Problem Finder can be that RainMaker!

On June 19, 2018, General Electric (GE)‘s 100 year run in the Dow Jones Industry Average came to an end. This was an enterprise known to be a big brand in the Corporate America. While such corporations are struggling to sustain themselves as a “Live Enterprise”, only strong Innovators who can find relevant problems to solve will continue to build resilient enterprises.

Elon Musk needs no introduction. He is the same genius who started a web software company (ZIP2), online financial services and payment company-Paypal, Space Exploration Technologies Corp (SpaceX), and also made Tesla the top American electric vehicle and clean energy company.?Did you ever ponder on how Elon Musk identified and solved these unknown problems that every other large corporation failed to find in the first place?

Today’s trends on faster bandwidth, powerful compute, elastic storage and Opensource adoption at very low-price points make problem finding achievable. Faster and inexpensive experiments/training of Machine Learning models (ML) is a great illustration of how Hyperscalers fuel the problem finder’s appetite. A Hyperscaler like Google Cloud offers easy-to-use Cloud Auto ML products to accelerate problem finding & experimentation in areas like medical research, weather prediction & so on.

To approach ‘Problem Finding’ and innovation, it is always good to draw inferences, study & tailor relevant practices from various well-established industries.

Here is an example from the Construction industry. We are all certainly very impressed with our fabulous Infy Buildings across DCs and Hubs. There is a lot of engineering and thinking behind these marvels. An intriguing aspect is how these construction engineering firms build a resilient structure that withstands earthquakes, natural disasters, and protects human lives in a repeatable and predictable manner.

These engineering firms follow various industry standard codes with respect to design loads for weight of building material, stored material, design for earthquake resistance, design for concrete elements and the design of steel structural elements.

In addition to adopting the above-said industry standard codes, the structural engineering teams define the list of design parameters, detailed design elements and its impact consideration on the building. The engineers determine the software model of the structural frame of the building that is generated for analyzing the various effects of vertical and lateral loads on the structure. There are software such as ETABS v2018 (one among the many tools in market) for analyzing the building loads. This analysis produces accurate impact to the structures, sensitivity of the various parameters and arrives at optimal structural frame characteristics that satisfy the stability and strength in all aspects.

I have tried to draw a parallel between the construction engineering and the Public Cloud industry where we design and provision infrastructure for various business architectures. Firstly, there are various architecture reference patterns available for different types of systems ranging from a Content Management System, B2C Web Portal, SAP ERP System, Microservices Architecture and so on. Secondly when we design a “Target Architecture” for a set of applications/workloads that need to be migrated to the Cloud, we determine the Compute, Memory, Network, Storage, Management etc., involved in such a future-state infrastructure. Once we determine the estimated sizing, we use various platforms such as the ?PolyCloud Management platform from our Infosys Cobalt ?Assets to provision the infrastructure or a PaaS service.

We could build the software capability to simulate the capacity planning in such a future Cloud state. We can have a “Sensitivity Analyzer” for analysis of using different types of VM instances offered by Public Cloud players. (For e.g., AWS is?expanding their Arm-based Graviton2 portfolio with C6gn instances that deliver up to 40% better price/performance for all workloads). We could analyze how such an adoption of a new offering across IaaS or PaaS could lead to an improved price/performance, quality of service & so on. This is an interesting problem to prod deep into and collect a set of industry benchmarks that can help achieve an interesting simulation. As trusted advisors & Orchestrators for Hybrid Cloud workloads for our clients, these capabilities will enhance our repertoire of Cobalt Assets.

As I reflect back on the above example, interesting problems can be found by studying alternate industries and utilizing the powerful offerings from Hyperscalers. ?

Needless to say, these ‘Problem Finders’ could be our distinguished Rainmakers!

Stay safe, stay curious!