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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 buy analytics to cellular gadgets and to interject a social networking approach to analytics, in order to encourage better collaboration.
Cognos 10 has a brand current materialize and consider, which IBM says mirrors individuals's daily utilize of technology, and too comprise loyal 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 champion in humor format, and to accomplish analytics purchasable to the broader organisation, increasing the variety of stakeholders that may utilize commerce intelligence in the resolution making technique.
"The Cognos 10 software gives you a completely current consumer event, which enables clients to achieve collective intelligence by passage of connecting with others, sharing insights and setting up determination networks, therefore redefining the traditional strategies of interplay companies utilize with assistance and the manner clients collaborate with their peers," stated Bashar Kilani, Bashar Kilani, enterprise Unit government, IBM application neighborhood, IBM core East.
IBM’s cloud computing method is clear-cut 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 designation Cognos 10 within the Cloud.
It’s a hosted service for businesses that requisite 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 espy 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 permeate for internet hosting your license. purchasers might too switch the license from on-premise to the IBM Cloud. consumers can too 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 utilize third-birthday party data to rep a archaic evaluation, espy a real-time trek or finish “what if,” evaluation.
less About Cloud, more About birth
It’s questionable if there is demand for a pure cloud computing provider. companies absorb abysmal investments in data facilities however they are seeking the passage to extend to a cloud atmosphere. That’s a fashion they espy 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 covet a spot to position their software license. That’s the birth IBM is making with Cognos 10 in the Cloud.
we've questions in regards to the IBM strategy. it’s just a wee a fragment of IBM’s cloud providing nonetheless it’s illustrative of a bigger approach to accomplish utilize of the cloud as an alternative start mannequin for typical software offerings.
IBM has dissimilar application commerce intelligence items together with Cognos Analytics (previously Cognos commerce Intelligence), Cognos TM1, Cognos specific, and Cognos Disclosure administration. IBM Cognos commerce Intelligence front-end can connect with SAP for HANA integration from IBM Cognos 10.2.1.9 onwards through JDBC records source connectivity (IBM, n.d.). based in 1966, Jabil Circuit, Inc is likely one of the greatest electronic components manufacturing capabilities companies masking a large-range of enterprise areas equivalent to deliver chain administration, electronic circuits, design engineering, logistics, telecommunications, and semiconductor add-ons for computing. GE is among the largest clients for Jabil Circuit (Universe, n.d.).
heritage of the problem
firms want closing mile facts analytics on their economic operations. After performing procurement of substances, creation of accomplished items, inventory management, supply chain administration, creating income orders, deliveries, transportation administration, superior shipping notifications, delivery, dealing with devices administration, invoice creation, ultimately the company has to function the fiscal shut taking the refined information from the disparate records sources of the aforementioned enterprise processes. In an commerce aid Planning device, total the above company processes are unified in a lone database with a structured layout. youngsters, organizations conducting the company transactions in assorted database techniques and spreadsheets will requisite to expend a few hours to assemble the records to consolidate the customary ledger postings, money circulation, and funds stream (Srilu, 2013).
The records that comes into excel spreadsheets may additionally now not absorb validation system to examine the enter of the values apart from checking numeric, alphanumeric mixtures. There isn't any ordinary pre-defined configuration installation in excel Spreadsheet migrated from another device. this may create problems for a corporation to consolidate and migrate the records from excel spreadsheets into monetary statements for every month conclusion. excel spreadsheets can drastically behind down the enterprise multiply and the revenues of the commercial enterprise. If the commerce can't visualize the profits and regional breakdown of the key efficiency warning signs, they are not able to forecast the multiply blueprint 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 financial nearby 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 commerce Analytics company Intelligence Analytics. The retort from Cognos Analytics presents the financial enterprise teams to create the analytics stories on their own. they can access the statistics points from BI Analytics through diverse sources, and with gauge 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 rig 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 assignment the trends of the facts into the longer term by passage 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 observe the premonitory watch signals notifications and traits can set the tempo for enterprise boom for each quarter to exhibit the records insights into revenues. EPM too can panoply screen the project plans developed with selected economic budgets for a bevy of a portfolio of mission options spanning a number of items to set up a knowledge-pushed company. The scorecards of EPM additionally indicates the areas the site 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, buy a seek at, report, and act. commerce workflow optimization drives automation of some choices. C-stage executives can manage the business-oriented decisions. These choices can probably approach up on reviewing the brand loyalty of the consumers regarding specific items for either launching a current product or multiply an present product in the organization. The prescriptive evaluation can too tap into the localization of the events equipped by the company catering a bevy of local markets (IBM, n.d.).
IBM commerce Intelligence offers predictive analytics for performing ultimate mile data mining analytics through wrangling the records with exploratory options to buy note the commerce perspectives from a variety of key performance indicators of commerce methods spawning from buying the uncooked substances via fiscal shut. organisations want insights to devise as a minimum 18 months forward of their superior planning optimization for supply chain administration processes. This may too 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 forecast and forecast the variety of products to launch on the commercial shelves for each retailer available in the market with who Jabil is conducting the enterprise transactions. basically, Jabil manufactures digital components to bring together a number of digital contraptions, telecommunication, and community gadget. This requires granularity of the market tendencies and the predictive analytics too can hook up to connect to R language for leveraging any extra statistical programs. IBM’s Apache Spark can function 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 rig extra (IBM, n.d.).
Governance, Compliance, and risk administration
corporations performing groups throughout the globe requisite to meet regulatory compliance requirements when buying and selling with different international locations. The dangers can approach up from the tax legal guidelines or customs tasks levied by passage of other nations throughout the globe. To steadiness the change hazards, the company transactions carried out within the commerce aid planning system or through multiple database methods should exist 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 total the above options. youngsters, a majority of the IBM Analytics options absorb been implemented. The benefits derived through Jabil via IBM Analytics solutions offered astounding insights to obtain the operational excellence for fiscal nearby and manufacturing vegetation to race their company correctly. The solution can too 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 method model to do together the built-in design of the circuits and chips. The grasp information management constructed via Jabil via their analytics, and commerce performance administration to panoply screen the principal 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 chance for relaxation of the industries in that angle to observe 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 commerce strategies. Retrieved November 9, 2015, from http://www.slideshare.net/srilu999/sap-modules-overview-and-business-methods
Thomson, S. (2012). IBM Analytics offers Jabil superior perception Into monetary performance. Retrieved November 9, 2015, from http://www-03.ibm.com/utility/businesscasestudies/us/en/corp?synkey=M525334P42483U27
Universe, F. (n.d.). Jabil Circuit, Inc. history. Retrieved November 9, 2015, from http://www.fundinguniverse.com/enterprise-histories/jabil-circuit-inc-background/
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IBM Cognos 10 Controller Developer
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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 kick properties to further enhance your Report Studio projects.
From the author of
The biggest additions to IBM Cognos Report Studio in version 10 absorb been vigorous Reports and Statistics objects. These two current ways of presenting data provide many current options for report authors to create reports that match the analytical needs of the user community. Entire classes absorb 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 aptitude to save Report Studio reports to exist used directly by commerce Insight Advanced authors. Using external data sources in Report Studio has been a customer request since IBM Cognos tools moved to a web-based architecture. This enhancement gives the report consumer current 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 complicated reports can receive assistance to build reports that absorb features beyond those normally available to commerce Insight Advanced users.
While the features I've discussed to this point are promoted as key features, other useful options in version 10 will multiply the effectiveness of reports to your users. Some of these features were previously available through complicated programming by the report developer, but IBM Cognos commerce Intelligence now makes this job much easier. As a report writer and instructor who can esteem the aptitude to create report designs designed to inform a story, I want to highlight 10 of these current 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 pattern 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.
Colored Regions, Plot area Fill, Material Effects
The Colored Regions, Plot area Fill, and Material Effects properties allow charts to absorb more creative designs in order to enhance the overall presentation of the charts. Mixed with other chart presentation options, these features alleviate report developers to give current polish to the presentation of content. pattern 2 shows the additional gradient options that are now available, along with the current properties for the charts.
New Gradient Fill Types
Moving beyond simple linear gradients, some current fill types provide more choices for enhanced presentations. Backgrounds behind pages, objects, and selected areas can now panoply a number of gradient designs. The fill types comprise rectangular frames and embedded circular gradients, with many parameters to customize the blending of colors (see pattern 3).
Chart Combinations (Primary Axis, Secondary Axis, Primary Bottom, Secondary Bottom)
IBM Cognos 8 commerce Insight had the aptitude 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. pattern 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 total bundled into the Query Calculations (see pattern 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can absorb enhanced calculations.
Summarize wee Slices and Exploded Slices
With pie charts, two improvements comprise summarizing wee slices and exploding slices. These two options greatly help the presentation of pie charts to emphasize the most principal information (see pattern 6). In my classes, these options absorb been requested for years, and now they're delivered in this release.
Trendlines, Category Baselines, and Numeric Baselines
Report Studio functions absorb now improved on the predictive aptitude behind charts, without having to utilize external functions to cipher the numbers (see pattern 7).
Series Color Synchronization
With the changes made to enhance series, this feature allows report writers to simplify the legends when the sequence information is repeated across the combinations. By changing the sequence Color property to Match (see the short arrow in pattern 8), the legend shows the different product lines and the nested measures differently (indicated by the longer arrow), increasing the effectiveness of the presentation.
Custom Properties for Prompts
Dimensional Set Definitions
As a welcome addition to dimensional functions, the Set Definition option (see pattern 10) allows report authors to create complicated subsets that are presented graphically. This feature can accomplish troubleshooting complicated sets much easier by providing a more modular approach to set development. This was the best current property for me, since it simplifies the evolution of sets that had to exist replete of nested functions.
Amazon Web Services, already a expansive player in databases, is launching a current commerce intelligence tool to alleviate non-technical people accomplish sense of total that data.
QuickSight, now in preview, is a “very mercurial cloud-powered commerce intelligence service,” according to Amazon (AMZN) senior vice president Andy Jassy, speaking at AWS Re:invent in Las Vegas.
QuickSight promises to enable commerce users to peer inside the data residing in various AWS repositories, including RedShift data warehouse, Elastic MapReduce, and Amazon relational database services. There they can espy relationships between data, the company said.
Then a tool called Autograph can alleviate them build an intelligence profile about total that data, said Matt Wood, general manager of product strategy for AWS.
But some, including Forrester Research senior analyst Paul Miller, espy SPICE (for Super-fast Parallel In-memory Calculation Engine), as the actual key here.
This is fragment of Amazon’s thrust to accomplish services apposite to commerce users as well as the developers who absorb 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 famed a tool with a designation that rhymes with “Hognos,” a reference to IBM’s (IBM) Cognos BI tool but it’s not arduous to espy QuickSight competing down the road with tools fancy Tableau as well, Miller said.
This narrative 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 commerce of technology.
This narrative was updated with additional analyst comment and to redress the spelling of QuickSight.
Artificial intelligence is transforming every commerce process. Developers are incorporating AI – in the configuration of abysmal learning, machine learning and kindred technologies – into cloud-native applications and commerce processes through tools that enable them to compose these features as data-driven microservices.
On Thursday, IBM Corp. and SiliconANGLE’s sister market research solid Wikibon held a #Think2019 conference community CrowdChat to debate how enterprises can accomplish 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 topic matter experts: Carlo Appugliese, Data Science and Machine Learning; Matthias Funke, Hybrid Data Management; Madhu Kochar, Cross-analytics; Hemanth Manda, IBM Cloud Private for Data; Anantha Narasimhan, UGI; and Jason Tavoularis, commerce Analytics.
Here were the most noteworthy responses from these and other participants to each of the CrowdChat questions:
Q: Rob Thomas, general manager of IBM Analytics, has said there is no AI without an IA or information architecture. How are you modernizing your data estate — the organization of your data assets — to rep ready for an AI and multicloud world?
Katie Schafer: “If anyone is looking to learn more about ICP for Data, exist sure to check-out session #2571, titled: Change the Game: Learn How to Win with AI happening on Wednesday, February 13th at 1:30pmPST in the great Theater on the Data & AI Campus.”
Hemanth Manda: “Having talked to a number of customers and commerce partners, this is an issue everyone is grappling with and they are addressing it through their current 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 censorious to absorb a trusted analytics data foundation. It starts with know your data, faith your data and utilize your data to further drive insights”
Matthias Funke: “I espy this question approach up everywhere. Modernization to gain agility, current insights faster, and absorb more people and commerce application capitalize from it … very often one needs to start at the bottom of the AI ladder and the question: How can I collect total the data I need, and accomplish it accessible to the perquisite people, at the perquisite 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 accomplish sure you account for 3 things … The perquisite skills, the perquisite process/ culture and finally the redress tools.”
Jason Tavoularis: “Of course! AI requires data. if there’s no infrastructure, there can’t exist much data, so you can’t hope the AI to exist very smart.”
Anantha Narasimhan: “Our customers are looking at AI to alleviate drive digital and potentially commerce transformation. At the core of AI are a) People & Culture, b) Process, c) Data … With data present total across the organization, getting a helpful ply 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 considerable enabler for AI. They requisite to recall that AI can alleviate us win quickly.. or tumble flat quickly. Because if the data is not of helpful quality, the models will hurl up atrocious 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 exist inconsistent, extraneous 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 rep this perquisite then ML and AI soar to current 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 perquisite data to drive feature engineering etc. Likewise, AI models can’t finish their jobs with high self-possession without upfront and ongoing training from fresh operational data … Infusing AI into the commerce requires that an operationalized data science pipeline with a strong real-time/streaming CI/CD workflow.”
David Floyer: “IMO, the future for analytics is real-time results. This means mercurial execution of operational AI/Analytics near the data. It too 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 identical problem, and many solutions to purchase … 1. There are two sources of AI solutions – internal, and external, the timehonored accomplish 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 help data accessibility, don’t miss The Road to AI—A Journey to Modernize Your Data Architecture session on Wednesday, February 13th at 3:30pmPST on the Data & AI Campus.”
Carlo Appugliese: “In Data Science … The key to success is replete access to total data.. In my experience, this is feverish topic and there is a equilibrium they absorb to play between Security and Innovation….My sustain is replete access to data for your Data Scientist and Data Engineers is censorious to your commerce 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 shadowy data is critical. It is too principal that they absorb helpful data virtualization story, acceptation you finish not always to trek your data…. Capability to associate your traditional data with IOT data, actual time streaming data is censorious to drive current analytics insights”
Jennifer Shin: “the notion of being able to access total 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 absorb total 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 requisite when I requisite is more principal than having access to total of it.”
David Floyer: “It is essential to absorb multiple sources of data around key commerce processes, products and services. The quality of AI/Advanced Analytics will exist dictated by the quality of the data sources.”
Q: Does your analytics strategy assume to trek data to analytics or analytics to data? Why?
Anantha Narasimhan: “Definitely analytics coming to data – so faster conclusion can exist taken at source or nearby to it…btw, there is an exciting session on Data Modernization strategy in a Multicloud World – by Madhu Kochar: https://myibm.ibm.com/events/think/all-sessions/session/7235A and virtualization: https://myibm.ibm.com/events/think/all-sessions/session/7223A”
Katie Schafer: “For more on business-ready data, don’t miss the Digital Transformation: A commerce Ready Data Hub for Advanced Analytics session at mediate 2019 happening Friday, February 15th at 9:30amPST on the Data & AI Campus.”
Madhu Kochar: “Data Gravity rules! You bring analytics to data, that is the most optimal…. Especially the world of multi-cloud strategy this is censorious that they champion 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 total kinds of issues (lineage, quality, latency, higher resource utilization and cost)”
Hemanth Manda: “always trek Analytics to Data .. that’s been their mantra . Data gravity should prescribe your strategy. stirring against the gravity means you would discontinuance up spending a ton of resources / money & is not sustainable”
Carlo Appugliese: “in my opinion, finish your analytics where the data is if you can.. There is no value in stirring lots of data, but there is significant commerce value in doing more analytics with your data. Its total 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 buy 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 current products or services find more value in using analytics for data. The best #datascience teams needs to find the equilibrium in doing both”
Jameskobielus: “In-situ/in-database analytics is a key foundation fo the expansive 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 trek & takes a lot of time. Data loses value over time. so, it is usually much cheaper to trek code to data than data to code. This is especially loyal for operational AI/analytics, which should exist moved nearby to data source where possible.…It is enchanting to observe 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 requisite to understand is that its a journey and they just requisite to start. most companies are learning and growing in this space…..I recommend, pick the one utilize case, do wee 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 principal than ever.”
Hemanth Manda: “very puny to exist 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 exist very valuable to attend : https://myibm.ibm.com/events/think/all-sessions/session/7181A “
Matthias Funke: “How principal are helpful policies if their ratification is not automated? abysmal integration across the analytics ‘stack’ can resolve for that”
Madhu Kochar: “Every CDO would want to teach YES to this. requisite ML/AI based solutions to automate these activities, and they in IBM analytics absorb solutions to accomplish this effortless (a arduous 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 principal portion of a machine driven future. Getting policy down and having machines pattern out current policies on the flee address both on demand AI and actual 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 absorb today!”
Q: Are protection and compliance regimes built into your analytics systems, or bolted on? Why?
Matthias Funke: “I espy it as a never-ending journey. One is never done. There is a legacy to start with, but every moment, current 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, wee and large, absorb to starting thinking about the data being collected and shared.”
Jennifer Shin: “#analytics systems typically absorb 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 requisite a company to absorb 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 refer to this as Data Preparation or Data Curation…. Here’s a helpful session at THINK, in case you are interested: https://myibm.ibm.com/events/think/all-sessions/session/6912A”
Carlo Appugliese: “In area of Data Science, typically they comprise a Data Engineer who toil side by side with Data Scientist and are censorious to buy findings and do into Catalog as well as provide key features needed to modeling phase…. You requisite a combination of a cross frictional team, the perquisite access to data and tools to build your AI foundation…. One the expansive areas they espy in AI is aptitude to complicated what your predictive models are doing and finish you faith them.. Let me interrogate everyone, finish you faith 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 principal and censorious to your organizations future as you incorporate key decisions using AI. So faith AI but verify :)”
Sarbjeet Johal: “it’s mainly done at LOB even in most of the companies I absorb worked with in advisory capacity. Central tools, policies and procedures requisite to exist built for data governance. I believe the WHAT of data cleansing and cataloging must sojourn with LOB and HOW with IT.”
Hemanth Manda: “as usual, there are multiple solutions too ply 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 censorious 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 pure 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 disagree if you mediate about IoT utilize cases with massive volumes of data points continuously produced. Aggregation and storage can befall at the edge. It’s not just data warehousing though.”
Jennifer Shin: “I absorb yet to espy a organization that has this process streamlined. Most established companies absorb many, many meetings about how data set is going to exist used internally and the logistics around it…. one of the advantages of structure cutting edge tech and creating current data products/services is that this is dealt with further down the line”
David Floyer: “This an principal requirement in the maturing of AI/advanced analytics. Solutions should champion distributed and multi-cloud data, and ideally champion orchestration and optimization of stirring code to data or vice versa.”
John Furrier: “Clean data in —> considerable ML and AI; not cleanly data in –> lots of cleanup. Just teach no to data pollution!!”
Q: What resources alleviate your enterprise deploy models anywhere, securely?
Madhu Kochar: “A built in governance for these models is censorious as well.. so you really requisite data engineers, data scientist, data stewards requisite to collaborate”
Carlo Appugliese: “Using Watson Machine learning really gives you aptitude 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 total people processes and tech aspects.”
Carlo Appugliese: “If you’re looking to build a current Data Science Team?…Here is a blog I do 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 principal when you requisite to substantiate that certain governance is in site within an #analytics system or requisite a current policy to exist do 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 taut access and integrity controls throughout.”
David Floyer: “End-to-end security from development, deployment, and updating is important, and not yet at total common!”
Q: How are your analytics users using data visualization and low-code evolution tooling?
Katie Schafer: “Here’s a considerable session that will showcase the current capabilities in IBM Cognos Analytics 11.1 and how it uses AI to provides smarter self-service analytics: https://myibm.ibm.com/events/think/all-sessions/session/3651A “
Anantha Narasimhan: “based on prior experience, when they want to accelerate self-service analytics, low code/no code become important… with Cognos Analytics 11.1, commerce Users can utilize natural language queries to rep insights into data.. and stunning visualization to clearly status trends or issues (sorry – unblushing plug in) :)”
Matthias Funke: “I espy 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 commerce users are too 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. espy 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 tumble short.. This is topic needs to exist evaluated in the dawn of your model development. They can really alleviate companies with this using tools.”
Madhu Kochar: “Bias in AI a very feverish topic and critical. There are considerable examples, i will share later on how many societal biases are in their datasets. So they really requisite tools and technology to alleviate on data traceability, explainability”
Jennifer Shin: “All models will absorb jaundice because they live in a world without consummate information, which is why being able to communicate the extent that the jaundice poses a risk is so essential in #AI….The best passage 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 great fragment they’re opinions embedded in code. AI systems are black boxes; the data goes in and the retort comes out without an explanation for the decision. Algorithms that learn are conjectural to become more accurate unless jaundice intrudes and amplifies stereotypes….Current ML models understand what’s explicitly stated, but less helpful at anticipating what’s not said or implied…@DameWendyDBE University of Southampton, Growing role of #AI in their lives is ‘too principal to leave to men’ …Must develop efficient mechanisms in algorithms to filter out biases and build ethics into AI with aptitude to read between the lines or what requires common sense.”
James Kobielus: “Debiasing models starts with debiasing data. Here’s a piece I published on the emerging best practices in this. From eventual year: https://www.informationweek.com/big-data/ai-machine-learning/debiasing-our-statistical-algorithms-down-to-their-roots/a/d-id/1331852
David Floyer: “This is an principal faith issue! If a company is shown not exist absorb addressed this issue, there are ascetic risk of brand damage. E.g., a store with cameras with AI to alleviate 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 sensible of this problem, that means, they are on their passage to resolve it!…. you absorb to remove jaundice from data input! Algos aren’t bias, data is! Always champion that in mind!”
Here’s the replete transcript of the CrowdChat and the polls. And save 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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