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I simply got lower back from attending IBM consider in San Francisco. although it became a brief commute across the nation, i used to be inundated with IBM’s imaginative and prescient, covering topics from A (i.e. synthetic intelligence) to Z (i.e. equipment Z) and every little thing in between.
regardless of the wide-ranging discussion, IBM’s main focal point changed into on three areas: 1) hybrid cloud, 2) advanced analytics, and 3) safety. for instance, IBM’s hybrid cloud discussion based on digital transformation and leaned closely on its purple Hat acquisition, while superior analytics covered artificial intelligence (AI), cognitive computing (Watson), neural networks, and so forth. To display its capabilities in these areas, IBM paraded out valued clientele corresponding to Geico, Hyundai credit service provider, and Santander bank, who're making a bet on IBM for video game-altering digital transformation projects.IBM's cybersecurity plans
As for cybersecurity, listed below are a number of of my take-aways about IBM's plans:
IBM’s protection portfolio is pretty solid, and the enterprise appears to be more energized than in the past. After attending IBM think, I do have a couple of cybersecurity suggestions for folks in Armonk and Cambridge, Massachusetts:
In accepted, Armonk need to keep in mind that the IBM manufacturer is a advertising obstacle when competing for mindshare with providers like CrowdStrike, FireEye, Palo Alto Networks, and so forth. accordingly, IBM security need to work more durable and smarter to get the be aware out.
Many because of IBM for hosting me in San Francisco this week. I’ll be again on the Moscone middle for RSA within the blink of a watch.
IBM is enforcing a technique wherein in change for helping convey the advantages of blockchain to a growing checklist of industrial clients, it’s taking pictures seat and cloud revenues.
although, from its monetary reporting, it’s not possible to examine how much of massive Blue’s exact line is attributable to its blockchain activities.
“demonstrate me” investors are left to invest and that i am supplementing our full place in IBM with short-dated calls expiring simply after successive salary releases.
Over a 12 months and a half in the past, I wrote my first article about blockchain and massive Blue (IBM) entitled, “may This Be IBM’s earnings Catalyst?” In it, i discussed a number of such initiatives including a world container transport blockchain led via Maersk (OTCPK:AMKBY), a fruit and vegetable consortium led by using Walmart (WMT), and a exchange finance blockchain involving quite a lot of foreign banks, HSBC (HSBC) amongst them. These programs stand to bring wonderful – “sooner, enhanced, more cost-effective” – effects for his or her participants:
In that first article, I postulated that IBM would monetize blockchain from seat licenses not diverse to Bloomberg’s very successful model. In a comply with-up piece, I linked to an IBM page that established the validity of this inspiration with fees correlating to the measurement of the blockchain participant; larger groups paying greater, dinky ones gaining free access to the platform. essential mass and extensive participation are essential to multilateral processing represented by means of blockchain.enterprise keeps Coming
issues haven’t stood nonetheless. Blockchain initiatives involving IBM are proliferating straight away and in areas that I could not have imagined in 2017. okay, so Abu Dhabi’s ADNOC oil blockchain turned into less complicated to foresee. however, past my field of vision had been a healthcare blockchain and an additional having to do with scientific drug trials, blockchains for overseas change and derivatives, a equipment to video display groundwater usage, yet another worried with mining, and a blockchain for digital advertising. infrequently a week goes through where we don’t see new blockchain initiatives spawned by using corporations desperate to work with IBM; at the least dozens of programs are underway involving a whole bunch of major groups.but Its impact is Opaque
transport, meals, finance, oil, healthcare, medication, foreign exchange, derivatives, water, mining, advertising – obviously it isn’t misplaced on any person that these are areas of large economic activity. For me, this makes it even more fascinating that nowhere of their numbers does IBM document earnings at once brought on by blockchain, neither with seat licenses, nor cloud revenues, nor cost-added services corresponding to AI. No, IBM has chosen to preserve blockchain-derived revenues opaque. We have no way of connecting the dots.
I don’t understand why IBM has assumed this posture. youngsters, as adversarial to obsessing about it, I actually have chosen to flow on to the query, “Given this opaqueness coupled with the large advantage significance of blockchain, how could one factor it into an IBM investment choice?” answer, actually: “with the aid of speculating, as in with acceptable bets that blockchain will power results in different areas for which IBM does account.”Speculating with Calls
in opposition t massive Blue’s mainly afflicted heritage of salary growth, cautious buyers say, “show me; show it”. To this I add, “sure, however we don’t want to miss out if issues continue to be high quality as we suspect they might.”
So, last month we begun an scan wherein I bought very short-dated at-the-cash calls appropriate earlier than IBM’s quarterly revenue free up. And, barring new information or sparkling insights, I plan to do so once more next quarter and successively thereafter.
exceptionally, and with apologies for less than one data aspect, the morning before IBM’s end-of-day income announcement of January twenty second, for $3.20 I purchased calls expiring Feb 1st. now not 24 hours later, I closed out the contracts for $eight.sixty six realizing a benefit of one hundred seventy%.
A side potential of buying and selling in IBM alternatives is that, in contrast to most others that expire but as soon as a month, IBM’s calls (and puts) are available with weekly expirations. This potential that one need not “pay for more time price” than is really warranted. If IBM’s salary are due out, say, on a fourth Tuesday, you can still purchase options expiring on that Friday; but pay attention as a result of things transpire right away.
options buying and selling is risky which is why brokerages pre-approve investors for it. As skilled as i am within the securities trade and as an investor, I simplest exchange alternate options a few times each and every yr when I actually have a compelling hunch. furthermore, I try this company not through my discount broking service-broking, but somewhat, through our full-carrier B-D wherein I confer with a relied on and professional professional through the lifestyles-cycle of every trade. I certainly not wager greater than we are able to have the funds for to lose and, on occasion, I lose it all, i.e. calls expiring nugatory. My adviser is aware of I have in mind and might handle the dangers.
it's obtrusive that IBM should re-set up its economic credibility by means of delivering consistent salary increase. Of the 22 analysts that observe the stock, wholly half have it as a “dangle” with the community forecasting a median price at about the place the stock is trading today. because it is with blockchain and other unique developments, I’m more confident than most in IBM which is why we are also lengthy the inventory. That talked about, I wouldn’t mind boosting our return which is why i will be able to pursue this call strategy for a long time.
Disclosure: i am/we are long IBM. I wrote this text myself, and it expresses my very own opinions. i'm not receiving compensation for it (apart from from seeking Alpha). I don't have any business relationship with any enterprise whose inventory is outlined in this article.
further disclosure: always do your own due diligence in session with an authorized and able economic adviser who is aware your wonderful needs and puts your interests ahead of their own. bear in mind, there are added concerns in owning international securities including these associated with ADR sponsorship, purchasing and selling the pinks, overseas withholding taxes on dividends, and charges. (All my proceeds from contributing to SA go to charity.)
In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts access patterns from executing SQL statements, uses computer discovering algorithms to opt for top-quality patterns and passes this counsel to the Db2 question optimizer for use via subsequent statements.desktop discovering on the IBM z Platform
In might also of 2018, IBM introduced edition 1.2 of its desktop gaining knowledge of for z/OS (MLz) product. here's a hybrid zServer and cloud utility suite that ingests efficiency records, analyzes and builds models that characterize the health fame of a lot of symptoms, screens them over time and gives actual-time scoring capabilities.
a few aspects of this product providing are aimed at supporting a group of model builders and executives. as an example:
This computing device learning suite became at first aimed toward zServer-based mostly analytics purposes. probably the most first glaring decisions was zSystem efficiency monitoring and tuning. gadget administration Facility (SMF) statistics which are instantly generated by using the working device provide the uncooked facts for equipment resource consumption reminiscent of relevant processor utilization, I/O processing, reminiscence paging etc. IBM MLz can assemble and save these facts over time, and construct and train fashions of device behavior, rating these behaviors, identify patterns now not effortlessly foreseen by means of people, improve key performance warning signs (KPIs) after which feed the model consequences lower back into the equipment to have an effect on gadget configuration changes that may enhance efficiency.
The subsequent step turned into to implement this suite to investigate Db2 performance facts. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) answer template, applies the computer gaining knowledge of expertise to Db2 operational facts to gain an knowing of Db2 subsystem fitness. it might probably dynamically construct baselines for key efficiency symptoms, supply a dashboard of these KPIs and provides operational body of workers precise-time insight into Db2 operations.
whereas everyday Db2 subsystem performance is a crucial component in typical software health and performance, IBM estimates that the DBA aid body of workers spends 25% or more of its time, " ... fighting entry route problems which cause efficiency degradation and repair affect.". (See Reference 1).AI comes to Db2
consider the plight of modern DBAs in a Db2 environment. In state-of-the-art IT world they must aid one or extra massive information purposes, cloud utility and database features, software installation and configuration, Db2 subsystem and utility performance tuning, database definition and management, catastrophe healing planning, and extra. query tuning has been in existence on account that the origins of the database, and DBAs are usually tasked with this as neatly.
The coronary heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the records, experiences the places of the objects to be accessed and develops an inventory of candidate records entry paths. These access paths can include indexes, desk scans, a number of desk join strategies and others. within the data warehouse and large statistics environments there are continually extra selections obtainable. One of those is the existence of abstract tables (once in a while called materialized question tables) that comprise pre-summarized or aggregated records, for this reason allowing Db2 to stay away from re-aggregation processing. one more choice is the starjoin entry direction, usual within the statistics warehouse, the place the order of table joins is changed for performance explanations.
The Optimizer then reviews the candidate access paths and chooses the access course, "with the bottom charge." charge during this context skill a weighted summation of useful resource usage including CPU, I/O, memory and different supplies. eventually, the Optimizer takes the lowest cost entry direction, outlets it in memory (and, optionally, within the Db2 directory) and starts entry path execution.
large statistics and facts warehouse operations now consist of application suites that permit the enterprise analyst to use a graphical interface to construct and manipulate a miniature facts mannequin of the information they wish to analyze. The packages then generate SQL statements in keeping with the users’ requests.
The difficulty for the DBA
to be able to do good analytics in your diverse records retailers you need an excellent understanding of the statistics requirements, an realizing of the analytical services and algorithms available and a high-efficiency statistics infrastructure. lamentably, the quantity and placement of records sources is expanding (both in measurement and in geography), information sizes are starting to be, and purposes continue to proliferate in number and complexity. How should still IT managers help this atmosphere, in particular with the most skilled and mature body of workers nearing retirement?
consider additionally that a big part of cutting back the full cost of possession of these techniques is to get Db2 functions to run faster and more successfully. This always interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information across the network. because it's regularly problematic to even identify which functions could improvement from performance tuning, one method is to automate the detection and correction of tuning considerations. this is where computer studying and artificial intelligence will also be used to awesome impact.Db2 12 for z/OS and artificial Intelligence
Db2 edition 12 on z/OS makes use of the computer gaining knowledge of amenities mentioned above to gather and keep SQL query textual content and entry path particulars, in addition to exact efficiency-connected historic information such as CPU time used, elapsed instances and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and outlets the facts in computing device getting to know fashions, with the mannequin evaluation results then being scored and made attainable to the Db2 Optimizer. The next time a scored SQL observation is encountered, the Optimizer can then use the model scoring records as enter to its access route option algorithm.
The effect should be a reduction in CPU consumption because the Optimizer uses mannequin scoring input to opt for greater entry paths. This then lowers CPU charges and speeds software response instances. a big knowledge is that the usage of AI software does not require the DBA to have facts science talents or deep insights into question tuning methodologies. The Optimizer now chooses the finest entry paths based mostly now not best on SQL query syntax and information distribution facts but on modelled and scored historic performance.
This will also be chiefly vital if you shop records in dissimilar places. as an instance, many analytical queries in opposition t huge records require concurrent entry to certain records warehouse tables. These tables are often known as dimension tables, and that they contain the statistics aspects continually used to control subsetting and aggregation. for example, in a retail ambiance agree with a table called StoreLocation that enumerates every shop and its place code. Queries against shop income records might also wish to combination or summarize income with the aid of location; therefore, the StoreLocation desk can be used with the aid of some large statistics queries. in this environment it is ordinary to take the dimension tables and copy them regularly to the big information utility. in the IBM world this place is the IBM Db2 Analytics Accelerator (IDAA).
Now consider about SQL queries from each operational applications, statistics warehouse clients and massive statistics business analysts. From Db2's point of view, all these queries are equal, and are forwarded to the Optimizer. besides the fact that children, within the case of operational queries and warehouse queries they should still certainly be directed to access the StoreLocation table in the warehouse. having said that, the question from the company analyst towards huge statistics tables should likely access the copy of the table there. This consequences in a proliferations of abilities access paths, and greater work for the Optimizer. fortunately, Db2 AI for z/OS can provide the Optimizer the information it needs to make smart entry course choices.how it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently right here:
There are additionally various person interfaces that supply the administrator visibility to the repute of the accrued SQL commentary performance records and mannequin scoring.summary
IBM's machine learning for zOS (MLz) providing is getting used to outstanding impact in Db2 version 12 to improve the efficiency of analytical queries in addition to operational queries and their linked functions. This requires administration attention, as you ought to investigate that your company is prepared to eat these ML and AI conclusions. How will you measure the prices and advantages of the use of computing device studying? Which IT guide team of workers ought to be tasked to reviewing the outcome of mannequin scoring, and maybe approving (or overriding) the results? How will you assessment and justify the assumptions that the application makes about entry direction decisions?
In different phrases, how neatly did you know your statistics, its distribution, its integrity and your current and proposed entry paths? this can determine the place the DBAs spend their time in assisting analytics and operational software performance.
# # #
John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 utility performance with computer researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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IBM partners are gearing up for a year of increased enablement, support and joint customer opportunities, as part of deepened channel plans across Australia and New Zealand.
According to Alison Freeman - general manager of global business partners A/NZ at IBM - the vendor is doubling down on its partner ecosystem on both sides of the Tasman, amid plans to drive more software-as-a-service (SaaS) opportunities through the channel.
"Our priorities for this year are very clear client segmentation, particularly from a software perspective on how and where we go to market with our partners,” Freeman said. “There’s a big focus on embedded solution agreements - where partners are developing solutions with our technology.
"We’ve had some great success and growth there in the past year. We’re getting the whole business behind achieving double digit growth for our partners in 2019, that’s our focus."
Last year, Big Blue wanted partners to have a larger handle on commercial business opportunities, an area the vendor will continue working through with partners specific to software.
"There’s a lot of work going into that this year," added Freeman, when speaking to ARN on the sidelines of PartnerWorld at Think in San Francisco. "We did that with a number of accounts last year, and this is a continuation.
"It’s a shift that allows partners to invest and bring on the right people and skills. We’re working with them by client, on which partners we shift, based on their skills. It’s a big move and that’s how we’ll help them grow, which will also help us grow."
Another point of focus for Freeman this year, is creating a better alignment with global system integrators (GSIs) such as KPMG, Wipro, HCL and DXC Technology.
This move may raise some intrigue over the competitiveness between IBM and such GSIs, but Freeman said the vendor was looking at how it can strengthen its go-to-market strategy in other areas of the business.
Another particular area that IBM is concentrating on is centred around providing more enablement and skills for partners, Freeman said.
“We’re providing the tools to enable our partners in the same way that we skill our IBM teams,” she said. “We’re pushing better enablement, better connection with our partners, protection under deal registration and much quicker pricing."
The tech giant formally rolled out new initiatives, including its Watson-based collaboration engine called IBM Business Partner Connect - which A/NZ partners piloted last year; My PartnerWorld; new software deal registration and IBM Power Systems, LinuxONE and Z ‘In It to Win It’ incentives.
In San Francisco, John Teltsch - partner ecosystem general manager at IBM - revealed that partners created more than 84,000 opportunities and drove over US$14 billion in revenue during 2018.
Such growth was triggered by the migration of multiple tools to My Sales Activity (MySA), a single portal that helps partners manage their business with the vendor.
Enhancements were also made to the PartnerWorld on-boarding process, cutting the time to on-board new partners by 70 per cent in the process, resulting in the addition of 13,000 net new ecosystem partners globally in 2018.
“From a partner point of view, we’re seeing a lot of growth in security, SaaS solutions, as well as the Internet of Things," Freeman added.
"We’re very strong in those areas. From a systems point of view, we’re seeing growth in storage and that’s another area where we’ll look to bring on new partners."
Julia Talevski attended PartnerWorld at Think in San Francisco as a guest of IBMJoin the newsletter!
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In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to decide on optimal patterns and passes this information to the Db2 query optimizer for use by subsequent statements.Machine Learning on the IBM z Platform
In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that represent the health status of various indicators, monitors them over time and provides real-time scoring capabilities.
Several features of this product offering are aimed at supporting a community of model developers and managers. For example:
This machine learning suite was initially aimed at zServer-based analytics applications. One of the first obvious choices was zSystem performance monitoring and tuning. System Management Facility (SMF) records that are automatically generated by the operating system provide the raw data for system resource consumption such as central processor usage, I/O processing, memory paging and the like. IBM MLz can collect and store these data over time, and build and train models of system behavior, score those behaviors, identify patterns not readily foreseen by humans, develop key performance indicators (KPIs) and then feed the model results back into the system to affect system configuration changes that can improve performance.
The next step was to implement this suite to analyze Db2 performance data. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational data to gain an understanding of Db2 subsystem health. It can dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational staff real-time insight into Db2 operations.
While general Db2 subsystem performance is an important factor in overall application health and performance, IBM estimates that the DBA support staff spends 25% or more of its time, " ... fighting access path problems which cause performance degradation and service impact.". (See Reference 1).AI Comes to Db2
Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must support one or more big data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, disaster recovery planning, and more. Query tuning has been in existence since the origins of the database, and DBAs are usually tasked with this as well.
The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to be accessed and develops a list of candidate data access paths. These access paths can include indexes, table scans, various table join methods and others. In the data warehouse and big data environments there are usually additional choices available. One of these is the existence of summary tables (sometimes called materialized query tables) that contain pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.
The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, memory and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in memory (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now include software suites that allow the business analyst to use a graphical interface to build and manipulate a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.
The Problem for the DBA
In order to do good analytics on your multiple data stores you need a good understanding of the data requirements, an understanding of the analytical functions and algorithms available and a high-performance data infrastructure. Regrettably, the number and location of data sources is expanding (both in size and in geography), data sizes are growing, and applications continue to proliferate in number and complexity. How should IT managers support this environment, especially with the most experienced and mature staff nearing retirement?
Understand also that a large part of reducing the total cost of ownership of these systems is to get Db2 applications to run faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and artificial intelligence can be used to great effect.Db2 12 for z/OS and Artificial Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to gather and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then use the model scoring data as input to its access path choice algorithm.
The result should be a reduction in CPU consumption as the Optimizer uses model scoring input to choose better access paths. This then lowers CPU costs and speeds application response times. A significant advantage is that using AI software does not require the DBA to have data science skills or deep insights into query tuning methodologies. The Optimizer now chooses the best access paths based not only on SQL query syntax and data distribution statistics but on modelled and scored historical performance.
This can be particularly important if you store data in multiple places. For example, many analytical queries against big data require concurrent access to certain data warehouse tables. These tables are commonly called dimension tables, and they contain the data elements usually used to control subsetting and aggregation. For example, in a retail environment consider a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will be used by some big data queries. In this environment it is common to take the dimension tables and copy them regularly to the big data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now think about SQL queries from both operational applications, data warehouse users and big data business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely be directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against big data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to make smart access path selections.How It Works
The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:
There are also various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.Summary
IBM's machine learning for zOS (MLz) offering is being used to great effect in Db2 version 12 to improve the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your business is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT support staff must be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access path choices?
In other words, how well do you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs spend their time in supporting analytics and operational application performance.
# # #
John Campbell, IBM Db2 Distinguished EngineerFrom "IBM Db2 AI for z/OS: Boost IBM Db2 application performance with machine learning"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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