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Pass4sure P2070-071 Practice Test | Download Updated Pass4sure IBM P2070-071 test sample | test prep - alphernet.com.au

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

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

Vendor IBM
Exam Number P2070-071
Exam Name IBM Information Management Content Management OnDemand Technical Mastery Test
Questions 38 Q & A
Recent Update February 18, 2019
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P2070-071 exam Dumps Source : IBM Information Management Content Management OnDemand Technical Mastery Test

Test Code : P2070-071
Test Name : IBM Information Management Content Management OnDemand Technical Mastery Test
Vendor Name : IBM
Q&A : 38 Real Questions

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overseas business Machines' (IBM) administration on this autumn 2018 consequences - income name Transcript | killexams.com Real Questions and Pass4sure dumps

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At consider conference, IBM launches new items and capabilities for managing diverse clouds | killexams.com Real Questions and Pass4sure dumps

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

the use of “multiclouds” is becoming fairly ordinary, with the IBM Institute for business cost estimating that ninety eight p.c of all companies will undertake hybrid guidance know-how architectures via 2021. companies are doing so in an effort to take capabilities of every cloud platform’s interesting capabilities, however they face difficulties in doing so for lack of consistent tools to manipulate and integrate distinct clouds.

That explains why IBM is including to its hybrid cloud equipment and functions offerings. on the IBM believe convention in San Francisco today, the company introduced a new Cloud Integration Platform that’s intended to make it less complicated to roll out utility applications across dissimilar clouds. It additionally introduced new features to help 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 new hybrid cloud play, connecting applications, application and functions throughout public and personal clouds and on-premises programs. The platform offers integration equipment for these apps which are attainable from a single building 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 new platform is being offered alongside new IBM services for cloud method and design. IBM is providing to help agencies control IT substances across their hybrid cloud infrastructures. furthermore, IBM is launching a new Cloud Advisory consulting carrier that goes even additional by means of assisting valued clientele architect their whole cloud strategies from beginning to conclusion. IBM noted groups will use open and comfortable multicloud concepts and its Cloud Innovate formula and tools to guide 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 new services to assist guard cloud workloads. The IBM Cloud Hyper offer 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 new 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 important analyst and vice president of Constellation research Inc. “As such, IBM must create new 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.”

picture: Abogawat/Pixabay since you’re right here …

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Perficient Named IBM 2019 Watson Commerce business accomplice of the 12 months | killexams.com Real 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 right through North the usa, announced it has been named IBM’s 2019 Watson Commerce company associate of the year. The IBM Excellence Award, announced all over IBM’s PartnerWorld at think 2019, recognizes Perficient’s ongoing boom and relationships with key customers, and concept management around the IBM Watson client Engagement Commerce platform as an fundamental part for digital transformation.

“Our method to commerce is concentrated on crafting a journey, connecting with customers, and delivering a seamless client event throughout channels and right through the enterprise, imperatives in today’s client-pushed world,” noted Steve Gatto, country wide earnings director, Commerce options, Perficient Digital. “collectively, with our shoppers, we’re remodeling corporations in a means that now not best drives increase but strengthens their standard brand, and we invariably evolve our offerings to maintain valued clientele on the true of their online game. We’re honored to be recognized via IBM, and we’re eager for sharing our innovative solutions all 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 dramatic 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 building 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 normal deliver chain, increase 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 answer 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 answer 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 name 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 effective experiences across their whole 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, our valued clientele proceed to engage us for assist with their digital commerce suggestions. We’re glad 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 think 2019

apart from its award-profitable commerce answer competencies, Perficient experts are reachable during the IBM suppose 2019 convention in booth #320 to talk about its event and knowledge 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 work with each agencies through this transition. And, our consultants will be accessible all through IBM consider 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 all facets of the commerce experience, to leading cloud, hosting, managed features and help solutions, agencies flip to Perficient as a go-to accomplice for their digital transformations.”

be part of a couple of Perficient area remember specialists and our customers as they latest throughout six IBM feel periods, together with:

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

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

About Perficient

Perficient is the leading digital transformation consulting company serving international 2000® and commercial enterprise consumers right 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 price with mind-blowing digital adventure, enterprise optimization, and business solutions. Our work permits customers to increase 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 level IBM business 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, visitwww.perficient.com.

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one of the vital statements contained during this information liberate that don't seem to be simply old statements discuss future expectations or state different forward-searching assistance regarding financial 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 our enterprise and our industry. make sure you be conscious that those statements best replicate our predictions. genuine activities or effects may fluctuate significantly. essential components that may trigger our 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 our annual record on form 10-k for the year ended December 31, 2017.

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Copyright enterprise Wire 2019


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The Data Lifecycle: Data Management in the Enterprise | killexams.com real questions and Pass4sure dumps

Overview

In the modern enterprise, effective use of data to run operations and improve business results is a fundamental competency. Yet, the complexity, diversity and available solutions have conspired to make data management a significant area 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, decision 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, help 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 call 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 all this data boil down to four basic issues — the 4 V’s:

  • Volume: The quantity of the data requires designing appropriate repositories to consume or manage that data with appropriate performance or service level 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 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 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 increase and decrease, sometimes quite dramatically. When the flow accelerates, it may be difficult for legacy systems to keep up; when the flow 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 business computing more than 50 years ago, the data generated by applications in finance, manufacturing, commerce, business operations, etc., has risen dramatically. Most companies are extensive users of business applications that interface with ERP, CRM, SCM and other systems. The data generated reflect the ebb and flow 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 business 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 advantage of schema-less data is the dynamic way in which the data store can be constructed, adding more data types as they are encountered. An example of schema-less data might be sentences in a verbatim response to a survey. JSON is a popular schema-less data structure standard.

    Schema-ful data is often associated with relational databases, but could include 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 example of schema-ful data is a data structure such as customer (which could include name, address, phone number) or order (which could include 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. All 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 business systems are extracted, transformed and loaded (a.k.a. ETL) into a warehouse. In some cases, it may be 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 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 reason for managing backup data is for business continuity and disaster recovery (BCDR). Equally important is the ability for an enterprise to efficiently use this data to restart operations in the event of a catastrophic failure.

    Generally, all the data that an enterprise generates or transforms as part 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 sufficient 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 have the luxury of starting from a clean slate; often legacy data sources must be supported and sustained.

    Relational Databases

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

    For various reasons, the semantic representation must then be “normalized” to eliminate any redundancy in the data model (i.e., the same 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 inconsistent under real world conditions).

    Relational databases are ideal for applications such as Ecommerce, website content management, ERP, CRM and countless other business 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 appear monolithic and accessible on demand, with no latency or other dependencies. Unfortunately, real 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 begin at the user’s device and only end inside the server responding to a request. For example, static content like 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 see 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 real world. In addition, most applications will have 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 dependent on business 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 noted 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 include DynamoDB (key-value), MongoDB, MemcacheDB (both hybrid).

    Relational Warehouses

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

    Identifying the correct facts and measures is a key part 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 business operations. The requirement for warehouse data is to be easily accessed and efficiently pulled into reports or compiled into other insights.

    Business Continuity and Disaster Recovery (BCDR)

    BCDR is a complex and growing field that cannot be easily summarized. However, the main concepts are to define an enterprise’s Recovery Point Objective (the maximum period for which data may be lost), its Minimum Acceptable Level of Service (the service level below which the business 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 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 business 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 dependent on transactional data generated by business 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). All 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 business operations:

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

    Hosted Databases

    While enterprises have been comfortable with on-premise database servers, especially for line-of-business applications, they have 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 fear of losing direct physical control of hardware.

    Virtualization in the Cloud was then a relatively painless transition, whereby the enterprise would still 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 last 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 all have fully hosted DaaS.

    Hosted Caches

    Caches have always been a critical part 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 complex 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 have brought this technology to wider range of developers. Further, the availability of scalable, hosted caches such as Amazon’s AWS ElastiCache or Microsoft Azure Redis have made caching relatively easy 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 business 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 include 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.

    Conclusion

    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 range from purely transactional issues of capturing real 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 deep appreciation of the available technologies so as to make the optimal tradeoffs.


    Mapping Critical Knowledge for Digital Transformation | killexams.com real 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 way to do 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 explain 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 New 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 big data – simply capturing and analyzing large 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 use of the digital data needs to be guided by deep insight into the company’s critical knowledge assets: its core competencies, intellectual property rights, market and industry comprehension, and customer understanding and expectations.

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

    MacMillanIhrigSteinhourFigure 1

    Figure 1: Strategic Knowledge Map

    We recognized that the application of the framework can illuminate recent strategies at Adobe. Through interviews with Adobe executives and key stakeholders, we researched the highly successful experience of Adobe in building a radically different rapid growth business model. Below, written as a stylized case, we use the map to illustrate how the strategic deployment of knowledge helped Adobe address three high-impact digital transformation challenges. Specifically, we describe how Adobe:

  • Produced significant value by recognizing and leveraging the tacit knowledge 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 business by leveraging tacit knowledge of subject matter experts

    As described in Harvard Business 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 business through a multi-pronged approach of growing organically within the company’s existing business; acquiring companies with strengths in adjacent categories; and shifting the business to allow Adobe to move beyond the company’s desktop heritage while building 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 price tag.  At the same time, the growth of new customers was anemic.  Younger creatives, an important source of new growth, were especially challenged to pay the price 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 ability to deliver innovation to just once every 18 to 24 months, making it tough to keep pace with the evolving needs.

    Senior strategists at Adobe did an analysis and found most new software companies were being founded with a cloud-based subscription model, and companies with high recurring revenue weathered the financial 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 knowledge (figure 1, lower left quadrant) in the form of deep employee expertise about pricing, product value, and customer behavior were cultivated through the pilot project and formed the basis of the knowledge 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 new subscription model could attract new users and increase the pace of upgrades by lowering the barrier to entry.  But to attract a broader customer base 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 think differently about our business. It enabled us to bring new 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 new “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 serious about moving into a significantly different market space. This required a “DNA shift” and the acquisition of new strategic knowledge 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 fast 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) business model involved selling and marketing directly to corporations and provided great insight into how to develop a direct, enterprise go-to-market business – a contrast to Adobe’s business selling to individual creatives through resellers and Adobe.com.

    Key to the successful integration of the Omniture business, Adobe embraced Omniture’s business model and culture, deliberately treating it as a strategic learning opportunity. In particular, the Adobe team systematically captured and developed the tacit knowledge of the marketing and sales experts from Omniture (figure 1, from lower right 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 business provided us valuable insight into how to run 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 our own marketing – we knew there was a lot to learn.”

    At that time, the whole notion of helping digital marketers drive performance through the use 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 like Forrester, Gartner and IDG.

    While it would be inaccurate to say that the acquisition of Omniture precipitated Adobe’s move to the Cloud, the acquisition did bring knowledge and expertise that added tremendous value to the transformation of the creative business.  Adobe’s proficiency in acquisition integration also played an important 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 knowledge 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 practice of packaging up proprietary (undiffused) knowledge 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 right (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 full spectrum of digital marketing technology) and rapidly build significant market share.  However, most customers associated Adobe with Acrobat and Photoshop and there was little awareness of its digital marketing business. Meantime, entrenched competitors with deep pockets, such as IBM, Google and Oracle, were also expanding their digital marketing technology offerings, which could potentially threaten Adobe’s ability 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 move 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 Adobe.com, the acquisition accelerated the process of transferring the tacit marketing analytics knowledge from the Omniture team to the broader Adobe organization.  Under Lewnes’ direction, marketing made moves to digitalize the business 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 be thought of as flip sides of the coin that helped move the company toward its own transformation.  Both were internal clients of Adobe software: using web content management and marketing analytics and measurement technology.

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

    Ron Nagy, Sr. Evangelist Adobe@Adobe, develops use 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 discuss 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 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 same time, it is an institutional function 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 new product development.

    Capturing and sharing the knowledge of Adobe practitioners, who possess deep 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 have to be individuals with knowledge of the tech, what is possible, and the business.  You need to take the input from practitioners and other sources then do the translation to what is relevant to the marketplace.” These Adobe@Adobe use cases are shared broadly to internal and external audiences. While the program aggregates and curates the knowledge 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 part of the IT organization, not part of sales; this deliberate separation, to bring an objective perspective. However, the marketing department, ecommerce department and the business 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 guide a firm’s own transformation.

    By capturing and organizing tacit knowledge (the confluence of technical and product knowledge, fueled by employee knowledge and enthusiasm, and guided to relevance by market needs) and then orchestrating the diffusion of that knowledge, Adobe has developed a masterful customer engagement 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 knowledge assets into the public domain, in the process creating value for Adobe and customers alike.  Knowledge in this case, are the insights derived from codifying an aggregate view of billions of digital data inputs (structured upper left quadrant of the knowledge 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 social media.  The accuracy of their predictions (36 of 37 predictions were spot on) resulted in a call from an executive from a major motion picture distributor who was keen to produce similar predictions.  “This is exactly what we 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 benefit is that ADI findings are syndicated broadly, thereby extending Adobe’s market reach which contributes to a significant increase in awareness of Adobe’s “big data” expertise.  For example, Adobe got great exposure with over 7,000 press stories including Good 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 way to go. Deep attention needs to be given to what digitization of what knowledge 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 standard digital metrics such as web and mobile traffic, video consumption, bounce rates and conversions.  “If we detect any anomalies then we dig deeper.  We ask ourselves questions and create hypothesis that we 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 be 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 like 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 take risks and sometimes fail.  Gaffney states, “We have a few explicit measures of success, such as total number of press articles, size of circulation, syndication by well-known publishers like Forbes, WSJ,” but equally important are the door openers or the conversation starters that stem from ADI findings.  Gaffney concludes, “ADI reports on important trends and indicators of future trends, which are significant topics for our target audiences, and it eases the way for our sales teams and executives to engage with our 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 new digital world order or risk becoming irrelevant. But digitization for the sake of digitization is not the way to go. Deep attention needs to be given to what digitization of what knowledge should be undertaken and why.  This is determined by mapping your major knowledge 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 knowledge gained through acquisition; to build critical customer credibility by diffusing tacit knowledge 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 knowledge assets will present its own set of context-specific digitization opportunities.

    Leading your firm in this new digital reality requires a thorough understanding of all of your critical knowledge assets, both explicit and tacit. Equipped with a strategic knowledge map, corporate leaders can craft a competitive strategy and make digital transformation a reality.


    Don't look down: The path to cloud computing is still missing a few steps | killexams.com real questions and Pass4sure dumps

    Don't look 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 moving to the cloud. There’s no doubt about that.

    Momentum for cloud computing has been building during the past year, after the new 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 General Services Administration became the center of gravity for cloud computing at civilian agencies, with the launch of a cloud storefront, Apps.gov, 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 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 take 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 all 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 last year defining what the cloud is, what are the various delivery models, deployments and characteristics,” Tseronis said. “We still continue to need to do 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 effort or service-provider interaction.

    The three delivery models include:

  • Software as a service (SaaS), which provides business 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 last 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 do 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 have been evaluating the benefits and risks for more than a year because a move to the cloud seems like a natural fit. “We are already fairly outsourced in terms of our 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 have concerns about data separation because they want to prevent a commingling of data with tenants in other environments. And they need access restrictions on data to make sure cloud hosting providers or other tenants don’t inadvertently or intentionally get access to sensitive data.

    “We are all struggling in the federal space with the right 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 need to follow 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 Business Productivity Online Services for the cloud, which is one example of how vendors could help address security requirements, he said.

    On the federal side, “we need to probably do 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 have a single, unified — to my knowledge — 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 have been addressing security requirements, and the Justice Department tackled the problem at a department level, Holgate said.



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