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C9550-273 - IBM Business Process Manager Advanced V8.0 Integration Development - Dump Information

Vendor : IBM
Exam Code : C9550-273
Exam Name : IBM Business Process Manager Advanced V8.0 Integration Development
Questions and Answers : 53 Q & A
Updated On : October 16, 2018
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C9550-273 Questions and Answers

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C9550-273 IBM Business Process Manager Advanced V8.0 Integration Development

Study Guide Prepared by Killexams.com IBM Dumps Experts


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C9550-273 exam Dumps Source : IBM Business Process Manager Advanced V8.0 Integration Development

Test Code : C9550-273
Test Name : IBM Business Process Manager Advanced V8.0 Integration Development
Vendor Name : IBM
Q&A : 53 Real Questions

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IBM IBM Business Process Manager

IBM Takes aim at Nutanix With Multicloud supervisor | killexams.com Real Questions and Pass4sure dumps

international business Machines(NYSE: IBM) has constructed an $18.5 billion cloud business through focusing on the needs of its customers. About 80% of mission-important workloads and delicate records nonetheless run on on-premises hardware because of efficiency or regulatory necessities, in keeping with Ovum. IBM's heavy focus on inner most cloud and hybrid cloud makes sense in that context.

another statistic driving IBM's cloud method: eighty five% of companies are the use of a couple of cloud ambiance, according to IBM's Institute for enterprise value. whereas Amazon internet features and Microsoft Azure dominate the marketplace for public cloud infrastructure, many groups are spreading workloads across dissimilar public clouds and on-premises hardware, in its place of going all in on a single public cloud. Managing those multi-cloud workloads can get complex quick.

IBM's Global Center for Watson IoT in Munich, Germany.© IBM IBM's world center for Watson IoT in Munich, Germany. A multi-cloud world

this is the place IBM is available in. The business introduced on Monday its Multicloud manager, a solution that enables valued clientele to manage complex multi-cloud workloads. The equipment provides a dashboard interface for managing container-primarily based purposes unfold across different cloud environments, even these of IBM's opponents, in addition to on-premises hardware.

The answer is optimized for IBM's cloud, however the enterprise above all mentioned guide for Amazon net functions, Microsoft Azure, and red Hat's inner most cloud. "With its open supply strategy to managing facts and apps across numerous clouds, the IBM Multicloud manager will enable companies to scale their many cloud investments and unleash the full business value of the cloud," spoke of IBM Hybrid Cloud senior vice chairman Arvind Krishna within the press unlock asserting the product.

while IBM claims that Multicloud supervisor is the "world's first multicloud administration technology," it is not entirely accurate. Managing workloads throughout different clouds is the bread and butter of Nutanix(NASDAQ: NTNX), a corporation that offers application that "melds private, public and distributed cloud operating environments and offers a single factor of manage to manage IT infrastructure and applications at any scale."

Nutanix generated $1.16 billion of earnings in fiscal 2018, with most of that coming from utility and help, which grew with the aid of forty seven% compared to 2017. there is a large and starting to be market for multi-cloud administration, and IBM's Multicloud manager is the company's effort to grab a piece of that pie.

IBM's key advantage over Nutanix should be its large trove of consumer relationships. IBM counts as valued clientele tremendous companies and governments, a few of which had been using IBM options for decades. As those corporations tiptoe into the cloud, IBM's Multicloud manager could be well placed to help simplify the system.

IBM's Multicloud Manager dashboard.© IBM IBM's Multicloud manager dashboard. manufacturer agnosticism

IBM has its own public cloud, so why is the company helping Amazon, Microsoft, and other rivals with its Multicloud supervisor? because at its core, IBM is an organization that solves complications for its shoppers. and infrequently an IBM-simplest answer doesn't cut it.

lower back within the Nineties, when outsider CEO Lou Gerstner changed into brought in to save the enterprise from give way, some of the pillars of the company's turnaround turned into manufacturer agnosticism. Gerstner wanted IBM's features company to be a one-cease shop in a position to constructing integrated solutions for its purchasers. however that required every now and then favoring competitors' products. Gerstner wrote in his e-book, Who Says Elephants can't Dance?:

To be truly a success, we would need to do issues that would shake the location to its roots. for instance, the functions unit would need to be capable of recommend the items of Microsoft, HP, sun, and all other most important IBM opponents if that, definitely, turned into the most desirable solution for the consumer. Of direction, we might must keep and repair these items as neatly.

With Multicloud manager, that manufacturer-agnostic approach is terribly a lot alive and neatly at IBM. It may not assist IBM win public cloud market share from Amazon and Microsoft, however will liberate a turning out to be marketplace for multi-cloud administration.

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John Mackey, CEO of total meals Market, an Amazon subsidiary, is a member of The Motley fool's board of directors. Teresa Kersten is an worker of LinkedIn and is a member of The Motley fool's board of directors. LinkedIn is owned via Microsoft. Timothy green owns shares of IBM. The Motley fool owns shares of and recommends Amazon. The Motley idiot recommends Nutanix. The Motley idiot has a disclosure policy.


IBM Unveils World's First Multicloud management know-how | killexams.com Real Questions and Pass4sure dumps

ARMONK, N.Y., Oct. 15, 2018 /PRNewswire/ -- IBM (NYSE: IBM) these days launched a new open know-how designed to make it less difficult to manage, circulate and combine apps across diverse cloud computing infrastructures. IBM's Multicloud supervisor offers an operations console for businesses as they increasingly include public and private cloud capabilities with present on-premises enterprise systems.

IBM Multicloud Manager is optimized on the IBM Cloud and extends the ability of businesses to manage, move and integrate apps across different cloud computing infrastructures.

in keeping with a new file from IBM's Institute for business value, essentially all corporations surveyed stated they're the usage of some type of cloud computing today, with 85 percent the usage of more than one cloud atmosphere. besides the fact that children, whereas the rush to cloud is colossal, research from Ovum suggests that eighty p.c of mission-vital workloads and delicate records are nevertheless running on on-premises business techniques, held again through issues of efficiency and regulatory requirements.

To handle this, IBM is introducing a new know-how that helps allow agencies to without problems create harmonized, 'multicloud' systems, with accelerated visibility of business approaches, governance and safety. The solution is optimized on the IBM Cloud, but extends the capability of companies to manage and integrate workloads on clouds from different suppliers akin to Amazon, pink Hat and Microsoft.

"With its open supply approach to managing records and apps across numerous clouds, the IBM Multicloud supervisor will position organizations to scale their many cloud investments and unleash the total company value of the cloud," spoke of Arvind Krishna, Senior vice president, IBM Hybrid Cloud. "In doing so, they will movement beyond the productivity economics of renting computing energy, to wholly leveraging the cloud to invent new company procedures and enter new markets."

a new generation of cloud expertise IBM's new Multicloud supervisor runs on the business's IBM Cloud private platform which is in response to Kubernetes container orchestration know-how – an open-supply method for 'wrapping' apps in containers thereby making them less complicated and cheaper to manipulate throughout distinct cloud environments – from on-premises methods to the public cloud. final week IBM announced that a whole bunch of international companies have turned to IBM Cloud inner most to help radically change their agencies.

Now, with Multicloud manager, IBM is extending these capabilities to interconnect distinctive clouds, even from distinctive suppliers, growing unified programs designed for accelerated consistency, automation and predictability. at the coronary heart of the new solution is a first of a kind dashboard interface for conveniently managing thousands of Kubernetes functions and spanning big volumes of information inspite of where within the firm they can be found.

huge benefits for business the new answer is anticipated to be a online game changer for modernizing groups around the world. as an example, if a automobile condominium enterprise uses one cloud for its AI functions, another for its bookings gadget and runs its economic processes on on-premises computer systems at workplaces around the world, IBM Multicloud supervisor can span the business's diverse computing infrastructures enabling customers to book a motor vehicle at extended speeds the use of the business's cellular app.

The inventive features and benefits of IBM's new Multicloud supervisor encompass:

  • multiplied visibility throughout clouds – operations and development groups get visibility of Kubernetes purposes and components across different clouds and clusters by the use of a single handle pane;
  • greater governance and security– using the integrated compliance and guidelines engine, groups can be sure that their Kubernetes functions aid their business compliance guidelines and protection necessities;
  • constant utility management – designed in order that clients can simplify IT and app operations management whereas expanding flexibility. they can also orchestrate and manipulate their Kubernetes environments with the click on of a button.
  • IBM Multicloud manager could be obtainable in October 2018.

    Analyst quotes "The ancient concept that every little thing would move to the general public cloud not ever took place," observed Stephen Elliot, program vice chairman, IDC. "in its place, the cloud market has developed to meet the needs of shoppers who are looking to keep on-premises programs while tapping a multitude of cloud platforms and carriers. The problem for this approach is integration. Many IT corporations had been talking about multicloud, but so far the user adventure has been fragmented. IT and business executives are trying to find multicloud capabilities that cut back the dangers, and convey extra automation all through their cloud journeys."

    "Cloud computing is at an important inflection aspect in its evolution," noted Roy Illsley, distinguished Analyst, Ovum. "whereas 20 % of company procedures have already moved to the cloud, 80 % of mission-crucial workloads and delicate information are still operating on on-premises business techniques on account of performance and regulatory necessities. despite the fact, in 2019 the percent of these workloads running within the cloud will boost to over forty percent and we assume that in the coming years commercial enterprise consumers will flow to multicloud techniques that cater for a huge range of enterprise and IT needs." 

    "operating in a multicloud atmosphere is a truth for many business features nowadays, even though too often it occurs in silos. instead of ignoring or attempting to stifle organizational forays onto assorted clouds, IT must get enhanced at facilitating, orchestrating and optimizing enterprise multicloud footing," in keeping with a brand new IBM Institute of enterprise price record. "enterprises that collect harmonized multicloud platforms now can place themselves for superior aggressive knowledge and lessen fees."IBM Institute of business cost file: Assembling Your Cloud Orchestra

    About IBM CloudWith $18.5B in annual cloud income, IBM is the world leader in commercial enterprise cloud with a platform designed to fulfill the evolving needs of company and society. relocating past productivity and value improvements, the IBM Cloud is tuned for the AI and records calls for which are using actual differentiation in modern day commercial enterprise. IBM's inner most, public and hybrid choices supply the global scale businesses deserve to guide innovation across industries.

    Media contacts:Jonathan BattyIBM Media members of the family (Europe)jjbatty@ibm.com

    Michael ZimmermanIBM Media members of the family (US)mrzimmerman@us.ibm.com

    The IBM Multicloud Manager dashboard interface is designed to help people manage thousands of Kubernetes applications and spans huge volumes of data regardless of where they're located in the organization.

    IBM Corporation logo. (PRNewsFoto/IBM Corporation) (PRNewsFoto/) (PRNewsfoto/IBM)

    Cision View long-established content to down load multimedia:https://www.prnewswire.com/news-releases/ibm-unveils-worlds-first-multicloud-administration-technology-300731206.html

    source IBM


    IBM Launches New business manner administration software and capabilities | killexams.com Real Questions and Pass4sure dumps

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    C9550-273 IBM Business Process Manager Advanced V8.0 Integration Development

    Study Guide Prepared by Killexams.com IBM Dumps Experts


    Killexams.com C9550-273 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    C9550-273 exam Dumps Source : IBM Business Process Manager Advanced V8.0 Integration Development

    Test Code : C9550-273
    Test Name : IBM Business Process Manager Advanced V8.0 Integration Development
    Vendor Name : IBM
    Q&A : 53 Real Questions

    Questions had been precisely equal as i bought!
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    GenSight’s Project Portfolio Management Solution Chosen by Major Pharmaceutical Company | killexams.com real questions and Pass4sure dumps

    DOYLESTOWN, Pa.--(BUSINESS WIRE)--GenSight, a leader in integrated software for new project and portfolio management, announced today that GlaxoSmithKline (GSK) has selected GenSight’s Enterprise portfolio application to help sustain its development of innovative new products.

    GSK, one of the world’s leading research-based pharmaceutical and healthcare companies, recognized the need to implement an advanced technology solution to enable their portfolio management process. After reviewing a number of PPM solutions, GSK selected GenSight’s Project Portfolio Management application as the best tool to help drive the company’s project and portfolio selection process.

    Hanif Patel, GSK Project Leader for the Integrated Resource Management program said: “GSK chose GenSight because of its advanced portfolio analytics functionality, ease of configuration and ease of integration with the company’s existing business processes and data sources. The GenSight tool supports our aim to improve the efficiency of R&D by helping to make visible where resources can be best deployed across the portfolio.”

    “It’s exciting to work with a company so focused on consumer value and continuous innovation” said Michael Menard, President and Co-Founder of GenSight. “GSK has developed a very impressive portfolio management process, and we are proud they have selected GenSight’s technology to automate and enable this important part of their business.”

    The latest version of its flagship project portfolio management solution, GenSight v8.0, is scalable and web-based for enterprise portfolio management. The GenSight solution enables directors and executives in new product development, innovation, finance, and business management to fundamentally improve their organization’s ability to analyze, view, select, track and communicate the portfolio of projects bringing the greatest value. This comprehensive capability optimizes resource allocation to accelerate successful product development, time-to-market and value creation.

    About The GenSight Group

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    The company’s co-founders, David Munt and Michael Menard, have over 25 years’ experience in technology development and portfolio management, respectively. www.gensight.com


    Advanced Campaign Management Solutions Market: Increase in Number of Digital Marketing Startups in the Asia Pacific Fuels Market | killexams.com real questions and Pass4sure dumps

    Albany, NY -- (SBWIRE) -- 10/01/2018 -- 'Campaign management' includes product development, sales, and public relations to create, observe, and measure marketing programs focused on the target audience. Under campaign management, demand for products and services is estimated, and a suitable cost-effective and efficient method is determined to reach out to customers.

    Campaign management involves development of pricing strategy along with sales strategy in order to maximize profits. Surveys are also conducted to assess the effectiveness of campaigns. Public responses (received after surveys) to the campaign strategy are tracked and analyzed to determine whether or not the desired results and goals have been met.

    Preview Report @ https://www.transparencymarketresearch.com/advanced-campaign-management-solutions-market.html

    Advanced campaign management solutions help optimize the planning process, simplify management, automate implementation of multi-channel campaigns, and ease tracking and measurement of performance of the campaign. These solutions also help to optimize workflow and create clear tasks that ensure resources are allocated seamlessly across the enterprise.

    Digital business landscape has transformed customer expectations and behavior. Today's multi-channel environment offers numerous channels, opportunities, and means of consumer engagement. This leads to a sharp spike in marketing costs (spends and Return on Investment (ROI). Therefore, it is vital for an enterprise to target the right consumer at the right time through the right channel. Advanced campaign management solutions plays a key role in achieving this objective.

    Benefits of advanced campaign management solutions include improvement in marketing effectiveness due to fact-based decision-making through evaluation and comparison of campaign performance; better customer acquisition through segmentation and analytical models such as look-alike modeling; accurate and objective measurement of the success of the campaign; and optimization of marketing budgets.

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    Changing trends in digital marketing and rise in customer expectations to view real-time cross-channel advertisements are factors driving the advanced campaign management solutions market.

    Fluctuation in dynamics of advertisement channels, increase in implementation of stringent regulations on mobile marketing in various countries, and technological complications in terms of compatibility are some of the factors hampering the demand for advanced campaign management solutions.

    The advanced campaign management solutions market can be segmented into by software, deployment, services, end users and industry verticals. The software deployment segment can be further classified into cloud based and on-premise deployment. Services are further categorized into training and consulting services, system implementation and integration services, and maintenance and support. The end user segment includes advertisers, enterprises, and publishers.

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    Industry verticals comprises of Banking, Financial services and Insurance (BFSI), healthcare and pharmaceuticals, retail, travel and tourism, transportation, media and entertainment, telecommunication and IT, and others. Regional segmentation is done as North America, Europe, Asia-Pacific, South America and Middle East and Africa.

    In terms of region, North America is anticipated to dominate the global advanced campaign management solutions market during the forecast period due to the strong growth in Banking, Financial services and Insurance (BFSI), IT and telecommunication, and retail and manufacturing sectors; and presence of major advanced campaign management solution vendors in the region. The market in North America is expected to be followed by that in Europe and Asia Pacific during the forecast period.

    Demand for advanced campaign management solutions is likely to increase significantly in Asia Pacific in the near future owing to the growth in penetration of BFSI, and IT and telecommunication companies; and increase in number of digital marketing startups in the region.

    Advancement in technologies has led to an increase in advanced mobility options, internet usage, and social networking. As a result, most of the companies have started aiming on high ROI solutions over the traditional marketing methods through advanced campaign management solutions. Furthermore, rise in customer centric solutions and proximity marketing campaigns is augmenting the global advanced campaign management solutions market.

    Prominent players operating in the advanced campaign management solutions market include Imagine Communications Corp., Adobe Systems Inc., Comarch SA, WNS (Holdings) Ltd., Salesforce.com Inc., Oracle Corporation, SAP SE, International Business Machines (IBM) Corporation, and Capgemini.


    Predictive Analytics in 2018: What’s Possible, Who’s Doing It, and How | killexams.com real questions and Pass4sure dumps

    In 2009, Netflix offered $1 million to anyone who could improve the quality of its recommendation engine by 10%. It took two years, but a team finally won. Netflix paid the bounty—then ignored the code.

    As it turned out, the enhanced algorithms “did not seem to justify the engineering effort needed to bring them into a production environment.”

    Not only did the winning prediction engine fail to scale economically, it also addressed an outdated problem: The shift from mail to streaming during that same two-year window gave Netflix all the data it needed to develop newer, better algorithms.

    Predictive analytics, in other words, wasn’t a panacea. Nor, in the decade since, has it become one. But, in 2018, incremental gains no longer cost $1 million either:

  • You have more data;
  • Storage is cheap; and
  • Cloud computing is almost infinitely scalable.
  • This post details those changes and shows how several businesses—and not just behemoths—have cultivated the predictive analytics landscape.

    What’s changed in the last decade? 1. More data, more storage, more computing power

    Massive, cloud-based repositories of customer interactions, often called data lakes, are the raw source material for predictive analytics applications.

    Many companies have taken advantage of cheap cloud storage to stow away data for years—without even considering its potential use. (How many neglected data points do you have in Google Analytics, Google Ads, MailChimp, Marchex, Stripe, and similar services?)

    That dual growth in scale—of data collected and accessibility to it—has solved two primary challenges of predictive analytics implementation.

    Historically, raw computing power has been the other. As Andrew Pearson of Intelligencia notes, “Without significant hardware investments, predictive analytics programs either weren’t possible or too slow to be useful.”

    That, Pearson continued, has also changed: “Cloud-based analytics systems have added massive computer power into the mix.” Increasingly powerful systems cracked open the door for real-time predictive analytics.

    2. A world of real-time predictions

    For some, the age of “real-time” predictive analytics is here. Judah Phillips, the co-founder and CTO of Vizadata and founder of SmartCurrent, explained:

    We already live in a world of “real-time” predictive analytics. A simple predictive analysis is your arrival time in Waze. A more complex real-time prediction occurs billions of times worldwide every millisecond in matching certain types of digital advertising.

    Further, companies like Mintigo and Versium now offer real-time solutions for lead scoring, showing that the transition is technically possible. Possible, however, doesn’t mean perfect. Sam Underwood, a vice president at Futurety, acknowledged the complexity of necessary integrations:

    Especially in the mid-market world, the tools that gather data to turn into predictive modeling—CRM systems, social media aggregators, logistics, and purchasing systems—often do not have friendly APIs or other easy mechanisms with which to quickly gather and interpret data.

    That disconnect still thwarts even the most fundamental business cases for real-time predictive analytics. David Longstreet, the chief data scientist at FanThreeSixty, offered an example:

    In our world of sports and entertainment, for example, most sports teams do not know how many people are in a stadium for a game. Teams know how many tickets were distributed; however, they do not know in “real time” how many people are in the venue or stadium during the event.

    That knowledge gap hampers efforts to staff and stock the stadium appropriately. It’s also why interest in predictive analytics is almost universal, even if it vastly outpaces adoption.

    3. Slow adoption but soaring interest

    So how many businesses are actively using predictive analytics? According to research from Dresner Advisory Services, about 23%, a figure essentially unchanged from the prior year.

    predictive analytics adoption

    Less than a quarter of businesses are using predictive analytics—though almost all aspire to do so. (Image source)

    Interest, however, exceeds implementation. The same research suggests that 90% of businesses “attach, at minimum, some importance to advanced and predictive analytics.”

    So which questions are those 23% answering with predictive analytics? Let’s take a look.

    Which questions can marketers answer with predictive analytics?

    “They want to predict everything,” according to Underwood. And who wouldn’t want to know the exact foot (or web) traffic by month, day, and hour to streamline staffing (or allocate server resources)?

    But, Underwood continued, he tries to focus clients on “the one thing that, if we could predict it for you, would revolutionize your business.”

    In digital marketing, Phillips outlined myriad use cases for predictive analytics, including the capability to predict:

  • which advertising will be most effective—however you define effective.
  • which marketing campaigns, channels, touches, behaviors, and demographics are contributing to a business outcome, a form of “machine learning–based attribution.”
  • which segment, test, or personalization a user is most likely to respond to.
  • the probability of users to click on an ad, to download a whitepaper, to respond to an email, to respond to an offer, and other customer response you define.
  • which leads will convert—however you define conversion.
  • which customers will buy one or more products for a cross-sell or upsell.
  • the number of purchases or revenue that will occur in the future.
  • which customers will have high/medium/low lifetime value.
  • customer churn.
  • The novel opportunity of predictive analytics, then, is not what you can predict but the fact that you can predict. The historical data you currently analyze can probably become a prediction.

    Just make sure you have the data.

    What do you need to get started with predictive analytics?

    Data, data, and data. “Priority 1A and 1B are data sources,” stated Underwood. That’s true whether you plan to license software or hire an outside organization. (Both options are detailed later.)

    All uses require training data. That training data, in turn, is used to build a predictive model to apply to current data. “The only limitation we’ve run into,” Phillips noted, “is a company’s available data for training.”

    How much data is enough? According to Phillips:

    A few thousand records with a sufficient amount of positive and negative outcomes can be sufficient for marketing, sales, and product prediction.

    Not all data is created (or stored) equally

    “You have to understand—I grew up tearing tickets.”

    FanThreeSixty’s Longstreet has heard that same explanation from venue managers who have spent countless hours counting stacks of stubs after games. It’s a reason why vital data sources may not be easily accessible, or accessible at all.

    In stadiums, Longstreet explained, point-of-sale machines and ticket scanners exist for a single purpose—to complete transactions quickly and keep lines moving. Those systems do not store data efficiently for extraction, nor can they handle incessant server requests (unless hungry fans don’t mind waiting).

    For Underwood, clients tend to fall into one of two buckets, with half in each:

  • “The ideal client has an internal database set up and ready to go. We pull in the data, build the model, and are off and running.”
  • The other half have a mix of data sources, which inevitably include an offshore SQL database (or ten) managed by an external vendor whom no one can track down.
  • Stitching data sources together is a major development project that may require creating custom connectors, setting up third-party FTP drops, and other complex but thankless tasks. That work, however, is necessary: Models and their predictions are only as accurate as the data they’re built upon.

    Don’t forget external data sources

    Not all data comes from internal sources, either. External data sources, like weather reports, are often a critical addition to data lakes, especially for small businesses. As Underwood explained:

    Restaurants may use analytics to trigger email sends; for example, we can set up the email platform to sync with National Weather Service data to send an email about iced tea when the temperature in a given metro area is above 90 degrees.

    Likewise, we can trigger an email to send to customers in a given city if the system detects wind gusts of 40+ MPH. Both of these use cases reach consumers in a key moment of need, negating downstream ad spend and beating competitors to the punch.

    So you have a large, well-organized dataset. What do you do with it?

    How do you turn data into predictions?

    While the limitation of insufficient data has faded, another remains:

    Companies require either a dedicated team of data scientists to parse through these sets, or a software suite powerful enough to do so rapidly. For most small and medium-sized businesses, this usually means settling for subpar software, or forgoing it entirely.

    For businesses of all sizes, solutions branch into two options:

  • Purchase software and create predictions in-house.
  • Pay an outside vendor to develop models and visualizations for you.
  • 1. Predictive analytics software

    The marketplace for predictive analytics software has ballooned: G2Crowd records 92 results in the category. Pricing varies substantially based on the number of users and, in some cases, amount of data, but generally starts around $1,000 per year, though it can easily scale into six figures.

    G2Crowd lists both IBM’s SPSS Statistics and SAS’s Advanced Analytics as market leaders at the enterprise level. Along with RStudio, the pair are also tagged as leaders for mid-market companies; only IBM retains a place in the “Leaders” quadrant for small businesses.

    Historically, however, even industry-leading predictive analytics software hasn’t been a simple, jump-right-in experience. Take these two examples from IBM’s SPSS Statistics and RapidMiner:

    ibm spss statistics screenshot

    rapid miner screenshot

    While these platforms are powerful, users must format data files, link nodes, and develop visualizations. Learning how to do this—and having the time to do it—is a specialized, full-time job. (To believe otherwise is to expect a Microsoft Word license to write your Great American Novel.)

    Not surprisingly, the market is shifting. RapidMiner has rolled out a SaaS beta that, with a bit of manual adjustment, translates an Excel sheet full of, say, employment data to a prediction of employee retention:

    rapid miner saas excel sheet

    A dataset in Excel—a starting point in which most marketers are already comfortable. rapid miner intake

    RapidMiner parses the Excel file prior to crunching the numbers. rapidminer predictive visualization

    The resulting visualization shows the tool’s prediction performance and correlations between datasets and retention.

    Some companies, like Vizadata’s Phillips, see the user-friendly SaaS model as the future:

    We are democratizing data science, so that people with limited or no data science or engineering skills can predict. You simply upload your data and click next. We do all the heavy lifting.

    Our intelligence determines your dependent and independent variables and the type of analysis to run. You can go with our selections or override them—from regression, where we can do forecasting and optimization, to both binary and multiclass classification, where we can predict the probability of outcomes.

    vizadata screenshot

    User-friendly SaaS models make predictive analytics more accessible to marketing teams without data scientists.

    Like Vizadata, MIT’s Endor pursues this path. The platform uses a query-builder to allow anyone to ask questions like “Where should we open our next store?” or “Who is likely to try product X?” It then mines targeted datasets to provide answers, often in a matter of minutes.

    The inclusion of tangential datasets that fall outside consideration—or feasibility—for human observers is a recurring advantage of predictive analytics. Endor’s creators offer an example:

    A marketing department for a bank asks, “Who is going to get a mortgage in the next six months?” Machine-learning engines may detect a pool of, say, 5,000 customers who have a bank credit card and a high credit score, and are married—many of which may be false positives.

    Endor detects more specific clusters of, say, couples about to get married or going through a divorce, founders who recently sold their startups to Facebook, or customers who recently graduated from a local real-estate course.

    Of course, if you want to outsource the process entirely, outside vendors can organize your data, build models, and visualize predictions for you.

    2. Outside vendors Agencies offering bespoke solutions

    For most clients, Futurety starts by identifying the key business question—not a specific metric or visualization. Clients may come in for one-off projects, annual re-runs of their data, or ongoing work.

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    “The end result is not always clear at the beginning,” Underwood explained. “When we’re delivering to someone close to the outcome, like a marketing manager, they’re typically happy with the model, the finding, and the math behind it.”

    The “end result” could be several things:

  • Integration with a third-party platform, like an email client, to automate predictive messaging.
  • Plain-text predictive answers to guide practitioners.
  • Robust visualizations to demonstrate the process and value to the C-Suite.
  • At the end of each engagement, Futurety delivers the model back to the client for management and maintenance.

    Predictive analytics at work
  • Futurety has a small business client that helps aspiring performing arts majors gain admission to their dream college. But few high schoolers have broad knowledge of good programs. More often than not, they know only one name: Julliard.
  • Futurety trained its model on three years of placement data. Then, using new student data entered into a common portal, predicted where students would get accepted and succeed academically.
  • The predictive analytics model, which Futurety updates annually, delivers a simple list of recommended schools for students based on factors like grades and exposure to different musical or artistic styles.
  • The model takes into account whether past placements graduated or won awards.
  • All-in-one niche providers

    FanThreeSixty serves a narrow market: sporting venues. Because they work with a comparatively consistent dataset—season ticket, concession, and souvenir sales—they know the range of business questions, data outcomes, and relevant visualizations.

    This consistency incentivizes niche vendors like FanThreeSixty to develop proprietary dashboards to roll out to all clients.

    The interface allows Longstreet’s team to keep data science in the background: “The secret of machine learning is when you’re being prompted behind the scenes.”

    Distilled fully, FanThreeSixty’s goal (and Longstreet’s explanation of his role at dinner parties) is to “help teams sell more tickets and hot dogs.”

    Predictive analytics at work
  • FanThreeSixty mines historical data to see which concessions are most commonly purchased with a hot dog at a Major League Soccer venue.
  • If a customer purchases a hot dog, concession staff are prompted to ask whether a customer would like to add the most popular accompaniment. That recommendation—a prediction of fan desire—changes based on other variables.
  • Predictions consider more than 20 datasets—everything from the home location of season ticket holders to the weather—to tailor messaging before, during, and after matches.
  • During cold-weather games, for example, FanThreeSixty can automate push notifications with tailored coupons, like buy-three-get-one-free hot chocolate for a family of four.
  • Whether solutions are internally or externally managed, they‘ve long been common in enterprise businesses.

    Predictive analytics use cases at the enterprise level

    Marketing departments in large organizations have used predictive analytics for years:

  • AutoTrader. AutoTrader uses data from its 40 million monthly visitors to better understand the sometimes lengthy customer journey. They built propensity models based on search behavior and created high-value lookalike audiences.
  • Editialis. The French publisher uses predictive analytics in its email campaigns to “anticipate engagement at an individual level.” As a result, they’ve seen click-through-rates increase “dramatically.”
  • Predictive analytics can also coordinate offline and online interactions, with two clear use cases for marketers whose companies have physical products or storefronts:

  • Improved pricing. Smartphone data registers in-store browsing habits to improve online or offline marketing targeting, approximating the advantages enjoyed by ecommerce companies.
  • Inventory management. Full warehouses cost money; empty shelves cost money. Folding online data, such as search patterns, into sales data can better manage inventory, especially at a regional and local level.
  • In addition to external marketing campaigns, predictive analytics also supports internal project management. Large marketing campaigns have many moving parts—a new ad campaign needs new creative, new copywriting, new landing pages, etc.

    Coordinating the involvement of those teams and accurately estimating the time-to-launch is complex. Many fail to get it right, sometimes at great expense.

    whiteboard planning

    Marketers and software businesses may use predictive analytics for internal project management in addition to external campaigns.

    Predictive algorithms, as McKinsey notes, use a wider lens that captures historical patterns and unique project elements in a single frame:

    While every development project is unique, the underlying complexity drivers across projects are similar and can be quantified. If companies understand the complexity involved in a new project, they can estimate the effort and resources required to complete it.

    Predictive analytics models “take into account not only the complexity of the project (both the functional and implementation aspects) but also the complexity of the team environment.”

    Predictive analytics at work:

    More accurate internal project management, in an example McKinsey offers, can have a major impact:

  • A company initially planned a product update to take roughly 300 person-weeks of effort, an estimate based on the limited number of changes between the current product and a new design.
  • However, that estimate failed to take into account the fact that planned updates would affect many different teams. Predictive analytics models did take it into account and estimated that the project would take three to four times as long.
  • As a result, the company limited the work to the original product team, enabling them to deliver the update on time.
  • In addition to helping companies solve internal and external challenges, predictive analytics is also the foundation for some businesses.

    Building a business on predictive analytics

    Ken Lazarus, CEO of the recruiting platform Scout Exchange, has an advantage—the company has been around for only five years.

    That means that the company’s data sources are already primed for extraction into its predictive models that pair companies with the right recruiter.

    scout exchange processScout Exchange’s predictions pair companies with the right recruiter.

    The single best predictor of job placement, Lazarus and his team have found, is the track record of job recruiters. In contrast, pairing the right job description with the right resume remains exceedingly difficult.

    “Job specs are horrible,” he lamented. “The data isn’t on the paper. CVs are pretty horrible, too.” (Data augmentation, such as skills testing and video interview decoding, Lazarus noted, offer potential improvements.)

    Nonetheless, holes remain. Candidates will never disclose negatives on their resume, and important information might forever remain “non-data,” such as whether a candidate is a good “culture fit.”

    Scaling data gathering

    Scout Exchange has honed its predictions by focusing on enterprise customers—its algorithms feast on hundreds or thousands of openings from Fortune 500 clients.

    As a result, the platform takes in roughly 1 million data points monthly, with each new job posting yielding an additional 50 data points.

    scout exchange platform

    Higher employer ratings indicate an employer is more responsive than his or her peers. Likewise, higher recruiter ratings suggest a recruiter is more likely to succeed in submitting acceptable candidates than his or her peers.

    Still, human assessment by a recruiter—and their client—is necessary. Lazarus drew a parallel: “Would you let machine learning pick your wife? No. But would you let it pick the right matchmaker to help you find a spouse? Yes.”

    Those who are trying to solve the most complex human issues aren’t even in the business world.

    Predictive analytics with life or death consequences

    The greatest challenges for predictive analytics are those that deal with complex, individualized human behavior, such as the likelihood that a patient or crisis-line texter will commit suicide.

    Because success or failure is measured in human lives, these challenges are also the most urgent. And while these projects operate beyond the scope of marketing and business, they suggest the potential for predictive analytics as it evolves.

    “REACH VET is not about trying to find the veteran who’s sitting in the car in a parking lot with a gun in his lap,” Aaron Eagan, Veteran Affairs deputy director for innovation told a Washington conference.

    “What we found,” Eagan continued, “is that veterans at highest risk of suicide [also have] significantly increased rates of all-cause mortality, accident morality, overdoses, violence, [and] opioids.” Proactive alerts that trigger physician check-ins have improved primary-care appointment attendance and reduced hospital admissions for mental health issues.

    crisis text line

    The project is similar to a collaboration between Periscope Data and Crisis Text Line, a text-based suicide hotline.

    Leaning on natural language processing and predictive analytics, the program analyzed conversations, forecasted trends, and trained more than 13,000 volunteers. The results?

  • Wait times decreased to less than 5 minutes, an operational goal.
  • Capacity increased by 10% during peak periods.
  • Responses were prioritized based on machine-identified urgency.
  • Endor’s technology has taken on similarly serious challenges. Using 15 million data points from 50 known ISIS supporters, Endor identified 80 lookalike accounts in less than half an hour, with only 35 false positives—expert investigation was still necessary yet feasible.

    In a collaborative project with the U.S. Defense Advanced Research Project Agency, the platform also analyzed mobile data to identify patterns to predict future riots.

    Conclusion

    Predictive analytics is not immune to criticism: GDPR rebuffs some of the same collection methods that swell data lakes. And not all predictions, even the most accurate, are well-received. (Famously, Target unwittingly informed a father of his teenage daughter’s pregnancy based on seemingly benign shopping habits.)

    Predictive analytics experts point out that their algorithms search for patterns among values, not the values themselves. Regardless, insufficient data is unlikely to hold back the expansion of the industry—the IoT, wearables, and other data collectors already supplement traditional web and app analytics.

    User-friendly SaaS platforms are still an emerging opportunity. For most businesses, creating models and predictions from historical data still requires a dedicated employee to navigate complex software solutions or the outsourcing of that work to a vendor.

    For those postponing predictive analytics projects until the SaaS options are more mature, you would be wise to keep filling your data lake.



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