Data driven organizations
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Data driven organizations Dutch Data Dude
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Data driven organizations
- 1. F A R E W E L L T O U R DUTCH DATA DUDE Data driven organizations JEROEN TER HEERDT Technical Evangelist @jeroenterheerdt http://www.dutchdatadude.com #farewelltour #dutchdatadude
- 3. 35
- 7. Artificial Intelligence in the news
- 9. “(Big) Data is like teenage sex: (Dan Ariely) so everyone claims they are doing it.” everyone thinks everyone else is doing it, nobody really knows how to do it, everyone talks about it,
- 10. How did we get here?
- 14. 1982
- 15. 1982 – $ 1.5 B 2013 $40.00
- 16. “This data currently has not enough value to bother deleting it.” (emphasis mine)
- 19. 2010 1.2 ZB 1 ZB = 1 ZettaByte =1021 bytes = 1.000.000.000.000.000.000.000 by
- 20. 2010 – 1.2 ZB 1 ZB = 1 ZettaByte =1021 bytes = 1.000.000.000.000.000.000.000 by 2020 35 ZB
- 21. Zettabyte (ZB) 35 35 ZB = 35 x 1021 bytes ≈ SUM( sand particles ( beaches ( world ) ) * 5 A
- 22. Drowning in a lake of data, breathless for insight (pun intended)
- 23. How did we get here? “Datafication” Cheap storage Information production ...and of course tools and processing power evolved.
- 27. Data is the new $ / € / £ / ¥ Can be used more than once (compare to resources). Data does not break or require maintenance (like a machine). Data does not get tired or suffer from burn-outs (like humans).
- 28. PICS OR IT DIDN’T HAPPEN. DATA OR IT WON’T HAPPEN.
- 31. Data Wrangler
- 32. Data Scientist
- 33. Data Artist
- 34. Technology + = value Art Science Security Authorization Compliance Quality Tools Platform
- 35. Action People Automated Systems Apps Web Mobile Bots Intelligence Dashboards & Visualizations Natural interfaces Automation Cognitive capabilities Dashboards and visualizations Information Management Data ingress Source management Data flow management Machine Learning and Analytics Advanced Analytics Real-time event processing Intelligence Machine Learning Big Data Stores Data warehouse Data Lake Data Sources Apps Sensors and devices Data
- 36. “Do or do not… there is no try.” MASTER YODA
- 37. 0. Start collecting 1. Data focus is everyone’s responsibility 2. Ask ‘where is the data?’ 3. Stop overthinking – start doing 4. Facilitate access to knowledge and tools 5. Postpone architecting and managing – but not indefinitely 6. Be honest and transparent about what you are doing and why 7. If you are failing, fail fast and fix fast
- 41. What went wrong? AI creates a model of the input. Shit in, shit out. Humans are always necessary. Interference, model adjustment, interpretation, etc.
- 42. F A R E W E L L T O U R DUTCH DATA DUDE Ask me anything JEROEN TER HEERDT Technical Evangelist @jeroenterheerdt http://www.dutchdatadude.com
Editor's Notes
- Dit is wat veel mensen vinden dat AI is, maar hier ontbreekt een component – namelijk proactiviteit
- Dit is al een stuk meer de juiste richting in.
- postures (for example as anti-theft system in cars) person sits in chair in a particular way. Enables starting.
- Another example of what we can datafy. Ook Halo voorbeeld
- More and more items are being treated as data, collected and stored. Samples of things that were no data but are now: location, clicks, postures
- Cost of storing data is approaching zero.
- Keep it, it may be of value sometime.
- We produced 90% of all data dat humanity has ever created in the last 5 years.
- Turnover smartphone example Zandkorrels (alle zandkorrels van alle stranden ter wereld in een grote bak en dan keer vijf) Floppies: stapel floppies zo hoog dat je er 100.000 keer heen en weer mee kunt naar de maan. Als je moderner bent en met DVDs werkt is het slechts een stapel die 12 keer heen en weer naar de maan reikt.
- Pun intended (datalake)
- Last one is not as big as an impact as most say.
- Data drives digital transformation
- Data is the new currency. From resources to machines to people to data
- Deze drie functies heb je nodig. Data Wrangler / Scientist / Sculptor (Artist). Het is aan een ieder om zijn rol te kiezen, daarbij ondersteund door technologie en platform die maakt dat je op je best bent.
- Each role has its own place in this platform (wrangler, scientist, artist) Cortana Intelligence delivers an end-to-end platform with an integrated and comprehensive set of tools and services to help you build intelligent applications that let you easily take advantage of Advanced Analytics and intelligence capabilities. First, Cortana Intelligence provides services to bring data in, so that you can analyze it. It provides information management capabilities like Azure Data Factory so that you can pull data from any source (relational DB like SQL or non-relational ones like your Hadoop cluster) in an automated and scheduled way, while performing the necessary data transforms (like setting certain data columns as dates vs. currency etc). Think ETL (Extract, Transform, Load) in the cloud. Event Hubs does the same for IoT type ingestion of data that streams in from lots of end points. The data brought in then can be persisted in flexible big data storage services like Data Lake Store and Azure SQL Data Warehouse. You can then use a wide range of analytics services from Machine Learning to Azure Data Lake Analytics to Azure HDInsight to Azure Stream Analytics to analyze the data stored in the big data storage. This means you can create analytics services and models specific to your business need (say real time demand forecasting). The resultant analytics services and models created by taking these steps can then be surfaced as interactive dashboards and visualizations via Power BI. These same analytics services and models created can also be integrated into various different UI (web apps or mobile apps or rich client apps), or with Cortana, so end users can naturally interact with them via speech etc., and so that end users can get proactively be notified by Cortana if the analytics model finds a new anomaly (unusual growth in certain product purchases- in the case of real time demand forecasting example given above) or whatever deserves the attention of the business users. Similar integration can occur with Cognitive Services or Bot Framework based applications. At a high level though, Cortana Intelligence capabilities are in three main areas: data, analytics and intelligence. <Transition>: We’re going to dive into each one, starting with data.
- 4: data scientist in every team or at least accessible. Provide training to everyone for free. 5: do not control the tools to use – let it grow organically – however you land on some tools be sure to consolidate to build a common platform / technology stack. 7) tay