Turning data from insights into value
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This document provides an overview of data platforms and the value of data-driven organizations. It discusses how organizations collect massive amounts of data from various sources but typically only analyze a small percentage. It emphasizes the importance of building the right team with skills in data engineering, analytics, and product development to fully leverage data. Finally, it lists some of the hottest in-demand professional skills for data-focused roles.
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Turning data from insights into value
- 2. Data Platform MVP Koray Kocabas https://tr.linkedin.com/in/koraykocabas https://twitter.com/koraykocabas koraykocabas@outlook.com https://medium.com/@koraykocabas Senior Business Intelligence Developer Instructor (Big Data & Analytics) Mentor Mentor
- 4. Everybody talks about Big Data, WTF?
- 5. For Marketing & Business
- 6. For some of BI Guys
- 12. 12
- 13. 13
- 15. Buraya bir örnek daha koy veri farklılığı çok olan
- 16. ERP / CRM WEB 2.0 INTERNET OF THINGS • Mobile • Advertising • eCommerce • Digital Marketing • Search Marketing • Recommendations • Clickstream • Sensors / RFID / Devices • Log Files • Spatial & GPS Coordinates • Social Media EVOLUTION OF DATA Modern Presentation • Payables • Payroll • Inventory • Contacts • Deal Tracking • Sales Pipeline Gigabytes Terabytes Petabytes Exabytes
- 17. Big Data Utility Gap Modern Presentation < 0.5 % begin operationalized 70 % of data generated by customers 3 % being prepared for analysis 80 % of data being stored 0.5 % begin analyzed
- 18. •Obsessively collect data •Keep it forever •Put the data in one place Store Everything •Cleanse, organize and manage your data •Make the right tools available •Use the resources wisely to compute, analyze and understand data Analyze Anything •Use insights to iteratively improve your productBuild the Right Thing
- 21. There is no bla bla bla bla...
- 23. Why companies need to be Data-Driven?
- 25. Controlling for other factors, data-driven organisations are 5% more productive Strength in numbers: how does data-driven decisonmaking affect firm performance? Analytics pays back $13/per $1 invested
- 26. Having lots of reports does not make you data-driven
- 27. Having lots of dashboards does not make you data-driven
- 28. Build & position the right team! Hiring not just as individuals but to complete team
- 31. How do we use data?
- 32. Direct / Indirect Revenue
- 36. Consumer Understanding Spotify • 20 Million songs • 24 Million Active Users • 8 Million Daily Active Users • 1 TB of Compressed Data Generated From Users Per Day • 700 node Hadoop Cluster
- 37. Big Data is not just data 65 + Million Members 50 Countries 1000 + Devices Supported ~25 PB Datawarehouse on Cloud (Read %10) ~550 Billion events daily
- 41. ~10 Billions row processed (Daily) ~750 Millions row result set (Daily)
- 48. The most memorable ads
- 51. 51
- 52. Retail Payment and Information Systems Business Development/ Relationship Management Recruiting Digital and Online Marketing User Interface Design Statistical Analysis and Data Mining Perl/Python/Ruby Cloud and Distributed Computing Mobile Development Social Media Marketing Perl/Python/Ruby Marketing Campaign Management Data Engineering and Data Warehousing Mobile Development Business Intelligence SEO/SEM Marketing Network and Information Security Storage Systems and Management Middleware and Integration Software Statistical Analysis and Data Mining The 10 Hottest Professional Skills2013 2014
- 53. Retail Payment and Information Systems Business Development/ Relationship Management Recruiting Digital and Online Marketing User Interface Design Statistical Analysis and Data Mining Perl/Python/Ruby Cloud and Distributed Computing Mobile Development Social Media Marketing Perl/Python/Ruby Marketing Campaign Management Data Engineering and Data Warehousing Mobile Development Business Intelligence SEO/SEM Marketing Network and Information Security Storage Systems and Management Middleware and Integration Software Statistical Analysis and Data Mining The 10 Hottest Professional Skills2013 2014
Notes de l'éditeur
- Today I will try to explain why data is important to companies. For examples Microsoft calls data is the new currency. Do you know who this guy is? He played the creative director role in mad men. Back in the day everything work well for the mad men. We dont need mad men today, we need the math men, we need the data, we need the analytics.
- Before the presentation I want to introduce myself. I worked as a BI developer at yemeksepeti. These are my social network accounts.
- You see big data everywhere. Everyone writes about big data. Everyone talks about big data. But it started to be like a cliche. Even HBR say that it is the sexiest job right now. But I dont feel like its true.
- Everyone defines big data differently. And I define big data in three sections according to my research and people’s opinion.
- For marketing & business guys, getting an insight from data, means big data. Even if they do something on excel with pivot table, marketing & business people think it means big data.
- For BI guys, its about the volume and velocity of data.
- For Nerdy software engineers, its about framework. Like hadoop. Do you guys know about hadoop. Hadoop is a framework. It helps you to analyze and store big data sets.
- What is the first thing you do in the morning. I dont believe if you say you wash your face.
- When we wake up in the morning, the first thing we do is to check our phones. To see if there is a message from your girlfriend or boyfriend. You check your facebook, you check your instagram, twitter. The clicking is data, but the wake up time is the information. If they analyze this, they can know when you wake up in morning, they can know when you start to work. Even they can find something that you dont know.
- The boeing engine produces 20 Terabytes of data. It’s actually huge. For example most of the companies in Turkey (except Telecom & banking) dont produce that in all of their lifetime.
- For example the weather company is a data as a service. They are getting data from iot sensors and they predict the weather. They get 25 billion requests in a day. By the way, the weather company acquired by IBM for 4 Billion dollars.
- 2000 + sensors, 200 GB data per a race
- When we look at the history of data, the first period is getting the data using ERP and CRM systems. The companies needed these systems to get the knowledge. The second period is internet era. Especially Digital marketing analyzing is too important for the decisions of managers. They needed to know cost and performance of marketing campaign. And the new era is IOT era. We have billions of rows information in a day using sensors.
- Actually the Big Data Utility Gap says everything you need. According to EMC that’s the biggest storage company 70 % of data generated by customers. The ratio will be down because of iot sensors. But we just analyze it only 0.5 % I can say the companies hasnt see the impact of data analysis yet.
- Back in the past, we just stored the available of analyzing data. Because storage cost is really expensive. But now, using cloud services, the storage cost is extremely cheap. So we can store everything to analyze on it for future.
- I just want to ask a question for you. Do you know, what is the most valuable startups and why?
- Companies want to be data oriented because rise of revenue. Also data oriented companies have prestige like spotify, amazon, netflix or tesla.
- Marketing people can do everything by themselves.
- Some of users generate many fake user account because of coupons. After they save lots of coupon, they sell those to other people. Our system define and detect the potential fraud user and put them all in blacklist. İn this year, 500 K users have been labeled. Think about it, we just detect the fraud users using prediction algorithms and the company save the money just analyzing the data.
- These are the sample of jackal
- Media Investment 2014 ~6 Billion TL
- Everybody ask to me why yemeksepeti doesnt predict a meal or product for me? He probably ordered for his girl friend. If a guy orders pizza or burger everyweek, it means he is a football fan and he watches the match on tv. If a guy orders pizza every two weeks, it probably means he is a extreme football fan and he has a combined ticket for stadium.
- But we can suggest the beverages. Some of users forget to add beverages to basket. So we suggest the beverage using data from their previous orders. Actually it’s too complicated because of stock problem of restaurants. It’s real time analytics. By the way I said AB testing is important. For example, if customer click the next or previous button, the convertion rate has risen %20.
- After recommend the beverages, we try to suggest a delicious desserts after meal. It’s a little bit different because when we check customers behaviour we see that they didnt buy their favourite desserts. They usually buy the cheapest one.
- Before the analyze of data, we think the sufle is the most selling product. So we think, the sufle has different banner to increase sales. But the results show that it is not too important to buying sufle.
- Media Investment 2014 ~6 Billion TL
- Media Investment 2014 ~6 Billion TL