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Towards better Data Analytics - Google Cloud Bootcamp

“Without data, you are another person with an opinion”

These words from Edward Deming, a management guru, are the best definition of what means to become a data-driven organization - a culture of making decisions based on data and facts throughout all levels of a company. Although this term, along with “digital transformation”, is being cited too often, the idea behind it is having a huge impact on modern organizations. Unarguably data analytics is booming and many leaders are facing the challenge of building a data culture backed by an effective toolset. According to the latest “Big Data and AI Executive Survey 2021” report, 99% of the surveyed companies are investing in data initiatives, while 91.9% of them confirm the pace is accelerating. However, only 24% have already created data-driven organizations.

Fortunately, the perception of Big Data is slowly changing from mysterious technology that can magically answer all questions (but hardly ever works properly), to rather a learning process of organizations on how to employ data in their daily operations. It is a journey that can be painful until you figure out that the only way to go is through experimentation and collaboration. The reason is that Big Data is about asking the right questions, shaping answers, iterating over another set of questions that might be more accurate and help you with your goals. It is like constantly running different experiments. Coming back to technology - as it has to support constantly changing environments and evolve together with your analytics maturity. Your platform has to be open and flexible - open to easily upgrade the way you do things as the analytics technologies are under heavy development and flexible to accommodate new ideas fairly quickly. 

Experimentation and open infrastructure are two fields where the public cloud with a variety of different ready-to-use services fits the best. You can easily design architecture and within a few clicks set it up in the cloud. If you want to change it, that would take another few clicks to try a new approach. This allows you to focus on the real use case while saving time on setting up infrastructure. This is pretty obvious but data analytics is purely about business scenarios, technology is just an enabler.

Keeping all the above in mind and being aware of how difficult it can be to make an initial step if there are so many options and challenges out there, we have designed a Cloud Analytics Bootcamp - a comfortable platform for advancing analytics capabilities in the organization. It is also suitable for organizations that are currently doing analytics, but want to experiment with more advanced concepts, like Machine Learning, or just evaluate how using the public cloud can speed up their experimentation and delivery processes.

About our Google Cloud Bootcamp

The program is designed as a complete package that should last 6-8 weeks and is split into a few blocks. We start with the Data Strategy Workshop - a platform for all stakeholders to establish a common understanding of the goals of the project. During the workshop, we help to define a strategy for the analytics environment by reviewing business strategies and use cases. Together we also decide on the priorities and choose use cases with the biggest value for the business that we should focus on in the first place. 

GetInData Google Cloud Bootcamp for Business

Towards data-driven organizations: Minimum Viable Product

The Minimum Viable Product (MVP) step is the main part of the program where we deliver the scope of the project. MVP splits into two main streams. Minimum Viable Platform is an initial version of a data platform that allows us to load, process, and transform data for further usage. Minimum Viable Data Product is an application whose primary objective is to use data to facilitate project goals, to bring value to business stakeholders. In our case, it is usually a report or visualization together with a data pipeline that loads and transforms data from the source. However, it can be also a real-time dashboard or data feed for external systems that include Machine Learning.

Demo meeting is an important part of the program where we show the functionalities of the platform and application we have built to satisfy the use case scenario. At this point, we can roll out the application to business users and gather feedback. It is important to emphasize that both Minimum Viable Platform and Minimum Viable Data Product are fully working applications that can evolve incrementally in the future by adding new data sources, new pipelines, and reports.

At this point in the program, we are organizing knowledge-sharing sessions with technical teams so they are capable of running and maintaining the application on their own. The program can be also extended by a set of technical and business-oriented training if you want to build internal capabilities regarding building and maintaining data-driven applications using Google Cloud Platform. However, such a package is usually tailored to the needs of the organization.

Becoming a data-driven organization is an iterative and rather gradual process with constant learning as a part of it so as the last part of the program we organize a workshop to summarize the experiences from building MVP product in the context of the organization’s Data Strategy and to draw a roadmap for data analytics.

This program is based on our experiences from working with customers being on various levels of analytics maturity. Although their goals varied, the beginning was always the same - a question of what shall they do to move forward with analytics. It turned out that a small, MVP project with a set of workshops works the best to reduce uncertainty and show this Big Data journey might be hard at the beginning, but is an extremely fascinating experience on the way.

If you have any questions about the first steps to drive your organization into a data-driven approach, we will be happy to answer them all.

big data
analytics
google cloud platform
getindata
open source
big data project
5 March 2021

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