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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 appellation : IBM Information Management Content Management OnDemand Technical Mastery Test
Vendor appellation : IBM
: 38 existent Questions

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overseas commerce Machines' (IBM) administration on this autumn 2018 consequences - income appellation Transcript | existent Questions and Pass4sure dumps

No influence found, are attempting newfangled keyword!foreign commerce 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 deem conference, IBM launches newfangled items and capabilities for managing diverse clouds | existent Questions and Pass4sure dumps

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

the exhaust of “multiclouds” is becoming fairly ordinary, with the IBM Institute for commerce cost estimating that ninety eight p.c of every companies will undertake hybrid guidance know-how architectures via 2021. companies are doing so in an endeavor to rob capabilities of every cloud platform’s enchanting capabilities, however they mug difficulties in doing so for want of consistent tools to manipulate and integrate distinct 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 newfangled Cloud Integration Platform that’s intended to construct it less complicated to roll out utility applications across dissimilar clouds. It additionally introduced newfangled features to embolden 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 newfangled 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 solitary structure environment, which means that builders need to write, test and comfy their code simplest once before rolling it out to probably the most suitable cloud.

the newfangled platform is being offered alongside newfangled IBM services for cloud course and design. IBM is providing to embolden agencies control IT substances across their hybrid cloud infrastructures. furthermore, IBM is launching a newfangled Cloud Advisory consulting carrier that goes even additional by means of assisting valued clientele architect their all cloud strategies from dawn to conclusion. IBM celebrated groups will exhaust open and restful 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 also introduced newfangled services to assist guard cloud workloads. The IBM Cloud Hyper proffer protection to Crypto provider provides encryption key administration by course 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 newfangled capabilities it receives from red Hat,” which it talked about remaining descend it could acquire in a $34 billion deal, spoke of Holger Mueller, most valuable analyst and vice president of Constellation research Inc. “As such, IBM must create newfangled layers that abstract different public clouds and on-premises capabilities, and IBM Cloud Integration systems is doing precisely that. however to be 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 commerce accomplice of the 12 months | existent 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 birthright 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 over IBM’s PartnerWorld at believe 2019, recognizes Perficient’s ongoing boom and relationships with key customers, and concept management around the IBM Watson client rendezvous Commerce platform as an fundamental allotment for digital transformation.

“Our course to commerce is concentrated on crafting a journey, connecting with customers, and delivering a seamless client event throughout channels and birthright through the enterprise, imperatives in today’s client-pushed world,” celebrated 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 augment but strengthens their criterion brand, and they invariably evolve their offerings to maintain valued clientele on the true of their online game. We’re honored to be recognized via IBM, and we’re fervent for sharing their innovative solutions every 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 melodramatic 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 structure 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 timehonored deliver chain, augment 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 own 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 own via cloud migration that presents a solitary 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 appellation 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 effectual experiences across their all enterprise,” pointed out Sameer Peera, commonplace supervisor, Perficient’s commerce apply. “With the fresh news 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 joyful to be their go-to accomplice as they navigate the altering market panorama and deliver for his or her clients.”

Perficient competencies in action at IBM believe 2019

apart from its award-profitable commerce own competencies, Perficient experts are reachable during the IBM suppose 2019 convention in booth #320 to talk about its event and learning 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 news 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 toil with each agencies through this transition. And, their consultants will be accessible every through IBM deem to talk about how to navigate the cloud market, participate 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 mug disruption,” stated Hari Madamalla, vice president, emerging solutions, Perficient. “With talents and adventure in every facets of the commerce experience, to leading cloud, hosting, managed features and embolden solutions, agencies flip to Perficient as a go-to accomplice for their digital transformations.”

be allotment of a couple of Perficient belt recall specialists and their customers as they latest throughout six IBM feel periods, together with:

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

For updates every through the event and after, connect with Perficient experts on-line by course 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 birthright 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 charge with mind-blowing digital adventure, enterprise optimization, and commerce solutions. Their toil permits customers to augment 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 toil 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 level IBM commerce 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 be simply ancient statements discuss future expectations or condition different forward-searching assistance regarding pecuniary consequences and enterprise outlook for 2018. those statements are matter to prevalent and unknown risks, uncertainties, and different components that might antecedent 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. construct sure you be conscious that those statements best replicate their predictions. genuine activities or effects may fluctuate significantly. essential components that may trigger their specific outcomes to be 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 profile 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 | existent questions and Pass4sure dumps


In the modern enterprise, effectual exhaust of data to hasten operations and improve commerce results is a fundamental competency. Yet, the complexity, diversity and available solutions be pleased conspired to construct data management a significant belt 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 be handled. Finally, it outlines some practical approaches.

In this brief, no attempt is made to discuss 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, determination 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 minuscule and large. Data about users, customers, operations, resources and other activities in a company, embolden an enterprise add value, maintain competitive advantage and grow. Data comes from many sources, but most importantly, from an enterprise’s own, usually proprietary listening systems, such as its website, customer convoke centers, product instrumentation, sales data and so on. It also comes from third parties or intermediaries, such as agencies, tracking systems, and others.

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

  • Volume: The quantity of the data requires designing preempt repositories to consume or manage that data with preempt performance or service level standards. Enterprises find that technologies which often toil with smaller data sets might not scale up in a cost-effective course or sometimes at all.
  • Variety: Data might be unstructured (for example, raw text, Twitter feeds, audio, etc.), semi-structured or structured. In order to derive insights from it, the data must either be transformed to give it a coherent structure or managed in an entirely different course using unstructured approaches.
  • Veracity: Collecting data is increasingly automated, but there noiseless 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 augment and decrease, sometimes quite dramatically. When the flood accelerates, it may be difficult for legacy systems to maintain up; when the flood is lessened, legacy systems can be prohibitively expensive to operate. More importantly, as the velocity of data changes, the rate at which insights can be found should also change, allowing for faster response times.
  • Sources of Data Transactional Data

    Since the emergence of commerce computing more than 50 years ago, the data generated by applications in finance, manufacturing, commerce, commerce operations, etc., has risen dramatically. Most companies are extensive users of commerce 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 kind 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 be unstructured. The most common problem with transactional data is its volume and velocity. A requirement for transactional data is that it be collected from commerce 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 noiseless unclear. With the emergence of standards and tools to organize the data, this constraint too was lifted. One of the first and noiseless 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 advantage of schema-less data is the dynamic course in which the data store can be constructed, adding more data types as they are encountered. An instance of schema-less data might be sentences in a verbatim response to a survey. JSON is a common schema-less data structure standard.

    Schema-ful data is often associated with relational databases, but could involve schema-driven data structures such as XML/XSD (perhaps a bit confusingly, XML without a corresponding XSD could be considered a schema-less data structure). An instance of schema-ful data is a data structure such as customer (which could involve name, address, phone number) or order (which could involve 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 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 commerce systems are extracted, transformed and loaded (a.k.a. ETL) into a warehouse. In some cases, it may be enough 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 be massaged, cleaned and summarized before it can be 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 be backed up and stored in a safe environment so that it can be reconstituted should the need arise. The primary intuition for managing backup data is for commerce continuity and cataclysm recovery (BCDR). Equally valuable is the talent for an enterprise to efficiently exhaust this data to restart operations in the event of a catastrophic failure.

    Generally, every the data that an enterprise generates or transforms as allotment of its operations should be backed up. This is primarily a concern when the data are managed on-premise, though merely storing the data in the Cloud may not be enough to satisfy 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 rise 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 be pleased the luxury of starting from a antiseptic slate; often legacy data sources must be supported and sustained.

    Relational Databases

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

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

    Relational databases are ideal for applications such as Ecommerce, website content management, ERP, CRM and countless other commerce solutions. There are numerous sturdy 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 issue monolithic and accessible on demand, with no latency or other dependencies. Unfortunately, existent 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 high 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 open at the user’s device and only halt inside the server responding to a request. For example, static content dote logos that change infrequently can be cached in a user’s browser; on the other hand, stock quotes or news headlines can be cached on a content server for many to notice 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 also to write through data that might be changed in the existent world. In addition, most applications will be pleased 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 relative on commerce 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 celebrated 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 involve DynamoDB (key-value), MongoDB, MemcacheDB (both hybrid).

    Relational Warehouses

    Data warehouses are typically constructed from relational databases. The relational model is resilient and well suited for such applications. A central concept in relational warehouses is to construct the data readily available for rapid retrieval, but only along well-defined, prescribed lines. In this respect, the remedy design of the warehouse is essential at the outset, since it can be 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 commerce to be 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 characterize the facts. For example, the sales fact table might advert to the related measures such as products, customer, sales persons, sales amount, geography, etc., every of which were aspects of sales. It’s not uncommon for facts to be pleased 20 or 30 related measures each.

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

    Business Continuity and cataclysm Recovery (BCDR)

    BCDR is a intricate and growing province that cannot be easily summarized. However, the main concepts are to define an enterprise’s Recovery Point Objective (the maximum era for which data may be lost), its Minimum Acceptable level of Service (the service level below which the commerce 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 be 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 be enough 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 commerce 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 relative on transactional data generated by commerce 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 sociable media or the Internet of Things (IoT). every 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 commerce operations:

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

    Hosted Databases

    While enterprises be pleased been restful with on-premise database servers, especially for line-of-business applications, they be pleased 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 awe of losing direct physical control of hardware.

    Virtualization in the Cloud was then a relatively painless transition, whereby the enterprise would noiseless handle 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 terminal 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 be pleased fully hosted DaaS.

    Hosted Caches

    Caches be pleased always been a critical allotment of practical computer systems architecture. The first caches were of course implemented in hardware. Indeed, the organization of computer systems could arguably be 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 intricate systems today. In the on-premise era, such an option was largely outside the reach of any but the most sophisticated and well-resourced organizations. In recent years, open source projects such as Redis and MemCached be pleased brought this technology to wider ambit of developers. Further, the availability of scalable, hosted caches such as Amazon’s AWS ElastiCache or Microsoft Azure Redis be pleased made caching relatively effortless to adopt. Indeed, no application developer should be satisfied with a design until considering how a database cache can alleviate bottlenecks and improve performance.

    Scalable Warehouses

    Once the data warehouse is designed and deployed, the practical challenge is scaling it efficiently and maintaining high levels of performance as it grows and commerce needs evolve. For an enterprise that has users in different locales, replication globally can be an added issue. In addition, optimizations for warehouse-oriented applications are usually needed to improve performance (sometimes by orders of magnitude) both in terms of time and cost. These optimizations involve columnar storage, zone maps and data compression. Parallelism and distributed computing are also 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 course through the enterprise. The problems posed by data ambit from purely transactional issues of capturing existent 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 be reduced by leveraging the infrastructure and investments of the leading SaaS providers. However, even such an approach requires considerable expertise and a profound appreciation of the available technologies so as to construct the optimal tradeoffs.

    Mapping critical learning for Digital Transformation | existent questions and Pass4sure dumps

    Companies in almost every industry these days are trying to Go digital. When digitalization is done in the context of a company’s strategic knowledge, powerful growth opportunities can be uncovered. One course to attain 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 interpret how the knowledge-mapping framework can shed light on recent strategic changes at Adobe, a software solid headquartered in San Jose, Calif.

    Ihrig is a clinical professor and associate dean at newfangled 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 high 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 roomy data – simply capturing and analyzing big 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 be uncovered. The exhaust of the digital data needs to be guided by profound insight into the company’s critical learning assets: its core competencies, intellectual property rights, market and industry comprehension, and customer understanding and expectations.

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

    MacMillanIhrigSteinhourFigure 1

    Figure 1: Strategic learning 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 suffer of Adobe in structure a radically different rapid growth commerce model. Below, written as a stylized case, they exhaust the map to illustrate how the strategic deployment of learning helped Adobe address three high-impact digital transformation challenges. Specifically, they characterize how Adobe:

  • Produced significant value by recognizing and leveraging the tacit learning of matter 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 merit and capture value for the firm.
  • Reinventing a commerce by leveraging tacit learning of matter matter experts

    As described in Harvard commerce 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 commerce through a multi-pronged approach of growing organically within the company’s existing business; acquiring companies with strengths in adjacent categories; and shifting the commerce to allow Adobe to stir beyond the company’s desktop legacy while structure 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 charge tag.  At the identical time, the growth of newfangled customers was anemic.  Younger creatives, an valuable source of newfangled growth, were especially challenged to pay the charge 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 evolution limited the company’s talent to deliver innovation to just once every 18 to 24 months, making it tough to maintain pace with the evolving needs.

    Senior strategists at Adobe did an analysis and found most newfangled software companies were being founded with a cloud-based subscription model, and companies with high recurring revenue weathered the pecuniary storm of 2008-2009 much better than those without. Adobe brought together internal matter 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 learning (figure 1, lower left quadrant) in the profile of profound employee expertise about pricing, product value, and customer behavior were cultivated through the pilot project and formed the basis of the learning needed to support a subscription model.  Learnings were institutionalized (moving from lower left quadrant to upper left, motif 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 newfangled subscription model could attract newfangled users and augment 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 believe differently about their business. It enabled us to bring newfangled 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 newfangled “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 also pursued a growth strategy targeting the enterprise software market. Narayen and his leadership team were sedate about affecting into a significantly different market space. This required a “DNA shift” and the acquisition of newfangled strategic learning 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 posthaste 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) commerce model involved selling and marketing directly to corporations and provided worthy insight into how to develop a direct, enterprise go-to-market commerce – a contrast to Adobe’s commerce selling to individual creatives through resellers and

    Key to the successful integration of the Omniture business, Adobe embraced Omniture’s commerce model and culture, deliberately treating it as a strategic learning opportunity. In particular, the Adobe team systematically captured and developed the tacit learning of the marketing and sales experts from Omniture (figure 1, from lower birthright 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 commerce provided us valuable insight into how to hasten 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 all notion of helping digital marketers drive performance through the exhaust 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 dote Forrester, Gartner and IDG.

    While it would be inaccurate to Tell that the acquisition of Omniture precipitated Adobe’s stir to the Cloud, the acquisition did bring learning and expertise that added tremendous value to the transformation of the creative business.  Adobe’s proficiency in acquisition integration also played an valuable role.  The company had a sturdy track record of retaining talent post-acquisition and, in this case, gave Omniture employees latitude and autonomy while leveraging embedded tacit knowledge. Learning and learning 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 participate and differentiation.

    Creating momentum in the market by sharing tacit experience

    The exercise of packaging up proprietary (undiffused) learning 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 birthright (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 slight awareness of its digital marketing business. Meantime, entrenched competitors with profound pockets, such as IBM, Google and Oracle, were also expanding their digital marketing technology offerings, which could potentially intimidate Adobe’s talent 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 stir 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 learning from the Omniture team to the broader Adobe organization.  Under Lewnes’ direction, marketing made moves to digitalize the commerce by reallocating the lion’s participate of advertising dollars to digital domains (such as display ads, sociable and search), while the IT organization helped replatform Adobe’s websites around the world so that marketing could measure the repercussion of the digital spend.   Marketing and IT could be thought of as flip sides of the coin that helped stir the company toward its own transformation.  Both were internal clients of Adobe software: using web content management and marketing analytics and measurement technology.

    The exhaust of the digital data needs to be guided by profound insight into the company’s critical learning 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 exhaust of these Adobe digital marketing solutions. This was of significant interest to customers, who were challenged to undertake the identical digital transformation themselves.  Adobe’s sharing of this learning with external audiences was, at first, ad-hoc and opportunistic.  However, they soon realized that codifying this internal learning and disseminating it publically (movement from the lower left to the upper birthright of the map) would provide a boost to Adobe’s credibility, and augment 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 construct the shift to digital.  This mainly focused on “people, processes and technologies.” They codified their learnings in on-demand videos to embolden scale the reach of this learning content.  In parallel, on the IT side, Adobe formed the Adobe@Adobe team to evangelize the exhaust of Adobe technology to address marketing exhaust cases.

    Ron Nagy, Sr. Evangelist Adobe@Adobe, develops exhaust case narratives through collaboration with customers, internal practitioners, product marketers and technologists.  He’s a solid 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 discuss topics that are selected via an internal voting process.  This internal forum invites constructive conversations where internal users of the products participate best practices and articulate areas for improvement.  Product marketing and engineering discuss 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 identical 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 evolution roadmap and to inspire newfangled product development.

    Capturing and sharing the learning of Adobe practitioners, who possess profound operational knowledge, is also a critical aspect of the program. However, Nagy notes that some translation of that message is needed: “If you are starting a program – there be pleased to be individuals with learning of the tech, what is possible, and the business.  You need to rob the input from practitioners and other sources then attain the translation to what is apropos to the marketplace.” These Adobe@Adobe exhaust cases are shared broadly to internal and external audiences. While the program aggregates and curates the learning of Adobe practitioners, it does not remove subject-matter experts from the process. Rather, developing the voice of the practitioner is also 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 allotment of the IT organization, not allotment of sales; this deliberate separation, to bring an objective perspective. However, the marketing department, ecommerce department and the commerce unit are also 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 learning (the confluence of technical and product knowledge, fueled by employee learning and enthusiasm, and guided to relevance by market needs) and then orchestrating the diffusion of that knowledge, Adobe has developed a masterful customer rendezvous 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 instance of how Adobe has deliberately diffused proprietary learning assets into the public domain, in the process creating value for Adobe and customers alike.  learning in this case, are the insights derived from codifying an aggregate view of billions of digital data inputs (structured upper left quadrant of the learning 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 be blockbusters, based on the analysis of commentary in sociable media.  The accuracy of their predictions (36 of 37 predictions were spot on) resulted in a convoke from an executive from a major motion picture distributor who was keen to produce 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 reach which contributes to a significant augment in awareness of Adobe’s “big data” expertise.  For example, Adobe got worthy exposure with over 7,000 press stories including first-rate Morning America, Today Show, CNBC Squawk Box and much more by identifying the average daily discounts for toys and electronics this past holiday season.

    Digitization for the sake of digitization is not the course to go. profound attention needs to be given to what digitization of what learning should be 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 criterion digital metrics such as web and mobile traffic, video consumption, bounce rates and conversions.  “If they detect any anomalies then they dig deeper.  They quiz 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 sequel may not be causal, but there is a sturdy correlation that suggests that timing of promotions is a prominent factor.”

    The course 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 dote thought leadership and earned media vs. conversion and sales.  The team reports into Marketing and is allowed to experiment, which allows them to be innovative and rob risks and sometimes fail.  Gaffney states, “We be pleased a few express measures of success, such as total number of press articles, size of circulation, syndication by well-known publishers dote Forbes, WSJ,” but equally valuable are the door openers or the conversation starters that stem from ADI findings.  Gaffney concludes, “ADI reports on valuable trends and indicators of future trends, which are significant topics for their target audiences, and it eases the course 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 need to transform their businesses in order to participate in the newfangled digital world order or risk becoming irrelevant. But digitization for the sake of digitization is not the course to go. profound attention needs to be given to what digitization of what learning should be undertaken and why.  This is determined by mapping your major learning 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 learning gained through acquisition; to build critical customer credibility by diffusing tacit learning 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 learning assets will present its own set of context-specific digitization opportunities.

    Leading your solid in this newfangled digital reality requires a thorough understanding of every of your critical learning assets, both express and tacit. Equipped with a strategic learning map, corporate leaders can craft a competitive strategy and construct digital transformation a reality.

    Don't peep down: The path to cloud computing is noiseless missing a few steps | existent questions and Pass4sure dumps

    Don't peep down: The path to cloud computing is noiseless 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 structure during the past year, after the newfangled administration trumpeted the approach as a course to derive greater efficiency and cost savings from information technology investments.

    At the behest of federal Chief Information Officer Vivek Kundra, the generic Services Administration became the hub of gravity for cloud computing at civilian agencies, with the launch of a cloud storefront,, that offers business, productivity and sociable 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 also 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 rob 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 of those areas are the missing rungs on the ladder to the clouds.

    “Cloud computing is not a technology that can just be 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 terminal year defining what the cloud is, what are the various delivery models, deployments and characteristics,” Tseronis said. “We noiseless continue to need to attain 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 be rapidly provisioned and released with minimal management endeavor or service-provider interaction.

    The three delivery models include:

  • Software as a service (SaaS), which provides commerce 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 terminal 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 attain 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 be pleased been evaluating the benefits and risks for more than a year because a stir to the cloud seems dote 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 be pleased concerns about data separation because they want to avert a commingling of data with tenants in other environments. And they need access restrictions on data to construct sure cloud hosting providers or other tenants don’t inadvertently or intentionally score access to sensitive data.

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

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

    First, cloud providers need to understand government security requirements and deliver services that satisfy those requirements. Microsoft recently created a federal version of its commerce Productivity Online Services for the cloud, which is one instance of how vendors could embolden address security requirements, he said.

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

    The federal government noiseless has a fragmented approach to security, he said. “We don’t be pleased a single, unified — to my learning — 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 be pleased been addressing security requirements, and the Justice Department tackled the problem at a department level, Holgate said.

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