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Short description of the talk
Data is one of the most valuable assets to nowadays business. However, most of them are struggling to manage it with their applications.
There are dozens of concepts and variables around data management, which can get confusing. That's why, before exploring some of those key definitions, let’s bust some myths around data management:
"A data management plan or a strategy is only for big companies." – Wrong! The sooner you become aware of the importance of having organized, manageable, and integrated data, the better positioned your company will be. Startups and small teams can increase their productivity and improve their customer interactions by managing data properly.
"Managing data is for mathematicians and scientists" – Definitely not. Though everyone is looking to hire Data Engineers and Data Scientists, the manual work involved and the real process of integrating data processes into applications often results in coding, architecting backend, and implementation of services.
“You need to learn Python to tackle data management” - Not necessarily. There are many easy-to-use Ruby-based solutions that allow you to collect, store, maintain, process, and integrate your data.
In this topic I will cover more about What is Data Management, Data Management Goals, Best Practices and Ruby Based Solutions for that.
Key Takeaways:
1️⃣ I will give a definition of what is Data Management and review three basic stages that it encompass: collection, storing, and processing
2️⃣ I will review real life examples of Enterprise Data Management Strategies and will learn how to build data processes aligned to your business objectives
3️⃣ You will learn best practices and techniques for managing data from Ruby language point of view, including libraries, frameworks and read-to-go boiler-plates.
4️⃣ Finally you will get a chance to look under the hood of working samples of Data Management concepts in a real production project.
Short description of the talk
Data is one of the most valuable assets to nowadays business. However, most of them are struggling to manage it with their applications.
There are dozens of concepts and variables around data management, which can get confusing. That's why, before exploring some of those key definitions, let’s bust some myths around data management:
"A data management plan or a strategy is only for big companies." – Wrong! The sooner you become aware of the importance of having organized, manageable, and integrated data, the better positioned your company will be. Startups and small teams can increase their productivity and improve their customer interactions by managing data properly.
"Managing data is for mathematicians and scientists" – Definitely not. Though everyone is looking to hire Data Engineers and Data Scientists, the manual work involved and the real process of integrating data processes into applications often results in coding, architecting backend, and implementation of services.
“You need to learn Python to tackle data management” - Not necessarily. There are many easy-to-use Ruby-based solutions that allow you to collect, store, maintain, process, and integrate your data.
In this topic I will cover more about What is Data Management, Data Management Goals, Best Practices and Ruby Based Solutions for that.
Key Takeaways:
1️⃣ I will give a definition of what is Data Management and review three basic stages that it encompass: collection, storing, and processing
2️⃣ I will review real life examples of Enterprise Data Management Strategies and will learn how to build data processes aligned to your business objectives
3️⃣ You will learn best practices and techniques for managing data from Ruby language point of view, including libraries, frameworks and read-to-go boiler-plates.
4️⃣ Finally you will get a chance to look under the hood of working samples of Data Management concepts in a real production project.
Level
Contact information
Possible days for the talk
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