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Adapting to Change

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26 February 2014


Adapting to Change

To remain agile, we must embrace and adapt to these new conditions. We must adopt changes in line with lean methodologies to stay productive.

Several changes in particular make a return to agility possible:

  • Choosing generalists over specialists
  • Preferring small teams over large teams
  • Using high-level tools and platforms: cloud computing, distributed systems, and platforms as a service (PaaS)
  • Continuous and iterative sharing of intermediate work, even when that work may be incomplete

In Agile Big Data, a small team of generalists uses scalable, high-level tools and cloud computing to iteratively refine data into increasingly higher states of value. We embrace a software stack leveraging cloud computing, distributed systems, and platforms as a service. Then we use this stack to iteratively publish the intermediate results of even our most in-depth research to snowball value from simple records to predictions and actions that create value and let us capture some of it to turn data into dollars. Let’s examine each item in detail.

Harnessing the power of generalists

In Agile Big Data we value generalists over specialists, as shown in Figure 1-3.

In other words, we measure the breadth of teammates’ skills as much as the depth of their knowledge and their talent in any one area. Examples of good Agile Big Data team members include:

  • Designers who deliver working CSS
  • Web developers who build entire applications and understand user interface and experience
  • Data scientists capable of both research and building web services and applications
  • Researchers who check in working source code, explain results, and share intermediate data
  • Product managers able to understand the nuances in all areas

Design in particular is a critical role on the Agile Big Data team. Design does not end with appearance or experience. Design encompasses all aspects of the product, from architecture, distribution, and user experience to work environment.

In the documentary The Lost Interview, Steve Jobs said this about design: “Designing a product is keeping five thousand things in your brain and fitting them all together in new and different ways to get what you want. And every day you discover something new that is a new problem or a new opportunity to fit these things together a little differently. And it’s that process that is the magic.”

Leveraging agile platforms

In Agile Big Data, we use the easiest-to-use, most approachable distributed systems, along with cloud computing and platforms as a service, to minimize infrastructure costs and maximize productivity. The simplicity of our stack helps enable a return to agility. We’ll use this stack to compose scalable systems in as few steps as possible. This lets us move fast and consume all available data without running into scalability problems that cause us to discard data or remake our application in flight. That is to say, we only build it once.

Sharing intermediate results

Finally, to address the very real differences in timelines between researchers and data scientists and the rest of the team, we adopt a sort of data collage as our mechanism of mending these disjointed scales. In other words, we piece our app together from the abundance of views, visualizations, and properties that form the “menu” for our application.

Researchers and data scientists, who work on longer timelines than agile sprints typically allow, generate data daily—albeit not in a “publishable” state. In Agile Big Data, there is no unpublishable state. The rest of the team must see weekly, if not daily (or more often), updates in the state of the data. This kind of engagement with researchers is essential to unifying the team and enabling product management.

That means publishing intermediate results—incomplete data, the scraps of analysis. These “clues” keep the team united, and as these results become interactive, everyone becomes informed as to the true nature of the data, the progress of the research, and how to combine clues into features of value. Development and design must proceed from this shared reality. The audience for these continuous releases can start small and grow as they become presentable (as shown in Figure 1-4), but customers must be included quickly.

References

[1] Russell Jurney, Agile Data Science, Data Syndrome LLC, Published by O’Reilly Media, Inc., 2014.


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