P2070-071 exam Dumps Source : IBM Information Management Content Management OnDemand Technical Mastery Test
Test Code : P2070-071
Test cognomen : IBM Information Management Content Management OnDemand Technical Mastery Test
Vendor cognomen : IBM
: 38 true Questions
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IBM Corp. is stepping up its hybrid-cloud thrust because it bids to spin into the go-to carrier issuer for companies that consume diverse public and private cloud structures.
the consume of “multiclouds” is becoming fairly ordinary, with the IBM Institute for trade cost estimating that ninety eight p.c of every bit of companies will undertake hybrid guidance know-how architectures via 2021. companies are doing so in an effort to buy capabilities of every cloud platform’s inviting capabilities, however they countenance difficulties in doing so for lack of consistent tools to exploit and integrate several 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 fresh Cloud Integration Platform that’s intended to develop it less complicated to roll out utility applications across dissimilar clouds. It additionally introduced fresh features to profit 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 fresh 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 separate structure environment, which means that builders exigency to write, test and comfy their code simplest once before rolling it out to probably the most suitable cloud.
the fresh platform is being offered alongside fresh IBM services for cloud routine and design. IBM is providing to profit agencies control IT substances across their hybrid cloud infrastructures. furthermore, IBM is launching a fresh Cloud Advisory consulting carrier that goes even additional by means of assisting valued clientele architect their all cloud strategies from genesis to conclusion. IBM celebrated groups will consume open and snug multicloud concepts and its Cloud Innovate formula and tools to usher 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 likewise introduced fresh 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 fresh capabilities it receives from red Hat,” which it talked about remaining tumble it could acquire in a $34 billion deal, spoke of Holger Mueller, most notable analyst and vice president of Constellation research Inc. “As such, IBM must create fresh layers that abstract different public clouds and on-premises capabilities, and IBM Cloud Integration systems is doing precisely that. however to live successful, organizations additionally want services, so IBM is adding these for the administration and operation of a multicloud environments.”picture: Abogawat/Pixabay since you’re right here …
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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 every bit of over IBM’s PartnerWorld at deem 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 fraction for digital transformation.
“Our routine 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,” 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 increase 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 live recognized via IBM, and we’re keen for sharing their innovative solutions every bit of over IBM believe 2019.”
Perficient Digital Takes Commerce solutions past Transactions to transform the client Lifecycle for a world diverse company
With branded manufacturers and distributors below pressure from the histrionic 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 prevalent 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 reply 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 reply via cloud migration that presents a separate 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 cognomen 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 all enterprise,” pointed out Sameer Peera, commonplace supervisor, Perficient’s commerce apply. “With the fresh advice 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 lighthearted to live their go-to accomplice as they navigate the altering market panorama and deliver for his or her clients.”
Perficient competencies in action at IBM deem 2019
apart from its award-profitable commerce reply 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 advice 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 travail with each agencies through this transition. And, their consultants will live accessible every bit of through IBM consider 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 countenance disruption,” stated Hari Madamalla, vice president, emerging solutions, Perficient. “With talents and adventure in every bit of facets of the commerce experience, to leading cloud, hosting, managed features and profit solutions, agencies flip to Perficient as a go-to accomplice for their digital transformations.”
be fraction of a yoke of Perficient belt recollect specialists and their customers as they latest throughout six IBM feel periods, together with:
As a Platinum IBM trade companion, Perficient holds more than 30 awards throughout its 20-12 months partnership heritage. The enterprise is an award-profitable, certified utility value Plus reply company and one of the crucial few companions to glean hold of dozens of IBM expert degree software competency achievements.
For updates every bit of through the event and after, join with Perficient experts on-line by way of viewingPerficient and Perficient Digital’s blogs, or ensue us on Twitter@Perficient and @PRFTDigital.
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 cost with mind-blowing digital adventure, enterprise optimization, and trade solutions. Their travail 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 travail 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 trade 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.
secure Harbor commentary
one of the vital statements contained during this information liberate that don't seem to live simply archaic statements debate 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. develop certain you live conscious that those statements best replicate their predictions. genuine activities or effects may fluctuate significantly. essential components that may trigger their specific outcomes to live materially different from the forward-looking statements consist of (however are not confined to) those disclosed beneath the heading “possibility components” in their annual record on contour 10-k for the year ended December 31, 2017.
View supply version on businesswire.com: https://www.businesswire.com/information/home/20190212005973/en/
source: Perficient, Inc.
Ann Higby, PR supervisor, Perficient, firstname.lastname@example.org
Copyright enterprise Wire 2019
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In the modern enterprise, effective consume of data to flee operations and ameliorate trade results is a fundamental competency. Yet, the complexity, diversity and available solutions own conspired to develop 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 live handled. Finally, it outlines some practical approaches.
In this brief, no attempt is made to debate 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 minute and large. Data about users, customers, operations, resources and other activities in a company, profit 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 summon centers, product instrumentation, sales data and so on. It likewise comes from third parties or intermediaries, such as agencies, tracking systems, and others.
The problems with every bit of this data boil down to four basic issues — the 4 V’s:
Since the emergence of trade computing more than 50 years ago, the data generated by applications in finance, manufacturing, commerce, trade operations, etc., has risen dramatically. Most companies are extensive users of trade applications that interface with ERP, CRM, SCM and other systems. The data generated reflect the ebb and flux of a business’ activities as it interacts with customers, vendors, partners, etc. For example, one typical kindhearted of transactional data is sales orders from an online Ecommerce website.
This data is considered transactional since it arises from the operations of the business. The bulk of transaction data is usually organized into relational databases, which are highly structured and defined by a schema. Some transactional data can live unstructured. The most common problem with transactional data is its volume and velocity. A requirement for transactional data is that it live collected from trade 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 soundless unclear. With the emergence of standards and tools to organize the data, this constraint too was lifted. One of the first and soundless 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 live constructed, adding more data types as they are encountered. An case of schema-less data might live 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 comprehend schema-driven data structures such as XML/XSD (perhaps a bit confusingly, XML without a corresponding XSD could live considered a schema-less data structure). An case of schema-ful data is a data structure such as customer (which could comprehend name, address, phone number) or order (which could comprehend an order number, a reference to a product and other information). Schema-ful data requires more supervision in designing, but can better advocate queries and transactional processing prerequisites, such as consistency and better advocate functions such as the joining of data sets. every bit of the four V’s are at play with unstructured data.Warehouse Data
Data warehouses are built from highly structured, schema-ful data as well as from unstructured schema-less data. Typically, the transactional data from trade systems are extracted, transformed and loaded (a.k.a. ETL) into a warehouse. In some cases, it may live sufficient to just extract and load, potentially delaying transformation to a later stage (ELT). In any case, the data is moved from one or more sources to a specially designed database, usually designated a warehouse (though there are mezzanine concepts such as data marts). Usually, the data must live massaged, cleaned and summarized before it can live stored in the warehouse.
With warehouse data, the key issues are variety, veracity and velocity.Backup Data
Business operations transaction data and warehouse data must live backed up and stored in a safe environment so that it can live reconstituted should the exigency arise. The primary intuition for managing backup data is for trade continuity and catastrophe recovery (BCDR). Equally notable is the ability for an enterprise to efficiently consume this data to restart operations in the event of a catastrophic failure.
Generally, every bit of the data that an enterprise generates or transforms as fraction of its operations should live backed up. This is primarily a concern when the data are managed on-premise, though merely storing the data in the Cloud may not live sufficient to 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 mount 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 own the extravagance of starting from a spotless slate; often legacy data sources must live supported and sustained.Relational Databases
Since their emergence in the early 1980’s relational databases (RDB) own become the criterion database model, supplanting network databases and file-based systems. They are model for transaction processing inherent in trade operations. To facilitate this, an RDB is designed to reflect the semantics of a trade problem — that is, it acts as a data model of the trade world. For example, a RDB might model a trade with tables to picture trade entities such as customers, sales, orders, products, and so on.
For various reasons, the semantic representation must then live “normalized” to eradicate any redundancy in the data model (i.e., the same data elements represented in more than one place). By doing this normalization, performance can live improved and data integrity enhanced (i.e., reduce the possibility that data can become discordant under true world conditions).
Relational databases are model for applications such as Ecommerce, website content management, ERP, CRM and countless other trade 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 issue monolithic and accessible on demand, with no latency or other dependencies. Unfortunately, true 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 affection logos that change infrequently can live cached in a user’s browser; on the other hand, stock quotes or advice headlines can live cached on a content server for many to espy 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 likewise to write through data that might live changed in the true world. In addition, most applications will own 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 conditional on trade 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 comprehend 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 develop 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 live 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 trade to live modeled, such as sales, purchase orders, shipments, and payments. Each of the facts is organized into its own fact table. Associated with these facts are measures, which together characterize or fully picture the facts. For example, the sales fact table might refer to the related measures such as products, customer, sales persons, sales amount, geography, etc., every bit of of which were aspects of sales. It’s not uncommon for facts to own 20 or 30 related measures each.
Identifying the correct facts and measures is a key fraction of the challenge and skill in designing a data warehouse. Warehouse data is several from transactional data in that it is not usually a direct output of trade operations. The requirement for warehouse data is to live easily accessed and efficiently pulled into reports or compiled into other insights.Business Continuity and catastrophe Recovery (BCDR)
BCDR is a complicated and growing territory that cannot live easily summarized. However, the main concepts are to define an enterprise’s Recovery Point Objective (the maximum term for which data may live lost), its Minimum Acceptable level of Service (the service level below which the trade effectively is out of operation), and its Maximum Acceptable Outage (longest duration for loss of operations). Once defined, the recovery processes, backup and recovery strategy can live determined.
The advent of Cloud services can address an enterprise’s needs. However, unless the service is fully managed, has its own BCDR strategy and is transparently tested, it may not live sufficient to ensure an enterprise’s own BCDR needs are met. Sometimes, it’s necessary to create database snapshots and propagate them to different geographic regions within a Cloud service provider’s network in order to explicitly ensure DR backups. Amazon AWS and other Cloud vendors provide infrastructure to advocate BCDR. But it’s incumbent on the enterprise to understand its trade needs and to design and build a system of both process and technology to advocate BCDR — and to regularly audit, test and update the BCDR system.Practical Approaches
Most enterprises are heavily invested in and conditional on transactional data generated by trade 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 convivial media or the Internet of Things (IoT). every bit of of this data has a role to play in helping the enterprise develop insights about its customers or business; one of the most common ways to achieve this is to marshal and summarize the transactional and other data into warehouses for analysis and reporting. In the enterprise information lifecycle, data is generated during the course of business, but then is summarized and analyzed to provide insights for the enterprise, thereby improving trade operations:
When trade data is managed in this way, internal users of the data can then count on a single, dependable source of verisimilitude upon which to develop decisions, build plans and grow the business.Hosted Databases
While enterprises own been snug with on-premise database servers, especially for line-of-business applications, they own 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 warning of losing direct physical control of hardware.
Virtualization in the Cloud was then a relatively painless transition, whereby the enterprise would soundless exploit 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 bit of own fully hosted DaaS.Hosted Caches
Caches own always been a faultfinding fraction of practical computer systems architecture. The first caches were of course implemented in hardware. Indeed, the organization of computer systems could arguably live viewed as the efficient management of successively larger caches, from registers in a microprocessor to virtual caches in applications to content distribution networks to archival storage.
Designing an application around fast, highly available caches is a typical requirement for complicated systems today. In the on-premise era, such an option was largely outside the attain of any but the most sophisticated and well-resourced organizations. In recent years, open source projects such as Redis and MemCached own brought this technology to wider scope of developers. Further, the availability of scalable, hosted caches such as Amazon’s AWS ElastiCache or Microsoft Azure Redis own made caching relatively easy to adopt. Indeed, no application developer should live satisfied with a design until considering how a database cache can alleviate bottlenecks and ameliorate 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 trade needs evolve. For an enterprise that has users in different locales, replication globally can live an added issue. In addition, optimizations for warehouse-oriented applications are usually needed to ameliorate performance (sometimes by orders of magnitude) both in terms of time and cost. These optimizations comprehend columnar storage, zone maps and data compression. Parallelism and distributed computing are likewise 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 scope from purely transactional issues of capturing true time events efficiently to processing data so that it yields information and then insights for the organization. Fortunately, the increased complexity of data management at scale can live reduced by leveraging the infrastructure and investments of the leading SaaS providers. However, even such an approach requires considerable expertise and a deep appreciation of the available technologies so as to develop the optimal tradeoffs.
Companies in almost every industry these days are trying to fade digital. When digitalization is done in the context of a company’s strategic knowledge, powerful growth opportunities can live 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 justify 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 fresh 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 much 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 live uncovered. The consume of the digital data needs to live guided by deep insight into the company’s faultfinding knowledge assets: its core competencies, intellectual property rights, market and industry comprehension, and customer understanding and expectations.
Strategic knowledge mapping helps to uncover these faultfinding 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 own access to it). Digitalization structures knowledge (moving it up the knowledge map), which then makes it workable to develop strategies to participate this knowledge and thereby create and capture value from this knowledge diffusion (systematically poignant it to the right of the map).
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, they researched the highly successful suffer of Adobe in structure a radically different rapid growth trade model. Below, written as a stylized case, they consume the map to illustrate how the strategic deployment of knowledge helped Adobe address three high-impact digital transformation challenges. Specifically, they picture how Adobe:
Reinventing a trade by leveraging tacit knowledge of matter matter experts
As described in Harvard trade 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 trade through a multi-pronged approach of growing organically within the company’s existing business; acquiring companies with strengths in adjacent categories; and shifting the trade to allow Adobe to scamper beyond the company’s desktop heritage 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 cost tag. At the same time, the growth of fresh customers was anemic. Younger creatives, an notable source of fresh growth, were especially challenged to pay the cost for the software and their needs were evolving rapidly. They were increasingly mobile, wanting connected workflows, faster innovation and more value. Yet, the perpetual-license model of software evolution limited the company’s ability to deliver innovation to just once every 18 to 24 months, making it tough to retain pace with the evolving needs.
Senior strategists at Adobe did an analysis and create most fresh 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 knowledge (figure 1, lower left quadrant) in the contour 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 advocate a subscription model. Learnings were institutionalized (moving from lower left quadrant to upper left, device 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 fresh subscription model could attract fresh 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 deem differently about their business. It enabled us to bring fresh 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 fresh “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 likewise pursued a growth strategy targeting the enterprise software market. Narayen and his leadership team were sedate about poignant into a significantly different market space. This required a “DNA shift” and the acquisition of fresh 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 hastily 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) trade model involved selling and marketing directly to corporations and provided much insight into how to develop a direct, enterprise go-to-market trade – a contrast to Adobe’s trade selling to individual creatives through resellers and Adobe.com.
Key to the successful integration of the Omniture business, Adobe embraced Omniture’s trade 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 faultfinding component of long-term success.
“Moving into the Digital Marketing trade provided us valuable insight into how to flee 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 consume 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 affection Forrester, Gartner and IDG.
While it would live inaccurate to relate that the acquisition of Omniture precipitated Adobe’s scamper 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 likewise played an notable 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 participate 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 replete spectrum of digital marketing technology) and rapidly build significant market share. However, most customers associated Adobe with Acrobat and Photoshop and there was puny awareness of its digital marketing business. Meantime, entrenched competitors with deep pockets, such as IBM, Google and Oracle, were likewise expanding their digital marketing technology offerings, which could potentially menace 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 scamper 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 trade by reallocating the lion’s participate of advertising dollars to digital domains (such as parade ads, convivial and search), while the IT organization helped replatform Adobe’s websites around the world so that marketing could measure the impact of the digital spend. Marketing and IT could live thought of as flip sides of the coin that helped scamper the company toward its own transformation. Both were internal clients of Adobe software: using web content management and marketing analytics and measurement technology.
The consume of the digital data needs to live guided by deep insight into the company’s faultfinding 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 consume 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 develop the shift to digital. This mainly focused on “people, processes and technologies.” They codified their learnings in on-demand videos to profit scale the attain of this learning content. In parallel, on the IT side, Adobe formed the Adobe@Adobe team to evangelize the consume of Adobe technology to address marketing consume cases.
Ron Nagy, Sr. Evangelist Adobe@Adobe, develops consume 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 debate 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 debate 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 role 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 fresh product development.
Capturing and sharing the knowledge of Adobe practitioners, who possess deep operational knowledge, is likewise a faultfinding aspect of the program. However, Nagy notes that some translation of that message is needed: “If you are starting a program – there own to live individuals with knowledge of the tech, what is possible, and the business. You exigency to buy the input from practitioners and other sources then Do the translation to what is apposite to the marketplace.” These Adobe@Adobe consume 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 likewise 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 fraction of the IT organization, not fraction of sales; this deliberate separation, to bring an objective perspective. However, the marketing department, ecommerce department and the trade unit are likewise 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 usher 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 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 case 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 live blockbusters, based on the analysis of commentary in convivial media. The accuracy of their predictions (36 of 37 predictions were spot on) resulted in a summon from an executive from a major motion picture distributor who was keen to bear 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 attain which contributes to a significant increase in awareness of Adobe’s “big data” expertise. For example, Adobe got much exposure with over 7,000 press stories including first-rate Morning America, Today Show, CNBC Squawk Box and much more by identifying the medium 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 live given to what digitization of what knowledge should live undertaken and why.
Extracting meaningful insights from vast data troves is a challenge which ADI attacks with a methodical approach starting with the monitoring of 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 question 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 outcome may not live causal, but there is a strong correlation that suggests that timing of promotions is a prominent factor.”
The way that ADI is managed and the expectations of the team are important: the group has been set up as an entrepreneurial team with no Adobe P&L responsibility and softer success metrics affection thought leadership and earned media vs. conversion and sales. The team reports into Marketing and is allowed to experiment, which allows them to live innovative and buy risks and sometimes fail. Gaffney states, “We own a few definite measures of success, such as total number of press articles, size of circulation, syndication by well-known publishers affection Forbes, WSJ,” but equally notable are the door openers or the conversation starters that flow from ADI findings. Gaffney concludes, “ADI reports on notable trends and indicators of future trends, which are significant topics for their target audiences, and it eases the way for their sales teams and executives to engage with their current and future customers.”
Whether the strategic intent of digital transformation is to meet customers’ expectations, to innovate, or to enable efficiencies, organizations increasingly are recognizing that they exigency to transform their businesses in order to participate in the fresh digital world order or risk becoming irrelevant. But digitization for the sake of digitization is not the way to go. deep attention needs to live given to what digitization of what knowledge should live 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 faultfinding 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 solid in this fresh digital reality requires a thorough understanding of every bit of of your faultfinding knowledge assets, both definite and tacit. Equipped with a strategic knowledge map, corporate leaders can craft a competitive strategy and develop digital transformation a reality.
Agencies navigate issues of interoperability, data migrations, security and standards
The federal government is poignant to the cloud. There’s no doubt about that.
Momentum for cloud computing has been structure during the past year, after the fresh administration trumpeted the approach as a way to derive greater efficiency and cost savings from information technology investments.
At the behest of federal Chief Information Officer Vivek Kundra, the common Services Administration became the focus of gravity for cloud computing at civilian agencies, with the launch of a cloud storefront, Apps.gov, that offers business, productivity and convivial 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 likewise can facilitate collaboration with scientists outside the agency.
NASA explores the cloud with Nebula
Cloud computing has appeal for Web applications
But the journey to cloud computing infrastructures will buy a few more years to unfold, federal CIOs and industry experts say.
Issues of data portability among different cloud services, migration of existing data, security and the definition of standards for every bit of of those areas are the missing rungs on the ladder to the clouds.
“Cloud computing is not a technology that can just live turned on overnight,” said Peter Tseronis, deputy associate CIO of the Energy Department and chairman of the Federal Cloud Computing Advisory Council.
“We spent a lot of terminal year defining what the cloud is, what are the various delivery models, deployments and characteristics,” Tseronis said. “We soundless continue to exigency 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 live rapidly provisioned and released with minimal management effort or service-provider interaction.
The three delivery models include:
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 Do you set up a service-level agreement? What are the data interoperability and portability issues?
The Bureau of Alcohol, Tobacco, Firearms and Explosives hasn’t launched a specific cloud project, but officials own been evaluating the benefits and risks for more than a year because a scamper to the cloud seems affection 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 own concerns about data separation because they want to avert a commingling of data with tenants in other environments. And they exigency access restrictions on data to develop certain cloud hosting providers or other tenants don’t inadvertently or intentionally glean access to sensitive data.
“We are every bit of 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 travail is necessary to hash out security requirements that federal agencies exigency to ensue to ensure that sensitive but unclassified and classified information is secure, Holgate said.
First, cloud providers exigency to understand government security requirements and deliver services that satisfy those requirements. Microsoft recently created a federal version of its trade Productivity Online Services for the cloud, which is one case of how vendors could profit address security requirements, he said.
On the federal side, “we exigency to probably Do a better job of articulating what those requirements are from a security perspective,” Holgate said.
The federal government soundless has a fragmented approach to security, he said. “We don’t own 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 own been addressing security requirements, and the Justice Department tackled the problem at a department level, Holgate said.
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