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#6 DataBeersBCN -"Data, Beer and Enterprise Architecture"

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#6 DataBeersBCN -"Data, Beer and Enterprise Architecture" by Julian Moore

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#6 DataBeersBCN -"Data, Beer and Enterprise Architecture"

  1. 1. beer… Data, and EA A presentation in orange by Julian Moore, Enterprise Architect @PantologEA
  2. 2. Why?Data Beer? What is the value of X?
  3. 3. Beer?Why Because… – It’s tasty – It’s refreshing – And even better when it’s free The question isn’t really Why Beer?, it’s Why Not! But… Why Data?
  4. 4. Data?Why The qualities of data… – Data can be pretty – Like beer, some data is also free – Data is existentially useful So, let’s put in context: Why and how is it useful? to us – and to Enterprise Architecture? And what is that?
  5. 5. What is Architecture: The knowledge of art, science & technology and humanity… in the service of designing, constructing and modifying physical structures EA? Enterprise Architecture knows people, processes & systems to develop better organisations and their capabilities , processes, services, hardware, software, data… Analogy
  6. 6. How EA Exploits Data How people, processes & systems connect What’s happening around us Breaking news Models & data choose the route Where are we now à Where we want to go We choose the future Orientation by strategy, standards & policies
  7. 7. Idea – Data is existentially useful – It really is a matter of life and death – but why? Data exploitation from non-technical viewpoints* – Scandinavian Mythology – The Ecology of the Galapagos – Chinese Polemology** * Now with added vampire references ** The study of war ReloadedData
  8. 8. Mythology: All-Father’s Eyes Munin(n) – Memory* Without memory, there is nothing to understand Hugin(n) - Thought Without thought, there is no understanding The Poetic Edda – Grímnismál I fear for Hugin, that he come not back, yet more anxious am I for Munin
  9. 9. BUFEE Understand the why and the how Elect a valuable future path as plan Forecast possible futures Behold the data Execute observe the results & repeat – like Slayer*, but different *Also like OODA, but different
  10. 10. InformationData Analysis makes outline models from data Models become plans Models make data about hypothetical futures The past as the history of the present Plans lead to outcomes Deviations from forecasts become part of history
  11. 11. Standard Lessons from the Galapagos Inheritance… – DNA Variation… – Sex – Mutation Selection… – Eat/be eaten – Looking fit Charles Darwin, 1840, by George Richmond, ©English Heritage
  12. 12. A Static Landscape © Randy Olson and Bjørn Østman, by permission
  13. 13. Evolution of Evolution Hidden Assumptions.. – Slow background change – Significant populations Are wrong in business, where – Everything changes – All the time – Only yours matters But, if change isn’t too rapid… Charles Darwin, ~1878. (Richard Milner Archive)
  14. 14. A Business Landscape © Randy Olson and Bjørn Østman, by permission
  15. 15. Is being “highly evolved” really good? T. Rex – Well adapted to 65 million BP Eagle – Well adapted to air Dodo – Well adapted to… one tiny island in Mauritius Archaeopteryx – Exploring a new niche Ï1 Ï
  16. 16. Sun Tzu’s Advice* to the General – individual, team leader, project manager, director, CEO… The general who wins a battle makes many calculations in his temple before fighting the battle. The general who loses a battle makes only a few calculations beforehand. If many calculations lead to victory, and few calculations to defeat, what if no calculation at all? To beat the competition, business must calculate… with data 孫子兵法 The Art of War
  17. 17. Want your business to die out? Of course not. So… Collect data – Goals, motivation, capabilities, resources… – The environment, the competition – And how they all change Decide where to go and calculate – Model ways forward – Evaluate for cost, benefit, risk & opportunity – Choose the best We’re naturally smart, business has to work at EA
  18. 18. The Problem with (EA) Data « stereo typ e» P ro je ctMile sto n e [C lass] {O w n ed attrib u tesh aveto b estereo typ ed <<P ro je ct Th em e> >., A llo fth eP ro jectTh em es,o w n ed b yaP ro jectMilesto n e,m u stb etyp ed b yth es am eP ro jec tTh em eSt atu s. } -In fo rm atio n Tech n o lo gyStan d ard C atego ry : Strin g [*] -m an d ated D ate : ISO 8 6 0 1 D ateTim e [0 ..] -retired D ate : ISO 8 6 0 1 D ateTim e [0 ..] -sh o rtN am e : Strin g [0 ..] -versio n : Strin g [0 ..] -cu rren tStatu s : Strin g [0 ..] « stereo typ e» Stan d ard [C lass] -/realized Exch an ge : O p eratio n alExch an ge [*] -id en tifier : Strin g « stereo typ e» N e e d lin e [A sso ciatio n , C o n n ecto r] -realized Exch an ge : R eso u rceIn teractio n [*] « stereo typ e» R e so u rce C o n n ecto r [C o n n ecto r] -id en tifier : Strin g -realized Exch an ge : R eso u rceIn teractio n [*] « stereo typ e» R e so u rce In te rface [A sso ciatio n , C o n n ecto r] -id en tifier : Strin g -/p ro d u cin gA ctivity : O p eratio n alA ctivity [*] -/co n su m in gA ctivity : O p eratio n alA ctivity [*] « stereo typ e» O p era tio n a lExch a n g e « stereo typ e» Su b jectO fO p era tio n a lSta teMa ch in e « stereo typ e» Su b jectO fR eso u rceSta teMa ch in e « stereo typ e» Su b jectO fO p era tio n a lC o n stra in t +co n n ecto rR eq u ired : B o o lean = tru e « stereo typ e» So aML::P o rt [P o rt] « stereo typ e» A ctu a lO rg a n iza tio n a lR eso u rce -/im p lem en ted B y : System sElem en t « stereo typ e» O p era tio n a lElem en t « stereo typ e» Su b jectO fR eso u rceC o n stra in t « stereo typ e» H igh Le ve lO p e ratio n alC o n ce p t [C lass] -id en tifier : Strin g -/co n su m in gFu n ctio n : Fu n ctio n [*] -/p ro d u cin gFu n ctio n : Fu n ctio n [*] « stereo typ e» R e so u rce In te ractio n [In fo rm atio n Flo w ] -/im p lem en ts : O p eratio n alElem en t « stereo typ e» System sElem en t -en d D ate : ISO 8 6 0 1 D ateTim e [0 ..] -startD ate : ISO 8 6 0 1 D ateTim e « stereo typ e» A ctu alP ro je ct [In stan ceSp ecificatio n ] « stereo typ e» Ma n u fa ctu red R eso u rceTyp e [C lass] « stereo typ e» Stan d ard O p e ratio n alA ctivity [A ctivity] -statem en t : V isio n Statem en t [*] « stereo typ e» En te rp rise V isio n [C lass] « stereo typ e» O p era tio n a lExch a n g eItem « stereo typ e» O rg a n iza tio n a lR eso u rce [C lass] « stereo typ e» P ro to co lIm p lem en ta tio n « stereo typ e» R eso u rceIn tera ctio n Item « stereo typ e» N o Lo n ge rU se d Mile sto n e [In stan ceSp ecificatio n ] « stereo typ e» O p e ratio n alState Mach in e [StateMach in e] -lo catio n D escrip tio n : Strin g « stereo typ e» P h ysicalLo catio n [D ataTyp e] « stereo typ e» O p e ratio n alA ctivityEd ge [A ctivityEd ge] « stereo typ e» O rgan izatio n alExch an ge [In fo rm atio n Flo w ] -d ate : ISO 8 6 0 1 D ateTim e « stereo typ e» A ctu alP ro je ctMile sto n e [In stan ceSp ecificatio n ] -d o ctrin e : C o n strain t [..*] « stereo typ e» C ap ab ilityC o n figu ratio n [C lass] -/filled B y : A ctu alP erso n [*] « stereo typ e» A ctu alP o st [In stan ceSp ecificatio n ] « stereo typ e» P articip an tA rch ite ctu re [C lass] « stereo typ e» C o n figu ratio n Exch an ge [In fo rm atio n Flo w ] « stereo typ e» O u tO fSe rvice Mile sto n e [In stan ceSp ecificatio n ] « stereo typ e» R e so u rce State Mach in e [StateMach in e] « stereo typ e» O p e ratio n alEve n tTrace [In teractio n ] +en co d in g : Strin g « stereo typ e» So aML::Me ssage Typ e [C lass, D ataTyp e] « stereo typ e» Syste m C o n n e cto r [A sso ciatio n , C o n n ecto r] « stereo typ e» C o m m u n icatio n sLin k [C o n n ecto r] « stereo typ e» R e so u rce C o m p o ne nt [P ro p erty] « m etaclass» In stan ce Sp e cificatio n « stereo typ e» So aML::R e q u e stP o in t [P o rt] « stereo typ e» Syste m Fu n ctio n Ed ge [A ctivityEd ge] « stereo typ e» In fo rm atio n Exch an ge [In fo rm atio n Flo w ] « stereo typ e» O p e ratio n alMe ssage « stereo typ e» R e so u rce Eve n tTrace « stereo typ e» Wh o le Life En te rp rise [C lass] -Missio n A rea : Strin g [*] « stereo typ e» Missio n [U seC ase] « stereo typ e» In cre m e n tMile sto n e [In stan ceSp ecificatio n ] « stereo typ e» So aML::Se rvice P o in t [P o rt] « stereo typ e» En viro n m en ta lTyp e « stereo typ e» D e p lo ye d Mile sto n e [In stan ceSp ecificatio n ] « stereo typ e» Mo ve m e n tO fP e o p le [In fo rm atio n Flo w ] « stereo typ e» R e tire m e n t [In stan ceSp ecificatio n ] -co d e/sym b o l : Strin g -serviceTyp e : Strin g « stereo typ e» A ctu alO rgan izatio n [In stan ceSp ecificatio n ] -id en tifier : Strin g « stereo typ e» In fo rm atio n Ele m e n t « stereo typ e» Lo gicalA rch ite ctu re [C lass] +en co d in g : Strin g « stereo typ e» So aML::A ttach m e n t [P ro p erty] « stereo typ e» Su b jectO fFo reca st « stereo typ e» R e so u rce Me ssage « stereo typ e» P erfo rm ed A ctivity « stereo typ e» O p e ratio n alA ctivity [A ctivity] « stereo typ e» U se d C o n figu ratio n [P ro p erty] « stereo typ e» O rg a n iza tio n R o le « stereo typ e» So aML::P articip an t [C lass] « stereo typ e» R eferred Lo ca tio n « stereo typ e» Mate rie lExch an ge [In fo rm atio n Flo w ] « stereo typ e» Se rvice O p e ratio n [O p eratio n ] « p ro file» UPDM L0 « stereo typ e» R e so u rce A rtifact [C lass] « stereo typ e» Se rvice Me ssage [Message] « stereo typ e» En te rp rise P h ase [C lass] +isID : B o o lean « stereo typ e» So aML::P ro p e rty [P ro p erty] « stereo typ e» K n o w n R e so urce [P ro p erty] « stereo typ e» O p e ratio n alN o d e [C lass] « stereo typ e» H u m an R e so u rce [P ro p erty] « stereo typ e» Su b O rgan izatio n [P ro p erty] « stereo typ e» En e rgyExch an ge [In fo rm atio n Flo w ] « m etaclass» In fo rm atio n Flo w « stereo typ e» Syste m Fu n ctio n [A ctivity] « stereo typ e» H o ste d So ftw are [P ro p erty] « stereo typ e» Se rvice Fu n ctio n [A ctivity] « stereo typ e» P ro b le m D o m ain [P ro p erty] « stereo typ e» A ctivitySu b ject « stereo typ e» Su b Syste m P art [P ro p erty] « stereo typ e» So aML::Exp o se [D ep en d en cy] « stereo typ e» C o n tro ls [In fo rm atio n Flo w ] « stereo typ e» C o m m an d s [In fo rm atio n Flo w ] « stereo typ e» D ataExch an ge [In fo rm atio n Flo w ] -U R L/U R I : Strin g « stereo typ e» U P D MElem en t « stereo typ e» En te rp rise G o al [C lass] -id en tifier : Strin g « stereo typ e» D ataEle m e n t [C lass] « stereo typ e» Ligh tC o n d itio n [D ataTyp e] « stereo typ e» R eso u rceR o le « stereo typ e» Syste m sN o d e [C lass] « stereo typ e» R eso u rce [C lass] « stereo typ e» P o stR o le [P ro p erty] « stereo typ e» C o n cep tItem « stereo typ e» N o d e [C lass] « stereo typ e» P latfo rm [P ro p erty] « stereo typ e» C ap ab ility [C lass] « stereo typ e» En d u rin gTask [C lass] « stereo typ e» C lim ate [C lass] « stereo typ e» N o d eP a ren t « stereo typ e» P erfo rm er [C lass] « stereo typ e» Fu n ctio n Ed ge [A ctivityEd ge] « stereo typ e» P ro to co l [C lass] « stereo typ e» O rgan izatio n [C lass] « stereo typ e» R e so u rce P o rt [P o rt] « stereo typ e» Syste m [C lass] « stereo typ e» En e rgy [C lass] « stereo typ e» C ap ab ility [C lass] « stereo typ e» N o d eC h ild « stereo typ e» Lo catio n [D ataTyp e] « stereo typ e» So aML::A ge n t [C lass] « stereo typ e» En viro n m e n t [C lass] « stereo typ e» En tityIte m [C lass] « stereo typ e» So ftw are [C lass] « stereo typ e» N o d e R o le [P ro p erty] « stereo typ e» P o st [C lass] « stereo typ e» C o m p e te n ce [C lass] « stereo typ e» Eq u ip m e n t [P ro p erty] « stereo typ e» P art [P ro p erty] « stereo typ e» Fu n ctio n [A ctivity] « stereo typ e» Exte rn alN o d e [C lass] « m etaclass» State Mach in e « m etaclass» P ro p e rty « m etaclass» U se C ase « m etaclass» D ataTyp e « m etaclass» A sso ciatio n « m etaclass» O p e ratio n « m etaclass» D e p e n d e ncy « m etaclass» A ctivity « m etaclass» C o n n e cto r « m etaclass» P o rt « m etaclass» Me ssage « m etaclass» A ctivityEd g e « m etaclass» In te ractio n « m etaclass» C lass Wh at, if an y is th e relatio n sh ip h ere in th e m eta-m o d el??? C lass_ServiceC ap ab ilit y -co n fo rm sTo * -m ilesto n e * -reso u rce * C lass_P articip an tA rch itectu re P o rt_P o rt -rep resen ted B y * -rep resen ts * D ataTyp e_Message Typ e -d efin ed B y * -d efin es * -u sed Fu n ctio n s * C lass_MessageTyp e -carried Item 0 ..* -carries * -/fu n ctio n sU p o n * -/su b ject * C lass_P articip an t -/su b ject * -/actsU p o n * « im p o rt» P ro p erty_A ttach m en t « im p o rt» -n o Lo n gerU sed B y ..* -carried Exch an ge * « im p o rt» P ro p erty_P ro p erty « im p o rt» -resp o n sib leFo r * « im p o rt» -u sed B y ..* -o w n ed Milesto n es ..* -realizes * -realized B y * -in h ab its 0 ..* -en viro n m en tC o n d itio n s 0 ..* -co n creteB eh avio r 0 .. -statem en tTasks * -en terp riseP h ase -go als * -exh ib its * -w h o le 0 .. -p art 0 ..* -d escrib ed Missio n 0 ..* -ratified B y * -ratified Stan d ard s * -im p lem en ts -en terp riseP h ase -visio n s * * -ab stractB eh avio r 0 .. -carries * « im p o rt» /m akes +/n eed s 0 ..* +p articip an t 0 ..1 /o ffers +/cap ab ilities 0 ..* +p articip an t 0 ..1 D ep en d en cy_C ap ab ilityR ealizatio n -carries * Too much data – As big as the enterprise, mapped into too many fuzzy concepts Too little meaning – Islands & archipelagoes instead of a continent Ineffective modelling – Like a weak neural network, bad EA just re-encodes its data EA fails ⅔ of the time* – Because data… and its lack of meaning *Roeleven & Broer, Why two thirds of Enterprise Architecture Projects Fail (2008) online here
  19. 19. Awakenings Jorge Santayana amended – – Those who cannot remember the structure of the past are condemned to repeat it* Though EA often sucks, it doesn’t have to – Those who best remember and understand the past will be the survivors of the future – They key to good is EA is better information – better data organised according to clearer concepts *Jorge Agustín Nicolás Ruiz de Santayana y Borrás, The Life of Reason, Vol. 1 Ch. XII Flux and Constancy in Human Nature (p284) online here
  20. 20. End game An English poet* famously said A little learning is a dangerous thing; Drink deep, or taste not the Pierian spring**: We need more semantic data – the data which gives other data meaning – in order to survive Why data? – Because used intelligently, Data Defeats Darwin… – It’s why we are Earth’s dominant species *Alexander Pope, An Essay on Criticism, 1709 ** sacred to the Greek muses and a source of knowledge & inspiration, in Macedonia
  21. 21. or eablog.pantologea.com Game Over Your Question Here ?

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