Acceleration at heart Strategy in mind

Transkript

1 Acceleration at heart Strategy in mind 1900 consultants in five countries 1200 Consultants in four locations in Sweden 2400MSEK turnover Consulting Enterprise Digital The Business Design Studio

2 Advanced analytics

3 Advanced Analytics - Agenda Den datadrivna organisationen Introduktion till advanced analytics & machine learning Hur fungerar det tillsammans med BI? Referenscase 3

4 Business Intelligence & Analytics - value curve Value What happened? Descriptiv e analytics Why did it happen? Diagnostic analytics What will happen? Predictive analytics How do we get it to happen? Prescriptive analytics Automation Difficulty 4 4

5 20% of all business content will be generated by algorithms year Source: Gartner 2016

6 HOW BUSINESSESS USE DATA WILL DEFINE THEIR SUCCESS TODAY AND TOMORROW DATA GROWTH Exponential growth of data Devices and sensors track more data with faster pace Everything gets connected CUSTOMER INTIMACY Expectations of a personal customer experience Complexity of customer journey New and fast changing sales and marketing channels NEW BUSINESS Data creates new business models Eco systems of platforms and partners transforms market Bi-modal is used to kill own darlings 6

7 The winners are building A DATA DRIVEN BUSINESS MODEL Known knowns Things you know Unknown knowns Questions you ask today Business Intelligence Data Analyst Questions you don t ask today Data Discovery Data Scientist 7 Known unknowns Things you don t know Unknown unknowns

8 VAD ÄR MACHINE LEARNING EGENTLIGEN? VAR KAN JAG APPLICERA TEKNOLOGIN? VAD ÄR DÅ ARTIFICIELL INTELLIGENS?

9 Begreppsförklaring

10 Typer av Artificiell intelligens Narrow AI Artificial General Intelligence Super intelligent AI

11 Hur lär man en maskin något? Supervised training Unsupervised training 11

12 Vad kan maskinell inlärning göra för er? Telemetrisk data analys Köpbenägenhets modeller Analys av sociala nätverk Proaktivt underhåll Webbapplikations optimering Övervakning smarta sensorer Vårdutlåtanden Kund-churn Kvalitetskontroll Prognoser Bedrägerispårning Biovetenskaplig forsknin Riktade kampanjer Spårning av nätverksintrång

13 Deep learning - en metod för att implementera Machine Learning

14 Data scientist BI Data Warehouse Machine Learning Business Workspaces Analyst Data set Data Scientist Custom Analysis Model BI tools Consumer Data Lake External Data Ad-hoc Insights

15 ACANDO HELPED MENIGO DECREASE FOOD WASTAGE Sales data as well as google analytics were analyzed by using machine learning and statistical methods (regression analysis). The result was presented in Power BI showing how likely it is that a particular customer buys a particular product on a certain day (taking seasonal variations into account). The new insights are used to promote products, which are likely to be wasted, in all sales channels (field, phone and web) with good results. How likely it is that a particular customer buys a particular product on a certain day 15

16 Utmaningen: Lösningen: Resultatet: Materielunderhåll är en stor kostnadspost En mängd olika data- och informationskällor Stora mängder data och information av olika kategorier och kvalitet Samordning och planering är nyckeln till lönsamhet i branschen Samla sensor-data och eventloggar Extrahera användbara delar Hitta mönster och avvikelser Skapa signaler och varningar Aggregera upp till dash-board med möjlighet till djupanalys rapporter för olika användarkategorier Ökad tillgänglighet Minskade kostnader för service och underhåll Minskade effekter /störningar av svåra förhållanden Frigjorda resurser

17 Acando helps Södra Älvsborgs Sjukhus to save lives Healthcare in Sweden are facing major challenges Large amounts of data and general feeling of unused potential Purpose: Answer the question: Why do some pneumonia patients become more ill than others? Why do some pneumonia patients become more ill than others? Efficient work method Involving different competences throughout the organization Design and develop IT-solution for advanced analytics for future work Support the project with expert knowledge 17

18 The data innovation lab creates immediate insights to Advanced Analytics using your data this i how! A Ideation and Hypothesis B C D E Data validation Analysis I Analys II and Conclusion Results DATA EXPERIENCE WORKSHOP The data innovation lab can be used in a number of situation e.g. as an activity to obtain general knowledge of Data Science and Advanced Analytics capabilities as proof of concept to verify that a specific business problem can be solved as an explorative activity to evaluate and identify data and analysis capabilities as a tool to understand business value of existing data as a tool for one-off business process improvement Use as a starting point for any Advanced Analytics related work with your data in focus

19 The data innovation lab creates immediate insights to Advanced Analytics using your data this i how! A Ideation and Hypothesis B C D E Data validation Analysis I Analys II and Conclusion Results DATA EXPERIENCE WORKSHOP What data is available? What analysis questions do you wish to have answered? Do you have a particular thesis at this stage? What is important for the next stage after the innovation lab? WHAT? WHO? HOW? WHEN? HOW MUCH? A F B E C D Activity output: Basic description of hypothesis/es to be analysed Selected data scope Plan for execution of Innovation Lab Confirmed customer stakeholder participation

20 Tack!