Imagine…imagine that you have been challenged to play Steph Curry, the greatest 3-point shooter in the history of the National Basketball Association, in a game of 1x1. Yea, a pretty predictable outcome for 99.9999999% of us.
But now image that Steph Curry has to wear a suit of knight’s armor as part of that 1x1 game. The added weight, the obstructed vision, and the lack of flexibility, agility and mobility would probably allow even the average basketball player to beat him.
Welcome to today’s technology architecture challenge!
For many companies, their technology architecture is becoming more of a hindrance than an enabler of value creation — it resembles an archeological dig, with layers of ancient technologies layered on top of even more ancient technologies. The result: added weight, obstructed vision, and lack of flexibility, agility and mobility.
Modern digital companies like Google, Facebook, Twitter, Apple, Netflix, Amazon, and AirBnB have taken a technology architecture approach that increasingly treats the technology infrastructure as “disposable” using open source technologies.
And the reason for this open approach, in my humble opinion, is two-fold:
Let’s drill into these two tenets to see the take-aways for any company interested in digitally transforming their business models can replicate.
Digital companies understand that trying to compete using traditional technologies is like Steph Curry being forced to wear full knight’s armor while competing in a basketball game. For a great lesson in modern technology, let’s look at Lyft’s strategy for structuring their technology architectures (from the article “What’s Behind Lyft’s Choices in Big Data Tech”).
Hive, Presto, Spark, Druid, Jupyter, Airflow, Kafka, Flink, Kubernetes…crazy-assed names for open source technologies that one is not going to find from the monolithic technology vendors of yesteryear (see Figure 1).
Figure 1: Source: Hortonworks
Lyft and other modern digital companies understand that their technology architecture should serve two basic purposes:
Data is the economic asset of lasting and differentiated value. Data is the source of customer, product and operational insights that the modern company uses to differentiate their products and services while driving towards operational excellence (e.g., reducing unplanned downtime, reducing procurement / logistics / inventory / manufacturing / distribution costs, increasing customer acquisition / retention / maturation / advocacy). And Data Science is the heart of the data value creation process.
These data and analytics-centric companies are tightly integrating the data science and business teams in order to define the parameters of analytics success; to identify, capture and operationalize the sources of customer, product and operational value creation (see Figure 2).
Figure 2: Data Science Value Engineering Framework
These organizations are mastering the integration of the DataOps, Data Science and DevOps disciplines to fuel their data monetization value chain (see Figure 3).
Figure 3: Data Monetization Value Chain
DataOps, Data Science, DevOps – all focused on accelerating the monetization of an asset that “The Economist” magazine declared “The world’s most valuable resource”. See "Data Curation: Weaving Raw Data into Business Gold" for more on monetizing the world’s most valuable resource.
So, what lessons can we take away from how modern digital companies?
These modern digital companies, through their aggressive open source architecture strategies, realize that they are not in the technology architecture business; they are in the data monetization and commercialization business.
Now, let’s take that stupid armor off of Steph Curry and let it rain 3’s!