It s all about the data: A Managerial Perspective By Ronald Damhof
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1 It s all about the data: A Managerial Perspective By Ronald Damhof ronald.damhof@prudenza.nl Linkedin: nl.linkedin.com/in/ronalddam hof/ Twitter: RonaldDamhof Blog: prudenza.typepad.com Website:
2 R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014 I am an opinionated kind a guy.
3 Who am I - My Data Manifesto The X commandments of data management I. Thou shall always respect & consider the context. Context is leading R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
4 Who am I - My Data Manifesto The X commandments of data management II. Thou shall love your (meta)data. Data is the ultimate proprietary asset: - Manage it - Govern it - Utilise it But do it ethically Most companies manage their parking lot better than their data Gartner, Frank Buytendijk (paraphrased) R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
5 Who am I - My Data Manifesto The X commandments of data management III.Thou shall stop centering apps over data:data first R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
6 Who am I - My Data Manifesto The X commandments of data management IV.Thou shall strive for accurate, relevant, timely, reliable and accessible data: It is all about the quality of the product Deming s point 3 of 14: Cease dependence on inspection to achieve quality. Eliminate the need for massive inspection by building quality into the product in the first place." R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
7 Who am I - My Data Manifesto The X commandments of data management V. Thou shall abstract R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
8 Who am I - My Data Manifesto The X commandments of data management VI.Thou shall make a fundamentalistic separation between facts & context R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
9 VI.Thou shall not forsake time R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
10 Who am I - My Data Manifesto The X commandments of data management VIII.Thou shall uphold, improve and teach the science and practice of Information- & data modeling R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
11 Who am I - My Data Manifesto The X commandments of data management IX.Thou shall Specify, Standardise, Automate & Productise R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
12 Who am I - My Data Manifesto The X commandments of data management X. Thou can not buy your way out of the data misery you are in R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
13 Who am I - My Data Manifesto The X commandments of data management XI There is a new saviour in town. Its name is Hadoop and it calls to us from its mountain: we got a lake and thou shall throw all your data in it. The water will be clean so you can drink it, the water will flow so it will irrigate your lands, grow your stock, feed your kids and of course bring you world peace.. R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
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15 R.D.Damhof Prudenza Oktober BV Copyright Norske - 22 mei 2014
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17 Logistics & Manufacturing
18
19 The Data Push Pull Point Push/Supply/Source driven Pull/Demand/Product driven Mass deployment Control > Agility Validation of ingredients Repeatable & predictable processes Standardized processes High level of automation Relatively high IT/Data expertise Piece deployment Agility > Control Plausibility User-friendliness Relatively low IT expertise Domain expertise essential All facts, fully temporal Truth, Interpretation, Context Business Rules Downstream
20 The Development Style Systematic User & developer are separated Defensive Governance Focus on non-functionals Centralised Proper system development User = developer Offensive governance Decentralised System development in production pportunistic
21 A Data Deployment Quadrant Systematic Push/Supply/Source driven Facts Data Push/Pull Point I Pull/Demand/Product driven Context II Development Style III IV Shadow IT, Incubation, Ad-hoc, Once off Research, Innovation & Design Opportunistic
22 6 Applications of the Quadrant
23 (1) How we produce
24 How we produce, process variants
25 How we produce, production lines Production-line: Poly Structured Eg. XBRL/JSON Production-line: Forms orientation Data Products Information Products Production-line: Data orientation Access to data Analytical tools Processing Power
26 (2) How we automate
27 (2) How we automate Rephrased - somewhat more nerdy: Model-driven, metadata driven Or Declarative instead of Imperative Rephrased - somewhat more popular: In Data, the developer is the data modeller
28 (3) How we organize
29 To centralize or to decentralize
30 (4) How do people excel
31 (5) How about Technology Storage: (R)DBMS Processing: Automation Software Data Quality: Validation, Profiling Development: Data Modeling Accessibility: Data Virtualization Storage: Pattern based Processing: Automation/limited ETL Data Quality: DQ rules/dashboards User tooling: Reporting, dashboards, Data Visualization Storage: Analytical Processing: Preptools for Data Analyst User tooling: Advanced Analytics, Data Visualization
32 (6) Business-,Information- or Data Modeling is key Natural Language Ontology Conceptual Logical Facts Relational At least the Logical Model drives the technical data architecture, design and implementation e.g Data Vault, Anchor Model e.g. Dimensional, hierarchical,flat
33 Oh data warehouse? DWH in Netherlands - since have increasingly been split-up between facts (Quadrant 1) and context (Quadrant 2). Quadrant 1 is morphing into an Integrated (Meta)Data Environment. An holistic view on data. Not only accepting feeds from other apps, but being the system-of-record for apps. At least integrated on the logical level, preferably on the conceptual level. The (meta)data(quality) is fiercely managed & governed centrally in orgs. Interestingly; quadrant II is becoming the classic - more Kimball style DWH, but where conformity is implicit and (technically) data virtualisation is key. Central Data Competencies, Decentral (close to demand) BI Competencies
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