Big Data Platforms in the Era of Transformation
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Abstract della presentazione di Giancarlo Vercellino, Research & Consulting Manager di IDC Italia, tenuta nel corso dell'executive meeting 'Big Data e Real-Time Analytics' svoltosi a Milano il 25 novembre 2015
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Big Data Platforms in the Era of Transformation
- 1. Looking for problems Big Data Platforms in the era of transformation Milan, November 25, 2015 REDUX
- 2. © IDC Visit us at IDC.com and follow us on Twitter: @IDC 2 (Source: Sole24Ore) Big Data Business Intelligence Business Analytics Talking about something that is everywhere … … but no one can see it (Source: Google Trends)
- 3. Goldmines of value rest wide-open under the sky © IDC Visit us at IDC.com and follow us on Twitter: @IDC 3 Source: IDC, Big Data Survey 2014 , Italian companies >100 employees (n=113, weighted percentages)
- 4. European Organizations Gaining Value from Big Data Industry-wide insight from one- off analysis Optimizing a previously manual process to run it at scale Data-driven business combining complex models and sensor data Danish wind-turbine provider runs on Big Data, combining fluid dynamic modelling with real-time sensor data to optimize turbine placement. French retailer identified the factors causing some stores to perform far better than others, with a causality analytics engine. German retailer saved EUR60m by optimizing distribution of perishable groceries, with a retail demand forecasting engine.
- 5. Barbarians at the gates of traditional IT © IDC Visit us at IDC.com and follow us on Twitter: @IDC 5 At the present churn rate, 75% of S&P will be replaced by 2027 (source: Innosight, R. Foster, S&P)
- 6. Why starting a Big Data Project? © IDC Visit us at IDC.com and follow us on Twitter: @IDC 6 Source: IDC, SW Survey 2015 (n=203, weighted percentages)
- 7. Slowly adopting, one step at a time © IDC Visit us at IDC.com and follow us on Twitter: @IDC 7 Source: IDC, SW Survey 2015 (n=223; employees >50; weighted percentages)
- 8. serendipity (n.) finding something good without looking for it v
- 9. IDC Italia Viale Monza 14 20127 Milano Tel: +39 02 28457339 gvercellino@idc.com Giancarlo Vercellino Research & Consulting Manager IDC Italy www.idc.com
Editor's Notes
- European organizations are gaining value from Big Data in many ways. It’s not just about saving costs, or increasing the scope of enterprise reporting – there are many ways Big Data can impact performance. Here are some examples: Data-driven business Vestas http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=PM&subtype=AB&htmlfid=IMC14702USEN Causal factors for one-off analysis A retailer had a number of stores that performed poorly, but could not work out why this was. Retailers generally know this from experience but in this particular case the management could not understand the reason for the problem. The company loaded about 200 attributes into a causality analytics engine and found out that there were two factors affecting store performance: firstly, the area (square meters) of the store, which all retailers know about; secondly the length of shelving at child height. The successful stores had more shelving at child height. This was something the retailer had never considered – now this success factor is something the whole industry can understand. So this is not a traditional use of BI in the sense of repeated, scheduled reports – rather it is an in-depth analysis that ran once, to solve a single problem. Automating a previously manual analytical process to run it at scale A large German retailer saved EUR 60m over a few years by replacing a spreadsheet and expertise based system with a predictive analytics engine. For produce retailers, particularly short shelf life goods like salad, sending the right stock levels to the right stores has a huge impact on the bottom line of the organization. The decisions on where to send these goods are affected by dynamic conditions including weather, sporting events etc. so a huge amount of data needs to be included in the process in order to make an accurate prediction. Background info on retail demand planning: http://www.blue-yonder.com/meldedownloads/automated-decision-making-saves-otto-millions.pdf
- Identify an existing problem List possible alternatives for solving the problem Select the most beneficial of these alternatives. Implement the selected alternative. Gather feedback to find out if the implemented alternative is solving the identified problem.