Data insights for breakfast, stockholm

1. Solita – The Data Company

2. Agenda • 08:00 Welcome: Future of data – Solita • 08:10 Faster, Better, Easier! Enabling the data-driven company -Snowflake • 08:25 The analytics platform that dirupted the world of business intelligence – Tableau • 08:40 Customer case: Finnish Transport Agency • 09:00 Wrap up and discussion, Q&A • 09:05-09:45 Demo labs open: • Demo lab/Solita: The 6 weeks approach – from zero to production ready • Demo lab/deep dive Snowflake • Demo lab/deep dive Tableau

3. We increase the value of your data asset. • 94 % of our 186 clients recommend us • Over 2 million daily users in maintained services • Extensive partner network in tech and insight • Sweden, Estonia, Finland, Germany 1996 FOUNDED 700 EMPLOYEES 8 CITIES 4 COUNTRIES 76M TURNOVER 2017 20% AVG. PROFITABLE GROWTH PER ANNUM

4. Solita x Snowflake x Tableau 30 12 2 15 25 3

5. Data flows are transforming from informational to suggestive and predictive. (AI, ML, DS & cloud) Data becomes a central part of business models generating revenues and business value. Not only focus on enhancing the existing business models with data. Technology-as-a-Service has taken us to a point where everyone is able to search and buy the latest solutions. Rather than worry about the loss of control, the most successful CIOs are the ones using data and therefore taking the driver seat in the data-driven transformation. CIO’s investment priorities in the next two years ● 84% of CIOs expect to be investing in cloud capabilities ● 79% of CIOs expect to be investing in data analytics ● 73% of CIOs expect to be investing in security Data is one of the most important assets for companies now and in the future

6. 91% of organizations have not yet reached a ‘transformational’ level of maturity in data and analytics, despite this being identified as a number one investment priority for CIOs. 60 %of respondents worldwide rated themselves in the lowest three levels. Source:https://www.gartner.com/en/newsroom/press-releases/2018-02-05- gartner-survey-shows-organizations-are-slow-to-advance-in-data-and-analytics

7. Data-driven transformation is like playing chess - in six dimensions! • Building a data strategy • Creating data-driven business cases • Changing the culture to be data-driven - view data as an asset • Building necessary skills, roles and ways of working • Technological enablement • Maintaining governance while enabling agility …and keeping the lights on at the same!

8. Data-informed Business Data-infused Business Data-driven Business https://blogs.sas.com/content/datamanagement/2017/11/30/data-driven-business/ There’s no “correct way” to be data-driven

9. Impact needs experimentation AND alignment

10. What is your greatest data-related challenge? www.menti.com Code: 45 82 47

11. https://www.mentimeter.com/s/f7c132e9b55d6041639cbd6f0c d042c4/9a6669fb3edc

13. Enabling All Companies to be Data-Driven

14. Infrastructure sized to fit the needs of the business at any time. Pay only for what is used. © 2018 Snowflake Computing Inc. All Rights Reserved. 3 What we need for modern business agility Comprehensive Understanding Fast Insights Affordable ScaleData Culture Data Enrichment Find the data that you need and integrate into your own data without friction. Self service analytics with a robust, flexible and governed data model. Rapid data ingestion, fast query performance, automatic insights. No silos. Complete view of customers, business and activity.

15. Why we don’t have Comprehensive Understanding © 2018 Snowflake Computing Inc. All Rights Reserved. 4 Organizational complexity – Everyone has their own tools and data stores Data type proliferation – It isn’t just CSV’s anymore

16. Why we can’t deliver Fast Insights © 2018 Snowflake Computing Inc. All Rights Reserved. 5 Limited resources Fixed compute capacity means fixed query potential Manual tuning Manual tuning methods (index, sort) require constant maintenance to be effective

17. Why Data Cultures are so hard to build © 2018 Snowflake Computing Inc. All Rights Reserved. 6 Concurrency Even when they have access to data and the tools they need, many people experienced delays and queues due to concurrency Restrictive viewpoints At many companies, data is like a sacred text, meant to be analyzed and interpreted by only a small priesthood of people. For this reason, BI tools are often not shared with the entire company.

18. Why is it so hard to Enrich Data? © 2018 Snowflake Computing Inc. All Rights Reserved. 7 Data providers • Complex process to prepare data for sharing • Poor ability to govern published data • Security headaches • Inefficiency Data consumers • Need to build infrastructure to collect and house data • Complex process to reconstruct data • Fragile pipeline • Difficult to keep data up to date Data Consumer Data Provider Data Repository Data & Metadata Staging Data Transfer Data Staging Data Reconstruction Current process for augmenting internal data with external data

19. Why it’s so hard to achieve Affordable Scale © 2018 Snowflake Computing Inc. All Rights Reserved. 8 Traditional databases are inflexible Usage varies Must be overprovisioned or underperformant By definition, you have to buy more than you need Scaling up and down is a challenging, drawn out exercise Must plan for capacity in the future

21. Traditional Database Architecture Is Compounding These Problems © 2018 Snowflake Computing Inc. All Rights Reserved. Shared-disk Single cluster Shared storage Shared-nothing Single cluster Decentralized, local storage

22. Databases Virtual Warehouse A Brand New Architecture Virtual Warehouse ETL & Data Integration Virtual Warehouse Data Scientists Virtual Warehouse Finance Dashboards Virtual Warehouse Marketing Dev, Test, QA Metadata

23. The Data Warehouse built for the Cloud Next-generation data warehouse built as a service to help you unify data, scale effectively, empower your team and deliver analytics. Affordable ScaleComprehensive Understanding Data EnrichmentFast Insights Data Culture

24. ”Data needs to be your primary focus or you are doomed tro fail!” Ville Brofeldt at Aktia Bank

27. © 2018 Snowflake Computing Inc. All Rights Reserved. 16 Fast Insights Fully managed with a pay-as-you-go model. Works on any data. Services to maintain materialized views and cluster keys. Multi petabyte-scale, up to 200x faster performance and 1/10th the cost SimplicityScale

29. Data Science SQL Analysts ETL & Processing BI & Analytics Tools Unlimited Concurrency © 2018 Snowflake Computing Inc. All Rights Reserved. 18 Management Optimization Security Availability Transactions Metadata Database Services

30. Dear Santa, For Christmas I want a warehouse solution with the following features, please: • SCALABLE, NO CONCURRENCY ISSUES • DATA WITH DIFFERENT DEGREE OF USAGE • FAST DATA PIPELINES• SEMISTRUCTURED DATA • SQL • CONNECTIVITY• MINIMAL MANAGEMENT • WORKS AS AN API BACKEND • MASTER DATA MANAGEMENT

31. Cloud Services Management Optimization Security Availability Transactions Metadata © 2018 Snowflake Computing Inc. All Rights Reserved. 21 Data Provider Snowflake Account Consumer 2 Snowflake Account SQL Analysts WH Multi-Tenant Snowflake Deployment Data Science WH Consumer 1 Snowflake Account Data to Share Secure Data Share Wants provider data for data scientists Wants provider data for SQL analysts Share Live Data Instantly Grant Secure Row Level Access Use One Share for Many NO FTP or EDI NO Duplicate Files or Storage NO Additional Charge Database Services Data Enrichment with Snowflake Data Sharing

33. Traditional databases are inflexible Snowflake uses the cloud to enable elasticity Usage varies Pay for only what you use with no overprovisioning Eliminate overbuy Scale compute up and down, transparently and automatically No need for capacity planning, make capacity decisions on the fly © 2018 Snowflake Computing Inc. All Rights Reserved. 23 Affordable Scale

34. “Building the Data-Driven cu

35. Snowflake at Finavia • Data Warehouse as a Service • Decisions now, must support requirements N years ahead • Per second pricing (!) • No-Admin !? ~Zero Admin • No Backups!? • Storage vs compute Separate compute workloads • On-line up/downscaling • No-hadoop etc. • ”It Just Works”™ • Love it. 22.11.2018 25

41. Tarek Qaddoumi Product Consultant EMEA Welcome!

42. Breakthrough Innovation

43. What does that mean? VizQL Driver Optimized SQL/MDX Aggregate Result Data Driver Processing VizQL Rendering Visualization • Blending • Aggregated Filters • Table Calculations • Reference & Trend Lines • Formatting • Layout • Local Filters •‘Hide’ • Table Calculations Interaction • Context Filters (tells DB to build temp tables) • Row-level filters

44. We help people see and understand their data.

45. People who know the data should ask the questions

46. Why? Opinion- Based Data- Driven WantCanWant Can We help people see and understand Data And enable more fact-based decision making

47. It allows people to create beautiful visuals! https://public.tableau.com/en-us/s/gallery

48. Communications, Media & Technology Financial Services Services Public Sector Retail & Consumer Goods Healthcare & Life Sciences Manufacturing Energy & Resources Travel & Transportation

49. What is visual analytics and why is it effective ?

50. Before

51. After

52. 70% 30% Be Visual

53. How many #9’s do you see?

54. Try now..

55. How about now?

56. Colour/Size Length

57. Preattentive Attributes

58. An exercise: What charts would you use to show this data?

59. At the heart is the cycle of visual analysis Visual analysis is a naturally iterative process

60. “Logic will take you from A to B. Imagination will take you anywhere” - Albert Einstein

61. • Testament to customer satisfaction and ability to drive value with a complete and flexible platform. • Customers rate Tableau extremely high with top scores in all aspects of customer experience, including best scores for user enablement, ethics and culture. • Gartner calls Tableau the “gold standard in visual exploration.” Calls out vast data connections both live and in-memory. • Gartner recognizes Tableau’s flexibility and choice with on-premises, public and fully cloud managed deployment options. • Gartner data indicates enterprises are growing their Tableau deployments with 52% of them saying they consider Tableau "The enterprise standard.” Tableau positioned as leader again. 6 years year in a row. Leading in execution since 2014.

62. A Leader for 6 years. Leading in Execution Since 2014. 2014 2015 2017 2016 2018

64. 2012 2013 2014 2015 2016 2017 2018 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 £0M £200M £400M £600M £800M £1,000M £1,200M Cummulative R&D Spend £0M £20M £40M £60M £80M £100M Quarterly R&D Spend £33M £94M £205M £409M £712M £1,046M £1,233M £10M £7M £7M £8M £13M £14M £15M £18M £22M £26M £29M £34M £42M £47M £55M £60M £71M £78M £75M £79M £84M £81M £84M £84M £94M £94M Source: https://ycharts.com/companies/DATA/r_and_d_expense

65. Understanding the Usage and Strain of the Rail Infrastructure Anssi Tikka Sales Director, International Business Image source: Finnish Transport Agency

66. 20billion € Finnish Transport Agency responds for the Finnish transport network Customers Comprehensive use of data assets Waterways 200 M€ Railways 4 600 M€ Roads 15 000 M€ Citizens: Safe and reliable traveling Industries: Transportation effectiveness and competitiveness Society: Regional balance, effective use of tax payers’ money, competitiveness Open data policy Currently: Investment prioritization, project planning, performance monitoring Aspiration: anticipating future events Data assets relating to traffic, infrastructure, conditions, maintenance planning and operations. Digitalization project renews IT systems and seeks new ways of collecting and using data

67. Image source: Finnish Transport Agency

68. Project execution Results: › 6 weeks lead time → goals met › Within budget › Technology capabilities confirmed › Foundation for a new cloud-based data platform FTA’s evaluation ● Desire for moving the outcome to production ● Large amount of development ideas End-to-end data pipeline in the first weeks ● Functional data pipeline for the visualizations ● Iterative expansion and addition of data entities Handful of status meetings / workshops ● Goals & metrics for project ● Project status and progression monitoring

69. Snowflake x Agile Data Engine x Tableau Storage Services Design and Deployment Management Runtime Workflow Orchestration Virtual Warehouses Self-service analytics

70. Solution architecture

71. Agile Data Engine is an out-of-the-box DataOps platform DW Load Orchestration with Dependency and Concurrency Control Continuous Deployment of DW packages with automatic DB schema deployment and metadata-driven code generation Cloud DW modelling and Load mappings and Custom SQL in a same tool for whole team Production-proof Software- Defined Cloud DW Infrastructure from Day One Infrastructure Designer Deployment Management Runtime

72. Example Automatic ELT loads

73. Customer evaluation and feedback 1.Earlier assumptions were proven wrong. 2.Incentive for business to become more data driven. 3.Modern analytics tools create value a lot faster compared to legacy systems.

74. Future possibilities for infrastructure maintenance 1.Data driven maintenance with intuitive self-service analytics. 2.Predictive rail element specific maintenance and lifecycle planning based on data. 3.Data sharing within the transport ecosystem.

75. ANSSI TIKKA Thank you! Questions? Sales Director, International Business +358 50 4433 789 anssi.tikka@solita.fi

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