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COG-605 IBM Cognos 10 Controller Developer

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COG-605 exam Dumps Source : IBM Cognos 10 Controller Developer

Test Code : COG-605
Test denomination : IBM Cognos 10 Controller Developer
Vendor denomination : IBM
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IBM IBM Cognos 10 Controller

IBM launches Cognos 10 | killexams.com true Questions and Pass4sure dumps

IBM has introduced the launch of the latest version of its company intelligence application, IBM Cognos 10.

The newest replace, which IBM says is the most colossal due to the fact that it bought Cognos, goals to elect analytics to cellular gadgets and to interpose a convivial networking approach to analytics, in order to embolden better collaboration.

Cognos 10 has a brand unique materialize and consider, which IBM says mirrors individuals's daily utilize of technology, and likewise comprehend true time analytics, and the capacity to deliver analytics to cellular instruments akin to iPhone and BlackBerry handsets.

The utility additionally extends the reporting of records, to existing analytics in a simpler to hold in intelligence format, and to invent analytics purchasable to the broader organisation, increasing the variety of stakeholders that may utilize commerce intelligence in the resolution making technique.

"The Cognos 10 software gives you a completely unique consumer event, which enables clients to achieve collective intelligence by course of connecting with others, sharing insights and setting up determination networks, therefore redefining the traditional strategies of interplay companies utilize with assistance and the manner clients collaborate with their peers," stated Bashar Kilani, Bashar Kilani, enterprise Unit government, IBM application neighborhood, IBM core East.


Cognos 10 and how it Represents the IBM Cloud mode | killexams.com true Questions and Pass4sure dumps

IBM’s cloud computing mode is patent to a degree with Cognos 10, the newest liberate of the company intelligence platform.

As a section of Cognos 10, IBM is delivering a cloud-based mostly provider they denomination Cognos 10 within the Cloud.

It’s a hosted service for businesses that exigency to switch its license to the cloud or entirely host online.

The mode gives some insight into the IBM software neighborhood’s approach to cloud computing.

We behold three distinctions to the Cognos strategy and how it displays on IBM’s method.

Hosted, no longer SaaS

The carrier has the identical elements that incorporates Cognos 10, arguably the spotlight of IBM’s suggestions on exact conference. It features predictive analytics and convivial collaboration capabilities via its integration with Lotus Connections.

Cognos 10 within the Cloud isn't within the vein of a traditonal SaaS service. You pay a license pervade for internet hosting your license. purchasers might likewise switch the license from on-premise to the IBM Cloud. consumers can likewise also opt to buy a hosted license with out investing in the on-premise service.

Streaming records from the Cloud

Cognos 10 is a cumbersome duty analytics engine. SPSS is baked in. It pulls in statistics from the cloud, including unstructured data. clients can utilize third-birthday party data to accumulate a former evaluation, behold a real-time plug or finish “what if,” evaluation.

less About Cloud, more About birth

It’s questionable if there is exact for a sheer cloud computing provider. companies abide profound investments in data facilities however they are seeking the course to extend to a cloud atmosphere. That’s a mode they behold plenty from SAP, VMware, Microsoft and a number of others.

IBM says it’s mode with Cognos 10 is much less about the cloud than start. customers covet a spot to position their software license. That’s the birth IBM is making with Cognos 10 in the Cloud.

we've questions in regards to the IBM strategy. it’s just a petite a section of IBM’s cloud providing nonetheless it’s illustrative of a bigger approach to invent utilize of the cloud as an alternative start mannequin for typical software offerings.


IBM Cognos main the style for great records analytics | killexams.com true Questions and Pass4sure dumps

Introduction

IBM has dissimilar application commerce intelligence items together with Cognos Analytics (previously Cognos commerce Intelligence), Cognos TM1, Cognos specific, and Cognos Disclosure administration. IBM Cognos commerce Intelligence front-end can connect with SAP for HANA integration from IBM Cognos 10.2.1.9 onwards through JDBC records source connectivity (IBM, n.d.). based in 1966, Jabil Circuit, Inc is likely one of the greatest electronic components manufacturing capabilities companies masking a large-range of enterprise areas equivalent to deliver chain administration, electronic circuits, design engineering, logistics, telecommunications, and semiconductor add-ons for computing. GE is among the largest clients for Jabil Circuit (Universe, n.d.).

heritage of the problem

firms want closing mile facts analytics on their economic operations. After performing procurement of substances, creation of accomplished items, inventory management, supply chain administration, creating income orders, deliveries, transportation administration, superior shipping notifications, delivery, dealing with devices administration, invoice creation, ultimately the company has to duty the fiscal shut taking the refined information from the disparate records sources of the aforementioned enterprise processes. In an commerce aid Planning device, outright the above company processes are unified in a solitary database with a structured layout. youngsters, organizations conducting the company transactions in assorted database techniques and spreadsheets will exigency to spend a few hours to assemble the records to consolidate the customary ledger postings, money circulation, and funds stream (Srilu, 2013).

The records that comes into excel spreadsheets may additionally now not abide validation system to examine the enter of the values apart from checking numeric, alphanumeric mixtures. There isn't any ordinary pre-defined configuration installation in excel Spreadsheet migrated from another device. this may create problems for a corporation to consolidate and migrate the records from excel spreadsheets into monetary statements for every month conclusion. excel spreadsheets can drastically slack down the enterprise increase and the revenues of the commercial enterprise. If the commerce can't visualize the profits and regional breakdown of the key efficiency warning signs, they are not able to forecast the increase plot for the subsequent quarter or subsequent month. this can influence supply chain operations, superior planning optimization, and corporation network collaboration, organisation community planning. in the conclusion, the organization will undergo losses. Jabil changed into stricken by these fundamental problems to shut the finance of the corporations on time (Thomson, 2012).

Key solutions

Jabil implemented IBM company Analytics, IBM Cognos enterprise Intelligence, IBM Cognos company standpoint, IBM Cognos Controller, and IBM Cognos TM1 to beat the fiscal nearby conundrums. IBM enterprise Analytics consists of a bevy of analytics solutions for organizations.

clever and Intuitive Analytics

Jabil implemented IBM Cognos Analytics, the appropriate arm of IBM commerce Analytics company Intelligence Analytics. The respond from Cognos Analytics presents the fiscal enterprise teams to create the analytics stories on their own. they can access the statistics points from BI Analytics through diverse sources, and with touchstone drag and drop innovations they can construct the self-serviceable reports. enterprise teams can access IBM Cognos even when they are cellular or operating browser-primarily based materiel on the web. Streamlined workflow gives quite a few notifications for the service provider groups to motion their specific initiatives (IBM, n.d.).

commercial enterprise Key performance warning signs

IBM commercial enterprise efficiency management offers the insights to measure the efficiency of the commercial enterprise in entirety by means of bridging the gaps discovered between the supply chain administration operations and the economic closing of the company books. It provides alternate options to build superior planning for the corporation to incorporate ideas to stint the trends of the facts into the longer term by course of gazing the ancient patterns of the information in every enterprise group. This gives forecasting for supply chain operations to manufacture the products into the market for numerous segments and valued clientele. corporations that may celebrate the premonitory watch signals notifications and traits can set the tempo for enterprise boom for each quarter to point to the records insights into revenues. EPM likewise can panoply screen the project plans developed with selected economic budgets for a bevy of a portfolio of mission options spanning a number of items to set up a knowledge-pushed company. The scorecards of EPM additionally indicates the areas the state the felony and regulatory necessities for the agencies in making ready the fiscal statements (IBM, n.d.).

Definitive Analytics

IBM Prescriptive Analytics performs prognosis on the agency ordinary efficiency with strategic management strategies. The diagnosis ends up in offering counseled alternate options to explain, elect a gape at, report, and act. commerce workflow optimization drives automation of some choices. C-stage executives can manage the business-oriented decisions. These choices can probably arrive up on reviewing the brand loyalty of the consumers regarding specific items for either launching a unique product or increase an present product in the organization. The prescriptive evaluation can likewise tap into the localization of the events equipped by the company catering a bevy of local markets (IBM, n.d.).

Presaging Analytics

IBM commerce Intelligence offers predictive analytics for performing ultimate mile data mining analytics through wrangling the records with exploratory options to elect note the commerce perspectives from a variety of key performance indicators of commerce methods spawning from buying the uncooked substances via fiscal shut. organisations want insights to devise as a minimum 18 months forward of their superior planning optimization for supply chain administration processes. This may likewise subsist executed through several forecasting fashions and utilized statistical methods on the statistics and glean the statistics via textual content-primarily based analytics to create trend-based fashions for the future. This helps to forecast and forecast the variety of products to launch on the commercial shelves for each retailer available in the market with who Jabil is conducting the enterprise transactions. basically, Jabil manufactures digital components to bring together a number of digital contraptions, telecommunication, and community gadget. This requires granularity of the market tendencies and the predictive analytics likewise can hook up to connect to R language for leveraging any extra statistical programs. IBM’s Apache Spark can duty the analytics in-memory in distinction to Apache Hadoop that puts IBM ahead of the pack on file-primarily based techniques. Integration of Apache Spark with IBM predictive analytics and R can raise the materiel extra (IBM, n.d.).

Governance, Compliance, and risk administration

corporations performing groups throughout the globe exigency to meet regulatory compliance requirements when buying and selling with different international locations. The dangers can arrive up from the tax legal guidelines or customs tasks levied by course of other nations throughout the globe. To steadiness the change hazards, the company transactions carried out within the commerce aid planning system or through multiple database methods should subsist audited. IBM has the analytics options for managing the chance. The risk administration analytics will scan through the database of the corporation and ascertain the dangers involved via actual-time analytics for understanding the ever-expanding hazards within the transactions (IBM, n.d.).

notwithstanding, Jabil carried out not outright the above options. youngsters, a majority of the IBM Analytics options abide been implemented. The benefits derived through Jabil via IBM Analytics solutions offered astonishing insights to obtain the operational excellence for fiscal nearby and manufacturing vegetation to elude their company correctly. The solution can likewise subsist utilized to a few different industries that are within the manufacturing of semiconductor, high-tech, telecommunications, storage instruments, and community equipment. The manufacturing technique comprises a particular mode model to effect together the built-in design of the circuits and chips. The grasp information management constructed via Jabil via their analytics, and commerce performance administration to panoply screen the famous thing performance indications can subsist utilized to a considerable number of different industries which are under an identical class. The success of Jabil can subsist a potential chance for relaxation of the industries in that aspect to celebrate in line with the necessities evaluation and implementation of Jabil.

References

IBM (n.d.). Cognos Analytics. Retrieved November 9, 2015, from http://www.ibm.com/analytics/us/en/technology/items/cognos-analytics/

IBM (n.d.). Cognos enterprise Intelligence 10.2.1. Retrieved December 28, 2015, from http://www-969.ibm.com/utility/studies/compatibility/clarity-stories/file/html/prereqsForProduct?deliverableId=1330380859450#!

IBM (n.d.). enterprise efficiency management. Retrieved November 9, 2015, from http://www-03.ibm.com/software/items/en/class/efficiency-administration

IBM (n.d.). Predictive Analytics. Retrieved November 10, 2015, from http://www.ibm.com/analytics/us/en/technology/predictive-analytics/

IBM (n.d.). Prescriptive analytics. Retrieved November 9, 2015, from http://www-03.ibm.com/application/items/en/class/prescriptive-analytics

IBM (n.d.). risk administration. Retrieved November 10, 2015, from http://www.ibm.com/analytics/us/en/enterprise/possibility-administration.html

Srilu (2013). SAP modules overview and commerce strategies. Retrieved November 9, 2015, from http://www.slideshare.net/srilu999/sap-modules-overview-and-business-methods

Thomson, S. (2012). IBM Analytics offers Jabil superior perception Into monetary performance. Retrieved November 9, 2015, from http://www-03.ibm.com/utility/businesscasestudies/us/en/corp?synkey=M525334P42483U27

Universe, F. (n.d.). Jabil Circuit, Inc. history. Retrieved November 9, 2015, from http://www.fundinguniverse.com/enterprise-histories/jabil-circuit-inc-background/


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IBM Cognos 10 Controller Developer

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Ten unique Features for Report Users in IBM Cognos 10 Report Studio | killexams.com true questions and Pass4sure dumps

Instead of just focusing on the major features of IBM Cognos 10 Report Studio, Roger Johnson, coauthor of IBM Cognos 10 Report Studio: Practical Examples, looks at some lesser-known tools and remonstrate properties to further enhance your Report Studio projects. From the author of 

The biggest additions to IBM Cognos Report Studio in version 10 abide been energetic Reports and Statistics objects. These two unique ways of presenting data provide many unique options for report authors to create reports that match the analytical needs of the user community. Entire classes abide been created to focus on the creation of reports using these formats.

Other capabilities in IBM Cognos 10 Report Studio comprehend the integration of external data sources and the ability to reclaim Report Studio reports to subsist used directly by commerce Insight Advanced authors. Using external data sources in Report Studio has been a customer request since IBM Cognos tools moved to a web-based architecture. This enhancement gives the report consumer unique opportunities to process just the information needed for a specific situation. By using a common report definition between applications, users who aren't accustomed to complex reports can receive assistance to build reports that abide features beyond those normally available to commerce Insight Advanced users.

While the features I've discussed to this point are promoted as key features, other useful options in version 10 will increase the effectiveness of reports to your users. Some of these features were previously available through complex programming by the report developer, but IBM Cognos commerce Intelligence now makes this job much easier. As a report writer and instructor who can prize the ability to create report designs designed to expound a story, I want to highlight 10 of these unique features.

Alternative Text

The Alternative Text property has been added to graphical objects, such as images and charts, to allow screen readers to translate the text (see pattern 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.

Colored Regions, Plot belt Fill, Material Effects

The Colored Regions, Plot belt Fill, and Material Effects properties allow charts to abide more creative designs in order to enhance the overall presentation of the charts. Mixed with other chart presentation options, these features profit report developers to give unique polish to the presentation of content. pattern 2 shows the additional gradient options that are now available, along with the unique properties for the charts.

New Gradient Fill Types

Moving beyond simple linear gradients, some unique fill types provide more choices for enhanced presentations. Backgrounds behind pages, objects, and selected areas can now panoply a number of gradient designs. The fill types comprehend rectangular frames and embedded circular gradients, with many parameters to customize the blending of colors (see pattern 3).

Chart Combinations (Primary Axis, Secondary Axis, Primary Bottom, Secondary Bottom)

IBM Cognos 8 commerce Insight had the ability to set Y1 and Y2 axes. Now combination charts can subsist stacked with two more axes for better analysis of related numbers. These options can subsist integrated into dashboard design to enhance the presentation of related measures. pattern 4 shows a chart with the additional axes selected.

Query Calculations in Query Explorer replace Calculated Member, Calculated Measure, Set Expression, and Intersection (tuple)

In IBM Cognos 8 Report Studio, different objects were created for each of the dimensional functions. Now they're outright bundled into the Query Calculations (see pattern 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can abide enhanced calculations.

Summarize petite Slices and Exploded Slices

With pie charts, two improvements comprehend summarizing petite slices and exploding slices. These two options greatly ameliorate the presentation of pie charts to emphasize the most famous information (see pattern 6). In my classes, these options abide been requested for years, and now they're delivered in this release.

Trendlines, Category Baselines, and Numeric Baselines

Report Studio functions abide now improved on the predictive ability behind charts, without having to utilize external functions to calculate the numbers (see pattern 7).

Series Color Synchronization

With the changes made to enhance series, this feature allows report writers to simplify the legends when the series information is repeated across the combinations. By changing the series Color property to Match (see the short arrow in pattern 8), the legend shows the different product lines and the nested measures differently (indicated by the longer arrow), increasing the effectiveness of the presentation.

Custom Properties for Prompts

Several unique properties abide been added to enhance the prompt options. These changes allow report developers to create customized prompt options without the additional JavaScript programming required in earlier versions. Most of the prebuilt text labels with these prompts can subsist customized to meet the needs of individual reports (see pattern 9).

Dimensional Set Definitions

As a welcome addition to dimensional functions, the Set Definition option (see pattern 10) allows report authors to create complex subsets that are presented graphically. This feature can invent troubleshooting complex sets much easier by providing a more modular approach to set development. This was the best unique property for me, since it simplifies the development of sets that had to subsist replete of nested functions.


Amazon takes on commerce intelligence with QuickSight | killexams.com true questions and Pass4sure dumps

Amazon Web Services, already a titanic player in databases, is launching a unique commerce intelligence appliance to profit non-technical people invent sense of outright that data.

QuickSight, now in preview, is a “very hasty cloud-powered commerce intelligence service,” according to Amazon (AMZN) senior vice president Andy Jassy, speaking at AWS Re:invent in Las Vegas.

QuickSight promises to enable commerce users to peer inside the data residing in various AWS repositories, including RedShift data warehouse, Elastic MapReduce, and Amazon relational database services. There they can behold relationships between data, the company said.

Then a appliance called Autograph can profit them build an intelligence profile about outright that data, said Matt Wood, general manager of product strategy for AWS.

But some, including Forrester Research senior analyst Paul Miller, behold SPICE (for Super-fast Parallel In-memory Calculation Engine), as the true key here.

This is section of Amazon’s propel to invent services apropos to commerce users as well as the developers who abide driven the public cloud’s success so far.

Oh, and QuickSight will subsist 1/10th the cost of traditional BI tools, which went unnamed although Jassy renowned a appliance with a denomination that rhymes with “Hognos,” a reference to IBM’s (IBM) Cognos BI appliance but it’s not hard to behold QuickSight competing down the road with tools enjoy Tableau as well, Miller said.

This tale will subsist updated during the Re:Invent keynote.

For more on Amazon Web Services check out the video:

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This tale was updated with additional analyst remark and to correct the spelling of QuickSight.


Recapping the mediate 2019 “Journey to AI” community CrowdChat: AI everywhere | killexams.com true questions and Pass4sure dumps

Artificial intelligence is transforming every commerce process. Developers are incorporating AI – in the form of profound learning, machine learning and kindred technologies – into cloud-native applications and commerce processes through tools that enable them to compose these features as data-driven microservices.

On Thursday, IBM Corp. and SiliconANGLE’s sister market research solid Wikibon held a #Think2019 conference community CrowdChat to dispute how enterprises can invent the journey to AI in the cloud. The hourlong online session was well-attended and there was vibrant discussion of many issues related to the journey to AI.

The CrowdChat featured the following IBM AI matter matter experts: Carlo Appugliese, Data Science and Machine Learning; Matthias Funke, Hybrid Data Management; Madhu Kochar, Cross-analytics; Hemanth Manda, IBM Cloud Private for Data; Anantha Narasimhan, UGI; and Jason Tavoularis, commerce Analytics.

Here were the most noteworthy responses from these and other participants to each of the CrowdChat questions:

Q: Rob Thomas, general manager of IBM Analytics, has said there is no AI without an IA or information architecture. How are you modernizing your data estate — the organization of your data assets — to accumulate ready for an AI and multicloud world?

Katie Schafer: “If anyone is looking to learn more about ICP for Data, subsist sure to check-out session #2571, titled: Change the Game: Learn How to Win with AI happening on Wednesday, February 13th at 1:30pmPST in the great Theater on the Data & AI Campus.”

Hemanth Manda: “Having talked to a number of customers and commerce partners, this is an issue everyone is grappling with and they are addressing it through their unique platform offering ICP for Data, an integrated data and AI platform for multi-cloud”

Madhu Kochar: “Every client discussion starts with this dialog … and very captious to abide a trusted analytics data foundation. It starts with know your data, dependence your data and utilize your data to further drive insights”

Matthias Funke: “I behold this question arrive up everywhere. Modernization to gain agility, unique insights faster, and abide more people and commerce application profit from it … very often one needs to start at the bottom of the AI ladder and the question: How can I collect outright the data I need, and invent it accessible to the prerogative people, at the prerogative time? And how can I integrate data assets across different locations and data sources?”

Carlo Appugliese: “We drudgery with clients on their Data Science Journey and biggest factor to winning with AI is to invent sure you account for 3 things … The prerogative skills, the prerogative process/ culture and finally the correct tools.”

Jason Tavoularis: “Of course! AI requires data. if there’s no infrastructure, there can’t subsist much data, so you can’t expect the AI to subsist very smart.”

Anantha Narasimhan: “Our customers are looking at AI to profit drive digital and potentially commerce transformation. At the core of AI are a) People & Culture, b) Process, c) Data … With data present outright across the organization, getting a obliging handle on it is the very first step … Collect, Organize and then dissect data. and then Infuse AI models in order to operationalize … ML is a remarkable enabler for AI. They exigency to recall that AI can profit us win quickly.. or plunge flat quickly. Because if the data is not of obliging quality, the models will sling up atrocious insights”

Tanmay Sinha: “Quality of AI models is directly proportional to the character of data used to train the model. Without an information architecture to serve high-quality data, the AI models can subsist inconsistent, impertinent or worse biased.”

John Furrier: “I mediate that he’s really nailing the core AI (and ML) angle meta data or information that feeds AI engines is super important. If companies accumulate this prerogative then ML and AI soar to unique heights”

Jameskobielus: “There’s no practical AI without data quality, governance, prep, and training in a high-performance data lake. Modernizing your data estate in the multicloud for AI demands an industrialized DevOps approach that automates much/most of these processes … AI can’t subsist smart if data scientists can find the prerogative data to drive feature engineering etc. Likewise, AI models can’t finish their jobs with tall self-possession without upfront and ongoing training from fresh operational data … Infusing AI into the commerce requires that an operationalized data science pipeline with a tenacious real-time/streaming CI/CD workflow.”

David Floyer: “IMO, the future for analytics is real-time results. This means hasty execution of operational AI/Analytics near the data. It likewise means low-latency connections between applications wanting to automate processes and the AI/analytics required … For example, if you are wanting to ensure that only employees are entering enterprise premises, there will subsist many enterprises with the identical problem, and many solutions to purchase … 1. There are two sources of AI solutions – internal, and external, the timehonored invent or buy decision. For products and services owned, it is vital that data is collected about those services in IA. However, there are many technologies it would subsist easier to buy.”

Q: How are you increasing workload and consumption flexibility in your analytics systems?

Katie Schafer: “To learn more about how you can build a proper data architecture to ameliorate data accessibility, don’t miss The Road to AI—A Journey to Modernize Your Data Architecture session on Wednesday, February 13th at 3:30pmPST on the Data & AI Campus.”

Carlo Appugliese: “In Data Science … The key to success is replete access to outright data.. In my experience, this is spicy topic and there is a balance they abide to play between Security and Innovation….My sustain is replete access to data for your Data Scientist and Data Engineers is captious to your commerce innovating….Here is sessions at #IBMThink where Experian will travel into detail about their AI journey. https://myibm.ibm.com/events/think/all-sessions/session/6869A  …. Here is a blog where I explained a recent AI project working with Experian. https://www.ibmbigdatahub.com/blog/how-data-science-elite-helped-uncover-gold-mine-experian”

Matthias Funke: “This is gold to me. Having a catalog of data assets at my disposal without worrying about where data resides. Avoid or minimize data movement to avoid lag and cost is of tremendous value.”

Madhu Kochar: “As I talk to multiple clients, access to data especially murky data is critical. It is likewise famous that they abide obliging data virtualization story, meaning you finish not always to plug your data…. Capability to join your traditional data with IOT data, true time streaming data is captious to drive unique analytics insights”

Jennifer Shin: “the notion of being able to access outright data sounds enjoy a dream I had once… then I woke up and remembered I drudgery with people and data is mess. The reality is they can abide outright the data in the world, but it’s useless if it’s not accurate or of impoverished quality…. In a competitive market, there will always subsist businesses and both internal and external clients who want their data to subsist kept private if it provides an advantage. Being able to access the data I exigency when I exigency is more famous than having access to outright of it.”

David Floyer: “It is essential to abide multiple sources of data around key commerce processes, products and services. The character of AI/Advanced Analytics will subsist dictated by the character of the data sources.”

Q: Does your analytics strategy assume to plug data to analytics or analytics to data? Why?

Anantha Narasimhan: “Definitely analytics coming to data – so faster decision can subsist taken at source or nearby to it…btw, there is an exciting session on Data Modernization strategy in a Multicloud World – by Madhu Kochar: https://myibm.ibm.com/events/think/all-sessions/session/7235A and virtualization: https://myibm.ibm.com/events/think/all-sessions/session/7223A”

Katie Schafer: “For more on business-ready data, don’t miss the Digital Transformation: A commerce Ready Data Hub for Advanced Analytics session at mediate 2019 happening Friday, February 15th at 9:30amPST on the Data & AI Campus.”

Madhu Kochar: “Data Gravity rules! You bring analytics to data, that is the most optimal…. Especially the world of multi-cloud strategy this is captious that they hold data where it is, thus technology enjoy data virtualization, having governance built in to dependence the data drives to trusted AI”

Matthias Funke: “Analytics to data. Any data movement or copying is expensive and leads to outright kinds of issues (lineage, quality, latency, higher resource utilization and cost)”

Hemanth Manda: “always plug Analytics to Data .. that’s been their mantra . Data gravity should ordain your strategy. stirring against the gravity means you would stay up spending a ton of resources / money & is not sustainable”

Carlo Appugliese: “in my opinion, finish your analytics where the data is if you can.. There is no value in stirring lots of data, but there is significant commerce value in doing more analytics with your data. Its outright about rate and pace of AI projects.”

Tanmay Sinha: “Data is growing exponentially within an enterprise. stirring becomes an avoidable expense if you can bring analytics to your data!”

Sarbjeet Johal: “When doing #ML #AI, for compute intensive scenarios enjoy human genome sequencing elect data to compute. For data intensive scenarios (especially input), bring compute to data. #rethink”

Jennifer Shin: “In my experience, companies already collecting data find value in turning data into analytics, whereas companies developing unique products or services find more value in using analytics for data. The best #datascience teams needs to find the balance in doing both”

Jameskobielus: “In-situ/in-database analytics is a key foundation fo the titanic data revolution. Data gravity. Now with the edge looming larger as a data source, analytics is stirring closer to those nodes and getting more sophisticated there. Distributed AI.”

David Floyer: “Data in volume is costly to plug & takes a lot of time. Data loses value over time. so, it is usually much cheaper to plug code to data than data to code. This is especially true for operational AI/analytics, which should subsist moved nearby to data source where possible.…It is enthralling to celebrate that when AI systems are deployed, 90%+ of the code is in operational AI, rather than ML model development.”

Q: How policy-driven are your data analytics visibility, detection and reporting activities?

Carlo Appugliese: “One of the biggest I’ve seen is that companies mediate they are behind vs other companies.. What companies exigency to understand is that its a journey and they just exigency to start. most companies are learning and growing in this space…..I recommend, pick the one utilize case, effect petite team on the project and start. If it fails, that is normal. just goto next one and for the wins, it will cancel out many failed projects.”

Tanmay Sinha: “To ensure unbiased AI models, policies on data analytics are more famous than ever.”

Hemanth Manda: “very tiny to subsist honest & I mediate this is huge issue given increased and diverse regulations , GDPR being the latest… Hemanth Manda…Here is a session on Data Virtualization @ mediate 2019 that would subsist very valuable to attend :  https://myibm.ibm.com/events/think/all-sessions/session/7181A “

Matthias Funke: “How famous are obliging policies if their ratification is not automated? profound integration across the analytics ‘stack’ can unravel for that”

Madhu Kochar: “Every CDO would want to remark YES to this. exigency ML/AI based solutions to automate these activities, and they in IBM analytics abide solutions to invent this easy (a hard problem)”

Jennifer Shin: “there’s always a policy, but the restrictions depend on the purpose associated with how the data is being used. When my #datascience team built models for negotiation purposes, even their internal status reports listed their drudgery as confidential.”

John Furrier: “Policy driven will subsist a very famous portion of a machine driven future. Getting policy down and having machines pattern out unique policies on the flee address both on exact AI and true time AI”

Sarbjeet Johal: “ML and AI are next frontiers in Data Governance Platforms and these models will drudgery in conjunction with policies! So it’s “policy driven ML enabled” approach which seems most practical with the tools they abide today!”

Q: Are protection and compliance regimes built into your analytics systems, or bolted on? Why?

Matthias Funke: “I behold it as a never-ending journey. One is never done. There is a legacy to initiate with, but every moment, unique data (sources) may accumulate added to your current landscape. Fun!”

Tanmay Sinha: “Data privacy regulations are coming whether they enjoy them or not. GDPR is already here, CCPA is coming soon. Enterprises, petite and large, abide to starting thinking about the data being collected and shared.”

Jennifer Shin: “#analytics systems typically abide several layers of protection and compliance regimes. accessing the platform is at a system flush whereas anonymizing data depends on the data set (as well any contracts associated with it)”

David Floyer: “Early days for establishing compliance and protection policies. It will probably exigency a company to abide a Wall Street Journal calamity to focus minds on this issue!”

Q: How does your organization administer profiling, cleansing and cataloging of data?

Anantha Narasimhan: “this is perhaps the core of organization’s journey to AI or even to a successful Data Lake, Data Science…. there is an excellent session at THINK, hosted by Jay @jaylimburn -https://myibm.ibm.com/events/think/all-sessions/session/6913A …. some organizations refer to this as Data Preparation or Data Curation…. Here’s a obliging session at THINK, in case you are interested: https://myibm.ibm.com/events/think/all-sessions/session/6912A”

Carlo Appugliese: “In belt of Data Science, typically they comprehend a Data Engineer who drudgery side by side with Data Scientist and are captious to elect findings and effect into Catalog as well as provide key features needed to modeling phase…. You exigency a combination of a cross frictional team, the prerogative access to data and tools to build your AI foundation…. One the titanic areas they behold in AI is ability to justify what your predictive models are doing and finish you dependence them.. Let me expect everyone, finish you dependence the decision made by an AI/ML model?…Model warp is something they are very focused on, especially from a dev ops perspective. Understanding this is famous and captious to your organizations future as you incorporate key decisions using AI. So dependence AI but verify :)”

Sarbjeet Johal: “it’s mainly done at LOB flush in most of the companies I abide worked with in advisory capacity. Central tools, policies and procedures exigency to subsist built for data governance. I believe the WHAT of data cleansing and cataloging must stay with LOB and HOW with IT.”

Hemanth Manda: “as usual, there are multiple solutions too handle this, but ICP for Data is a platform that includes and enforces these capabilities by default .. Learn more @ this mediate session : https://myibm.ibm.com/events/think/all-sessions/session/5478A….here is a 3rd party listing of vendors offering cleansing tools : https://www.analyticsindiamag.com/10-best-data-cleaning-tools-get-data/“

Madhu Kochar: “Besides Profiling, cleansing, cataloging, Data classification is another captious attribute. Here is where Ml automation can travel a long way. IBM Information Server provides complete solution”

Pouya Fakhari: “An edge computing approach is made for the concept of the data warehouse, while sheer cloud computing fundamentally contradicts the concept. It is generally accepted that only edge computing makes sense for systems that collect data on a massive scale thoughts hybrid cloud edge…. E. g. an Edge Computing Device can outsource simple computing tasks to a cloud using a Function-As-A-Service concept. Here, the cloud does not store anything and no backend is set up on it. The cloud only offers computing power for any functions that are transmitted on the fly

Matthias Funke: “Would conform if you mediate about IoT utilize cases with massive volumes of data points continuously produced. Aggregation and storage can happen at the edge. It’s not just data warehousing though.”

Jennifer Shin: “I abide yet to behold a organization that has this process streamlined. Most established companies abide many, many meetings about how data set is going to subsist used internally and the logistics around it…. one of the advantages of structure cutting edge tech and creating unique data products/services is that this is dealt with further down the line”

David Floyer: “This an famous requirement in the maturing of AI/advanced analytics. Solutions should support distributed and multi-cloud data, and ideally support orchestration and optimization of stirring code to data or vice versa.”

John Furrier: “Clean data in —> remarkable ML and AI; not cleanly data in –> lots of cleanup. Just remark no to data pollution!!”

Q: What resources profit your enterprise deploy models anywhere, securely?

Madhu Kochar: “A built in governance for these models is captious as well.. so you really exigency data engineers, data scientist, data stewards exigency to collaborate”

Carlo Appugliese: “Using Watson Machine learning really gives you ability to train. deploy and monitor your models.. This really gives you model portability so you can train and deploy anywhere..”

Sarbjeet Johal: “Data Governance Policies + Data Governance Skills + Stated Policies. That covers outright people processes and tech aspects.”

Carlo Appugliese: “If you’re looking to build a unique Data Science Team?…Here is a blog I effect out on how to build a rock star Data Science Team! https://www.ibm.com/blogs/business-analytics/rock-star-ibm-data-science-elite-team/ “

Jennifer Shin: “In my experience, IT and operations teams are very famous when you exigency to corroborate that inevitable governance is in state within an #analytics system or exigency a unique policy to subsist effect in place… the best resource for deploying models anywhere, securely is a IT or technology team that is knowledgable, experienced and responsive!”

James Kobielus: “The core platform that enables enterprises to deploy models anywhere is a data-science CI/CD toolchain that can serve to any target device, node, hardware, container, and runtime environment. The “securely” requires tense access and integrity controls throughout.”

David Floyer: “End-to-end security from development, deployment, and updating is important, and not yet at outright common!”

Q: How are your analytics users using data visualization and low-code development tooling?

Katie Schafer: “Here’s a remarkable session that will showcase the unique capabilities in IBM Cognos Analytics 11.1 and how it uses AI to provides smarter self-service analytics:  https://myibm.ibm.com/events/think/all-sessions/session/3651A “

Anantha Narasimhan: “based on prior experience, when they want to accelerate self-service analytics, low code/no code become important… with Cognos Analytics 11.1, commerce Users can utilize natural language queries to accumulate insights into data.. and stunning visualization to clearly state trends or issues (sorry – impudent plug in) :)”

Matthias Funke: “I behold two categories of analytics users: Data Scientists using dev tooling enjoy jupyter notebooks and OSS visualization libraries, vs LoB users using canned reports and dashboards.”

Hemanth Manda: “I tried using Tableau, but gave up after a few days. Nothing beats Cognos especially after the latest improvements in 11.1”

James Kobielus: “Increasingly, analytics developers are using declarative, visual, low-code tooling to program AI/ML, with the tooling leveraging auto-ML to compile models for optimized execution on target platforms…. Analytics commerce users are likewise using self-service, visual tooling to build predictive and other advanced analytics for decision support–eg Cognos…. ML-driven augmented programming, leveraging low-code visual front-ends, is a huge research focus here at Wikibon. behold my report from a year ago: https://wikibon.com/augmented-programming-ml-development/ ”

Jennifer Shin: “I find more teams are using #datavisualization across an organization ranging from creating a realtime dashboard for the c suite to using it as a a tracking appliance for day to dat operations.”

Q: What is your organization doing to manage and mitigate warp in your models?

Katie Schafer: Here’s a session happening at mediate 2019 that will dive into Detecting and Mitigating warp in AI: https://myibm.ibm.com/events/think/all-sessions/session/3449A”

Carlo Appugliese: “What I’ve seen is companies are doing this manually but after the fact and really plunge short.. This is topic needs to subsist evaluated in the birth of your model development. They can really profit companies with this using tools.”

Madhu Kochar: “Bias in AI a very spicy topic and critical. There are remarkable examples, i will participate later on how many societal biases are in their datasets. So they really exigency tools and technology to profit on data traceability, explainability”

Jennifer Shin: “All models will abide warp because they live in a world without impeccable information, which is why being able to communicate the extent that the warp poses a risk is so essential in #AI….The best course to manage and mitigate warp in your model is to understand #statistics, #mathematics, #data, #science, #engineering and people…. algorithms aren’t in and of themselves bias, but it can increase the warp depending on how it is designed… Developing appropriate reporting and monitoring for models and algorithms implemented in productions is essential for limiting bias”

Steve Ardire: “Most people mediate algorithms are objective but in great section they’re opinions embedded in code. AI systems are black boxes; the data goes in and the respond comes out without an explanation for the decision. Algorithms that learn are putative to become more accurate unless warp intrudes and amplifies stereotypes….Current ML models understand what’s explicitly stated, but less obliging at anticipating what’s not said or implied…@DameWendyDBE University of Southampton, Growing role of #AI in their lives is ‘too famous to leave to men’ …Must develop effective mechanisms in algorithms to filter out biases and build ethics into AI with ability to read between the lines or what requires common sense.”

James Kobielus: “Debiasing models starts with debiasing data. Here’s a piece I published on the emerging best practices in this. From eventual year: https://www.informationweek.com/big-data/ai-machine-learning/debiasing-our-statistical-algorithms-down-to-their-roots/a/d-id/1331852

David Floyer: “This is an famous dependence issue! If a company is shown not subsist abide addressed this issue, there are strict risk of brand damage. E.g., a store with cameras with AI to profit employees meet, greet or challenge customers entering the store should subsist especially careful!”

Sarbjeet Johal: “always subsist training your models! Context injection mechanisms are impoverished with current toolings but they are conscious of this problem, that means, they are on their course to unravel it!…. you abide to remove warp from data input! Algos aren’t bias, data is! Always hold that in mind!”

Here’s the replete transcript of the CrowdChat and the polls. And reclaim this date for the Journey to Cloud CrowdChat, 9 a.m. PST Jan. 30, at https://www.crowdchat.net/think2019.

Image: Marcus Spiske/Unsplash

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