Getting platforms right when data is currency and partnerships a force multiplier

With their ability to plug and play and consume services, platforms are today’s go-to business model. The agility allows organisations to embrace an ever-widening network — or ecosystem — of value-adding contributors.

In this environment, data is the new currency and partnerships are the force multiplier — yet many organisations haven’t changed the way they partner or reward performance. Too many use outdated agreements and KPIs at odds with a successful platform business.

Business platforms require a number of layers in the stack, and we are seeing each of the major IT platforms reach well beyond compute and infrastructure into enterprise systems (such as ERP, CRM, Marketing, ServiceRequest, Resourcing etc.) to now include natively integrated layers for DevOps and orchestration, Internet of Things, identity and security, machine learning and artificial intelligence, even blockchain.

Thanks to advances in computer processing power, memory, storage, and data tools, machine learning and artificial intelligence can be integrated into the core enterprise systems that form the heart of most organisational IT infrastructures. This is how platform-based and born- digital businesses are able to achieve accelerated innovation.

After seeing the rise of Salesforce and new market entrants such as Amazon, software companies have been aggressively transforming their own businesses into cloud-based full-service platforms. Now, rather than selling licenses, they sell subscriptions, and generate revenue based on the volume of active consumption of their services. These platform businesses look like this:

  • Integrated platform stack that not only includes core proprietary applications (ERP, CRM, Marketing, Service Request, etc.), but also DevOps and orchestration, identity and security, IoT, analytics, machine learning, AI bots and now blockchain. These organisations acquired or partnered with “best-of-breed” cloud-based companies, bringing them into their platform architecture. To open it up externally, they use APIs to enable third parties, client and partners to develop services around their platform.
  • App store for developers and partners to use these APIs and developer tools, to build out ready-to-use business process services built on the platform stack, all available for prospective clients at the click of a button. The store provides significant market access and data analytics on customer likes and preferences.

Already, platforms have significant economic value. The Center for Global Enterprise valued it at $4.5 trillion USD in 2015 (Rise of the Platform Survey 2015). But only one business from either Australia or New Zealand was worthy of mention in their list – Atlassian. While Atlassian is a born-digital tech company, the platform economy isn’t just for tech companies – it is for all companies, no matter what industry you are in.

The limited use of a platform approach in Australia could be because our region didn’t experience the extreme pressures of the Global Financial Crisis. Therefore we weren’t in the crucible of “re-invent or die.”

But we can’t rest on past success. Platform-based businesses are accelerating the pace of innovation and raising the bar on the minimum expected level of experience that customers will accept. Organisations that do not adapt will find themselves being disrupted via disintermediation.

Businesses can’t deny that technology is becoming a multi-faceted enabler of change and innovation. Organisations and governments must now consider how new cloud platforms will form part of their own platform infrastructure. The money (whether it’s revenue growth, or cost reduction) is in the services provided on top of these platforms.

Let’s look at some of the use cases for how organisations can use these cloud-based platforms to drive business performance:

Better hiring – Machine learning can identify the patterns between the CVs of your staff and their subsequent performance, identifying the most suitable candidates for interviews out of thousands of applicants. It can also recognize which job descriptions and job advertisements to push to diverse groups of top talent.

Personalised service experience– Combining natural language voice assistants with access to thousands of historical records of customer service data, along with ideal customer journeys, can help organisations triage customers and ensure they get advice that is tailored to their exact needs. The customer can be guided through a process or a transaction and complete their outcome in a single interaction.

Asset maintenance – By automatically sifting through drone and satellite images and videos over vast areas and comparing them to a “normal” state, organisations can identify equipment and places in need of immediate maintenance. This can also be applied to healthcare provisioning. For example, a detailed review of images, based on millions of image results can detect defects that require immediate specialist referral.

Lower cost operations – By creating a complete digital replica (digital twin) of an area of operations, organisation can then use machine learning to examine historical and real-time data. Through scenario analysis, the system can identify areas of waste and consolidation, and the organization can implement automation. This is an ongoing cycle of operational improvement and cost reduction as the system keeps learning and you keep automating.

Safety and security – By comparing a real-time analysis of video, social media, Internet of Things and sensor data, against historical data, users can learn patterns and identify safety hot spots or even fraudulent activity that needs immediate attention.

Procurement and inventory management – When employees use devices to take/upload photos of what they need, it opens a process to purchase the item (or next closest item for which the employee has authority to purchase), based on the employee profile. Or, perhaps, uploaded photos from a retail store sets off a process where machine learning reviews purchasing patterns for the store area and recommends appropriate items for restock and how to change displays to improve purchasing.

Risk management and contract enforcement – Capturing senior legal expertise has relied on professionals with years of experience to draw up and defend legal agreements, and this has been difficult to disrupt. However imagine that with digital forensics, biometrics, neuro-imaging, IoT sensors and deep learning, together with blockchain contracts, we can track and prove “the truth” at any point in time.

In each use case, data is central to value creation.

Data becomes the currency in the platform economy.

Planning a business model requires an understanding of what data you will keep inside the firewall and what data you will share and make available to others to help create new value.

For example, say you are a bank. There is significant amount of data that, by regulation, you cannot share. However there may be adjacent data (purchasing behviour by category, by suburbs) that, if shared, could create new value . By sharing this data, automotive manufacturers, for example, may be able to create new services for their connected cars.

This creates a completely new value add for car manufacturers and the bank gains in mind share. In return, you attain significant information about what services people want, where and when they want it when travelling in their car. You can now offer un-precedented levels of personalised services that are geo-fenced and context aware.

The other key element in each of these use cases is the need for a digital ecosystem of partners.

In the above example, the bank and automotive manufacturer become part of each other’s digital ecosystem. Many companies are forming revolutionary partnerships and alliances in this way, however establishing and navigating these partnerships is very different to previous alliance structures.

Partnership ecosystems are mandatory in the platform economy. They are the force multiplier for innovation and growth that makes the platform work.

So as part of the business design, you must be clear on what data you will share and create value around, and therefore what partners you need to connect to, so new value can be created. This will inform the nature of your partnership agreements. Consider:

  • What data you provide access to and share
  • The unit of value, or metric used for recognising value created
  • The usage-based metric pricing and revenue expectations
  • Your expectations of partners – how they use your data, how they create value, how you gain access to their data created

This way of thinking is new for many partnership agreements. Typically in many large organisations and government, procurement will lead the negotiation agreements. Whether you are on the buy side or sell side, your organization is likely not considering “data on consumption” as a key value metric, or writing agreements to monitor, incentivise and monetise this.

As value is now being created outside company walls, there is a joint responsibility to manage the data being used and ensure it creates value for both parties. This means agreements need to consider co-creation of value as the new “balance of trade” item, rather than how much spend each company has.

In turn, this must be reflected in the KPIs for employees. Organisations need to measure and reward collaboration and sharing with new ecosystem partners, which, in some cases, may have been fierce competitors or strict supplier relationships in the past.

Leaders must shed their corporate power paradigms to build a culture that breaks down hierarchies, silos and external walls to lean-in with trust, empower sharing and co-creation.


Building a new world: Moving to the platform era

Choosing the right collaboration platform saves money and time

Why businesses can’t ignore the API economy


  1. […] Getting platforms right when data is currency and partnerships a force multiplier […]


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