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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 title : IBM Cognos 10 Controller Developer
Vendor title : IBM
: 94 true Questions

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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 occupy analytics to cellular gadgets and to introduce a social networking approach to analytics, in order to cheer better collaboration.

Cognos 10 has a brand modern materialize and consider, which IBM says mirrors individuals's daily expend of technology, and moreover embrace lawful 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 reserve in wit format, and to earn analytics purchasable to the broader organisation, increasing the variety of stakeholders that may expend industry intelligence in the resolution making technique.

"The Cognos 10 software gives you a completely modern consumer event, which enables clients to achieve collective intelligence by way of connecting with others, sharing insights and setting up determination networks, therefore redefining the traditional strategies of interplay companies expend 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 method | killexams.com true Questions and Pass4sure dumps

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

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

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

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

We contemplate 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 demand conference. It features predictive analytics and social 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 impregnate for internet hosting your license. purchasers might moreover switch the license from on-premise to the IBM Cloud. consumers can moreover also opt to buy a hosted license with out investing in the on-premise service.

Streaming records from the Cloud

Cognos 10 is a hefty duty analytics engine. SPSS is baked in. It pulls in statistics from the cloud, including unstructured data. clients can expend third-birthday party data to fetch a faded evaluation, contemplate a real-time walk or Do “what if,” evaluation.

less About Cloud, more About birth

It’s questionable if there is demand for a unadulterated cloud computing provider. companies own abysmal investments in data facilities however they are seeking the way to extend to a cloud atmosphere. That’s a mode they contemplate plenty from SAP, VMware, Microsoft and a number of others.

IBM says it’s method with Cognos 10 is much less about the cloud than start. customers desire 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 small a fragment of IBM’s cloud providing nonetheless it’s illustrative of a bigger approach to earn expend of the cloud as an alternative start mannequin for typical software offerings.


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

Introduction

IBM has dissimilar application industry intelligence items together with Cognos Analytics (previously Cognos industry Intelligence), Cognos TM1, Cognos specific, and Cognos Disclosure administration. IBM Cognos industry 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 role the fiscal shut taking the refined information from the disparate records sources of the aforementioned enterprise processes. In an industry aid Planning device, every single the above company processes are unified in a single database with a structured layout. youngsters, organizations conducting the company transactions in assorted database techniques and spreadsheets will need 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 surpass spreadsheets may additionally now not own 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 surpass Spreadsheet migrated from another device. this may create problems for a corporation to consolidate and migrate the records from surpass spreadsheets into monetary statements for every month conclusion. surpass spreadsheets can drastically tedious down the enterprise multiply and the revenues of the commercial enterprise. If the industry can't visualize the profits and regional breakdown of the key efficiency warning signs, they are not able to foretell the multiply scheme 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 monetary proximate conundrums. IBM enterprise Analytics consists of a bevy of analytics solutions for organizations.

clever and Intuitive Analytics

Jabil implemented IBM Cognos Analytics, the commandeer arm of IBM industry Analytics company Intelligence Analytics. The acknowledge from Cognos Analytics presents the monetary enterprise teams to create the analytics stories on their own. they can access the statistics points from BI Analytics through diverse sources, and with standard 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 task the trends of the facts into the longer term by way 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 commemorate the premonitory watch signals notifications and traits can set the tempo for enterprise boom for each quarter to bespeak the records insights into revenues. EPM moreover can display 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 dwelling 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, occupy a stare at, report, and act. industry workflow optimization drives automation of some choices. C-stage executives can manage the business-oriented decisions. These choices can probably promote up on reviewing the brand loyalty of the consumers regarding specific items for either launching a modern product or multiply an present product in the organization. The prescriptive evaluation can moreover tap into the localization of the events equipped by the company catering a bevy of local markets (IBM, n.d.).

Presaging Analytics

IBM industry Intelligence offers predictive analytics for performing ultimate mile data mining analytics through wrangling the records with exploratory options to occupy note the industry perspectives from a variety of key performance indicators of industry 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 moreover 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 foretell 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 moreover can hook up to connect to R language for leveraging any extra statistical programs. IBM’s Apache Spark can role 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 need to meet regulatory compliance requirements when buying and selling with different international locations. The dangers can promote up from the tax legal guidelines or customs tasks levied by way of other nations throughout the globe. To steadiness the change hazards, the company transactions carried out within the industry 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 discover the dangers involved via actual-time analytics for understanding the ever-expanding hazards within the transactions (IBM, n.d.).

notwithstanding, Jabil carried out not every single the above options. youngsters, a majority of the IBM Analytics options own been implemented. The benefits derived through Jabil via IBM Analytics solutions offered astonishing insights to obtain the operational excellence for fiscal proximate and manufacturing vegetation to avoid their company correctly. The solution can moreover 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 method model to build together the built-in design of the circuits and chips. The grasp information management constructed via Jabil via their analytics, and industry performance administration to display screen the significant 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 commemorate 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 industry 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 modern 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 demur properties to further enhance your Report Studio projects. From the author of 

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

Other capabilities in IBM Cognos 10 Report Studio embrace the integration of external data sources and the ability to redeem Report Studio reports to subsist used directly by industry 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 modern 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 intricate reports can receive assistance to build reports that own features beyond those normally available to industry Insight Advanced users.

While the features I've discussed to this point are promoted as key features, other useful options in version 10 will multiply the effectiveness of reports to your users. Some of these features were previously available through intricate programming by the report developer, but IBM Cognos industry Intelligence now makes this job much easier. As a report writer and instructor who can prize the ability to create report designs designed to bid a story, I want to highlight 10 of these modern 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 figure 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.

Colored Regions, Plot zone Fill, Material Effects

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

New Gradient Fill Types

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

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

IBM Cognos 8 industry 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. figure 4 shows a chart with the additional axes selected.

Query Calculations in Query Explorer supersede 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 every single bundled into the Query Calculations (see figure 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can own enhanced calculations.

Summarize Small Slices and Exploded Slices

With pie charts, two improvements embrace summarizing small slices and exploding slices. These two options greatly better the presentation of pie charts to emphasize the most significant information (see figure 6). In my classes, these options own been requested for years, and now they're delivered in this release.

Trendlines, Category Baselines, and Numeric Baselines

Report Studio functions own now improved on the predictive ability behind charts, without having to expend external functions to compute the numbers (see figure 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 figure 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 modern properties own 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 figure 9).

Dimensional Set Definitions

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


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

Amazon Web Services, already a titanic player in databases, is launching a modern industry intelligence tool to serve non-technical people earn sense of every single that data.

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

QuickSight promises to enable industry 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 contemplate relationships between data, the company said.

Then a tool called Autograph can serve them build an intelligence profile about every single that data, said Matt Wood, common manager of product strategy for AWS.

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

This is fragment of Amazon’s thrust to earn services germane to industry users as well as the developers who own 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 notable a tool with a title that rhymes with “Hognos,” a reference to IBM’s (IBM) Cognos BI tool but it’s not difficult to contemplate QuickSight competing down the road with tools fancy Tableau as well, Miller said.

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

For more on Amazon Web Services check out the video:

Subscribe to Data Sheet, Fortune’s daily newsletter on the industry of technology.

This myth was updated with additional analyst observation and to reform 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 industry process. Developers are incorporating AI – in the contour of abysmal learning, machine learning and kindred technologies – into cloud-native applications and industry processes through tools that enable them to compose these features as data-driven microservices.

On Thursday, IBM Corp. and SiliconANGLE’s sister market research difficult Wikibon held a #Think2019 conference community CrowdChat to debate how enterprises can earn 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 subject 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, industry Analytics.

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

Q: Rob Thomas, common 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 fetch 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 big Theater on the Data & AI Campus.”

Hemanth Manda: “Having talked to a number of customers and industry partners, this is an issue everyone is grappling with and they are addressing it through their modern 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 faultfinding to own a trusted analytics data foundation. It starts with know your data, trust your data and expend your data to further drive insights”

Matthias Funke: “I contemplate this question promote up everywhere. Modernization to gain agility, modern insights faster, and own more people and industry application capitalize from it … very often one needs to start at the bottom of the AI ladder and the question: How can I collect every single the data I need, and earn it accessible to the right people, at the right time? And how can I integrate data assets across different locations and data sources?”

Carlo Appugliese: “We work with clients on their Data Science Journey and biggest factor to winning with AI is to earn sure you account for 3 things … The right skills, the right process/ culture and finally the reform 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 serve drive digital and potentially industry transformation. At the core of AI are a) People & Culture, b) Process, c) Data … With data present every single across the organization, getting a cordial wield 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 distinguished enabler for AI. They need to remember that AI can serve us win quickly.. or Fall flat quickly. Because if the data is not of cordial quality, the models will throw up substandard insights”

Tanmay Sinha: “Quality of AI models is directly proportional to the quality 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 fetch this right then ML and AI soar to modern 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 right data to drive feature engineering etc. Likewise, AI models can’t Do their jobs with high assurance without upfront and ongoing training from fresh operational data … Infusing AI into the industry requires that an operationalized data science pipeline with a strong real-time/streaming CI/CD workflow.”

David Floyer: “IMO, the future for analytics is real-time results. This means fleet execution of operational AI/Analytics near the data. It moreover 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 same problem, and many solutions to purchase … 1. There are two sources of AI solutions – internal, and external, the usual earn 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 better 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 complete access to every single data.. In my experience, this is passionate topic and there is a equipoise they own to play between Security and Innovation….My undergo is complete access to data for your Data Scientist and Data Engineers is faultfinding to your industry innovating….Here is sessions at #IBMThink where Experian will walk 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 unlit data is critical. It is moreover significant that they own cordial data virtualization story, import you Do not always to walk your data…. Capability to combine your traditional data with IOT data, true time streaming data is faultfinding to drive modern analytics insights”

Jennifer Shin: “the notion of being able to access every single data sounds fancy a dream I had once… then I woke up and remembered I work with people and data is mess. The reality is they can own every single the data in the world, but it’s useless if it’s not accurate or of poor 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 need when I need is more significant than having access to every single of it.”

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

Q: Does your analytics strategy presume to walk data to analytics or analytics to data? Why?

Anantha Narasimhan: “Definitely analytics coming to data – so faster conclusion can subsist taken at source or proximate 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 industry 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 faultfinding that they reserve data where it is, thus technology fancy data virtualization, having governance built in to trust the data drives to trusted AI”

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

Hemanth Manda: “always walk Analytics to Data .. that’s been their mantra . Data gravity should impose 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, Do your analytics where the data is if you can.. There is no value in stirring lots of data, but there is significant industry value in doing more analytics with your data. Its every single 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 fancy human genome sequencing occupy 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 modern products or services find more value in using analytics for data. The best #datascience teams needs to find the equipoise 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 walk & takes a lot of time. Data loses value over time. so, it is usually much cheaper to walk code to data than data to code. This is especially lawful for operational AI/analytics, which should subsist moved proximate to data source where possible.…It is involving to commemorate 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 need to understand is that its a journey and they just need to start. most companies are learning and growing in this space…..I recommend, pick the one expend case, build small 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 significant than ever.”

Hemanth Manda: “very dinky 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 significant are cordial policies if their ratification is not automated? abysmal integration across the analytics ‘stack’ can solve for that”

Madhu Kochar: “Every CDO would want to assure YES to this. need ML/AI based solutions to automate these activities, and they in IBM analytics own solutions to earn this light (a difficult 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 work as confidential.”

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

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

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

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

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

Jennifer Shin: “#analytics systems typically own several layers of protection and compliance regimes. accessing the platform is at a system even 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 need a company to own a Wall Street Journal catastrophe 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 cordial session at THINK, in case you are interested: https://myibm.ibm.com/events/think/all-sessions/session/6912A”

Carlo Appugliese: “In zone of Data Science, typically they embrace a Data Engineer who work side by side with Data Scientist and are faultfinding to occupy findings and build into Catalog as well as provide key features needed to modeling phase…. You need a combination of a cross frictional team, the right access to data and tools to build your AI foundation…. One the titanic areas they contemplate in AI is ability to complicated what your predictive models are doing and Do you trust them.. Let me query everyone, Do you trust the conclusion made by an AI/ML model?…Model jaundice is something they are very focused on, especially from a dev ops perspective. Understanding this is significant and faultfinding to your organizations future as you incorporate key decisions using AI. So Trust AI but verify :)”

Sarbjeet Johal: “it’s mainly done at LOB even in most of the companies I own worked with in advisory capacity. Central tools, policies and procedures need 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 wield 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 faultfinding attribute. Here is where Ml automation can walk 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 unadulterated 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 accord if you mediate about IoT expend cases with massive volumes of data points continuously produced. Aggregation and storage can occur at the edge. It’s not just data warehousing though.”

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

David Floyer: “This an significant 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 —> distinguished ML and AI; not spotless data in –> lots of cleanup. Just assure no to data pollution!!”

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

Madhu Kochar: “A built in governance for these models is faultfinding as well.. so you really need data engineers, data scientist, data stewards need 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 every single people processes and tech aspects.”

Carlo Appugliese: “If you’re looking to build a modern Data Science Team?…Here is a blog I build 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 significant when you need to authenticate that inevitable governance is in dwelling within an #analytics system or need a modern policy to subsist build 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 tight access and integrity controls throughout.”

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

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

Katie Schafer: “Here’s a distinguished session that will showcase the modern 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, industry Users can expend natural language queries to fetch insights into data.. and stunning visualization to clearly state trends or issues (sorry – shameless plug in) :)”

Matthias Funke: “I contemplate two categories of analytics users: Data Scientists using dev tooling fancy 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 industry users are moreover using self-service, visual tooling to build predictive and other advanced analytics for conclusion support–eg Cognos…. ML-driven augmented programming, leveraging low-code visual front-ends, is a huge research focus here at Wikibon. contemplate 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 tool for day to dat operations.”

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

Katie Schafer: Here’s a session happening at mediate 2019 that will dive into Detecting and Mitigating jaundice 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 Fall short.. This is topic needs to subsist evaluated in the nascence of your model development. They can really serve companies with this using tools.”

Madhu Kochar: “Bias in AI a very passionate topic and critical. There are distinguished examples, i will share later on how many societal biases are in their datasets. So they really need tools and technology to serve on data traceability, explainability”

Jennifer Shin: “All models will own jaundice because they live in a world without faultless information, which is why being able to communicate the extent that the jaundice poses a risk is so essential in #AI….The best way to manage and mitigate jaundice in your model is to understand #statistics, #mathematics, #data, #science, #engineering and people…. algorithms aren’t in and of themselves bias, but it can multiply the jaundice depending on how it is designed… Developing commandeer 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 big fragment they’re opinions embedded in code. AI systems are black boxes; the data goes in and the acknowledge comes out without an explanation for the decision. Algorithms that learn are supposed to become more accurate unless jaundice intrudes and amplifies stereotypes….Current ML models understand what’s explicitly stated, but less cordial at anticipating what’s not said or implied…@DameWendyDBE University of Southampton, Growing role of #AI in their lives is ‘too significant to leave to men’ …Must develop efficient 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 final 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 significant trust issue! If a company is shown not subsist own addressed this issue, there are severe risk of brand damage. E.g., a store with cameras with AI to serve employees meet, greet or challenge customers entering the store should subsist especially careful!”

Sarbjeet Johal: “always subsist training your models! Context injection mechanisms are poor with current toolings but they are sensible of this problem, that means, they are on their way to solve it!…. you own to remove jaundice from data input! Algos aren’t bias, data is! Always reserve that in mind!”

Here’s the complete transcript of the CrowdChat and the polls. And redeem 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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