect a better job of articulating what those requirements are from a security perspective,” Holgate said.

    The federal government still has a fragmented approach to security, he said. “We don’t hold a single, unified — to my lore — federal voice that everyone has agreed to and signed up to as the authoritative version of what the federal government considers sufficiently secure in a cloud-type environment,” he said.

    GSA and the National Institute of Standards and Technology hold been addressing security requirements, and the Justice Department tackled the problem at a department level, Holgate said.

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    ESPA [1 Certification Exam(s) ]
    Esri [2 Certification Exam(s) ]
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    McAfee [8 Certification Exam(s) ]
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    mySQL [4 Certification Exam(s) ]
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    Nokia [6 Certification Exam(s) ]
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    Novell [37 Certification Exam(s) ]
    OMG [10 Certification Exam(s) ]
    Oracle [279 Certification Exam(s) ]
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    Pegasystems [12 Certification Exam(s) ]
    PEOPLECERT [4 Certification Exam(s) ]
    PMI [15 Certification Exam(s) ]
    Polycom [2 Certification Exam(s) ]
    PostgreSQL-CE [1 Certification Exam(s) ]
    Prince2 [6 Certification Exam(s) ]
    PRMIA [1 Certification Exam(s) ]
    PsychCorp [1 Certification Exam(s) ]
    PTCB [2 Certification Exam(s) ]
    QAI [1 Certification Exam(s) ]
    QlikView [1 Certification Exam(s) ]
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    RACC [1 Certification Exam(s) ]
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    RES [5 Certification Exam(s) ]
    Riverbed [8 Certification Exam(s) ]
    RSA [15 Certification Exam(s) ]
    Sair [8 Certification Exam(s) ]
    Salesforce [5 Certification Exam(s) ]
    SANS [1 Certification Exam(s) ]
    SAP [98 Certification Exam(s) ]
    SASInstitute [15 Certification Exam(s) ]
    SAT [1 Certification Exam(s) ]
    SCO [10 Certification Exam(s) ]
    SCP [6 Certification Exam(s) ]
    SDI [3 Certification Exam(s) ]
    See-Beyond [1 Certification Exam(s) ]
    Siemens [1 Certification Exam(s) ]
    Snia [7 Certification Exam(s) ]
    SOA [15 Certification Exam(s) ]
    Social-Work-Board [4 Certification Exam(s) ]
    SpringSource [1 Certification Exam(s) ]
    SUN [63 Certification Exam(s) ]
    SUSE [1 Certification Exam(s) ]
    Sybase [17 Certification Exam(s) ]
    Symantec [134 Certification Exam(s) ]
    Teacher-Certification [4 Certification Exam(s) ]
    The-Open-Group [8 Certification Exam(s) ]
    TIA [3 Certification Exam(s) ]
    Tibco [18 Certification Exam(s) ]
    Trainers [3 Certification Exam(s) ]
    Trend [1 Certification Exam(s) ]
    TruSecure [1 Certification Exam(s) ]
    USMLE [1 Certification Exam(s) ]
    VCE [6 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [58 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]

    References :

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