2. Key Objectives Introduce the Requirements âIcebergâ Identify the relevant aspects of Analysis Consider an Agile Approach to Requirement Analsysis
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4. Relax! Not a new process! No new rules! No additional documentation! Just to inspire your thoughtsâŠ. and help with communication.
14. The Requirements ICEBERG Seemingly Simple Report Request ⊠is but the surface! Reporting Tool Constraints Complex Transformations The majority is yet to be DISCOVERED Ambiguous Business Rules How to detect Change ? Onboard new Data Source Upstream System Dependencies
15. But back to the QuestionsâŠFor WHOm? Simple answer: âThe Customerâ (next Grisham novel?) Really:The Business More aptly: For what purpose? (Business Case) Getting a solution request from customer does not cover all the requirements yet.
35. Where Requirements & Analysis meet What does customer ask for? What is really needed? What do we already have? Identify Gap! Close Gap! ANALYSIS REQUIREMENTS STATUS QUO NEEDS
36. Whoâs the CUSTOMER ? External Third Party (e.g. Affiliate, Provider) Product Mgmt Operations (e.g. Finance, Support) Peer Team (in CIO org i.e. Marketing) EDW (Technical Debt)
37. Why does the WHO matter? Different people, teams, business functions have different understandings of⊠Completeness Quality Performance Accuracy Precision Donât project one customerâs expectations on another
38. The NEED Forward Looking (the âTO BEâ) Reconcile what the customer asks for with what the business needs What does the customer intend to do with the solution (s)he asks for? Get underlying Business Context! Distinguish quantitativefrom qualitativeasks: Tangible metrics calculation(validate understanding), versus More acurate, timely, faster information(clarify meaning!) Note the difference between⊠ASK=> Request NEED=> Context REQUIREMENT=> Details
39. The STATUS QUO BackwardLooking(the âWHAT ISâ)⊠Reports Views Reporting Data (Structure, Content) Persistent Staging Data (a.k.a. ODS, rpt_ tables) Source Data (internal) External Data
40. Identify GAP Metrics donât exist Not sliceable as desired (dimensions) Data Quality(coverage, numbers, attribution) More Detail More History Subject Area not covered yet Change Delivery Format(CSV, view, XLS, BO, Tableau) More timely Data (finer time grain, or performance) Business Rules changed (ETL/backfill processing)
41. Close GAP Onboard new external data possibly new data acquisition method Onboard new internal data more likely via existing channels Profile Source Data Design Reporting Data (Sub) Model Build Data Structures Transform Source Data into Reporting Structures (ETL) Build customer-accessible Reporting Views Build Reports
42. More Details on Gaps Customer Need: What is the underlying calculation for the needed Business Metric? What Business Rules apply for this Report/Metric? Existing EDW: What does the existing view query code do? How does the existing ETL logic work? What is covered by the source data? How can we access the 3rd party data feed?
43. Gap Analysis Matrix NEED to find out about BUSINESS⊠Business Concepts (Terms, Meanings, Relationships) Business Process Work Flow Decision Points, Criteria Business Rules Possible States Changes/Transitions (criteria, scenarios) Formulas, Parameters HAVE understanding about the BUSINESS⊠Business Concepts (Terms, Meanings, Relationships) Business Process Work Flow Decision Points, Criteria Business Rules Possible States Changes/Transitions (criteria, scenarios) Formulas, Parameters HAVE in EDW⊠Reports (Metrics, Slicers) Views (Query Logic) ETL (Transformation Logic) Tables(Facts, Dimensions, Reporting, Staging) Data(Coverage, Quality, History) Source(Availability, Connection, Understanding) NEED in EDW⊠Reports (Metrics, Slicers) Views (Query Logic) ETL (Transformation Logic) Tables(Facts, Dimensions, Reporting, Staging) Data(Coverage, Quality, History) Source(Availability, Connection, Understanding)
44. Business Process â Flow & Steps Ad-hoc model drives better understanding We do it all the time on the white board Helps you understand the steps and concepts involved
45. Business Process - Outcomes Understand the relevant outcomes of Business Processes And how the influence each other Most likely we have to model, capture and report on these in the EDW Also known as âState Transitionsâ
46. Decision Tree Understand what leads to outcomes What decisions influence the process? Similar: âFishboneâ Diagrams
47. Conceptual Data Model Understand the Business Entities And their Relationships among each other Gauge Impact & Dependencies Distinguish Business⊠Actors Event State (as in Status) Metric (Amount, Count, Ratio)
48. Data Flow Understand Data Lineage Change Dependencies & Impact This example is very detailed. You can apply this to higher level, Conceptual Model as well In fact, you can combine any of these diagrams with each other, to illustrate a focused subject area
50. Use these Models! You can combine any of these diagrams with each other, to illustrate a focused subject area Use them with your customer! Have them understand and validate it (incentivize that the alternative is for them to look at ETL code, physical data models, raw source data :-)
56. Setting Scope You might not know the details of what youâre analyzing, but you can⊠set a clear scope of what you plan on analyzing, and what findingswill satisfy the need for Analysis. Determine the Type of Analysis upfront (then the approach will become clearer) Are you looking for:then: Data Relationships?ï Profile / Model Change Dynamics?ï Observe Process/Data Flow The Meaning of something?ï Ask People Process Dependencies?ï Monitor Status Bug Impact?ï Reverse Engineer Implementation Gap?ï Gap Analysis Root Cause?ï Root Cause Analysis
57. How to establish Progress Make sure each iteration delivers a new insight, incrementing the knowledge base. Specify clear artifacts expected from each Iteration of Analysis
58. A quick view on COMPLEXITY How many objects can you see? Are you sure they are triangles? Why are they colored? If this were a movie, what would they do next? What is this picture about? What is this useful for?
59. The Elements of COMPLEXITY SCALE-> Size DYNAMICS-> Change SEMANTICS -> Meaning CONTEXT-> Relationships CERTAINTY-> Confidence And this is the simple version. âEmergenceâ anyone? BTW, the Cartoon illustrates a typical human reaction to Complexity: resort to the known tools/approaches. A phenomenon also referred to as âif all you have is a hammer, every problem becomes a nailâ. The opposing extreme is: throwing everything and the kitchen sink at it
60. Complexity in the EDW â A Quiz Number of Rows Distinct PK Combinations Inserts, Updates, Deletes Track Change History (SCD Type 2) 85% column values are NULL rpt_site_activity vs. bfgmq_known join FSR with DPC
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66. BI Examples Correlate the abstract to something tangible⊠Conceptual business process model -> Physical Data Model You see, format/presentation, of the same underlying thing, can make all the difference in clarity/meaning. BI Example: reporting tables vs. views vs. Reports)
67. Consider this⊠Differentiate between making things complex (blowing things out of proportion), and mapping an existing complex Process Structure Concept/Phenomenon Context: Divide & Conquer, Zoom/pan, Abstract/Concrete, Generic/Specific Did you knowâŠ. Albert Einstein was a SCRUM pioneer? âEverything should be made as simple as possible, but not simplerâ In other words: âWe can always complicate things laterâ
68. Mix & Match Letâs correlate our questions w/ Complexity⊠SCALE How much/many? What is involved? (components) DYNAMICS What happens? How does it work? MEANING What is the purpose? What is the root cause? What types of ⊠? How do you define concept <X> CONTEXT What belongs together? How does (a) relate to (b) ?
Hinweis der Redaktion
MEANING: What is it? A âCellular Automatonâ (rather: the output of such algorithm)Root cause: mathematical algorithmsPurpose: simulate/model natural processes, like biological, or chemicalSCALE: number of triangles (uncountable) -> group by size, colorDYNAMICS: this picture doesnât move, but during itâs creation, youâd see the triangles evolveCONTEXT:Computer ScienceCERTAINTY:Mathematical algorithm well knownBackground of picture without sub title only known to experts
For our very unscientific view here, letâs say the main contributors to COMPLEXITY are:SCALE: SizeVolume (number of elements)DYNAMICS:ChangeSpeedMEANINGPurpose/IntentRoot CauseCONTEXT:RelationshipsDependencies (upstream)Impact (downstream)EnvironmentScope, QualifierFocus
A solution is often inFocusing scope, summarizing, generalizing, aggregating, grouping. One can aways make it more complicated later
Inspired by Occamâs Razor: â a principle which ⊠recommends selecting the ⊠hypothesis that makes the fewest new assumptions;  ⊠that suggests we should tend towards simpler theories until we can trade some simplicity for increased explanatory powerâ*and yes, we are not scientists here, this wouldnât hold up to scrutiny ï