Top 10 Platform Engineering Next Practices

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Platforms are revolutionizing businesses be it in banking, insurance, entertainment, consumer products to pharma and vaccine development and production. Platforms are emerging as the most interesting opportunity constructs for enterprises that are ready to pivot into digital in a massively transformative rather than incremental manner. Platforms are not games for the faint-hearted companies that are not digitally ambitious and competitive on innovations. Platform engineering practices are also team games, where the businesses and technology providers and service partners merge into the enterprise value-stream. All these stakeholder entities build and co-own the platform engineering services and best practices, as explained below:

1- Focus on DfE- Design for Experience:

Digital businesses are about delivering unique 'Wow' business moments- value consumption can be digital moments or an analog utility curve of experience moments but with constant n not marginal utility. Think of running shoes as experience delivery mechanisms, including all its hard and soft (data, algorithms, automation, IoT) components. Autonomous car platforms are as much software and data platforms as they are hardware. 

2- Pivot to integrative platforms across business and tech ecosystems:

Pivot your business and technology strategy thinking into an ecosystem of value delivery across shareable secure data and algorithmic expertise. Eg imagine, design, build and deploy integrative value-streams e.g. combining running shoes to heath-tech IoT monitoring to insurance to digital persona analytics and security. Consider location tracking and PII data usage in risk free manner, e.g. in cloud data-lakes holding integrated data-streams of the running avatar persona of a customer:  Combining IoT sensors data from shoes to fitness data to heath service providers and  data services providers. All these digital data streams and value-streams must be designed and delivered with clear and direct benefits to the customer persona. Then the next steps are extending to adjacent business ecosystems e.g. link to health insurance for instance. Customer should have full control, visibility and transparency on her persona, e.g.  where her data is being tracked and stored and used by whom and what algorithms for what impact on her life parameters. 

3- Build composable platform architecture:

Shift from monolithic to composable dynamic self organizing and optimizing system architecture for all business systems and data ecosystems designed to deliver that integrative experience eg shoes to insurance, as described above. 

4- Build systems from collections of Experience as Code lego blocks 

Flexibility and agility (speed of change) are the best value levers from platformized business models. For instance the running experience components in the digitally engineered shoes: IoT sensors data and RT analytics algorithms/ ML models running in federated cloud AI infra. Similarly: Autonomous vehicles passenger experience components as code- thermal sensors to ambience management to entertainment to imp info and conversational and emotive AI

5- Build CoExp (cost of experience) models for transparency: 

Leverage the cloud costing and pricing models logic and structure, e.g. pay-per-use and rapid scale-up/ down based on demand surges and usage patterns analyzed by time and resource costs. For platform services,  WYU-is-WYPF (What You Use Is What You Pay For) is the ideal and optimal costing and pricing structure. 

6-  Design and build real-time risk dashboards:

Risk management being an integral real-time capability expected from platformized businesses, real-time risk dashboards are must-have elements, ideally self-managed and with smart optimizers and orchestrators built within. They should be integrated logically and data-wise at the business ops backend, e.g. self-triggering smart automation to act upon instantly if a high-risk situation or a potential threat or breach scenario arises. This actionable and smart risk dashboard should have both business and tech operational risks as well as strategic risks covered and should aim at delivering stakeholder and user info in real time. This is not just for experience and happiness but in the best value added form that will impact the customers' lives in the most optimal way. Transparency into risks and costs vs returns build trust. Trust builds acceptance. Acceptances scales adoption and subsequent value realization, in the digital customer experience value-chain. 

7- Practice the PfT (Platform-for-Trust) principle:

Platformized businesses, through operational transparency, must ensure and aim to inculcate trust in the minds of the users. Therefore, Platform-for-Trust should be the guiding principle in developing and running the platform and lifecycles of the assets and services on top of it: Make trust your key business objective for platformized business. As shown in 6, trust is the key ingredient for customer comfort and experience. 

8- Focus on 4 M's- Metrics, Measure, Monitor, Manage: 

One of the key challenges emerging in the early platform tech implementation case studies is the difficulty in measuring and articulating the overall business value generated, supported and delivered by the platform, to the enterprise balance sheet as well as to the ecosystem. Yes there are loads of intangible parameters like NPS, that indirectly impact and can be attributable to business strategic metrics such as profitability and market share. Try to connect these value dots and quantify and measure whatever you can- combining, cascading and integrating the tangible and intangible value metrics. What you can measure and monitor, you can manage. 

9- Build a different talent strategy for your business platforms: 

Pivot your talent strategy, rather overhaul it,  based on the actions required above. Accept the fact none of the talents that are required for the practices mentioned above are commonly available talent or skill from the technology/ full-stack certified resource pools alone. Know the constraints and build a multi-faceted talent evaluation and prioritization strategy, rather than taking an ostrich policy or relying entirely on partner tech platform certifications. Design thinking for instance must be prioritized as mandatory skill, which should be evaluated from case examples delivered by the resources, with manifested results 

10- Partners ecosystem is key. 

Don’t try to hoard all the talents or do everything yourself, from tech to design to ops. That way you will end up elevating the constraints and not solve them. Be real. Focus on platformizing your core business on the practices mentioned above, with help from specific experts