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COG-605 exam Dumps Source : IBM Cognos 10 Controller Developer
Test Code : COG-605
Test designation : IBM Cognos 10 Controller Developer
Vendor designation : IBM
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IBM IBM Cognos 10 Controller
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 assume analytics to cellular gadgets and to interpose a gregarious networking approach to analytics, in order to animate better collaboration.
Cognos 10 has a brand modern materialize and consider, which IBM says mirrors individuals's daily exhaust of technology, and moreover comprise birthright 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 keep in intellect format, and to beget analytics purchasable to the broader organisation, increasing the variety of stakeholders that may exhaust traffic 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 artery of connecting with others, sharing insights and setting up determination networks, therefore redefining the traditional strategies of interplay companies exhaust with assistance and the manner clients collaborate with their peers," stated Bashar Kilani, Bashar Kilani, enterprise Unit government, IBM application neighborhood, IBM core East.
IBM’s cloud computing manner is clear-cut to a degree with Cognos 10, the newest liberate of the company intelligence platform.
As a allotment of Cognos 10, IBM is delivering a cloud-based mostly provider they designation 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 manner gives some insight into the IBM software neighborhood’s approach to cloud computing.
We notice 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 require conference. It features predictive analytics and gregarious 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 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 heavy duty analytics engine. SPSS is baked in. It pulls in statistics from the cloud, including unstructured data. clients can exhaust third-birthday party data to rep a extinct evaluation, notice a real-time saunter or Do “what if,” evaluation.
less About Cloud, more About birth
It’s questionable if there is require for a unadulterated cloud computing provider. companies stand profound investments in data facilities however they are seeking the artery to extend to a cloud atmosphere. That’s a vogue they notice plenty from SAP, VMware, Microsoft and a number of others.
IBM says it’s manner with Cognos 10 is much less about the cloud than start. customers want 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 allotment of IBM’s cloud providing nonetheless it’s illustrative of a bigger approach to beget exhaust of the cloud as an alternative start mannequin for typical software offerings.
IBM has dissimilar application traffic intelligence items together with Cognos Analytics (previously Cognos traffic Intelligence), Cognos TM1, Cognos specific, and Cognos Disclosure administration. IBM Cognos traffic 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 traffic aid Planning device, sum 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 exigency to disburse 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 stand 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 unhurried down the enterprise extend and the revenues of the commercial enterprise. If the traffic can't visualize the profits and regional breakdown of the key efficiency warning signs, they are not able to call the extend 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).
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 traffic Analytics company Intelligence Analytics. The respond 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 yardstick 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 paraphernalia 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 chore the trends of the facts into the longer term by artery 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 prove 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 residence the felony and regulatory necessities for the agencies in making ready the fiscal statements (IBM, n.d.).
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, assume a study at, report, and act. traffic workflow optimization drives automation of some choices. C-stage executives can manage the business-oriented decisions. These choices can probably near up on reviewing the brand loyalty of the consumers regarding specific items for either launching a modern product or extend 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.).
IBM traffic Intelligence offers predictive analytics for performing ultimate mile data mining analytics through wrangling the records with exploratory options to assume note the traffic perspectives from a variety of key performance indicators of traffic 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 exist 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 call 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 paraphernalia 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 near up from the tax legal guidelines or customs tasks levied by artery of other nations throughout the globe. To steadiness the change hazards, the company transactions carried out within the traffic aid planning system or through multiple database methods should exist audited. IBM has the analytics options for managing the chance. The risk administration analytics will scan through the database of the corporation and learn the dangers involved via actual-time analytics for understanding the ever-expanding hazards within the transactions (IBM, n.d.).
notwithstanding, Jabil carried out not sum the above options. youngsters, a majority of the IBM Analytics options stand been implemented. The benefits derived through Jabil via IBM Analytics solutions offered fabulous insights to obtain the operational excellence for fiscal proximate and manufacturing vegetation to hasten their company correctly. The solution can moreover exist 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 manner model to residence together the built-in design of the circuits and chips. The grasp information management constructed via Jabil via their analytics, and traffic performance administration to display screen the distinguished thing performance indications can exist utilized to a considerable number of different industries which are under an identical class. The success of Jabil can exist a potential random for relaxation of the industries in that side to celebrate in line with the necessities evaluation and implementation of Jabil.
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 traffic 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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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 protest properties to further enhance your Report Studio projects.
From the author of
The biggest additions to IBM Cognos Report Studio in version 10 stand been energetic 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 stand been created to focus on the creation of reports using these formats.
Other capabilities in IBM Cognos 10 Report Studio comprise the integration of external data sources and the competence to rescue Report Studio reports to exist used directly by traffic 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 involved reports can receive assistance to build reports that stand features beyond those normally available to traffic Insight Advanced users.
While the features I've discussed to this point are promoted as key features, other useful options in version 10 will extend the effectiveness of reports to your users. Some of these features were previously available through involved programming by the report developer, but IBM Cognos traffic Intelligence now makes this job much easier. As a report writer and instructor who can esteem the competence to create report designs designed to divulge a story, I want to highlight 10 of these modern features.
The Alternative Text property has been added to graphical objects, such as images and charts, to allow screen readers to translate the text (see design 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.
Colored Regions, Plot region Fill, Material Effects
The Colored Regions, Plot region Fill, and Material Effects properties allow charts to stand more creative designs in order to enhance the overall presentation of the charts. Mixed with other chart presentation options, these features abet report developers to give modern polish to the presentation of content. design 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 comprise rectangular frames and embedded circular gradients, with many parameters to customize the blending of colors (see design 3).
Chart Combinations (Primary Axis, Secondary Axis, Primary Bottom, Secondary Bottom)
IBM Cognos 8 traffic Insight had the competence to set Y1 and Y2 axes. Now combination charts can exist stacked with two more axes for better analysis of related numbers. These options can exist integrated into dashboard design to enhance the presentation of related measures. design 4 shows a chart with the additional axes selected.
Query Calculations in Query Explorer supplant 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 sum bundled into the Query Calculations (see design 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can stand enhanced calculations.
Summarize petite Slices and Exploded Slices
With pie charts, two improvements comprise summarizing petite slices and exploding slices. These two options greatly better the presentation of pie charts to emphasize the most distinguished information (see design 6). In my classes, these options stand been requested for years, and now they're delivered in this release.
Trendlines, Category Baselines, and Numeric Baselines
Report Studio functions stand now improved on the predictive competence behind charts, without having to exhaust external functions to design the numbers (see design 7).
Series Color Synchronization
With the changes made to enhance series, this feature allows report writers to simplify the legends when the succession information is repeated across the combinations. By changing the succession Color property to Match (see the short arrow in design 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
Dimensional Set Definitions
As a welcome addition to dimensional functions, the Set Definition option (see design 10) allows report authors to create involved subsets that are presented graphically. This feature can beget troubleshooting involved sets much easier by providing a more modular approach to set development. This was the best modern property for me, since it simplifies the development of sets that had to exist plenary of nested functions.
Amazon Web Services, already a ample player in databases, is launching a modern traffic intelligence appliance to abet non-technical people beget sense of sum that data.
QuickSight, now in preview, is a “very lickety-split cloud-powered traffic intelligence service,” according to Amazon (AMZN) senior vice president Andy Jassy, speaking at AWS Re:invent in Las Vegas.
QuickSight promises to enable traffic 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 notice relationships between data, the company said.
Then a appliance called Autograph can abet them build an intelligence profile about sum that data, said Matt Wood, common manager of product strategy for AWS.
But some, including Forrester Research senior analyst Paul Miller, notice SPICE (for Super-fast Parallel In-memory Calculation Engine), as the true key here.
This is allotment of Amazon’s push to beget services apropos to traffic users as well as the developers who stand driven the public cloud’s success so far.
Oh, and QuickSight will exist 1/10th the cost of traditional BI tools, which went unnamed although Jassy famous a appliance with a designation that rhymes with “Hognos,” a reference to IBM’s (IBM) Cognos BI appliance but it’s not hard to notice QuickSight competing down the road with tools fancy Tableau as well, Miller said.
This yarn will exist 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 traffic of technology.
This yarn was updated with additional analyst comment and to amend the spelling of QuickSight.
Artificial intelligence is transforming every traffic process. Developers are incorporating AI – in the profile of profound learning, machine learning and kindred technologies – into cloud-native applications and traffic processes through tools that enable them to compose these features as data-driven microservices.
On Thursday, IBM Corp. and SiliconANGLE’s sister market research hard Wikibon held a #Think2019 conference community CrowdChat to debate how enterprises can beget 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, traffic 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 rep ready for an AI and multicloud world?
Katie Schafer: “If anyone is looking to learn more about ICP for Data, exist confident 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 traffic 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 critical to stand a trusted analytics data foundation. It starts with know your data, faith your data and exhaust your data to further drive insights”
Matthias Funke: “I notice this question near up everywhere. Modernization to gain agility, modern insights faster, and stand more people and traffic application profit from it … very often one needs to start at the bottom of the AI ladder and the question: How can I collect sum the data I need, and beget it accessible to the birthright people, at the birthright time? And how can I integrate data assets across different locations and data sources?”
Carlo Appugliese: “We toil with clients on their Data Science Journey and biggest factor to winning with AI is to beget confident you account for 3 things … The birthright skills, the birthright process/ culture and finally the amend tools.”
Jason Tavoularis: “Of course! AI requires data. if there’s no infrastructure, there can’t exist much data, so you can’t anticipate the AI to exist very smart.”
Anantha Narasimhan: “Our customers are looking at AI to abet drive digital and potentially traffic transformation. At the core of AI are a) People & Culture, b) Process, c) Data … With data present sum across the organization, getting a superior maneuver on it is the very first step … Collect, Organize and then analyze data. and then Infuse AI models in order to operationalize … ML is a Great enabler for AI. They exigency to recall that AI can abet us win quickly.. or tumble flat quickly. Because if the data is not of superior quality, the models will sling up basis insights”
Tanmay Sinha: “Quality of AI models is directly proportional to the attribute of data used to train the model. Without an information architecture to serve high-quality data, the AI models can exist inconsistent, impertinent or worse biased.”
John Furrier: “I reckon that he’s really nailing the core AI (and ML) angle meta data or information that feeds AI engines is super important. If companies rep this birthright 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 exist smart if data scientists can find the birthright data to drive feature engineering etc. Likewise, AI models can’t Do their jobs with elevated assurance without upfront and ongoing training from fresh operational data … Infusing AI into the traffic requires that an operationalized data science pipeline with a stalwart real-time/streaming CI/CD workflow.”
David Floyer: “IMO, the future for analytics is real-time results. This means lickety-split 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 exist many enterprises with the selfsame problem, and many solutions to purchase … 1. There are two sources of AI solutions – internal, and external, the natural beget 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 exist 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 plenary access to sum data.. In my experience, this is tart topic and there is a poise they stand to play between Security and Innovation….My sustain is plenary access to data for your Data Scientist and Data Engineers is critical to your traffic innovating….Here is sessions at #IBMThink where Experian will Go 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 obscure data is critical. It is moreover distinguished that they stand superior data virtualization story, significance you Do not always to saunter your data…. Capability to relate your traditional data with IOT data, true time streaming data is critical to drive modern analytics insights”
Jennifer Shin: “the notion of being able to access sum data sounds fancy a dream I had once… then I woke up and remembered I toil with people and data is mess. The reality is they can stand sum the data in the world, but it’s useless if it’s not accurate or of poverty-stricken quality…. In a competitive market, there will always exist businesses and both internal and external clients who want their data to exist kept private if it provides an advantage. Being able to access the data I exigency when I exigency is more distinguished than having access to sum of it.”
David Floyer: “It is essential to stand multiple sources of data around key traffic processes, products and services. The attribute of AI/Advanced Analytics will exist dictated by the attribute of the data sources.”
Q: Does your analytics strategy postulate to saunter data to analytics or analytics to data? Why?
Anantha Narasimhan: “Definitely analytics coming to data – so faster conclusion can exist 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 traffic Ready Data Hub for Advanced Analytics session at reckon 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 critical that they keep data where it is, thus technology fancy data virtualization, having governance built in to faith the data drives to trusted AI”
Matthias Funke: “Analytics to data. Any data movement or copying is expensive and leads to sum kinds of issues (lineage, quality, latency, higher resource utilization and cost)”
Hemanth Manda: “always saunter Analytics to Data .. that’s been their mantra . Data gravity should prescribe your strategy. poignant against the gravity means you would quit 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 poignant lots of data, but there is significant traffic value in doing more analytics with your data. Its sum about rate and pace of AI projects.”
Tanmay Sinha: “Data is growing exponentially within an enterprise. poignant 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 assume 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 poise in doing both”
Jameskobielus: “In-situ/in-database analytics is a key foundation fo the ample data revolution. Data gravity. Now with the edge looming larger as a data source, analytics is poignant closer to those nodes and getting more sophisticated there. Distributed AI.”
David Floyer: “Data in volume is costly to saunter & takes a lot of time. Data loses value over time. so, it is usually much cheaper to saunter code to data than data to code. This is especially birthright for operational AI/analytics, which should exist moved proximate to data source where possible.…It is Interesting 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 reckon 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 exhaust case, residence 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 distinguished than ever.”
Hemanth Manda: “very runt to exist honest & I reckon this is huge issue given increased and diverse regulations , GDPR being the latest… Hemanth Manda…Here is a session on Data Virtualization @ reckon 2019 that would exist very valuable to attend : https://myibm.ibm.com/events/think/all-sessions/session/7181A “
Matthias Funke: “How distinguished are superior policies if their ratification is not automated? profound integration across the analytics ‘stack’ can resolve for that”
Madhu Kochar: “Every CDO would want to boom YES to this. exigency ML/AI based solutions to automate these activities, and they in IBM analytics stand solutions to beget this facile (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 toil as confidential.”
John Furrier: “Policy driven will exist a very distinguished portion of a machine driven future. Getting policy down and having machines design out modern policies on the wing address both on require AI and true time AI”
Sarbjeet Johal: “ML and AI are next frontiers in Data Governance Platforms and these models will toil in conjunction with policies! So it’s “policy driven ML enabled” approach which seems most practical with the tools they stand today!”
Q: Are protection and compliance regimes built into your analytics systems, or bolted on? Why?
Matthias Funke: “I notice it as a never-ending journey. One is never done. There is a legacy to commence with, but every moment, modern data (sources) may rep 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, petite and large, stand to starting thinking about the data being collected and shared.”
Jennifer Shin: “#analytics systems typically stand several layers of protection and compliance regimes. accessing the platform is at a system flat 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 stand a Wall Street Journal disaster 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 advert to this as Data Preparation or Data Curation…. Here’s a superior session at THINK, in case you are interested: https://myibm.ibm.com/events/think/all-sessions/session/6912A”
Carlo Appugliese: “In region of Data Science, typically they comprise a Data Engineer who toil side by side with Data Scientist and are critical to assume findings and residence into Catalog as well as provide key features needed to modeling phase…. You exigency a combination of a cross frictional team, the birthright access to data and tools to build your AI foundation…. One the ample areas they notice in AI is competence to clarify what your predictive models are doing and Do you faith them.. Let me anticipate everyone, Do you faith the conclusion made by an AI/ML model?…Model prejudice is something they are very focused on, especially from a dev ops perspective. Understanding this is distinguished and critical to your organizations future as you incorporate key decisions using AI. So faith AI but verify :)”
Sarbjeet Johal: “it’s mainly done at LOB flat in most of the companies I stand worked with in advisory capacity. Central tools, policies and procedures exigency to exist built for data governance. I believe the WHAT of data cleansing and cataloging must linger with LOB and HOW with IT.”
Hemanth Manda: “as usual, there are multiple solutions too maneuver this, but ICP for Data is a platform that includes and enforces these capabilities by default .. Learn more @ this reckon 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 critical attribute. Here is where Ml automation can Go 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 coincide if you reckon about IoT exhaust 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 stand yet to notice a organization that has this process streamlined. Most established companies stand many, many meetings about how data set is going to exist 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 distinguished requirement in the maturing of AI/advanced analytics. Solutions should advocate distributed and multi-cloud data, and ideally advocate orchestration and optimization of poignant code to data or vice versa.”
John Furrier: “Clean data in —> Great ML and AI; not clean data in –> lots of cleanup. Just boom no to data pollution!!”
Q: What resources abet your enterprise deploy models anywhere, securely?
Madhu Kochar: “A built in governance for these models is critical as well.. so you really exigency data engineers, data scientist, data stewards exigency to collaborate”
Carlo Appugliese: “Using Watson Machine learning really gives you competence 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 sum people processes and tech aspects.”
Carlo Appugliese: “If you’re looking to build a modern Data Science Team?…Here is a blog I residence 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 distinguished when you exigency to corroborate that certain governance is in residence within an #analytics system or exigency a modern policy to exist residence 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 sum common!”
Q: How are your analytics users using data visualization and low-code development tooling?
Katie Schafer: “Here’s a Great 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, traffic Users can exhaust natural language queries to rep insights into data.. and stunning visualization to clearly situation trends or issues (sorry – unblushing plug in) :)”
Matthias Funke: “I notice 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 traffic 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. notice 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 prejudice in your models?
Katie Schafer: Here’s a session happening at reckon 2019 that will dive into Detecting and Mitigating prejudice 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 tumble short.. This is topic needs to exist evaluated in the birth of your model development. They can really abet companies with this using tools.”
Madhu Kochar: “Bias in AI a very tart topic and critical. There are Great examples, i will participate later on how many societal biases are in their datasets. So they really exigency tools and technology to abet on data traceability, explainability”
Jennifer Shin: “All models will stand prejudice because they live in a world without perfect information, which is why being able to communicate the extent that the prejudice poses a risk is so essential in #AI….The best artery to manage and mitigate prejudice in your model is to understand #statistics, #mathematics, #data, #science, #engineering and people…. algorithms aren’t in and of themselves bias, but it can extend the prejudice 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 reckon algorithms are objective but in great allotment 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 hypothetical to become more accurate unless prejudice intrudes and amplifies stereotypes….Current ML models understand what’s explicitly stated, but less superior at anticipating what’s not said or implied…@DameWendyDBE University of Southampton, Growing role of #AI in their lives is ‘too distinguished to leave to men’ …Must develop efficient mechanisms in algorithms to filter out biases and build ethics into AI with competence 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 last 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 distinguished faith issue! If a company is shown not exist stand addressed this issue, there are stern risk of brand damage. E.g., a store with cameras with AI to abet employees meet, greet or challenge customers entering the store should exist especially careful!”
Sarbjeet Johal: “always exist training your models! Context injection mechanisms are poverty-stricken with current toolings but they are aware of this problem, that means, they are on their artery to resolve it!…. you stand to remove prejudice from data input! Algos aren’t bias, data is! Always keep that in mind!”
Here’s the plenary transcript of the CrowdChat and the polls. And rescue 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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