Silo

 

Senwes tahıl siloları (275 bin ton kapasiteli), Güney Afrika

UniCredit ile ortak çalıştığımız günlerde birçok İtalyan iş arkadaşımız vardı. Biri de Ricardo. Çok iyi bir yazılım mühendisiydi Ricardo... Eminim duymuşsunuzdur, yapısal programa dilleriyle yazılım geliştirirken programınızın yukardan aşağıya tam bir mantık duruluğu içerisinde akıp, başarıyla bitmesi istenir. GOTO komutu ile akışın bozulması çok kötü karşılanır. Bu tip "yapısı bozuk" programlara spagetti sitili programlar denir. İşte Ricardo bu benzetmeye cepheden karşı çıkardı. "Spagetti mükemmeldir ve böylesine kötü bir yazılım pratiğine spagetti denmesi saçmalık" derdi. Hatta, benzer sebeplerle, yapısı düzgün programlara lazanya stili programlama denmesine de isyan ederdi.

Gerçekten de iş hayatı birçok klişeleşmiş benzetme ve etiketlemeye boğulmuş durumda. Öyle ifadeler var ki sadece iş hayatında kullanım sahası bulabilmiş, hayatın esas zenginliği içerisinde hiçbir bağlama oturmuyor ve oldukça sığ kalıyor. "Siloları yıkmak" bu kalıplaşmış laflardan biri.

Hayatında silo görmemiş, silo inşa etmenin inceliklerinden bihaber, silonun faydalarını hiç düşünmemiş niceleri, siloları yıkmanızı öğütlemiştir eminim sizlere de... İş hayatının derin düşünmeye muhalif, her olguyu bir kapsül gibi yaygın biçimde, her tarafa tatbik etme eğilimi ve yarattığı zihinsel kısırlık mücadele etmemiz gereken bir şey bence. Ayrıca, ben genel olarak her şeyi mukayese veya benzetme ile tarif etme yöntemini çok ilkel buluyorum.

Gelin, silolara biraz yakından bakalım. 

Silolar tarım, gıda, kimya ve yapı sektörleri için son derece hayati saklama üniteleridir. Barındıracakları maddenin doğasına göre özel izolasyon, statik, havalandırma, direnç, yükleme ve boşaltma karakteristiklerine sahip olmaları gerekir. Farklı silo üniteleri arasında materyal transferini sağlayabilmek adına oldukça iyi tasarlanmış aktarım mekanizmaları ve güç üniteleri ile desteklenirler. Optimum maliyetle maksimum materyali saklama ihtiyacını karşılayabilmek için özel geometrileri olmalıdır. Üstelik, silolar sadece pasif depolama amacıyla değil, bir fabrikada üretim sürecine entegre bekletme, biriktirme veya aktarma istasyonu olarak da konumlandırılabilirler.

Uzun lafın kısası, silolar olmasa birçok endüstrinin dengesi bozulur, bazıları da yok olur. İyi bir silo ortaya koyabilmek için malzeme bilimi, kimya, iklimlendirme, inşaat ve süreç mühendisliği gibi alanlarda beceriye sahip olmak gerekir. Yani, anlamadan, bilmeden siloları yıkarsanız başınıza kötü şeyler gelebilir.

Silolara direkt savaş açan kişiler, silo derken işletmelerdeki departmanları kastediyorlar. Oysa departmanlaşma salt negatif bir anlama gelemez. Aksine, modüler tasarımın ve kontrollü otonominin bir yansımasıdır. Bir bakış açısıyla, düzeni, yapısallığı ve hiyerarşiyi temsil ederler.

Medeniyet düzen, yapısallık ve hiyerarşi üzerinde kurulmuştur. Örneğin, hayat ağacı diye betimlenen olgu insanın kaosa karşı kutsadığı yapısal hiyerarşiyi temsil eder. Kökler, ana gövde, dallar, alt dallar, yapraklar ve bu düzende yer altından göğün en yüksek mertebelerine yükselme...

Görüldüğü üzere, bir konuya yakından bakmadan ve derinlemesine akıl yürütmeden bir takım kalıp düşüncelerin izinde yol almak son derece sağlıksız bir tutum.

Bir de Conway kanunu var. Ünlü bilgisayar bilimci Melvin Conway 1960'larda ortaya koymuş bu kanunu. Conway özetle diyor ki bir organizasyonun tasarladığı sistem o organizasyonun içsel iletişim yapısının bir kopyasıdır. Bu öylesine geçerli bir içgörü ki daha ortada yazılım mühendisliği disiplini yokken böylesine bir ilişkiyi sarsılmaz bir kesinlikte tespit etmek ve devamında bu yaklaşımın empirik olarak defalarca teyit edilmiş olması inanılmaz. Bugün dahi "domain driven design" yaklaşımında anti-Conway manevraları önerilmektedir. Tabi ki Conway'in zamanında işletmeler yazılım sistemleri üretiyordu, yani tanımlayan ve baskın olan taraf organizasyon idi. Şimdilerde neredeyse işletmeler yazılım tarafından tanımlanır vaziyette. Şirketlerin yapısı bilgisayar sistemlerine o kadar bağlı ki şirkete ait yazılım sistemlerini ayakta tutabilmek adına organizasyonun orijinal yapısında yer almayan birçok departman teşkil ediliyor. Bu tersine dinamikte, Conway kanunu  nasıl ele alınmalı diye düşünmekte fayda var ama bu başka bir yazının konusu olabilecek kadar büyük bir başlık. Neticede, Conway kanununu hesaba katmadan siloları yıkarsanız, yazılım sisteminiz de yıkılabilir ve herhangi bir "anti-corruption-layer" sizi kurtaramaz.

Vasat bir var oluş için kalıplar iyi birer kılavuz olabilir. Fakat, amacınız zengin ve özgün bir yaşam sürmekse, siloları hemen yıkmayın, önce bir inceleyin.

Daha iyisini beceremeyecekseniz dokunmayın.

Eski Çamlar

Geçen ay meşhur bir "Data & Analytics" etkinliğine katıldım. Aşağı yukarı yer aldığım her seansta yapay zekadan bahsedildi. Yapay zekaya dair en çok yönetişim faktörleri hakkında konuşuldu. Hangi kullanım türlerini nasıl devreye almak lazım, yönetim kurullarına bu işleri nasıl sunmak lazım başlıkları da boldu. "Don't boil the ocean" en çok kullanılan deyimdi. "Her şeyi hemen yapmaya kalkma, büyük düşün küçük adımlarla başla" denildi... Diğer bir yapay zeka sloganı ise "trust" idi. Yapay zeka yazılımlarına dair öngörülemezlik bu işin çözülemeyen bir parçası olduğundan, denetleme değil, güvene dayalı ilerleme önerildi. Sektörde pek yerini bulamamış olsa da Chief Data & Analytics Officer (CDAO) rollerinde yer alanlar ne yapmalı, nasıl yapmalı değerlendirmelerine yer verildi. Data, bahsetmesi nispeten kolay ve temel bir kavram olduğundan "AI-ready Data" etiketiyle veri kalitesi ve yapay zeka ilişkisi güçlü bir şekilde kuruldu. Fakat, analitik biraz öksüz kaldı. "Traditional AI" filan diyerek analitik işlere yapay zeka denilmeye çalışıldı ama pek tutmadı. Bizde bir laf vardır "eski çamlar bardak oldu" denir... Gözlemlediğim kadarıyla, eski analitikler de yapay zeka olmuş :) Ama tam da olamamış... 

Bu dejenerasyona birkaç farklı yazımda da değinmiştim. Yapay zeka, analitik, daha öncesinde veri madenciliği, onun da öncesinde iş zekası ve en temelde bilgisayar bilimi konuları hakkında kafalar karışık. Bilgi eksikliği ve sektörde yer edinme çabasıyla ayağı yere basmayan, ticari modelden yoksun hikayeleştirmeler üzerinden yaygın iletişim yapılıyor. Sıklıkla klasik hesaplama görevlerine yapay zeka denildiğine şahit oluyorum: "Ambara kaç kilo malzeme koyacağımıza yapay zeka ile karar verdik", "Satış hedeflerini yapay zeka ile veriyoruz", "Müşteri segmentlerini yapay zeka ile belirledik" vb. sözler dile getiriliyor.

Bu yazıda yapay zeka nedir, akademik taksonomide yeri nerededir, analitikten farkı nedir gibi tanımlamalara girmeyeceğim ama şunu belirtmekte fayda görüyorum: Bugünlerde yapay zeka ile ilgili yaklaşım ve ürün geliştirme faaliyetleri dünyada 4-5 büyük firma tarafından ve akademik disiplinden kopuk, puslu bir şekilde ilerliyor. Bunun farkında olmamız lazım. Bilişim tarihine baktığımızda bu bir ilk. Bu alandaki temel adımlar hep açık, akademik yanı güçlü, erişilebilir ve irdelenebilir bir nitelikteydi. Devlet girişimi de olsa, özel sektör girişimleri de olsa teknik spesifikasyonlar hep açıktı ve bir standartlaşma mantığı hakimdi. İlk işletim sistemleri, bilgisayar mimarileri, işlemci yapıları, kullanıcı etkileşim modelleri, veri tabanları, programlama dillerine bakıldığında bu makul yaklaşımın izi daima sürülebilir. Yapay zeka alanında, tarihte ilk kez en ileri çalışmalar üniversiteler dışlanarak yürütülüyor. Her şey ticari ve politik, dijital egemenlik kurmaya odaklı. Bir yerlerde daima belirsizlik ve kapalı kutular var. Standartlaşma yok. Bununla beraber, her şeyden haberi var gibi davranan büyükçe bir kitle var. Bu tezat ile makul bir istikamete varmak çok zor...

Ticari Unix işletim sistemi ve kaynak kodu kapalı yazılım lisanslaması tutumuna karşı tepki olarak özgür yazılım akımı başlamıştı ve yıllar içinde zenginleşerek kendine azımsanamayacak ölçüde yer buldu. Fakat, o zamanlar bir yetenekli ekip veya bir kişi azim gösterip işletim sistemi, compiler, editör, driver vb. geliştirebiliyordu. Bu tarz bir özgürlükçü bireysel tutumun yapay zeka alanında boy göstermesi çok zor çünkü ihtiyaç duyulan veri miktarı, donanım gücü ve enerji miktarı bireysel ölçeğin çok ötesinde. Belki akademi tarafından çok daha etkin bir yapay zeka yaklaşımı ortaya konulabilirse özgürleşme kapısı bir nebze aralanabilir.

Dolayısıyla, bilimselliğin ticari arzuların gerisinde kaldığı tekinsiz bir zeminde yapay zeka değerlendirmeleri yapmaya çalışıyoruz. Bu konularda hizmet ve teknoloji sunan firmalar da bu eksende çok yoğun propaganda yapıyorlar. Her yöneticide bir "geride kaldık" tedirginliği var. Acilen bir yapay zeka üretimi yapmak isteniyor.  

Sektör liderlerine tavsiyem, tarihi boyunca kavram ve sunum dosyası üretmiş firmalardan kavram ve sunum almaları, yazılım/donanım üretmiş firmalardan yazılım/donanım almaları ve ticari kar sağlayacak özgün rotaya bağımsızca karar vermeleri. Mevcut durumda sunum ve kavram üretmekte mahir firmalar teknoloji, teknoloji firmaları hukuki yaklaşım, hukuk firmaları da yapay zeka konusunda felsefe üretmeye meyilli. Dedim ya kafalar karışık.

Kendini ispatlamış 3 üretken yapay zeka vakası biliyorum: (i) müşteri destek hizmetleri (Service Now), (ii) akıllı doküman uyarlamaları (JP Morgan), (iii) yazılım geliştirme asistanı (Intuit). Bilgimin kaynağı Evangelos Simoudis. Somutlaştırmadan, yaklaşımın başarısını ispat etmeden basit analitik ve iş zekası uygulamaları, bazen de birinci seviyeden mantık yürütme ve aritmetik ile çözülecek birçok problemi yapay zeka ile çözüyoruz demek ve yapay zekaya mistik bir anlam yüklemek bence pek doğru değil.

Benim bugün itibarıyla bu konulara dair görüşlerim şu şekilde:

1. Yapay zeka çok disiplinli bir alandır ama neticede, çalışan hali bir yazılım sistemidir. Yazılım mühendislerinin aktifleşmesi gerek. Çok sessizler. İşlem otonomisi, etkileşim tasarımı, hesaplama teknikleri, bilgi teorisi ve yeni bilgi temsil şekilleri ortaya koyma açılarından yapay zeka alanında bilgisayar mühendislerine çok iş düşüyor.
2. Analitik, "demokratikleşmiş", iş zekası gibi zaman içinde tabana inmiş bir kavramdır. Her iş kolu bağımsız analitik görev yürütebilmelidir. Bilgi teknolojileri bölümleri bu amaca hizmet edecek kaliteli veri ve kullanması kolay analitik platformları iş kollarına sunabilmelidir. Veriye dayalı her fikir saatler mertebesinde iş kolları tarafından test edilebilmeli, uygunsa devreye alınabilmelidir. Teknolojik altyapı bu çevikliği destekler nitelikte olmalıdır.
   
3. Veri bilimi, tanımı itibarıyla çokça sorgulanan bir dal olarak belirmişti. Her kurumun ve otoritenin kendine has bir veri bilimi Venn diyagramı vardı. Neticede bu ekipler kuruldu, gelişti... Başta her yönetici "PhD arakadaşlarla bomba gibi ekip kurduk" diye yola çıktılar. Bu arkadaşlar genelde Python programlama dili ile kısa kodlar yazıp meşhur birkaç library kullandılar. Daha net olmak gerekirse, neredeyse hepsi xgboost ve lightgbm ile iş kollarının verdikleri target değişken için en iyi Gini katsayısını yakalamaya çalıştı. "Gini fetişi" diyordu tanıdığım bir CDAO bu duruma. Bir bilgi sisteminin temel taşı olan özgün veri yapısı tasarımı hiç hayata alınamadı. Çok kolonlu, "flat", değişken kümeleriyle ilerlendi. Uzatmayayım, veri bilimi çabaları mantıksal temellendirme, planlama ve stratejik karar otomasyonları alanlarında hayal edildiği ölçüde başarılı olamadı. Zaten belli bir sınırın geçilemeyeceğine dair teorik çerçeveler hep vardı ama bugün yapay zeka için yaratılmış mistik tutum o zamanlar da veri bilimi için yaratılmıştı. Korona salgını esnasında zaman serisi verileri üzerinden salgının bitme zamanını tahmin çabalarını ve yaşanan hüsranı hatırlayın. Evreni deterministik bir düzende işliyor sanarak veriye sahip olanın geleceği bileceği inancına kapılmaktan kaynaklı bir durum. Felsefi niteliği bir yana, günümüzde, ortamda kaliteli veri var ise veri bilimi faaliyetleri teknik olarak tümüyle otomasyona alınabiliyor. Bu konuda birçok başarılı ticari ürün var. Diğer yandan, şu sıralar kimse veri biliminden bahsetmiyor, herkes yapay zekaya yöneldi. Oysa yapay zeka, veri biliminin devamı veya ilerlemiş hali değildir.
   
4. "Yazılım artık makineler tarafından yapılıyor" inancına sahip birçok yeni mezun bilgisayar mühendisine rastlamaya başladım. Kodu yazılım sanıyorlar. Bu noktada öğretim üyelerine büyük iş düşüyor. Yenilikçi bilgi sistemleri üretebilecek kalibrede bir zihin yapısına dönülmesi gerek. Bugünlerde veri, bilgi sistemi, yazılım, yapay zeka vb. birbirinden bağımsız kavramlarmışçasına bir güdülenme var ve bence hatalı. Hiç kimse her şeyi bilemez ama bilişim tarihini iyi anlatmak ve ilişkilerin doğru kurulmasını sağlamak bir çıkış yöntemi olabilir.
5. Üretken yapay zeka kullanarak kod üreten ve bu yaklaşımla profesyonel yazılım mühendisliği yapabileceğini sanan, bazı mesleki yeterlilik sınavlarında dahi bu davranışı sergileyen kurnaz ve dar görüşlü bir kitle türedi ve sayıları hızla artıyor. Yazılım ürünleri üzerinden ticari faaliyet yürüten kurumların bu kitleye karşı dirençli yöntemler geliştirmesi gerek.
   

Yine hiçbir şeyi beğenmeyip, her şeye bir kulp takmış gibi bir tutum sergiledim ama bunu daha önceki bir yazımda izah etmiştim: Ben ÖYS neslinden bir fen liseliyim. Seçenekler arasından en uygununa razı olmak bizim anlayışımıza ters düşüyor. Bizde formül ezberlemek yok. Temeli öğrenip formülleri her seferinde baştan türetebilmek üzerine eğitildik. Biz önce soruya bakarız enine boyuna, ve gerekirse "bu soru yanlış" deriz. Kalemi atarız, sonuçlarına da katlanırız :) 

CIO

 

Mimisbrunnr image by Rim Baudey

Data has always been in the center scene. Information processing, including communications, has always been the primary purpose. Different types of computers have been developed. Different layers of software have been devised. We put the computers in dedicated facilities and called them data centers. It was not by chance, data has always been the prima donna.

Of course, in time, we needed to formally define organizational bodies for governing data centers and computer systems. Normally, it began in different state departments first such as one computer for Military Ballistics Research Department, another for US Census etc. Then it became enterprise level and we needed central, enterprise level planning, execution and control of computer systems within public and private legal entities. At this point in time, Chief Information Officers emerged (CIO).

The name CIO were suggesting everything about its responsibility area. A role, built around information. Computer and communications systems’ importance got higher and higher every day which resulted in the increased power of CIOs. They were not just managers of cost centers but major business partners. Beyond that, they were business enablers. CIOs were prevalent in governmental bodies and private companies, and they still are.

However, in the last decade, especially in private sector, CIO visibility has been deteriorating. What happened to CIOs? Where are they?

CIO role is splitting and giving birth to other roles. 

One is Chief Technology Officer (CTO). If you give the right meaning to the term “technology”, CTO role must be superior to CIO role because information and communication technologies referred in the name CIO is just a subset of technology. But in practice, in private sector, if you are managing information technology infrastructure (e.g. servers, network, data center, platforms, client computers and peripherals) you are the CTO. Some CTOs also handle software development function, especially in relatively smaller companies. CTOs commonly don’t carry profit generation responsibility. These were all CIO duties…

Other is Chief Data Officer (CDO). It is the most interesting, actually. When this role was invented, most of the CIOs could not figure out the organizational location and the responsibility perimeter of CDOs. They thought CDOs should report to CIOs. It is understandable. CDOs are usually responsible of data governance, data analysis, business intelligence & critical reporting, education & culture, data architecture, data privacy, database management and data platforms, data engineering. They are supposed to have P&L accountability but in practice, usually they do not. The list can be shorter or longer depending on the setup. Looking at the topic of CDO definition from that point of clarity, it was right for CIOs to be confused and to feel their premises lost to CDOs because all those have always been CIO tasks.

The other role is Chief Information Security Officer (CISO).  As the name suggests, CISOs are taking care of all information security tasks such that information protection & confidentiality, identity and access management, investigations, information risk management, information incident management, regulatory security compliance etc. These were all CIO responsibilities actually but CISO roles mostly have been established by regulations as an independent senior position for preventing conflict of interests between CIOs and information security directors under them.

As a result, in the private companies, number of CIOs are declining; CISOs are increasing in a robust way; CDOs are vaguely surviving, there are reasons for this but these are beyond the scope of this post; CTOs are increasing as the new form of CIOs. At the government side (I follow USA structure, both civilian and military organizations) CIOs are powerfully going on their mission.

That’s the question: Why? Why are CIOs losing their castles? In theory, there were “data” centers, enterprise level management needs for “information” & communication systems and perfectly matching role description as the CIO… What happened in a sudden?

Frankly, I don’t know all the answers but I can put one or two things: Characters of CIOs have an effect. Most of the CIOs could not contribute to company profit through business development. Being an enabler was not powerful enough anymore. It was very hard for a CIO to over-achieve. Managing a bunch of engineers and very complex information systems became a sterile factor within companies.  Burden was so heavy on the shoulders of CIOs… CEO and CFO were considering them as cost black holes; moreover, it was very difficult to be compliant with regulations, growing daily. Being a good CIO has been like being a sort of super hero. On top of all those, accelerated change in information technologies required more specialized managers… 

But… But. 

At the end, it is business, it is life.  Things happen, there is entropy in the universe and increase in complexity is inevitable. That’s for sure, one man could not solve everything but if CIOs could have showed bolder character on their domains, things would be different.

Personally, I still am supporting the notion of CIO rather than other Cs emerged from CIO lndscape. Because, to me, life is information processing and the "I" in the middle of CIO represents a lot. 

Think.

Whose language is it?

I do not like Martin Heidegger as a person because of his actions and words during Nazi era but if you are interested in philosophy, it is impossible not to cite him. He stands still in the center of modern philosophy like a big rock. Heidegger attributed a powerful meaning to language by saying "language is the house of Being". This claim shaped existential and post-modern philosophy deeply. There has been many open questions but most of the people agreed that for being an authentic individual, one needs to build and talk his/her own language rather than repeating others' language. If one shapes your language, you are under that person's existential power.

Moving from this perspective, I wanted to have a look at the language in our daily business lives. It won't be a comprehensive examination of course but I will try to give some examples of common use.

Be thrilled & be delighted

During COVID-19 pandemic, because of the predefined grammar embedded in LinkedIn job change function, everyone was constantly thrilled or delighted to announce something. Nowadays, no thrills.

Unlock & Unleash

These two words are used oftenly. When we analyze the meaning, we see that imposing these words presumes there is a blocked potential of the subject. This potential is hidden from you but known by the narrator. The only thing to do is following the narrator's guidence and removing the blocking components from the system. It is very similar to the motto of the coaching: "Every solution and answer is in yourself, I can only guide you to find it, you are to find". We all know that every business hasn't got that much potential locked mysteriously.

Democratize

This word expresses the necessity of positioning a particular technology for the use of as much people as possible so that those users can achieve their business objectives by themselves rather than demanding solutions from the "elite" departments. The analogy is rooted in the number of people involved and of course, it is a naive mentality. I accept that technology is encapsulated knowledge. Therefore, if a person uses a kind of technology, that person accesses the knowledge served by this technology. However, using a sort of encapsulated knowledge through a tool does not imply that user has the knowledge embedded; user just makes use of it. It is like driving a Ferrari at 300 km/h speed without knowing the internals of V12 engine. In that example, we democratize the mobility by making cars accessible to persons: Everyone can go whenever and wherever desired by using a car. On the other hand, we did not democratize mechanical engineering, chemical engineering, aerodynamics, fluid mechanics etc. In the common use cases of "democratize", narrators are saying "democratize software development, machine learning, artificial intelligence etc. by using our technology". It is like suggesting democratizing fluid dynamics by giving someone a car. Non-sense. Business lines in the companies want to achieve their financial goals. It is their job to do. Their job is not to use fancy looking tools. For the special technology development, elite departments will always be needed. We must remember that respresentative democracy is the most common form of democracy in the world.

Revolutionize & Disrupt

A couple of years ago, the word "disrupt" and its different forms were heavily in use and it was related to innovation but nowadays, it stepped down from its throne and gives its fancy crown to the word "revolutionize". It seems innovation is not enough any more, we need revolution. Revolutionize is used very similar to the words "unleash" and "unlock" but in a revolutionized way :) In that case, there is no hidden potential in your company, everything about your company or the system you are in is old fashioned and must be replaced by new things. For example, "banks are big losers, you need to revolutionize financial sector by fintech companies", if you wish... Or "capitalism is rotten and DeFi platforms will revolutionize world trade system". Besides, those "revolutionizations" are proposed as if there exists an already solved problem: There is a very practical tool or method, you just need to buy it and wait for the revolution to happen: "Revolutionize UX by harnessing generative AI". Simple. I believe, we must understand evolution better.

Hybrid

As the name suggests, a little bit from this, a little bit from that, some green, some red... When you cannot propose a robust solution, you can propose a hybrid one. When I hear hybrid, I run away. If you like it, it is up to you.

Toxic 

I think it emerged from the prevalent promotion and pratice of mindfulness. If you don't like a person, an attitude or a thing; you can easily label them as toxic. When you do that, you externalize the problem and responsibility. You find your scapegoat. The rest is nice and easy: Detox, there are programs for that :) I think, we have to face our problems, take the responsibility and solve them as an adult would do. Blaming, canceling or labeling are childish traits.

Talent

As Andy Warhol famously said: “In the future, everyone will be famous for 15 minutes”, every employee is a talent today. Interesting but it is the fact. It is impossible to hire average Joe. This sort of language imposes leadership myth, curses managers, praise team members and completes the composition by saying: "Hello Joe, it is not you, it is your narrow minded and old fashioned manager. You are a talent. It is not you, but your manager's decisions. You need a supreme leader instead of your mortal manager and you need to embrace our methods for salvation. For example, fusion teams and DevSecMLOps and excessive use of post-its & board markers" :)

Version Numbers

It is a good example of vulgarization. Versioning is a notion in software engineering and there are strict definitions and styles of assigning version identifiers to software products. You need to consider, at least, backward compatibility besides other issues like alpha, beta, major, minor, patch identifiers etc. However, nowadays it is very easy for everyone to assign version numbers to any thing or concept such as Web 2.0, Banking 3.0, Industry 4.0, Payments 2.0... Moreover, every new version is a major one and ends with zero. I need somebody to explain the meaning of that zero to me. That love for versioning gets along with the mentality of revolutionizing explained above: Manifestation of a new version, forget about the older one. 

There are some more trendy jargons being circulated following the zeitgeist but I stop here. 

Let's build our language, our homes, where we dwell. We need that shelter to live, grow and share. Without that, we are nothing.

Momentum

 

Mustafa Kemal Atatürk in the field with staff, during the Turkish War of Independence

In classical cognitive science, it is assumed that peripheral sensors of your body perceive the signals and then, those signals are transferred to your brain through your nerves. After the transfer, your brain symbolizes the signals received, processes and stores the symbolic information in order to make decisions, solve problems, manage memory operations and trigger actions in an organized fashion. It is a very central way of explaining human cognition. All the intellect is sourced in the head.

To me, how we design our organizations and information systems is very similar to the classical account of cognition. We collect the information at the edge. Sometimes a sensor reads the data, sometimes a sales team enters data to their tablet computers, sometimes your branches are there for receiving information from your related parties. Edges are connected to the center. You may call it head quarter, head office, mainframe, data center etc. In the center, stored data is processed by "intelligent" systems and "intelligent" professionals. Central decisions are made and the actions are pushed to the edge units. Perception-cognition-action.

The familiar story of the organizations. It is proven to get the job done. However, it would be better for us to explore the flipside. We model information systems and expect collected symbolic data to fit in the models designed. If there is a mismatch, central system rejects the data sent... Very similar to blood-brain barrier. Time passes and you come up with mountains of stored data in your "very well modelled" central data structures. Now it is time to do necessary analytics so that you can build your decisions and organize related actions. What is missing here?

In the center, almost always, you don't know the field. All the interactions with your customers, partners, thousands of non-deterministic and emotional clues, wins and losses. There is a huge amount of information in the dynamics of the field. And for sure, our field units are not able to encode all of the live information and send it to pre-modelled central structures. Tacit knowledge is missed. The gap between tacit knowledge and the symbolic knowledge is like the qualitative difference between the experience of talking to a person face-to-face and reading an e-mail from the same person. Former is always real, latter is always incomplete.

Therefore, in central analytics and information processing model, we only do static analysis. It is reductionism. It is like reading as many facts as you can on two basketball teams and trying to predict the winner in an accurate way. The players, all the statistics, past performance of the coach, anything you can read about the teams. But you never know all the governing dynamics to make correct decision. In the dynamics of the basketball match, coaches and players are managing millions of parameters... Some are tactical, some are intuitive, some are relative to the state of the opponent at a given time, some are highly related to the hormon levels of the moment, so on and on. It is similar in warfare too. With the stored information on the map, you cannot win the battle. 

To make the story short, I can say that being present in the field during the action is crucial for success. Do you remember impulse and momentum from the physics class? This is the equation: 

M . ∆V = F . ∆ t          

Where M is mass, F is force, ∆V is change in velocity and ∆ t is change in time. 

In our organizations, we want to create momentum and/or make an impulse for being in relatively advantageous positions in the market. When we do just static analysis by using symbolic central information, we can just know the amount of mass and the force. On the other hand, in the field, velocity of the mass and timing for applying the force heavily affect the impulse and momentum. In the battlefield, victor is the one who masters the mass, the power, timing and speed at the same time. History is full of supporting anectodes.

As you can see, physics gives a clue for success. Also, cognitive science has another explanation called embodied cognition. According to embodied cognition approach, it is said that we process information by using our whole body, not just our central nervous system. We probe the environment with our body, sense our situation in immersive environments, use our full nervous system to make multiple mental simulations and act as a result. So in contrast to the classical cognitive model, embodied cognition is not that sterile. It considers distributed cognition, action orientedness and situatedness. There are many real life practices supporting embodied cognition. Examples can be found in the fields of robotics, artificial intelligence, cognitive psychology and analytical philosophy. The position of your body affects how you give meaning to the world such that if you put a pencil between your teeth, which gives you a forced smile, and a friend tells a joke to you; you find the joke more fun compared to the case you don't smile. Similarly, positional up is better than down position. We use our body for cognitive offloading as well. Remember the times you use your fingers for counting or how you use your hands and mimics while you are talking. We also translate some mental states into bodily forms during solving problems. Assume that you are given a task to mentally rotate a cube on your computer screen 60 degrees. You may find yourself with your head turned 60 degrees instead of rotating the cube in your mind.   

The question is whether we can use the notion of embodied cognition for enhancing the way we organize our corporations and come up with better designed information systems. 

I think the answer is yes. 

It requires to be present in the field with all the central capabilities of the company. Doing the analytics at the edge. Enriching the symbolic knowledge with muscle memory. Putting human in the center. Being action oriented. You can translate it as being result oriented. It sounds like a usual term in the business jargon. However, how you interpret it can make the difference. 

For example, take the analytics as it is today. By definition, it is reactive and it tries to predict relying on simplified static data accumulated. Trying to predict the future is painful and does not guarantee the success all the time. What if you accept that you are living in a complex world where is (1) unknown, (2) unpredictable and (3) constantly changing? How can you win in such a complex world? What decision making process should you execute? 

Just act in a bodily way! Do not try to predict but build your muscles to define the future. You can define the future by being the first mover in the context so that you take the initiative and create a situation. If your body is agile and alive to optimize mass, force, speed and timing, it is possible for you to define, at least, near future. Winning is relative to your rivals. War is a series of temporary conditions. As you can see, action orientedness might be a viable way forward. The key is being able to act first at low costs compared to your challengers. And this ability is heavily depending on your information system architecture. Rapid application development, easy deployment, high availability, enabling users in the field, capturing dynamic information are the key concepts of being action/result oriented.

While I am coming to the end, I'd like to say that creating concepts and following ruling heuristics in a vigilant manner are necessary qualities for the organizations. If we visit cognitive science again, it is called metacognition. Metacognition is the ability of monitoring and executing your cognitive processes. In other words, self-awareness! 

We need eyes wide open for winning.

Forget about Failing!

Agile mindset and culture have been promoted for years. Organizations are getting slimmer, hierachies are vanishing, the world is trying to do everything through scrum teams. People are thinking on post-its, working with post-its. Humanity is doing digital transformation with post-its. Perfect!

Coaches, facilitators, mentors, consultants and all the other members of the familia are cursing the "old school" doctrines, burrying the wicked waterfall software development method, so on and on...

And of course the notion of failing! 

"Fail fast, fail often" motto, told by literally every one to every one. Written everywhere. People like the shocking effect of the slogan which is a linguistic poison in my idea. 

The word "fail" is being repeated so often and pervasively that it starts to disturb the mindsets of the young professionals. I am against the tendency of promoting failure culture. And never praise it.

Of course, it is easily understandable that the people who are advising to fail for success are aiming to emphasize the importance of learning new things about the universe. This is not a new notion. It is called "trial and error", one of the fundemantal methods of learning and problem solving. The issue here is that, it is not the perfect way of problem solving. Actually, it costs more when compared with the alternative ways. It is possible to learn things without failing. 

Moreover, it is not proper to start projects with the "failure in your mind". Nowadays, the narrative came to a point that, as if it is impossible to be successful without failing. It is not correct. It is shallow. It is a reflection of a way of popularism which praises mediocrity. Saying "you can be an average person, don't worry, there is a method to save you: if you fail enough, you'll be victorious at the end". It is not true. No ultimate methods can save you. Extraordinary people and extra ordinary events shaped the history and marked the characteristics of humanity. Repeatable methods merely work well if you want to scale up with average masses. Therefore, if you are to launch creative initiatives proposing real added-value, failure driven iterations are not the ideal paths to follow.

Let's look at problem solving.

When you face a problem in your way, in most of the times, you use heuristics to find a solution. It's a much more faster and efficient method than trial and error is. Or you may apply algorithmic approaches for solving the problem. Or you may follow trial and error. However, if you don't have any hypotheses before starting the trial and error process, you end up with no information gain. So the main goal in problem solving is to gain new information to apply to the problem and it is obvious that there are many ways of information gaining, or learning, methods in the world. You don't have to fail to learn. In most of the times, success is not a function of your knowledge and competence. Environmental factors, let's call them context or ecosystem dynamics, are playing a crucial role. If you analyse the journeys of a set of startups, you can see that successful and unseccessful ones are doing exactly the same things. I wonder how deterministic failure prophets would explain this situation. Methodologically, there is no lock-in to failure driven prodecures, there are better alternatives of reaching success.

Take the motivational factors. 

You need to feel the fire and the desire for success while you are starting things up. Failure is not a catalyst in the process. You must be brave enough to take the risks and move forward after facing blocking factors in your route. The magical factor here is not how you are good at failing but how you are resilient and solid after facing obstacles. It is you, not the method. The better you are at applying the methods, the more chance you have to be successful. Virtuosity matters. And it is about practice and training. No matter what your profession is, you must separate the training session and the performance session. Train like hell. Get down, get up, read, sweat, bleed, meet people, ask questions, expand yourself and get perfectly ready to the performance. During the performance, which is your professional daily life, never ever fail. Get the job done! Get the job done every time! Get the job done perfectly! If you face unexpected situations, just improvise like Tango performers do. No one superimposes any coreography to Tango performers, they are the experts of fundemantal steps and patterns. And they improvise perfectly on the stage. They simply handle things. For being able to improvise during the performence, you had to do your training well enough. Otherwise, you will become a failure machine. And believe me, it will not make you victorious in the end.

As I am getting to the conclusion, I want to remind that implying scientific methods to business is valuable if you know the essence of the science. It is well known that science and rationalism are being used as management tools by the authorities. A government may state that "scientific norm of the human psyche is defined as XXX, if you are not in the area of XXX you must take that pill, or the authority may send you to hospital as an obligatory measure". There are many examplars of that management style. We must never forget that science is humble in nature. Science never asserts that it will solve everything about the universe or it will bring the ultimate salvation. Science always tries to minimise the grey area, finds some variables for explaining some situations under well defined conditions. In most of the times, science gets back to you with greater but new questions, new unknowns. It is a never ending spiral. Therefore, proposing some formulae as an ultimate solution to a problem in business is almost always shallow and it is a sort of fashion. People may refer to scientific research outcomes for fortifying their formulae. It changes nothing. Be a free, intellectually sufficient, brave, hard working and creative person. 

On the stage, never ever fail!