Power of Control Charts and IT-Chart Concept (Part 1)

This is the video presentation about Control Charts. It is based on my workshop I have already run a few times. It shows how to read and use Control Charts for reporting and analyzing IT systems performance (e.g. servers, applications) . My original IT-(Control) Chart concept within SEDS (Statistical Exception Detection System) is also presented.

The Part 2 will be about "How to build" control chart using R, SAS, BIRT and just 

If anybody interested I would be happy to conduct this workshop again remotely via Internet or in person. Just put a request or just a comment here.


UPDATE: See the version of this presentation with the Russian narration:

How To Build IT-Control Chart - Use the Excel Pivot Table!

Continuing the topic of the previous post “One Example of BIRT Data Cubes Usage for Performance Data Analysis” I am showing here the way how to transform raw data to a “SEDS DB” format suitable for IT- Control Chart building or for exception detection. Based on the published on this blog SEDS-lite introduction it is “...building data for charting/detecting” task which is seen on the picture:

But in this case it is strictly manual process (unless someone wants to use VBA to automate that within MS Excel….) and requires the same basically approach as Data Cube/CrossTable usage in BIRT and in MS Excel it is called “PivotTable and PivotChart report“ listed under “data” menu item.

Below are a few screenshots that could help someone who is a bit familiar with EXCEL to understand how to build IT-Control Charts in order to analyze performance data in SEDS terms.

The input data is the same as in the previous post – just date/hour stamped system utilization metric (link to it). Additionally three calculated variables were added: Weekday (using Excel WEEKDAY () function) and weekhour as seen on the next picture:

/CPUdata/ sheet

Then the pivot table was built as shown on the next screenshot against raw data plus calculated weekhour field, which is actually is specified in “row” section of Pivot Table Layout Wizard (it is a bit similar with CrossTable object in BIRT; indeed, the Excel Pivot Table is the another way to work with Data Cubes too!):

/PivotForITcontrolChart/ sheet

Then three other columns were added right next to the pivot table to be able to compare Actual vs. Base-line and calculate Control limits (UCL and LCL). To do that, the “CPU util. Actual” data were referenced from the raw /CPUdata/  sheet where the last week data considered as Actual. Control limits calculation was done by usual spreadsheet formula and the picture shows that formula for UCL.

The last step was to build a chart against the data range,  which includes pivot table and those three additional fields. See result IT Control Chart on the final picture:

Do you see where exceptions (anomalies)  happened there?

Note that is IT-Control chart where the last day with actual data at the very right last 24 hours on Saturday. So that report made by Excel or BIRT is good to run once a week (e.g. by Sundays before work hours) to get all last week exceptions. To be more dynamic this report should be a bit modified (by adding "refreshing" birder) to run it daily, so minor exception first happened in the Thursday could be captured at least on Friday morning and one could make some proactive measures to avoid overutilization issue the chart shows for Friday and especially Saturday. The most dynamic way is to run that hourly (Excel is not good for that - use BIRT!) to be able to react on the first exception with a few next hours! See live example how that's suppose to be here: http://youtu.be/NTOODZAccvk or here: http://youtu.be/cQ4bk1HNuRk

By the way, I plan to prepare one another workshop type of presentation to demonstrate the technique  discussed in my last posts and also to share actual reports maybe during some CMG.org events in the nearest future...

One Example of BIRT Data Cubes Usage for Performance Data Analysis

I have got the comment on my previous post “BIRT based Control Chart“ with questions about how actually in BIRT the data are prepared for Control Charting. Addressing this request I’d like to share how I use BIRT Cube to populate data to CrossTab object which was used then for building a control chart.


As I have already explained in my CMG paper (see IT-Control Chart), the data that describes the  IT-Control Chart (or MASF control chart) has actually 3 dimensions (actually, it has 2 time dimensions and one measurement - metric as seen in the picture at the left). And the control chart is a just a projection to the 2D cut with actual (current or last) data overlaying. So, naturally, the OLAP Cubes data model (Data Cubes) is suitable for grouping and summarizing time stamped data to a crosstable for further analysis including building a control chart. In the past SEDS implementations I did not use Cubes approach and had to transform time stamped data for control charting using basic SAS steps and procs. Now I found that Data Cubes usage is somewhat simpler and in some cases does not require a programming at all if the modern BI tools (such as BIRT) are used.

Below are the some screenshots with comments that illustrates the process of building the IT-Control Chart by using BIRT Cube.

Data source (Input data) is a table with date/hour stamped single metric with at least 4 months history (in this case it is the CPU utilization of some Unix box). That could be in any database format; in this particular example it is the following CSV file:

The result (in the form of BIRT report designer preview) is on the following picture:(Where UCL – Upper Control Limit; LCL is not included for simplicity)

Before building the Cube the three following data sets were built using BIRT “Data Explorer”:

(1) The Reference set or base-line (just “Data Set” on the picture) is based on the input raw data with some filtering and computed columns (weekday and weekhour) and 
(2) the Actual data set which is the same but having the different filter: (raw[“date”} Greater “2011-04-02”)

 

(3) To combine both data sets for comparing base-line vs. actual, the “Data Set1” is built as a “Joint Data Set” by the following BIRT Query builder:

Then the Data Cube was built in the BIRT Data Cube Builder with the structure shown on the following screen:

Note only one dimension is used here – weekhour as that is needed for Cross table report bellow.

The next step is building report starting with Cross Table (which is picked as an object from BIRT Report designer “Pallete”):

The picture above shows also what fields are chosen from Cube to Cross table.

The final step is dropping “Chart” object from “Palette” and adding UCL calculation using Expression Builder for additional Value (Y) Series:

To see the result one needs just to run the report or to use a "preview' tab on the report designer window:

 

                FINAL COMMENTS

- The BIRT report package can be exported and submitted for running under any portals (e.g. IBM TCR).

- Additional Cube dimensions makes sense to specify and use, such as server name or/and metric name.

- The report can be designed in BIRT with some parameters. For example, good idea is to use a server name as the report parameter.

- To follow the “SEDS” idea and to have the reporting process based on exceptions, the preliminary exception detection step is needed and can be done again within a  BIRT report using the SQL script similar with published in one of the previous post: 

   

BIRT based Control Chart

Recently implementing some solution using IBM TCR I have noticed that one of the default reports in TCR/BIRT is a Control Chart in the classical (SPC) version. Looks like that was one of the requirements for ability to build a consistent reports using TCR/BIRT as it's written here: Tivoli Common Reporting Enablement Guide

So I have built a few TCR reports with control chart against Tivoli performance data and that was somewhat useful.


I believe the IT-Control Chart (see my post about that type of control chart here) would give much more value for analyzing time stamped historical data. Is that possible to build using BIRT?


The BIRT is open source free BI tool (can be downloaded from here). I have downloaded and installed that on my laptop and have built a few reports for one of my customers. One of them was to filter out the exceptionally "bad" objects (servers) using EV criteria (see the linked post here).

Then I have built the IT-Control chart using BIRT. Below is the result: 

Yes, it is possible with some limitation I have noticed in the current version of BIRT report designer. You could see it if you compare that with oher IT-Control Charts I have build using R (See example here), SAS (Example here) or EXCEL (here).


Anyway, could you see how that chart reports pro-actively on an issue?


So it is another way (not to program like in R or SAS and not to make manually like in EXCEL) to build IT-Control charts. After it is built that could be submitted to TCR (or other reporting portals) to be seen/run on a web.

CMG'11 papers about non-statistical ways to capture outliers/anomalies and trends

from  The CMG'11 Abstract report  :

Monitoring Performance QoS using Outliers

Eugene Margulis, Telus

Commonly used Performance Metrics often measure technical parameters that the end user neither knows nor cares about. The statistical nature of these metrics assumes a known underlying distribution when in reality such distributions are also unknown. We propose a QoS metric that is based on counting the outliers - events when the user is clearly “dis”-satisfied based on his/her expectation at the moment. We use outliers to track long term trends and changes in performance of individual transactions as well as to track system-wide freeze events that indicate system-wide resource exhaustion.

BTW I have already tried to "count" outliers  ; see my 
2005 paper listed here: http://itrubin.blogspot.com/2007/06/system-management-by-exception.html

I used the SEDS database to count and analyze exceptions:

Introduction to Wavelets and their Application for Computer Performance Trend and Anomaly Detection: 
Introduction to wavelets and their application for computer performance analysis. Wavelets are a set of waveforms that can be used to match a signal or noise. There are various families of wavelets unlike Fourier Analysis. Wavelets are stretched(scaled) in time AND frequency and correlated with the signal. The correlation in time and frequency is displayed as a heat map. The color is the intensity, the X axis is the time and the Y axis is the frequency. The heat map shows the time the trends or anamoly starts and when it repeats(frequency).

CMG'11 Abstract Report shows my virtual presence

The CMG'11 agenda is online now. The Abstract report shows the following paper related to this blog subject:

1. A Real-World Application of Dynamic Thresholds for Performance Management by Jonathan B Gladstone


He published some material on this blog that most likly is included in his CMG paper: 

Jonathan Gladstone: Threshold Management Diagram 

Feb 17, 2011

Jonathan Gladstone has worked with a team to implement pro-active Mainframe CPU usage monitoring, basing his design partly on presentations and conversations with Igor Trubin (currently of IBM) and Boris Ginis (of BMC Software).

Here is the abstract form the Abstract report:

The author describes a real application of dynamic thresholds as developed at BMO Financial Group. The case shown uses performance management data from IBM mainframes, but the method would work equally well for detecting deviations from normal patterns in any time-series data including resource utilization in distributed systems, storage, networks or even in non-IT applications such as traffic or health management. This owes much to previous work by well-regarded CMG participants Igor Trubin (currently at IBM), Boris Zibitsker (BEZ Systems) and Boris Ginis (BMC Software).

2. Automatic Daily Monitoring of Continuous Processes in Theory and Practice by Frank Bereznay

Monitoring large numbers of processes for potential issues before they become problematic can be time consuming and resource intensive. A number of statistical methods have been used to identify change due to a discernable cause and separate it from the fluctuations that are part of normal activity. This session provides a case study of creating a system to track and report these types of changes. Determining the best level of data summarization, control limits, and charting options will be examined as well as all of the SAS code needed to implement the process and extend its functionality.

I believe that paper is based on the presentation he did at Southern CA CMG this year, which I have already mentioned in my following post: "The Master of MASF"

I have not written any paper for this year (1st time for the last 10 years!) but I glad that the technology I have been promoting for years still have presented in this year CMG conference with some references to my work!

"The Master of MASF"

The following paper has been recently presented at  Southern California CMG (SCCMG)

Automatic Daily Monitoring of Continuous Processes
Theory and Practice
by 

MP Welch – Merrill Consultants

Frank Bereznay - IBM

That is another great paper that promotes the MASF approach in System performance monitoring, which is actually the main subject of this blog. Most likely that paper will be presented again and publish at the international  CMG'11 conference.

I am very proud that I was called "The Master of MASF" at that presentation! Thank you, Frank!

Here is the link to the presentation file I have found via google, which has the following pages referencing my work and also this blog:

[PPT] 

Automatic Daily Monitoring of Continuous Processes Theory and Practice

The paper also has good references to Ron Kaminski and  Dima Seliverstov work. Both authors as well as Frank  Bereznay have already  been mentioned in this blog already: 


See the following posts for Frank  Bereznay work:

System Management by Exception: CMG'06: Performance Data ...

Aug 13, 2007

2006 Best Paper Award paper: Did Something Change? Using Statistical Techniques to Interpret Service and Resource Metrics. Frank M. Bereznay, Kaiser Permanente LINK: http://cmg.org/conference/cmg2006/awards/6139.pdf ...

CMG'09: Performance Data Statistical Exceptions Analysis (Review)

Nov 05, 2010

Brian Barnett, Perry Gibson, and Frank Bereznay. That paper has a deep discussion about normality of performance data, showing examples where MASF approach does not work. The Survival Analysis that does not require any knowledge of how...

For Ron Kaminski work:

cmg'08 trip report

Jan 24, 2009

and ron kaminski who expressed some interest in my ev algorithm to capture recent bad trends as that solves some problems of workload pathology recognition on which he has been working recently. so you want to manage your z-series mips?

And for Dima Seliverstov work:

The Exception Value Concept to Measure Magnitude of Systems ...

Dec 10, 2010

At CMG'10 conference I met BMC software specialist Dima Seliverstrov and he mentioned of referencing my 1st CMG'01 paper in his CMG presentation (scheduled to be presented TODAY!). I looked at his paper "Application of Stock Market... 

My CMG publication statistics from Microsoft

Googleing one of my CMG paper (for the purpose of  IBM certification, which I am doing now sitting on a "bench" between projects) I ran across interesting new site called "Microsoft Academic Search". That references my last CMG papers with some statistics. I saw similar information in other specialized search engines (e.g. CiteSeerX or DBLP), but this one looks most accurate (I was not aware of this site until now, they got me themselves somehow - CMG?); it provides actual citations and has a nice "silverlite" charting. I like charts, so I cannot resist to copy the snapshot here:

UCL=LCL : How many standard deviations do we use for Control Charting? Use ZERO!

How many standard deviations do we use for upper (UCL) and lower (LCL) limits calculations on a control charts? 3? 1? What about 0 st. dev.!? Indeed, the simplest way to build MASF data for exception detection is to use 168 weekly hours averages as a baseline, so that would be the case when ZERO st. Dev is used to make UCL=LCL! Plus for further simplification the current data could be included in wider historical baseline (Why not?). My EV meta-metric in this case would be just difference between actual metric value and the average over baseline!

Here is example of DB2-like SQL script to implement that approach. It is based on real script I developed and successfully tested against real data for BIRT tool to get exceptional servers list:

 

(In this SQL script example metrics names as well as some other fields parsing (e.g. week day and hour) are dropped to increase readability; for real usage that script needs to be adjusted to the real PDB type, metrics names and schema.)

So the SEDS-lite project could be implemented using BIRT reporting (no real programming needed – just SQL scripting and report design!) and the script above could be a good example of ETL step on the SEDS-lite architecture for building SEDS DB out of raw data (see my other post linked here) for charting and exception detecting. 

"Все гениальное - просто" = KEEP IT SIMPLE!  

GACMG presentation about Statistical Pattern Recognition Techniques

Tim Browning, one of my guest blogger (check his post here) and the author of interesting book "Capacity Planning for Computer Systems" will be presenting the following paper on this spring Greater Atlanta CMG meeting:


Statistical Pattern Recognition Techniques for Performance Analysts and Capacity Planners.

CMG Canada Paper about Threshold Managment

 CMG  Canada will  have a paper presented by J. Gladstone related to the following posting on this blog: http://itrubin.blogspot.com/2011/02/jonathan-gladstone-threshold-management.html

Jonathan Gladstone: Threshold Management Diagram

Jonathan Gladstone has worked with a team to implement pro-active Mainframe CPU usage monitoring, basing his design partly on presentations and conversations with Igor Trubin (currently of IBM) and Boris Ginis (of BMC Software).

His system does not generate any alerts on this basis, but it’s a good place to go to

• find out what’s been running hot (or cool) at the system level, and/or
• figure out why at the service class level.

It compares each interval (in this case every 10 minutes) of the most recent day’s utilization (by system and by service class) with the average for a given hour on a given day of the week over the past six weeks. Each interval is compared to the set of the last 36 values in a similar timeframe. If more than one interval in an hour is higher than the 98^th percentile for its hour & day, the hour is marked yellow; if more than four intervals are high the hour is marked red. If more than one interval is lower than the 2^nd percentile for its hour & day, the hour is marked blue. Anything in between (i.e. anything that falls within roughly x-bar±2SD) is green.

Here’s the main “CPU Overview” page from his system:

The thumbnails give an idea of what’s going on – green is within normal range. Let’s look at the Sunday, Jan. 23^rd (just because all the colours are there). Clicking on any thumbnail shows that day close up:

Without going into details about what runs in which systems, we can see that they’re listed in reverse alpha order and, of course, anyone who’s looking at this knows which system is which. The user can see that a lot of systems were running well below their normal utilization on this particular Sunday. That’s mostly because of some special testing: our developers were asked to stay off the systems if they could. To see more detail let’s choose SCA6, which has all of the colours. If we click anywhere on the bar for SCA6, this next level of detail is shown:

  

(Igor Trubin: That is very similar to IT-control charts: see  http://itrubin.blogspot.com/2009/05/seds-charts-at-scmg.html , but in the older version of 24-hour format; I prefer now to use 7x24 weekly profile shown here:  https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjizmXcXEYkyoRxutIr4xS2dj83eQeVurIX3NQhrWlge4a6BYZeXM1JU9mCvdwiwvZEtMX-m4Q93BC98g9vhEKm_vSaJguMlE7RmVyU7ih_RViHc9FmjjscNzDPgsLtYglQ_SxCgERsii4/s1600-h/Untitled.jpg) 

That chart shows the system’s total utilization (from SMF70s) for individual 10-minute intervals (green area) compared to the average, high (98%ile) and low (2%ile) values for each hour based on the last six weeks. We see why some hours are marked red, yellow or blue instead of green according to the rules above. Clicking  anywhere on the green area gets a long page full of control charts that show the same information for each defined service class within that system (from SMF72s).

Among them the following, BATH_A6, is high-priority batch. Clearly it was driving some of the yellow and red flags for this system in the 2-3 and 5-7h windows:

(This post is published here with Jonathan’s Gladstone permission. He retains all publication rights and copyright for this material)

Tim Browning: the review of cloud computing article "Optimal Density of Workload Placement"

Bottom line: a cloud computing resource is really a data center with virtualized components.  A GUI-frontend to an outsourcing arrangement.

           

Maybe the only true “cloud computing” takes place in aircraft. Although, that is debatable.

The Cloud Hype in the paper:

The author proclaims that cloud computing “is not simply the re-branding and re-packaging of virtualization”…then proceeds to show that it is just that. He also states that capacity planning’s use of “trend-and-threshold” analytics is not useful in the cloud infrastructure, yet he defines ‘strategic optimization’ as “proactive, long-term placement of resources based on detailed analysis of supply and demand (compacting)”.  I  assume he does not understand that ‘supply’ is a threshold – we only have a finite amount of ‘supply’ -  and that ‘long-term’ is a trend?

He also states

“Rather than the trend-and-threshold model of planning that is typically employed in legacy physical environments, this new form of planning [my emphasis] is based on discrete growth models (at the VM and/or workload level) and the use of permutations and combinations to determine when to rebalance, when to add or remove capacity, and how the environment will respond to different growth, risk and change scenarios.” 

So, I ask myself,  what’s new about ‘discrete growth models’? Where does he get the “growth, risk and change scenarios” -- (wait, don’t tell me…from trend-and-threshold thinking)?  Maybe he is being discreet about the discrete models (thus avoiding being discreetly indiscrete)?

 Permutations and combinations say nothing about end-state solutions relative to (long or short term) time-series load patterns. They are time static, so ‘when to add or remove’ is not part of those computational functions. Perhaps, what arrangement is ‘best’ is what he is meaning?  Perhaps he is thinking of ‘on demand’ capacity wherein capacity planning is replaced by ‘instant’ capacity in response to ‘change’? Which is to say, there is no planning…just rapid and efficient deployment of some kind of limitless unseen capacity?

What is ‘new’ about combinations and permutations? The newest development I know of in this area is perhaps combinatorial optimization, which consists of finding the optimal solution to a mathematical problem in which each solution is associated with a numerical “cost”. It operates on the domain of optimization problems, in which the set of feasible solutions is discrete or can be reduced to discrete (in contrast to continuous), and in which the goal is to find the best solution (lowest cost). (Developed in the early 70’s as linear and integer programming in operations research and similar to the root mean square error criteria for evaluating competing forecast models using neural networks or statistical methods).

So, knowing how many ways you can combine 887 disks on the same I/O path (combinatorics) tells me when to add or remove some if referenced to a discrete growth model? Wow.. yes, that is so NEW…well, for 1968, maybe. 

 Subsequently, he states

 “the natural changes in utilization over time caused by organic growth will tend to push the limits on the configured capacity. Furthermore, the ability to configure capacity is relatively new to IT, and there are typically no existing processes in place to catch misallocation situations.” 

Perhaps the ability to “configure capacity” is new to him, it is in no way new to enterprise IT.  So, trend – a legacy term -  is not, per the author, ‘changes in utilization over time’ and ‘configured capacity’ is not a threshold? There are ‘no existing processes in place’ to catch misallocation situations? What? None? I suppose by ‘misallocation situation’ he means that a capacity shortfall isn’t a capacity issue, it’s an “allocation issue”. Somewhere – over the rainbow -  there is capacity going to waste, but it’s not available for some reason. It’s just been ‘misallocated’. Sort of…misplaced. We must go find it. Instantly.

OK….So do I like anything about this paper?

Some ideas in the paper I DO like:

Workload density – the degree of consolidation of work into one image (of the OS) - is a cool concept where ‘contention for resources’ is a boundary condition for ‘workload placement’. How is this done? “Contention probability analysis”, which involves analyzing the operational patterns and statistical characteristics of running workloads in order to determine the risk of workloads contending for resources. The author uses the phrase, “Patterns and statistical characteristics”. So, in effect, ‘contention probability analysis’ is a ‘trend-and-threshold’ technique (although he thinks it isn’t). I am surprised he didn’t rebrand ‘statistical cluster analysis’ as also something new and revolutionary just hot from computer science labs - yet another form of blessed combinatorics optimization. Where this idea has been usefully applied at KC: SAP Batch Workload time density – the degree of consolidation of batch work into the same time intervals.  In this case a boundary condition for workload ‘time placement’ would examine workload (demand) leveling and distribution to avoid unnecessary spikes for time-movable workloads.

Another idea I like:

He suggests that workloads are best characterized by their statistical properties, rather than “up front descriptions of their demand characteristics”. Thus workloads are ‘placed’ using segmentation of the resource demand profiles (to avoid imbalance, etc.). Which is to say, workloads are aggregations of activity with  common ‘demand characteristics’. In queuing theory, the classification of incoming transactions into resource-based profiles which are used for priority dispatching protocols against an array of appropriately resource mapped servers will always produce a more optimal process model in terms of throughput and average response times in contrast to a queuing network where transactions are not classified based on resource requirements. This was the basis for batch initiator job class definitions in the mainframe world of the 1970’s. It worked then also. It will work for ‘clouds’ too. 

The only ‘up front descriptions of demand characteristics’ that I know of would be the results of demand/performance modeling and/or LoadRunner-type benchmarking. This is still useful for ‘start-state’ sizing of the target landscape.

So…bottom line: interesting concepts or ‘new ways of conceptualizing’ the functional parametric states of virtualized landscapes. Suggestions (but no concrete explanations) that combinatorial optimization techniques can be utilized for capacity planning (implying it is not now being used). Interesting and useful applications for event densities and statistical profiling.

It seems so important, especially to vendor environments, to reinvent the wheel – a legacy object -   by their services or products, and suggest that they have superior knowledge of all things new and different and these new and different things are not ‘legacy’. After all, in vendor gadget technology what isn’t ‘new’ is ‘bad’ and ‘if it works, it’s out of date’. Thus, legacy means ‘bad’ because it’s  not ‘new’ (even if it uses new components) and, most importantly, it’s not what they are selling.

Just because “2 + 2 = 4” is legacy math, i.e. old, and thus bad, it doesn’t mean that it’s no longer true in cloud math.  It is still true, but needs to be repackaged.

So, in the interest of actionable market relevance, here is a new, fresh, cloud hyped- up version of “2+2=4”:

“It has been newly (re)discovered that ‘2 + 2 is optimally 4 and exceptionally relevant for business purposes.  The scope of this process is enhanced for sufficiently configured integer values of {2,4} in a dynamic web-enabled hi definition virtual presence wherein it has locality of reference within the set of all integer number segments of the arithmetic cloud infrastructure. This will provide a competitive edge to your business as newly revealed by the appropriate cloud-centric data mining tools (c1, c2, … cn, ) - with price guarantees, if you act now! -  at current release, version and maintenance levels in dynamic optimal adaptive combination. This fabulous offering is expertly administered under the guidance of cloud certified  analysts, at an attractive hourly rate, who are not now, nor ever have been, legacy experts and thus ‘new’ and ‘fresh’ with exciting social networking added value potential. (Please join us on the Facebook group “I like integer addition with cloud computing”).”

                                   

Of course, I might be preaching the choir (rather than the clouds) on this one. It seems, nevertheless, that corporate IT vendors demonstrate a kind of ‘math neurosis’:

A math-psychotic does NOT believe that 2+2=4.

A math-neurotic knows that 2+2=4 is true, but hates it. It must be repackaged for resale and aggressively marketed with a customer focused strategy.

If mathematics is the art of giving the same name to different things (J. H. Poincare), then IT marketing is the art of giving a new name to the same things and using pretty charts.

  

THE theologically orthodox  axiom for information technology services/product vendors:

"Absolutum Obsoletum"

(TimLatin translated: "If it works,  it’s out of date").

How to make 3 mice out of 2 mice by making 2 = 1:

How to increase shareholder value by reducing labor costs:

(Posted with the Tim's Browning permission)